DATA IMPUT

Understanding How GPS, Remote Sensing and IDI Work http://educationally.narod.ru/gis39photoalbum.html image processing and analysis http://educationally.narod.ru/gis311photoalbum.html Image Processing and Analysis Introduction Image Processing and Analysis can be defined as the "act of examining images for the purpose of identifying objects and judging their significance" Image analyst study the remotely sensed data and attempt through logical process in detecting, identifying, classifying, measuring and evaluating the significance of physical and cultural objects, their patterns and spatial relationship. Digital Data In a most generalized way, a digital image is an array of numbers depicting spatial distribution of a certain field parameters (such as reflectivity of EM radiation, emissivity, temperature or some geophysical or topographical elevation. Digital image consists of discrete picture elements called pixels. Associated with each pixel is a number represented as DN (Digital Number), that depicts the average radiance of relatively small area within a scene. The range of DN values being normally 0 to 255. The size of this area effects the reproduction of details within the scene. As the pixel size is reduced more scene detail is preserved in digital representation. Remote sensing images are recorded in digital forms and then processed by the computers to produce images for interpretation purposes. Images are available in two forms - photographic film form and digital form. Variations in the scene characteristics are represented as variations in brightness on photographic films. A particular part of scene reflecting more energy will appear bright while a different part of the same scene that reflecting less energy will appear black. Digital image consists of discrete picture elements called pixels. Associated with each pixel is a number represented as DN (Digital Number), that depicts the average radiance of relatively small area within a scene. The size of this area effects the reproduction of details within the scene. As the pixel size is reduced more scene detail is preserved in digital representation. Data Formats For Digital Satellite Imagery Digital data from the various satellite systems supplied to the user in the form of computer readable tapes or CD-ROM. As no worldwide standard for the storage and transfer of remotely sensed data has been agreed upon, though the CEOS (Committee on Earth Observation Satellites) format is becoming accepted as the standard. Digital remote sensing data are often organised using one of the three common formats used to organise image data . For an instance an image consisting of four spectral channels, which can be visualised as four superimposed images, with corresponding pixels in one band registering exactly to those in the other bands. These common formats are: Band Interleaved by Pixel (BIP) Band Interleaved by Line (BIL) Band Sequential (BQ) Digital image analysis is usually conducted using Raster data structures - each image is treated as an array of values. It offers advantages for manipulation of pixel values by image processing system, as it is easy to find and locate pixels and their values. Disadvantages becomes apparent when one needs to represent the array of pixels as discrete patches or regions, where as Vector data structures uses polygonal patches and their boundaries as fundamental units for analysis and manipulation. Though vector format is not appropriate to for digital analysis of remotely sensed data. Image Resolution Resolution can be defined as "the ability of an imaging system to record fine details in a distinguishable manner". A working knowledge of resolution is essential for understanding both practical and conceptual details of remote sensing. Along with the actual positioning of spectral bands, they are of paramount importance in determining the suitability of remotely sensed data for a given applications. The major characteristics of imaging remote sensing instrument operating in the visible and infrared spectral region are described in terms as follow: Spectral resolution Radiometric resolution Spatial resolution Temporal resolution Spectral Resolution refers to the width of the spectral bands. As different material on the earth surface exhibit different spectral reflectances and emissivities. These spectral characteristics define the spectral position and spectral sensitivity in order to distinguish materials. There is a tradeoff between spectral resolution and signal to noise. The use of well -chosen and sufficiently numerous spectral bands is a necessity, therefore, if different targets are to be successfully identified on remotely sensed images. Radiometric Resolution or radiometric sensitivity refers to the number of digital levels used to express the data collected by the sensor. It is commonly expressed as the number of bits (binary digits) needs to store the maximum level. For example Landsat TM data are quantised to 256 levels (equivalent to 8 bits). Here also there is a tradeoff between radiometric resolution and signal to noise. There is no point in having a step size less than the noise level in the data. A low-quality instrument with a high noise level would necessarily, therefore, have a lower radiometric resolution compared with a high-quality, high signal-to-noise-ratio instrument. Also higher radiometric resolution may conflict with data storage and transmission rates. Spatial Resolution of an imaging system is defines through various criteria, the geometric properties of the imaging system, the ability to distinguish between point targets, the ability to measure the periodicity of repetitive targets ability to measure the spectral properties of small targets. The most commonly quoted quantity is the instantaneous field of view (IFOV), which is the angle subtended by the geometrical projection of single detector element to the Earth''s surface. It may also be given as the distance, D measured along the ground, in which case, IFOV is clearly dependent on sensor height, from the relation: D = hb, where h is the height and b is the angular IFOV in radians. An alternative measure of the IFOV is based on the PSF, e.g., the width of the PDF at half its maximum value. A problem with IFOV definition, however, is that it is a purely geometric definition and does not take into account spectral properties of the target. The effective resolution element (ERE) has been defined as "the size of an area for which a single radiance value can be assigned with reasonable assurance that the response is within 5% of the value representing the actual relative radiance". Being based on actual image data, this quantity may be more useful in some situations than the IFOV. Other methods of defining the spatial resolving power of a sensor are based on the ability of the device to distinguish between specified targets. Of the concerns the ratio of the modulation of the image to that of the real target. Modulation, M, is defined as: M = Emax -Emin / Emax + Emin Where Emax and Emin are the maximum and minimum radiance values recorded over the image. Temporal resolution refers to the frequency with which images of a given geographic location can be acquired. Satellites not only offer the best chances of frequent data coverage but also of regular coverage. The temporal resolution is determined by orbital characteristics and swath width, the width of the imaged area. Swath width is given by 2htan(FOV/2) where h is the altitude of the sensor, and FOV is the angular field of view of the sensor. How to Improve Your Image? Analysis of remotely sensed data is done using various image processing techniques and methods that includes: Analog image processing Digital image processing. Visual or Analog processing techniques is applied to hard copy data such as photographs or printouts. Image analysis in visual techniques adopts certain elements of interpretation, which are as follow: The use of these fundamental elements of depends not only on the area being studied, but the knowledge of the analyst has of the study area. For example the texture of an object is also very useful in distinguishing objects that may appear the same if the judging solely on tone (i.e., water and tree canopy, may have the same mean brightness values, but their texture is much different. Association is a very powerful image analysis tool when coupled with the general knowledge of the site. Thus we are adept at applying collateral data and personal knowledge to the task of image processing. With the combination of multi-concept of examining remotely sensed data in multispectral, multitemporal, multiscales and in conjunction with multidisciplinary, allows us to make a verdict not only as to what an object is but also its importance. Apart from these analog image processing techniques also includes optical photogrammetric techniques allowing for precise measurement of the height, width, location, etc. of an object. Elements of Image Interpretation Primary Elements Black and White Tone Color Stereoscopic Parallax Spatial Arrangement of Tone & Color Size Shape Texture Pattern Based on Analysis of Primary Elements Height Shadow Contextual Elements Site Association Digital Image Processing is a collection of techniques for the manipulation of digital images by computers. The raw data received from the imaging sensors on the satellite platforms contains flaws and deficiencies. To overcome these flaws and deficiencies inorder to get the originality of the data, it needs to undergo several steps of processing. This will vary from image to image depending on the type of image format, initial condition of the image and the information of interest and the composition of the image scene. Digital Image Processing undergoes three general steps: Pre-processing Display and enhancement Information extraction Pre-processing consists of those operations that prepare data for subsequent analysis that attempts to correct or compensate for systematic errors. The digital imageries are subjected to several corrections such as geometric, radiometric and atmospheric, though all these correction might not be necessarily be applied in all cases. These errors are systematic and can be removed before they reach the user. The investigator should decide which pre-processing techniques are relevant on the basis of the nature of the information to be extracted from remotely sensed data. After pre-processing is complete, the analyst may use feature extraction to reduce the dimensionality of the data. Thus feature extraction is the process of isolating the most useful components of the data for further study while discarding the less useful aspects (errors, noise etc). Feature extraction reduces the number of variables that must be examined, thereby saving time and resources. Image Enhancement operations are carried out to improve the interpretability of the image by increasing apparent contrast among various features in the scene. The enhancement techniques depend upon two factors mainly The digital data (i.e. with spectral bands and resolution) The objectives of interpretation As an image enhancement technique often drastically alters the original numeric data, it is normally used only for visual (manual) interpretation and not for further numeric analysis. Common enhancements include image reduction, image rectification, image magnification, transect extraction, contrast adjustments, band ratioing, spatial filtering, Fourier transformations, principal component analysis and texture transformation. Information Extraction is the last step toward the final output of the image analysis. After pre-processing and image enhancement the remotely sensed data is subjected to quantitative analysis to assign individual pixels to specific classes. Classification of the image is based on the known and unknown identity to classify the remainder of the image consisting of those pixels of unknown identity. After classification is complete, it is necessary to evaluate its accuracy by comparing the categories on the classified images with the areas of known identity on the ground. The final result of the analysis consists of maps (or images), data and a report. These three components of the result provide the user with full information concerning the source data, the method of analysis and the outcome and its reliability. Pre-Processing of the Remotely Sensed Images When remotely sensed data is received from the imaging sensors on the satellite platforms it contains flaws and deficiencies. Pre-processing refers to those operations that are preliminary to the main analysis. Preprocessing includes a wide range of operations from the very simple to extremes of abstractness and complexity. These categorized as follow: Feature Extraction Radiometric Corrections Geometric Corrections Atmospheric Correction The techniques involved in removal of unwanted and distracting elements such as image/system noise, atmospheric interference and sensor motion from an image data occurred due to limitations in the sensing of signal digitization, or data recording or transmission process. Removal of these effects from the digital data are said to be "restored" to their correct or original condition, although we can, of course never know what are the correct values might be and must always remember that attempts to correct data what may themselves introduce errors. Thus image restoration includes the efforts to correct for both radiometric and geometric errors. Feature Extraction Feature Extraction does not mean geographical features visible on the image but rather "statistical" characteristics of image data like individual bands or combination of band values that carry information concerning systematic variation within the scene. Thus in a multispectral data it helps in portraying the necessity elements of the image. It also reduces the number of spectral bands that has to be analyzed. After the feature extraction is complete the analyst can work with the desired channels or bands, but inturn the individual bandwidths are more potent for information. Finally such a pre-processing increases the speed and reduces the cost of analysis. Radiometric Corrections Radiometric Corrections are carried out when an image data is recorded by the sensors they contain errors in the measured brightness values of the pixels. These errors are referred as radiometric errors and can result from the Instruments used to record the data From the effect of the atmosphere Radiometric processing influences the brightness values of an image to correct for sensor malfunctions or to adjust the values to compensate for atmospheric degradation. Radiometric distortion can be of two types: The relative distribution of brightness over an image in a given band can be differ Map Analysis http://innovativegis.com/basis/MapAnalysis/, Map Analysis online book http://innovativegis.com/basis/, Papers, presentations, instructional and other materials on map analysis and GIS modeling Hardcopy books and companion CDs for hands-on exercises by the author on map analysis and GIS moeling include— http://www.innovativegis.com/basis/Books/MapAnalysis/Default.htm, Map Analysis by J. K. Berry (GeoTec Media 2007) http://www.innovativegis.com/basis/Books/AnalyzingNRdata/Default.htm, Analyzing Geospatial Resource Data by J. K. Berry (BASIS 2005) http://www.innovativegis.com/basis/Books/AnalyzingGBdata/Default.htm, Analyzing Geo-Business Data by J. K. Berry (BASIS 2003) http://www.innovativegis.com/basis/Books/AnalyzingPAdata/Default.htm, Analyzing Precision Ag Data by J. K. Berry (BASIS 2002) http://www.innovativegis.com/basis/Books/spatial.htm, Spatial Reasoning for Effective GIS by J. K. Berry (Wiley 1995) http://www.innovativegis.com/basis/Books/bm_des.html, Beyond Mapping: Concepts, Algorithms and Issues in GIS by J. K RASTER GIS CAPABILITIES http://www.geo.wvu.edu/~elmes/geog350/unit05.htm A. INTRODUCTION 2) B. DISPLAYING LAYERS a) Basic display b) Other types of display 3) C. LOCAL OPERATIONS a) Recoding b) Overlaying layers 4) D. OPERATIONS ON LOCAL NEIGHBORHOODS (FOCAL - Tomlin) a) Filtering b) Slopes and aspects 5) E. OPERATIONS ON EXTENDED NEIGHBORHOODS a) Distance b) Buffer zones c) Visible area or "viewshed" 6) F. OPERATIONS ON ZONES (GROUPS OF PIXELS) a) Identifying zones b) Areas of zones c) Perimeter of zones d) Distance from zone boundary e) Shape of zone 7) G. COMMANDS TO DESCRIBE CONTENTS OF LAYERS a) One layer b) More than one layer c) Zones on one layer 8) H. ESSENTIAL HOUSEKEEPING 9) REFERENCES 10) EXAM AND DISCUSSION QUESTIONS NOTES This unit continues the overview of raster GIS. If possible, we suggest that you replace and/or supplement the graphics provided with this unit with graphics generated by the raster program your students will be using in their labs. Alternatively, the best way to illustrate this unit may be through the use of a laboratory demonstration. Consider providing handouts to the students that summarize the commands for the raster GIS program you will be using in labs. Check your program''s manual for a command summary or do a screen dump of the appropriate help screen if there is one. UNIT 5 - RASTER GIS CAPABILITIES Compiled with assistance from Micha Pazner, University of Manitoba A. INTRODUCTION A raster GIS must have capabilities for: - Input of data - Various housekeeping functions - Operations on layers, like those encountered in the previous unit - recode, overlay and spread - Integration with vector GIS operations - Output of data and results - The range of possible functions is enormous, current raster GISs only scratch the surface - Because the range is so large, some have tried to organize functions into a consistent scheme, but no scheme has been widely accepted yet - The unit covers a selection of the most useful and common - Each raster GIS uses different names for the functions IDRISI is a commonly used and powerful raster based GIS developed by Dr. Eastman at Clark University in Worcester, MASS. It is now a commercial success. IDRISI TUTORIAL on line at Univ. British Columbia ArcGIS Spatial Analyst provides powerful tools for comprehensive, raster-based spatial modeling and analysis Find suitable locations Calculate the accumulated cost of traveling from one point to another Perform land use analysis Predict fire risk Analyze transportation corridors Determine pollution levels Perform crop yield analysis Determine erosion potential Perform demographic analysis Conduct risk assessments Model and visualize crime patterns ESRI Clips B. DISPLAYING LAYERS Basic display - The simplest type of values to display are integers - On a color display each integer value can be assigned a unique color - There must be as many colors as integers - If the values have a natural order we will want the sequence of colors to make sense - E.g. elevation is often shown on a map using the sequence blue-green-yellow-brown-white for increasing elevation - There should be a legend explaining the meaning of each color - The system should generate the legend automatically based on the descriptions of each value stored with the data layer IDRISI TUTOR display.htm - Simple display (IDRISI) - On a dot matrix or laser printer shades of grey can be generated by varying the density of dots - If there are too many values for the number of colors, may have to recode the layer before display Other types of display - It may be appropriate to display the data as a surface - Contours can be "threaded" through the pixels along lines of constant value - The searching operation for finding contours is computer-intensive so may be slow - The surface can be shown in an oblique, perspective view FIGURE - Perspective view - This can be done by drawing profiles across the raster with each profile offset and hidden lines removed - The surface might be colored using the values in a second layer (a second layer can be "draped" over the surface defined by the first layer) - The result can be very effective - FLY Overs -- "LA The Movie" was produced by Jet Propulsion Lab by draping a Landsat image of Los Angeles over a layer of elevations, then simulating the view from a moving aircraft we''ve come along way in 15 years - Google Earth and Microsoft''s Virtual Earth - common place. - These operations are also computer-intensive because of the calculations necessary to simulate perspective and remove hidden lines C. LOCAL OPERATIONS - Produce a new layer from one or more input layers - The value of each new pixel is defined by the values of the same pixel on the input layer(s) - Neighboring or distant pixels have no effect - Note: arithmetic operations make no sense unless the values have appropriate scales of measurement (see Unit 6) - You cannot find the "average" of soils types 3 and 5, nor is soil 5 "greater than" soil 3 Recoding / reclassing - using only one input layer - Examples: 1. Assign a new value to each unique value on the input layer - Useful when the number of unique input values is small 2. Assign new values by assigning pixels to classes or ranges based on their old values - E.g. 0-499 becomes 1, 500-999 becomes 2, >1000 becomes 3 - Useful when the old layer has different values in each cell, e.g. elevation or satellite images 3. Sort the unique values found on the input layer and replace by the rank of the value - E.g. 0, 1, 4, 6 on input layer become 1, 2, 3, 4 respectively - Applications: assigning ranks to computed scores of capability, suitability etc. - Some systems allow a full range of mathematical operations - E.g. newvalue = (2*oldvalue + 3) 2 Overlaying layers - An overlay occurs when the output value depends on two or more input layers - Many systems restrict overlay to two input layers only - Examples: 1. Output value equals arithmetic average of input values 2. Output value equals the greatest (or least) of the input values 3. Layers can be combined using arithmetic operations - x and y are the input layers, z is the output - Some more examples: Z = X + Y Z = X * Y Z = X / Y 4. Combination using logical conditions - E.g. if y>0, then z = y , otherwise z = x - Note: in many raster packages logical conditions cannot be done directly from input layers - must first create reclassified input images so that cells have 0 if they do not meet the condition and 1 if they do Boolean logical operations on rasters (2 pages) 5. Assign a new value to every unique combination of input values - E.g. LAYER 1 LAYER 2 OUTPUT LAYER 1 A 1 1 B 2 2 A 3 2 B 4 etc. D. OPERATIONS ON LOCAL NEIGHBORHOODS - the value of a pixel on the new layer is determined by the local neighborhood of the pixel on the old layer Filtering - A filter operates by moving a "window" across the entire raster - E.g. many windows are 3x3 cells - The new value for the cell at the middle of the window is a weighted average of the values in the window - By changing the weights we can produce two major effects: - Smoothing -- a "low pass" filter, removes or reduces local detail - Edge enhancement -- a "high pass" filter, exaggerates local detail - Weights should add to 1 - Example filters: 1) 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 0.11 - Replaces each value by the simple unweighted average of it and its eight neighboring values - Severely smoothes the spatial variation on the layer 2) 0.05 0.05 0.05 0.05 0.60 0.05 0.05 0.05 0.05 - Gives the pixel''s old value 12 times the weight of its neighboring values - Slightly smoothes the layer 3) -0.1 -0.1 -0.1 -0.1 1.8 -0.1 -0.1 -0.1 -0.1 - Slightly enhances local detail by giving neighbors negative weights Spatial filtering - Filters can be useful in enhancing detail on images for input to GIS, or smoothing layers to expose general trends Three examples from IDRISI 1.Smoothing - low pass filters G:GEOG250idrtutors_tools3.htm#lowpass 2.Edge enhancing, edge detecting - high pass filters G:GEOG250idrtutors_tools3.htm#highpass 3.Directional filters Enhance or detect directional structures in the filtered images. LAPISELMES$GEOG250idrtutors_tools3.htm - directional Slopes and aspects - If the values in a layer are elevations, we can compute the steepness of slopes by looking at the difference between a pixel''s value and those of its adjacent neighbors - The direction of steepest slope, or the direction in which the surface is locally "facing", is called its aspect - Aspect can be measured in degrees from North or by compass points - N, NE, E etc. (Cyclic level of measurement) - Slope and aspect are useful in analyzing vegetation patterns, computing energy balances and modeling erosion or runoff - Aspect determines the direction of runoff - This can be used to sketch drainage paths for runoff Computing Slope and Aspect in IDRISI with SURFACE http://gis01.ame.umontreal.ca/APA/6237/idrtutor/s_tools4.htm E. OPERATIONS ON EXTENDED NEIGHBORHOODS Distance - calculate the distance of each cell from a cell or the nearest of several cells - each pixel''s value in the new layer is its distance from the given cell(s) Buffer zones - Buffers around objects and features are very useful GIS capabilities - E.g. build a logging buffer 500 m wide around all lakes and watercourses - Buffer operations can be visualized as spreading the object spatially by a given distance - The result could be a layer with values: 1 if in original selected object 2 if in buffer 0 if outside object and buffer - Applications include noise buffers around roads, safety buffers around hazardous facilities - in many programs the buffer operation requires the user to first do a distance operation, then a reclassification of the distance layer - The rate of spreading may be modified by another layer representing "friction" - E.g. the friction layer could represent varying cost of travel - This will affect the width of the buffer - narrow in areas of high friction, etc. Visible area or "viewshed" - Given a layer of elevations, and one or more viewpoints, compute the area visible from at least one viewpoint - E.g. value = 1 if visible, 0 if not - useful for planning locations of unsightly facilities such as smokestacks, or surveillance facilities such as fire towers, or transmission facilities F. OPERATIONS ON ZONES (GROUPS OF PIXELS) Identifying zones - By comparing adjacent pixels, identify all patches or zones having the same value - Give each such patch or zone a unique number - Set each pixel''s value to the number of its patch or zone Areas of zones - Measure the area of each zone and assign this value to each pixel instead of the zone''s number - Alternatively output may be in the form of a summary table sent to the printer or a file Perimeter of zones - Measure the perimeter of each zone and assign this value to each pixel instead of the zone''s number - Alternatively output may be in the form of a summary table sent to the printer or a file - Length of perimeter is determined by summing the number of exterior cell edges in each zone - Note: the values calculated in both area and perimeter are highly dependent upon the orientation of objects (zones) with respect to the orientation of the grid Overhead - Area and perimeter functions in rasters - However, if boundaries in the study area do not have a dominant orientation such errors may cancel out Distance from zone boundary - Measure the distance from each pixel to the nearest part of its zone boundary, and assign this value to the pixel - Boundary is defined as the pixels which are adjacent to pixels of different values Shape of zone - Measure the shape of the zone and assign this to each pixel in the zone - One of the most common ways to measure shape is by comparing the perimeter length of a zone to the square root of its area - by dividing this number by 3.54 we get a measure which ranges from 1 for a circle (the most compact shape possible) to 1.13 for a square to large numbers for long, thin, wiggly zones - Commands like this are important in landscape ecology - Helpful in studying the effects of geometry and spatial arrangement of habitat - E.g. size and shape of woodlots on the animal species they can sustain - E.g. value of linear park corridors across urban areas in allowing migration of animal species G. COMMANDS TO DESCRIBE CONTENTS OF LAYERS - Important to have ways of describing a layer''s contents - Particularly new layers created by GIS operations - Particularly in generating results of analysis One layer - generate statistics on a layer - e.g. mean, median, most common value, other statistics More than one layer - Compare two maps statistically - E.g. is pattern on one map related to pattern on the other? - E.g. chi-square test, regression, analysis of variance Zones on one layer - Generate statistics for the zones on a layer - E.g. largest, smallest, number, mean area H. ESSENTIAL HOUSEKEEPING - List available layers - Input, copy, rename layers - Import and export layers to and from other systems - Other raster GIS - Input of images from remote sensing system - Other types of GIS - Identify resolution, orientation - "Resample" - Changing cell size, orientation, portion of raster to analyze - Change colors - Provide help to the user - Exit from the GIS (the most important command of all!) CARTOGRAPHIC MODELING EXAMPLE Harvard Graduate School of Design REFERENCES Berry, J.K., 1987. "Fundamental operations in computer-assisted map analysis," International Journal of Geographical Information Systems 1:119-136. Describes a logical and consistent way of classifying and grouping raster GIS functions. Burrough, P.A., 1986. Principles of Geographical Information Systems for Land Resource Assessment, Clarendon, Oxford. Chapter 5 is a comprehensive review of raster GIS. Star, J.L. and J.E. Estes, 1990. Geographic Information Systems: An Introduction, Prentice Hall. A comprehensive text on GIS, with excellent treatment of raster systems. Tomlin, C.D., 1990. Geographic Information Systems and Cartographic Modeling, Prentice-Hall, Englewood Cliffs, NJ. A comprehensive approach to some qoestions Cartographic Appeal Clearly, how a map looks - especially if it is being used in a presentation - determines its effectiveness. Appropriate color choices, linetypes, and so on add the professional look you want and make the map easier to interpret. Since display information often is not included in the source data set or is filtered out by conversion software, you may need to add it yourself or purchase the map from a vendor who does it for you. Map display information should convey the meaning of its underlying attribute data. Various enhancements will increase a map''s usefulness and cartographic appeal. Feature Colors and Linetypes. Colors and line representations should be chosen to make the map''s meaning clear. For example, using double-line roads can be quite helpful. Many GIS data sets only include road centerline information. Actual road width is not given. So maps with centerlines only can look like spider webs, which is visually unappealing. Some software and conversion systems can draw roads as double lines, with distance between lines varying according to road type. Centerlines can be included, if necessary. Double-line maps are appropriate for detailed studies of small areas, such as subdivisions, or maps where right-of-way information is important. Naming Roads. Naming, or labeling, roads are important for proper map interpretation. This information should be legible, positioned in the center of the road or offset from the center, and drawn at intervals suited to the scale of the final map or its purpose. Landmark Symbols. A good set of symbols should be used to indicate landmarks, such as hospitals, schools, churches, and cemeteries. The symbols should be sized appropriately in relation to map scale. Polygon Fills. Polygon features, such as lakes or parks, should be filled with an appropriate color or hatch pattern. Zoom Layer Control. If the GIS software platform permits, map layers should be set up so that detailed, high-density information only appears when the user zooms in for a close-up of part of the map. For example, when a large area is displayed, only the major roads should appear; for a smaller area, both major and minor roads should appear. Layering Most GIS software has a system of layers, which can be used to divide a large map into manageable pieces. For example, all roads could be on one layer and all hydrographic features on another. Major layers can be further classified into sub-layers, such as different types of roads - highways, city streets, and so on. Layer names are particularly important in CAD-based mapping and GIS programs, which have excellent tools for handling them. Some digital maps are layered according to the numeric feature-classification codes found in their source data sets. For example, a major road might be on the 170-201 layer. However, this type of system is not very useful. A well-thought-out layering scheme can make any data set much easier to use because it allows the user to control the features with which you want to work. A good layering standard has layer names that are mnemonic (suggest their meanings) and hierarchical (have a structured classification scheme that makes it easy to choose general or specific classes). For example, a map could have its roads on a layer called RD, its railroads on a layer called RR, its road bridges on a layer called RD-BRIDGE, and its railroad bridges on a layer called RR-BRIDGE. This scheme is mnemonic because it is easy to tell a layer''s contents from its name, and it''s hierarchical because the user can easily select all the roads, railroads, bridges, road bridges, or railroad bridges. Maps and Map Analysis Automated Mapping Computer Aided Mapping has its limitations. Goal of GIS is not only to prepare a good map but also perform map analysis. Maps are the main source of data for GIS. GIS, though an accurate mapping tool, requires error management. MAP is a representation on a medium of a selected material or abstract material in relation to the surface of the earth (defined by Cartographic association). Maps originated from mathematics. The term Map is often used in mathematics to convey the motion of transferring the information from one form to another just as Cartographers transfer information from the surface of the earth to a sheet of paper. Map is used in a loose fashion to refer to any manual display of information particularly if it is abstract, generalised or schematic. Process involved in the production of Maps: Selection of few features of the real world. Classification of selected features in to groups eg. Railway in to different lines. Classification depends upon the purpose. Simplification of jaggered lines like the coast lines. Exaggeration of features. Symbolisation to represent different classes of features. Drawing Digitization of Maps. Maps can be broadly classified in to two groups: Topographical maps Thematic maps Topographical Maps It is a reference map showing the outline of selected man-made and natural features of the earth. It often acts as a frame for other features Topography refers to the shape of surface represented by contours or shading. It also shows lands, railway and other prominent features. Thematic maps Thematic maps are an important source of GIS information. These are tools to communicate geographical concepts such as Density of population, Climate, movement of goods and people, land use etc. It has many classifications. DATA INPUT Compiled with assistance from Jeffrey L. Star, University of California at Santa Barbara, and Holly Dickinson, SUNY Buffalo A. INTRODUCTION Modes of data input B. DIGITIZERS Hardware The digitizing operation Problems with digitizing maps Editing errors from digitizing Digitizing costs C. SCANNERS Video scanner Electromechanical scanner Requirements for scanning D. CONVERSION FROM OTHER DIGITAL SOURCES Automated Surveying Global Positioning System (GPS) E. CRITERIA FOR CHOOSING MODES OF INPUT F. RASTERIZATION AND VECTORIZATION Rasterization of digitized data Vectorization of scanned images G. INTEGRATING DIFFERENT DATA SOURCES Formats Projections Scale Resampling rasters REFERENCES DISCUSSION AND EXAM QUESTIONS NOTES This unit examines the common methods of data input. This may be a good time to take a field trip to a local GIS shop to show students the operation of these various devices. If you can''t find local examples, the slide set contains some examples of the hardware items described. UNIT 7 - DATA INPUT Compiled with assistance from Jeffrey L. Star, University of California at Santa Barbara, and Holly Dickinson, SUNY Buffalo A. INTRODUCTION - need to have tools to transform spatial data of various types into digital format - data input is a major bottleneck in application of GIS technology - costs of input often consume 80% or more of project costs - data input is labor intensive, tedious, error-prone - there is a danger that construction of the database may become an end in itself and the project may not move on to analysis of the data collected - essential to find ways to reduce costs, maximize accuracy - need to automate the input process as much as possible, but: - automated input often creates bigger editing problems later - source documents (maps) may often have to be redrafted to meet rigid quality requirements of automated input - because of the costs involved, much research has gone into devising better input methods - however, few reductions in cost have been realized - sharing of digital data is one way around the input bottleneck - more and more spatial data is becoming available in digital form - data input to a GIS involves encoding both the locational and attribute data - the locational data is encoded as coordinates on a particular cartesian coordinate system - source maps may have different projections, scales - several stages of data transformation may be needed to bring all data to a common coordinate system - attribute data is often obtained and stored in tables Modes of data input - keyboard entry for non-spatial attributes and occasionally locational data - manual locating devices - user directly manipulates a device whose location is recognized by the computer - e.g. digitizing - automated devices - automatically extract spatial data from maps and photography - e.g. scanning - conversion directly from other digital sources - voice input has been tried, particularly for controlling digitizer operations - not very successful - machine needs to be recalibrated for each operator, after coffee breaks, etc. B. DIGITIZERS http://educationally.narod.ru/gis301photoalbum.html SOCIO-ECONOMIC DATA http://www.geo.wvu.edu/~elmes/geog350/unit08.htm SOCIO-ECONOMIC DATA Aggregate and disaggregated data Cross-sectional and longitudinal data B. SOCIO-ECONOMIC DATA FOR GIS Sources of socio-economic data "Geography" Issues in using secondary socio-economic data C. SOURCES OF SOCIO-ECONOMIC DATA Population census Economic census Agricultural census Labor force statistics Land records Transportation and infrastructure inventories Administrative records D. US CENSUS OF POPULATION AND HOUSING Process of taking the census Content Processing of returns Geographic referencing Census reporting zones Availability of Census data E. TIGER DATA Development Content Marketing TIGER files Non-census uses for TIGER F. Land Records Issues in land records modernization REFERENCES EXAM AND DISCUSSION QUESTIONS notes It may be useful to illustrate this unit with several different examples of the data products described, including examples of census products such as summary reports, maps and even digital tapes. UNIT 8 - SOCIO-ECONOMIC DATA Compiled with assistance from Hugh Calkins, State University of New York at Buffalo A. INTRODUCTION Socio-Economic Data -are data about humans, human activities, and the space and/or structures used to conduct human activities -specific classes include -demographics (age, sex, ethnic and marital status, education) -housing (quality, cost) -migration -transportation -economics (personal incomes, employment, occupations, industry, regional growth) -retailing (customer locations, store sites, mailing lists) Aggregate and disaggregate data -disaggregated data - data about individuals or single entities, for example: -a person''s age, sex, level of education, income, occupation, etc. -gross sales, number of employees, profit, etc. for a retail store -registration number and type for a single vehicle -aggregated data - describing a group of observations with the grouping made on a defined criterion -geographical data are often grouped by spatial units such as a census tract, traffic zone, etc. -aggregation can also be by time interval -e.g. number of persons leaving area in 5 years -also by socio-economic grouping -e.g. persons aged 5 through 14 years -examples of aggregated data are: -number of persons, average income, median housing value for a census tract -number of commute trips and average trip length from a suburban traffic zone to the central business district Cross-sectional and longitudinal data -cross-sectional data gives information on many areas for the same single slice or interval of time -e.g. average income in census tracts of Los Angeles for 1990 -e.g. numbers migrating out of each state in the period 1971-95 -longitudinal data gives information on one or more areas for a series of times -e.g. average income for State of New York from 1970-1997 by year B. SOCIO-ECONOMIC DATA FOR GIS Sources of Socio-economic data Field surveys -much data used in marketing gathered by door-to-door or street interview -field surveys require careful sampling design is -how to obtain a representative sample -how to avoid bias toward certain groups in street interviews Government statistics -statistics collected and reported by government as part of required activities, e.g. Bureau of the Census -usually based on entire population, except sampling is used for some Census questions -government administrative records -records are collected by government as part of administrative functions, e.g. tax records, auto registrations, property taxes -these are useful sources of data provided confidentiality can be preserved -usually available only to government or for research purposes -secondary data collected by another group, often for different purposes -e.g. the original mandated purpose of the Census was to provide data for congressional districting -increasingly socio-economic data is available in digital form from private sector companies -retailers and direct-mail companies are major clients for these companies -includes data originally from census augmented from other sources and surveys -data can be customized for clients (special sets of variables, special geographical coverage or aggregation) -customizing justifies costs, which are often higher than for "raw" census data "Geography" -for use in GIS, socio-economic statistics are of little use without associated "geography," the term often used to describe locational data -e.g. data on census tracts must be supported by digital information on locations of census tract boundaries -geography also allows data to be aggregated geographically, e.g. by merging data on individual cities into metropolitan regions -thus, many suppliers of socio-economic data also supply digitized geography of reporting zones -boundaries of many standard types of reporting zones change from time to time -e.g. changes occur occasionally in county boundaries -e.g. census enumeration districts are redefined for each census (see Redistricting in Unit 56) -difficult to assemble longitudinal data for such units due to changing geography -data is often needed for one set of reporting zones, only available for another set -e.g. data available for census tracts, required for school districts which do not follow same boundaries -such problems of cross-area estimation are facilitated by GIS technology -these problems are often grouped into the area of modifiable area problems (MAP) -considerable effort has been expended recently to develop statistically sound techniques to deal with these problems (see Openshaw, 1981) Issues in using secondary socio-economic data Cost -usually secondary data is much less expensive than field surveys -large expenditures by government agencies on data collection (e.g. US Census) are indirect subsidies to users, who often pay much less than real cost of data Documentation -quality of documentation, supporting information (e.g. maps) is usually high for data collected by government Data quality -major difficulty is undercounting - census and other social surveys tend to miss certain groups, leading to bias in results -undercounting in US Census may be as high as 25% for certain social groups Data conversion -conversion steps may be necessary to make data useful in GIS -e.g. format, type of data may be incompatible Aggregation -are data available with suitable level of spatial, temporal aggregation? -e.g. study to change elementary school district boundaries will require data at resolution of city blocks or higher -e.g. location for gas station will require city block level data, for regional shopping mall much lower resolution (greater aggregation of data) is adequate Currency -social data changes rapidly, can be quickly out of date because of births, deaths, migration, changing economy -competitive edge in retailing depends on having current data -US has a major census only every 10 years, so its data may be 10 years old -often have to estimate current or future patterns based on old data Accuracy of location -census locates people by place of residence - "night-time" census -"daytime" data would show locations during the day (place of work, school etc.) but is generally not available from standard sources -medical records often locate individuals by place of treatment (hospital), not residence or workplace e.g. consider implications for detecting exposure to cancer-causing agents C. SOURCES OF SOCIO-ECONOMIC DATA Population census -questions on age, sex, income, education, ethnicity, migration, housing quality etc. -summary statistics used in research, planning, market research, available at high level of geographic resolution in many countries -see detailed discussion following for US case (Census of Population and Housing) http://www.census.gov/ Economic census -enumeration and tabulation of business activity is conducted in the US by the Census Bureau in years ending in 2 and 7 -detailed information on classes of industry -low level of geographic resolution (i.e. large reporting zones) -data collected in many countries through annual, quarterly or monthly returns of information from companies Agricultural census -annual data on crops, yields, livestock etc. -more extensive periodic surveys of farm economy -available in spatially disaggregated form to e.g. county level in US Labor force statistics -enumeration of employment, unemployment -produced from periodic (e.g. monthly) sample surveys of workforce -other special-purpose surveys often combined with regular labor force survey - e.g. household expenditures, recreation activities -often available for small areas, e.g. parts of city Land records -record of land parcel description, ownership and value for taxation purposes -updated on a regular basis (e.g. annually) by municipality or county government -also used for land use planning -source of current demographic information in some countries/states (i.e. local census) -see detailed discussion following Transportation and infrastructure inventories -planning, management and maintenance of facilities -includes roads and streets, power lines, gas lines, water, sewer lines -collected by local utilities, responsible government departments -valuable to variety of users -e.g. construction companies needing information on buried pipes -e.g. emergency management departments needing data on hazardous facilities -compiling agency often sees a substantial market for such data which can offset costs of collection Administrative records -vehicle registrations, tax returns etc. -useful for various marketing, research purposes -based on 100% sample so can be disaggregated spatially -however, disaggregation causes problems over confidentiality of records D. US CENSUS OF POPULATION AND HOUSING Process of taking the census -purpose is to enumerate the population for redefining election districts -taken every ten years (l960, l970, etc.) -April lst is census day, although complete enumeration takes a "few" weeks -most households receive forms in mail, some require visit by enumerator Content handout - 1990 Census content -two types of items - those completed by "100%" of the population, those by random sample Processing of returns -automated encoding to digital form -automated editing to correct obvious inconsistencies -some missing items can be assigned automatically using simple rules -other missing items are assigned based on probabilities -data assembled into master database -sample surveys processed to produce statistical summaries Geographic referencing -initially returns are identified by street address -address is converted into geographic location using a digital referencing system -for the 1980 census, DIME (Dual Independent Map Encoding) files were used for digital geographic referencing of urbanized portions of the US -for the 1990 census, TIGER files covering every county were used for the 2000 census TIGER files will have greater positional accuracy (Local checking and update) -since TIGER files will have a major impact on GIS databases in the next decade, they are discussed in detail in the next section Census reporting zones -range from blocks to states overhead - Hierarchy of census areas, 1990 handout - 1990 US Census units -as noted previously, the geographic boundaries and definitions of these areas may change from one census to the next Availability of Census data -tabulation of statistics by reporting zones, e.g. population by county, population by age by county -crosstabulation, e.g. population by age and sex by county -special tabulations, e.g. for unusual combinations of characteristics, or for unusual or custom reporting zones -number of possible tabulations and crosstabulations is infinite, volume of census products vastly exceeds volume of data collected -alternative formats for products -printed reports -magnetic media - tapes, disks -microfiche, microfilm, CD-ROMs, WWW Sources of census data -State data centers distribute Census data -private firms repackage and customize data, produce custom reports (e.g. tabulation of population by distance from proposed mall location) -geography products available -base maps showing reporting zones -atlases produced for urban areas -digital products - boundary files, TIGER handout (cont) - 1990 US Census products E. TIGER Development -TIGER stands for Topologically Integrated Geographic Encoding and Referencing -designed to: -support pre-census geographic and cartographic functions in preparation for the 1990 and 2000 Censuses -to complete and evaluate the data collection operations of the census -to assist in the analysis of the data as well as to produce new cartographic products -TIGER files were created by the Bureau of the Census with the assistance of the US Geological Survey Content -TIGER/line files are organized by county -they contain: -map features such as roads, railroads and rivers -census statistical area boundaries -political boundaries -in metropolitan areas, address ranges and ZIP codes for streets Marketing TIGER files -Census Bureau -1990 Census versions of TIGER/Line files available from the Census Bureau and Depository libraries -the 50 states plus DC on tape cost $87,450 in 1991!!!!! -TIGER files are also available on CD-ROM for $250 per disk, 40 disks are required for coverage of the entire country (all prices as of Jan. 1990) -Third party vendors -VALUE-ADDED sales: as of December 1989, 25 vendors had notified the Census Bureau that they will market repackaged versions of TIGER/Line files, in many cases with software which will enable users to access this data easily and quickly -many of these products are being designed for use on micro-computers Non-census uses for TIGER -TIGER files are valuable for other purposes -e.g. locating customers from address lists -e.g. planning vehicle routes through city streets, for parcel delivery, cab dispatching -for these purposes TIGER files need to be kept current at all times, but Bureau of the Census only requires them to be current every 10 years -see Unit 29 for technical details of TIGER files F. Land Records -many systems have been developed by local governments in the US to manage land, particularly in urban areas -in other countries there has been more effective coordination at provincial and national levels, e.g. Australia -practices in different countries depend on the system of land tenure -the basic entity in land records systems is the land parcel, i.e. the basic unit of ownership -traditionally ENVIRONMENTAL AND NATURAL RESOURCE DATA Contents of environmental databases B. CHARACTERISTICS Spatial management units C. SOURCES OF DATA Thematic Topographic Remote sensing D. REMOTE SENSING AND GIS Wavelengths Scale in images Elevation Image interpretation Classification Problems in classification Using remotely sensed data in GIS E. EXAMPLE DATABASE - MLMIS Minnesota Land Management Information System (MLMIS) Example use of MLMIS data layers REFERENCES EXAM AND DISCUSSION QUESTIONS NOTES You may prefer to use a local example of a natural resources database in place of the section on the MLMIS. This section can then serve as an outline for the organization of information about your local example. Examples of different air photos (low level, high level, oblique), satellite (natural color, false color) and radar images would be useful illustrations for this unit. UNIT 9 - ENVIRONMENTAL AND NATURAL RESOURCE DATA Compiled with assistance from Charles Parson, Bemidji State University and Jeffrey L. Star, University of California, Santa Barbara A. INTRODUCTION - natural resource-based GISs may be used: - as an inventory tool - to better manage the marketing of the resource - to protect the resource from improper development - to model the complex interactions between phenomena so that forecasts can be used in decision-making Contents of environmental databases overhead - Environmental database themes - there are several different kinds of information needed in an environmental database - many of these are obvious: geology, vegetation, hydrology, soils - however, to address a range of issues, the environmental database must include several characteristics that are not generally perceived as "natural" - transportation network - political boundaries - management unit boundaries - other data may be needed for modeling, e.g. variables relating to: - erosion - groundwater flow - soil productivity B. CHARACTERISTICS - natural resource data in GIS is comparatively static - update can be infrequent - spatial resolution can be relatively low - e.g. grid cells covering large areas - historically, natural resource GIS have been raster-based - adequate for many planning and management applications - can provide comprehensive coverage of a jurisdiction at reasonable cost - could often run on existing mainframes - hardware requirements were modest Spatial management units - the actual management units of most natural resources in North America are pseudo-rasters - square, forty acre parcels are the standard building block for PLSS areas (areas surveyed under the Public Land Survey System) of the Midwest, and Western United States, and much of Canada - "forties" are frequently broken into ten acre units, or combined into: - quarter sections (160 acres) - sections (640 acres, 1 square mile) - townships (6x6 miles) - farms are managed in rectangular fields and forest resources are sold in similar acreage units - however, natural resources do not commonly conform to PLSS grids - vector-based systems appear better able to accurately represent them - on the other hand, satellite imagery, which is an important source of environmental data is raster-based C. SOURCES OF DATA Thematic - thematic map series are compiled by various agencies: - soil maps (e.g. Soil Conservation Service) - land use (e.g. USGS land use series) - vegetation (forestry agencies, many state governments) - surficial geology (US and state geological surveys) Topographic - topographic maps can supply: - elevations - roads and railroads - cultural features - streams and lakes - political and administrative boundaries - public land survey system (PLSS) - "township and range" - Data from USGS topographic maps is available in digital form as DLG (digital line graph) files - Elevation data is available from the USGS in the form of DEMs, (digital elevation models) at various resolutions US Geological Survey supplies 30 m resolution data for much of US Digital Orthophotographic Quadrangles DOQ High resolution 1 meter, recent, may be digital color. http://www.dep.state.wv.us/metadata/index.html handout - USGS Digital elevation models Remote sensing - remotely sensed imagery data can be interpreted to yield many layers - e.g. urban/rural, vegetation, crops, surface geology, land use - LANDSAT and TM (Thematic Mapper) are commonly used sources D. REMOTE SENSING AND GIS - definition of remote sensing - "In the broadest sense, the measurement or acquistion of information of some property of an object or phenomena, by a recording device that is not in physical or intimate contact with the object or phenomena under study" (Manual of Remote Sensing) - aircraft and satellite platforms can be used - selection of a platform involves balancing a number of competing goals: - ability to schedule the acquisition - atmospheric distortions vs. platform stability - the available suite of sensors for a given application - issues of coverage and scale - cost - data can be captured in analog (photographs) or digital form (data, transmitted to a ground station or recorded onboard) demo - display a selection of air photos and satellite views Wavelengths - key issue in a remotely sensed observation is the range of wavelengths of energy that will be observed overhead - Electomagnetic spectrum and satellite coverages - the human eye sees only a limited range of wavelengths - photographs capture visible light - remotely sensed observations may include information in the infrared portion of the spectrum which is not visible to human eyes - infrared sensors allow recording of the thermal characteristics of the earth''s surface - microwave wavelengths can also be used - Radar is a form of microwave system - sometimes of particular value due to the ability to penetrate clouds and carry their own source of illumination - i.e. radar systems generate and collect radiation - they are active sensors - objects with large differences in their electrical properties may be discriminated, and the size of the object compared to the wavelength of the radar system is also important demo - display radar image, natural color image and infrared image Scale in images - key concern is the scale of the images, and how the scale varies within each image due to distortion - many sources of distortion - focal length of the optical system, viewing geometry, surface topography greatly affect the scale at each location in the image overhead - Effect of elevation and oblique view on scale Elevation - information on elevation can be obtained by comparing photographs taken from different camera positions, i.e. stereographic images - the simplest devices for viewing pairs of photographs in stereo, called stereoscopes, effectively recreate the illusion of one''s eyes being in the same position as the camera lenses when the photographs were taken - produce the impression of 3-D images - more complex instruments known as stereoplotters allow operators to use pairs of photographs to develop accurate topographic maps and contours - thus, by understanding the geometrical details of the camera system and the Earth''s surface, one can determine both horizontal and vertical positions of objects with high accuracy and precision - an analytical plotter is a partially automated form of stereoplotter which obtains contours by automatically comparing photographs Image interpretation - the identification of objects and determination of their significance involves: Identification - recognizing features on the image Measurement - once features have been identified, can make measurements (i.e., the distance between objects, the number of features per unit area) Interpretation - normally based on a systematic examination of the primitive elements of the photograph, in conjunction with a wide range of ancillary data - primitive elements include tone, color, size, shape, texture, pattern, shadow, site, association - automated image analysis typically relies on only the first few primitive elements (tone, color, size) - ancillary data are often very diverse, may include maps, vegetation phenologies, and many kinds of information about human activities in the general area - human experts bring all these elements, plus their acquired skills and knowledge of related disciplines - the best photointerpreters have expertise in such related disciplines as physical geography, geology and plant biology and ecology - human interpretation also includes a significant perceptual or subjective component overhead - Conceptual Framework of Image Analysis Classification - the information obtained from a remote sensing instrument consists of reflectance measurements, often in several different bands or parts of the electromagnetic spectrum - measurements are in discrete units with fixed range, e.g. 0-255 - the process of classification, an important part of image interpretation, attempts to assign each pixel to one of a number of classes based on its reflectance in one or more bands - e.g. vegetation types or land use classes ("urban", "pasture", "cropland", "water", "forested") - many techniques exist for classification - supervised classification develops the rules for assigning reflectance measurements to classes using a "training area", based on input from the user, then applies the rules automatically to the remaining image - unsupervised classification develops the rules automatically Problems in classification - since reflectances vary with time of day, season of the year, etc., classification rules vary from image to image - classification is often uncertain or inaccurate - also pixels may often contain several classes - mixed pixels - despite this, classification assigns a single class to every pixel, ignoring uncertainty - there is no best method of classification - successful classification is time-consuming and can be expensive Using remotely sensed data in GIS - often difficult or time consuming to develop systematic products of known accuracy - complex operations are required to force images to correspond to a known map projection and/or to have a consistent scale - difficult to go from image (varying reflectance or emissivity in different wavelength bands) to interpreted features and objects - however, since the value of a GIS is directly related to the quality and currency of its internal data - remote sensing offers a suite of tools for quickly creating current, consistent datasets for input to a GIS - conversely, remotely sensed data is best interpreted when additional spatial datasets (representing other dates, other scales, other sensors, other methods for acquiring data about the earth) are employed - such data may be obtained from a GIS - thus, strong links between remote sensing and GIS can improve both technologies E. EXAMPLE DATABASE - MLMIS Minnesota Land Management Information System (MLMIS) - one of the most extensive natural resource databases - a statewide inventory of layers for natural resource management and planning handout - MLMIS data themes - list is the result of over fifteen years of involvement in projects that added data to the system - referred to as MLMIS40 because the fundamental structure is a raster with 40 acre cells - to improve spatial resolution, this is being gradually replaced with - vector files at a common scale of 1:24,000 (line-width resolution 12 m) - raster files with hectare grid cells Example use of MLMIS data layers - how might the database (and a GIS) be used to assist a county to locate a waste disposal incinerator? handout - Siting a waste disposal incinerator - handout lists several MLMIS data layers and the criteria that might be considered in planning for the siting of a waste disposal incinerator REFERENCES Marble, D.F. et al., 1983. "Geographic information systems and remote sensing," Manual of Remote Sensing. ASPRS/ACSM, Falls Church, VA, 1:923-58. Reviews the various dimensions of the relationship between the two fields. Niemann, Jr., B.J., et al, 1988. "The CONSOIL project: Conservation of natural resources through the sharing of information layers," Proceedings GIS/LIS ''88, San Antonio, TX, pp. 11-25. Reviews a multi-agency project in Wisconsin to design and evaluate an LIS for soil conservation. Radde, G.L., 1987. "Under the Rainbow: GIS and Public Land Management Realities," Proceedings, IGIS ''87, Arlington, VA, 3:461-472. A discussion of the MLMIS, describes some projects that have made use of the system and how policy makers attitudes towards GIS have changed. Star, J.L., and J. Estes, 1990. Geographic Information Systems: An Introduction, Prentice-Hall, Englewood Cliffs, NJ. Chapter 5 reviews data sources. Sullivan, J.G., and B.J. Niemann, Jr., 1987. "Research Implications of eleven natural resource GIS applications," Proceedings, IGIS ''87, Arlington, VA, 3:329-341. A short review of several LIS for natural resource applications, discusses common themes, problems and techniques. http://www.geo.wvu.edu/~elmes/geog350/unit09.htm SPATIAL DATABASES AS MODELS OF REALITY Definition Standards B. DATABASE CONTENT AND AN ORGANIZATION''S MISSION Organization mandates Database contents Database design C. FUNDAMENTAL DATABASE ELEMENTS Entity Object Entity types Spatial object type Object classes Attributes Attribute value Database model Layers D. DATABASE DESIGN Steps in database design Desirable database characteristics Issues in database design REFERENCES EXAM AND DISCUSSION QUESTIONS NOTES This begins a three unit section covering some basic principles of spatial databases. As these issues are very fundamental, many of them are introduced here but dealt with in much greater detail in later units. UNIT 10 - SPATIAL DATABASES AS MODELS OF REALITY Compiled with assistance from Timothy L. Nyerges, University of Washington A. INTRODUCTION - the real world is too complex for our immediate and direct understanding - we create "models" of reality that are intended to have some similarity with selected aspects of the real world - databases are created from these "models" as a fundamental step in coming to know the nature and status of that reality Definition - a spatial database is a collection of spatially referenced data that acts as a model of reality - a database is a model of reality in the sense that the database represents a selected set or approximation of phenomena - these selected phenomena are deemed important enough to represent in digital form - the digital representation might be for some past, present or future time period (or contain some combination of several time periods in an organized fashion) Standards - many of the definitions in this Unit have been standardized by the proposed US National Digital Cartographic Standard (DCDSTF, 1988) - these standards have been developed to provide a nationally uniform means for portraying and exchanging digital cartographic data - these cartographic standards will form part of a larger standard being developed for the digital representation of all earth science information B. DATABASE CONTENT AND AN ORGANIZATION''S MISSION Organization mandates - organizations have mandates to perform certain tasks that carry out their missions - mandates are the reasons they exist as organizations - organizations have different needs for data depending on their mandates and the activities required to carry out these mandates - mandates often help identify and define entities of interest, requiring a certain view of the world - what might seem at first glance to be the same data need in two different organizations can actually be quite different when we look at a more detailed level - e.g. wildlife and forestry departments both need information on vegetation but the detail needed is different Database contents Example: Transportation - highway data from the different points of view of a natural resources organization and a highway transportation organization - a natural resource organization might only need logging roads and the connecting access to state highways - the transportation organization''s main interest is in characterizing highways used by the public - the database might also be used to store detailed highway condition and maintenance information - we would expect their need for highway data to be more detailed than would the natural resource organization''s Example: wetlands - wetlands data from the different points of view of an ecological organization and a taxing authority - ecological organization might define wetlands as a natural resource to be preserved and restricted from development - that perspective might require considerable detail for describing the area''s biology and physical resources - a taxing authority might define a wetland to be a "wasteland" and of very little value to society - that description might require only the boundary of the "wasteland" in the database Database design - in each organization only certain phenomena are important enough to collect and represent in a database - the data collection process involves a sampling of geographic reality, to determine the status of that reality (whether past, present or future) - identifying the phenomena and then choosing an appropriate data representation for them is part of a process called database design - see Units 11 and 66 for more on database design C. FUNDAMENTAL DATABASE ELEMENTS - elements of reality modeled in a GIS database have two identities: 1. the element in reality - entity 2. the element as it is represented in the database - object - a third identity that is important in cartographic applications is the symbol that is used to depict the object/entity as a feature on a map or other graphic display - these definitions and the following concepts are based on those defined by the DCDSTF, 1988 (see references) handout - Definition of terms Entity - an entity is "a phenomenon of interest in reality that is not further subdivided into phenomena of the same kind" - e.g. a city could be considered an entity and subdivided into component parts but these parts would not be called cities, they would be districts, neighborhoods or the like - e.g. a forest could be subdivided into smaller forests Object - an object is "a digital representation of all or part of an entity" - the method of digital representation of a phenomenon varies according to scale, purpose and other factors - e.g. a city could be represented geographically as a point if the area under consideration were continental in scale - the same city could be geographically represented as an area if we are dealing with a geographic database for a state or a county Entity types - similar phenomena to be stored in a database are identified as entity types - an entity type is any grouping of similar phenomena that should eventually get represented and stored in a uniform way, e.g. roads, rivers, elevations, vegetation - provides convenient conceptual framework for describing phenomena at a general level - organizational perspective influences this interpretation to a large degree - precise definitions should be generated for each entity type - helps with identifying overlapping categories of information - aids in clarifying the content of the database - the proposed US National Standard for Digital Cartographic Data Volume 2 (DCDSTF 1988) includes a large number of definitions for entity types handout - Sample entity definitions - the first step in database development is the selection and definition of entity types to be included - this is guided by the organization''s mandate and purpose of the database - this framework can be as important as the actual database because it guides the development - the second step of database design is to choose an appropriate method of spatial representation for each of the entity types Spatial object type - the digital representation of entity types in a spatial database requires the selection of appropriate spatial object types - the National Standard for Digital Cartographic Databases specifies a basic list of spatial objects and their characteristics - this classification is based on the following definition of spatial dimensions: 0-D - an object that has a position in space, but no length - a point 1-D - an object having a length - composed of two or more 0­D objects - a line 2-D - an object having a length and width - bounded by at least three 1­D line segment objects - an area 3-D - an object having a length, width and height/depth - bounded by at least four 2­D objects - a volume overhead - Spatial object types (3 pages) handout (cont) - Spatial object types - note very specific definitions for line segment, string, link, chain - spatial objects as representations of reality are dealt with in depth in Unit 11 Object classes - an object class is the set of objects which represent the set of entities - e.g. the set of points representing the set of wells Attributes - an attribute is a characteristic of an entity selected for representation - usually non-spatial - though some may be related to the spatial character of the phenomena under study - e.g. area, perimeter Attribute value - the actual value of the attribute that has been measured (sampled) and stored in the database - an entity type is almost always labeled and known by attributes - e.g. a road usually has a name and is identified according to its class - e.g. alley, freeway - attributes values often are conceptually organized in attribute tables which list individual entities in the rows and attributes in the column - entries in each cell of the table represent the attribute value of a specific attribute for a specific entity - note: attribute table is not an official DCDSTF term Database model - is a conceptual description of a database defining entity type and associated attributes - each entity type is represented by specific spatial objects - after the database is constructed, the database model is a view of the database which the system can present to the user - other views can be presented, but this one is likely useful because it was important in the conceptual design - e.g. the system can model the data in vector form but generate a raster for purposes of display to the user - need not be related directly to the way the data are actually stored in the database - e.g. census zones may be defined as being represented by polygons, but the program may actually represent the polygon as a series of line segments - examples of database models can be grouped by application area - e.g. transportation applications require different database models than do natural resource applications Layers - spatial objects can be grouped into layers, also called overlays, coverages or themes - one layer may represent a single entity type or a group of conceptually related entity types - e.g. a layer may have only stream segments or may have streams, lakes, coastline and swamps - options depend on the system as well as the database model - some spatial databases have been built by combining all entities into one layer D. DATABASE DESIGN - almost all entities of geographic reality have at least a 3­dimensional spatial character, but not all dimensions may be needed - e.g. highway pavement actually has a depth which might be important, but is not as important as the width, which is not as important as the length - representation should be based on the types of manipulations that might be undertaken - map-scale of the source document is important in constraining the level of detail represented in a database - e.g. on a 1:100,000 map individual houses or fields are not visible Steps in database design 1. Conceptual - software and hardware independent - describes and defines included entities - identifies how entities will be represented in the database - i.e. selection of spatial objects - points, lines, areas, raster cells - requires decisions about how real-world dimensionality and relationships will be represented - these can be based on the processing that will be done on these objects - e.g. should a building be represented as an area or a point? - e.g. should highway segments be explicitly linked in the database? 2. Logical - software specific but hardware independent - sets out the logical structure of the database elements, determined by the data base management system used by the software - this is discussed in greater detail in Unit 43 3. Physical - both hardware and software specific - requires consideration of how files will be structured for access from the disk - covered in Unit 66 Desirable database characteristics - database should be: - contemporaneous - should contain information of the same vintage for all its measured variables - as detailed as necessary for the intended applications - the categories of information and subcategories within them should contain all of the data needed to analyze or model the behavior of the resource using conventional methods and models - positionally accurate - exactly compatible with other information that may be overlain with it - internally accurate, portraying the nature of phenomena without error - requires clear definitions of phenomena that are included - readily updated on a regular schedule - accessible to whoever needs it Issues in database design - almost all entities of geographic reality have at least 3-dimensional spatial character, but not all dimensions may be needed - e.g. highway pavement has a depth which might be important, but is not as important as the width, which is not as important as the length - representation should be based on types of manipulations that might be undertaken - map-scale of the source document is important in constraining the level of detail represented in a database - e.g. on a 1:100,000 map individual houses or fields are not visible REFERENCES Codd, E. F., 1981. "Data Models in Database Management," ACM SIGMOD Record 11(2):112-114. Explains the nature of data models, their role in constructing databases. DCDSTF - Digital Cartographic Data Standards Task Force. 1988. "The proposed standard for digital cartographic data," The American Cartographer 15(1). Summary of the major components of the proposed US National Standard. Robinson, A., R. Sale, J. Morrison, and P. Muehrcke, 1984. The Elements of Cartography, (5th ed.), John Wiley and Sons, New York. Useful survey of cartographic terminology and models. Unwin D., 1981. Introductory Spatial Analysis, Methuen, London. A spatial analysis perspective on spatial data models. http://www.geo.wvu.edu/~elmes/geog350/unit10.htm SPATIAL OBJECTS AND DATABASE MODELS http://www.geo.wvu.edu/~elmes/geog350/unit11.htm Contents of environmental databases A. INTRODUCTION B. POINT DATA C. LINE DATA Network entities Network characteristics Attributes Networks as linear addressing systems D. AREA DATA 1. Environmental/natural resource zones 2. Socio-economic zones 3. Land records Areal coverage Holes and islands E. REPRESENTATION OF CONTINUOUS SURFACES General nature of surfaces Data structures for representing surfaces Spatial interpolation REFERENCES EXAM AND DISCUSSION QUESTIONS NOTES This unit continues the development of basic concepts about representing reality as spatial data. Here we look at how the representation of reality in the form of entities is handled with the spatial objects points, lines and areas. UNIT 11 - SPATIAL OBJECTS AND DATABASE MODELS Compiled with assistance from Timothy L. Nyerges, University of Washington A. INTRODUCTION - the objects in a spatial database are representations of real-world entities with associated attributes - the power of a GIS comes from its ability to look at entities in their geographical context and examine relationships between entities - thus a GIS database is much more than a collection of objects and attributes - in this unit we look at the ways a spatial database can be assembled from simple objects - e.g. how are lines linked together to form complex hydrologic or transportation networks - e.g. how can points, lines or areas be used to represent more complex entities like surfaces? B. POINT DATA - the simplest type of spatial object - choice of entities which will be represented as points depends on the scale of the map/study - e.g. on a large scale map - encode building structures as point locations · e.g. on a small scale map - encode cities as point locations FIND an ATM location: http://www.visa.com/ -Coordinates of each point can be stored as two additional attributes - information on a set of points can be viewed as an extended attribute table - each row is a point - all information about the point is contained in the row - each column is an attribute - two of the columns are the coordinates Point data attribute table - here northing and easting represent y and x coordinates - each point is independent of every other point, represented as a separate row in the database model C. LINE DATA Network entities - infrastructure networks - transportation networks - highways and railroads - utility networks - gas, electric, telephone, water - airline networks - hubs and routes - natural networks - river channels Network characteristics - a network is composed of: - nodes - junctions, ends of dangling lines - links - chains in the database model - valency of a node is the number of links at the node - ends of dangling lines are "1-valent" - 4-valent nodes are most common in street networks - 3-valent nodes are most common in hydrology - a tree network has only one path between any pair of nodes, no loops or circuits are possible - most river networks are trees Attributes - Examples of link attributes: - direction of traffic, volume of traffic, length, number of lanes, time to travel along link - diameter of pipe, direction of gas flow - voltage of electrical transmission line, height of towers - number of tracks, number of trains, gradient, width of most narrow tunnel, load bearing capacity of weakest bridge - Examples of node attributes: - presence of traffic lights, presence of overpass, names of intersecting streets - presence of shutoff valves, transformers - note that some attributes (e.g. names of intersecting streets) link one type of entity to another (nodes to links) - some attributes are associated with parts of network links - e.g. part of a railroad link between two junctions may be inside a tunnel - e.g. part of a highway link between two junctions may need pavement maintenance - many GIS systems require such attributes to be attached to the network by splitting existing links and creating new nodes - e.g. split a street link at the house and attach the attributes of the house to the new (2-valent) node - e.g. create a new link for the stretch of railroad which lies inside the tunnel, plus 2 new nodes - this requirement can lead to impossibly large numbers of links and 2-valent nodes - e.g. at a scale of 1:100,000, the US rail network has about 300,000 links - the number of links would increase by orders of magnitude if new nodes had to be defined in order to locate bridges on links Networks as linear addressing systems Often need to use the network as an addressing system, e.g. street network · address matching is the process of locating a house on a street network from its street address Seattle Property Finder http://www.ci.seattle.wa.us/realprop/ - e.g. if it is known that the block contains houses numbers from 100 to 198, house #124 would probably be 1/4 of the way along that link - points can be located on the network by link number and distance from beginning of link - this can be more useful than the (x,y) coordinates of points since it links the points to a location on the network - this approach provides an answer to the problem of assigning attributes to parts of links - keep such entities (houses, tunnels) in separate tables, link them to the network by link number and distance from beginning of link - need one distance for point entities, two for extended entities like tunnels (start and end locations) - the GIS can then compute the (x,y) coordinates of the entities if needed - links need not be permanently split in this scheme - Dynamic referencing D. AREA DATA - is represented on Area Class maps or DASYMETRIC maps, Choropleth maps - boundaries may be defined by natural phenomena, e.g. lake, or by man, e.g. forest stands or by ''FIAT'' -- political decision E.g. census tracts, ZIP or Area code boundaries Several types of areas can be represented 1. Environmental/natural resource zones - examples include - land cover data - forests, wetlands, urban - geological data - rock types - forestry data - forest "stands", "compartments" - soil data - soil types - boundaries are defined by the phenomenon itself - e.g. changes of soil type - almost all junctions are 3-valent 2. Socio-economic zones - includes census tracts, ZIP codes, etc. - boundaries defined independently of the phenomenon, then attribute values are enumerated - boundaries may be culturally defined, e.g. neighborhoods 3. Land records - Land parcel boundaries, land use, land ownership, tax information Boone County, Kentucky http://www.boonecountygis.com/ Data Sources Areal coverage Diagram on handout 1. entities are isolated areas, possibly overlapping - any place can be within any number of entities, or none - e.g. areas burned by forest fires - areas do not exhaust the space 2. any place is within exactly one entity - areas exhaust the space - every boundary line separates exactly two areas, except for the outer boundary of the mapped area - areas may not overlap - any layer of the first type can be converted to one of the second type - each area may now have any number of fire attributes, depending on how many times it has been burned - unburned areas will have none Holes and islands - areas often have "holes" or areas of different attributes wholly enclosed within them Diagram on handout - the database must be able to deal with these correctly - this has not always been true of GIS products - cases can be complex, for example: - Lake Huron is a "hole" in the North American landmass - Manitoulin Island is a "hole" in Lake Huron - Manitoulin Island has several large lakes, including one which is the largest lake on an island in a lake anywhere - some of these lakes have islands in them - some systems allow area entities to have islands - more than one primitive single-boundary area can be grouped into an area object - e.g. the area served by a school or shopping center may have more than one island, but only one set of attributes REFERENCES Burrough, P. A., 1986. Geographical Information Systems for Land Resources Assessment, Clarendon Press, Oxford. See chapter 2 for a review of database models. Dueker, K. J., 1987. "Geographic Information Systems and Computer-Aided Mapping," American Planning Association Journal, Summer 1987:383-390. Compares database models in GIS and computer mapping. Mark, D.M., 1978. "Concepts of Data Structure for Digital Terrain Models," Proceedings of the Digital Terrain Models (DTM) Symposium, ASP and ACSM, pp. 24-31. A comprehensive discussion of DEM database models. Marx, R. W., 1986. "The TIGER System: Automating the Geographic Structure of the United States Census," Government Publications Review 13:181-201. Issues in the selection of a database model for TIGER. Nyerges, T. L. and K. J. Dueker, 1988. Geographic Information Systems in Transportation, Federal Highway Administration, Division of Planning, Washington, D. C. Database model concerns in transportation applications of GIS. Peuquet, D.J., 1984. "A conceptual framework and comparison of spatial data models," Cartographica 21(4):66-113. An excellent review of the various spatial data models used in GIS. B. CHARACTERISTICS Spatial management units C. SOURCES OF DATA Thematic Topographic Remote sensing D. REMOTE SENSING AND GIS Wavelengths Scale in images Elevation Image interpretation Classification Problems in classification Using remotely sensed data in GIS E. EXAMPLE DATABASE - MLMIS Minnesota Land Management Information System (MLMIS) Example use of MLMIS data layers REFERENCES EXAM AND DISCUSSION QUESTIONS NOTES You may prefer to use a local example of a natural resources database in place of the section on the MLMIS. This section can then serve as an outline for the organization of information about your local example. Examples of different air photos (low level, high level, oblique), satellite (natural color, false color) and radar images would be useful illustrations for this unit. UNIT 9 - ENVIRONMENTAL AND NATURAL RESOURCE DATA Compiled with assistance from Charles Parson, Bemidji State University and Jeffrey L. Star, University of California, Santa Barbara A. INTRODUCTION - natural resource-based GISs may be used: - as an inventory tool - to better manage the marketing of the resource - to protect the resource from improper development - to model the complex interactions between phenomena so that forecasts can be used in decision-making Contents of environmental databases overhead - Environmental database themes - there are several different kinds of information needed in an environmental database - many of these are obvious: geology, vegetation, hydrology, soils - however, to address a range of issues, the environmental database must include several characteristics that are not generally perceived as "natural" - transportation network - political boundaries - management unit boundaries - other data may be needed for modeling, e.g. variables relating to: - erosion - groundwater flow - soil productivity B. CHARACTERISTICS - natural resource data in GIS is comparatively static - update can be infrequent - spatial resolution can be relatively low - e.g. grid cells covering large areas - historically, natural resource GIS have been raster-based - adequate for many planning and management applications - can provide comprehensive coverage of a jurisdiction at reasonable cost - could often run on existing mainframes - hardware requirements were modest Spatial management units - the actual management units of most natural resources in North America are pseudo-rasters - square, forty acre parcels are the standard building block for PLSS areas (areas surveyed under the Public Land Survey System) of the Midwest, and Western United States, and much of Canada - "forties" are frequently broken into ten acre units, or combined into: - quarter sections (160 acres) - sections (640 acres, 1 square mile) - townships (6x6 miles) - farms are managed in rectangular fields and forest resources are sold in similar acreage units - however, natural resources do not commonly conform to PLSS grids - vector-based systems appear better able to accurately represent them - on the other hand, satellite imagery, which is an important source of environmental data is raster-based C. SOURCES OF DATA Thematic - thematic map series are compiled by various agencies: - soil maps (e.g. Soil Conservation Service) - land use (e.g. USGS land use series) - vegetation (forestry agencies, many state governments) - surficial geology (US and state geological surveys) Topographic - topographic maps can supply: - elevations - roads and railroads - cultural features - streams and lakes - political and administrative boundaries - public land survey system (PLSS) - "township and range" - Data from USGS topographic maps is available in digital form as DLG (digital line graph) files - Elevation data is available from the USGS in the form of DEMs, (digital elevation models) at various resolutions US Geological Survey supplies 30 m resolution data for much of US Digital Orthophotographic Quadrangles DOQ High resolution 1 meter, recent, may be digital color. http://www.dep.state.wv.us/metadata/index.html handout - USGS Digital elevation models Remote sensing - remotely sensed imagery data can be interpreted to yield many layers - e.g. urban/rural, vegetation, crops, surface geology, land use - LANDSAT and TM (Thematic Mapper) are commonly used sources D. REMOTE SENSING AND GIS - definition of remote sensing - "In the broadest sense, the measurement or acquistion of information of some property of an object or phenomena, by a recording device that is not in physical or intimate contact with the object or phenomena under study" (Manual of Remote Sensing) - aircraft and satellite platforms can be used - selection of a platform involves balancing a number of competing goals: - ability to schedule the acquisition - atmospheric distortions vs. platform stability - the available suite of sensors for a given application - issues of coverage and scale - cost - data can be captured in analog (photographs) or digital form (data, transmitted to a ground station or recorded onboard) demo - display a selection of air photos and satellite views Wavelengths - key issue in a remotely sensed observation is the range of w RELATIONSHIPS AMONG SPATIAL OBJECTS http://www.geo.wvu.edu/~elmes/geog350/unit12.htm Three types of relationship B. EXAMPLES OF SPATIAL RELATIONSHIPS Point-point Point-line Point-area Line-line Line-area Area-area C. CODING RELATIONSHIPS AS ATTRIBUTES Example - "flows into" relationship Example - "is contained in" relationship D. OBJECT PAIRS E. CARTOGRAPHIC AND TOPOLOGICAL DATABASES Strict definition of "topological" Usage of "topological" in GIS F. PLANAR ENFORCEMENT Process Objective G. RELATIONSHIPS IN RASTER SYSTEMS REFERENCES EXAM AND DISCUSSION QUESTIONS NOTES This final unit in the spatial databases module looks at the complex issue of relationships and how they can be coded. The important concept of planar enforcement, introduced here, is referred to several times in later units. UNIT 12 - RELATIONSHIPS AMONG SPATIAL OBJECTS Compiled with assistance from Gerald White, California State University, Sacramento A. INTRODUCTION - there are a vast number of possible relationships in spatial data - many are important in analysis - e.g. "is contained in" relationship between a point and an area is important in relating objects to their surrounding environment - e.g. "intersects" between two lines is important in analyzing routes through networks - relationships can exist between entities of the same type or of different types - e.g. for each shopping center, can find the nearest shopping center (same type) - e.g. for each customer, can find the nearest shopping center (different types) Three types of relationship 1. relationships which are used to construct complex objects from simple primitives - e.g. relationship between a line (chain) and the ordered set of points which defines it - e.g. relationship between an area (polygon) and the ordered set of lines which defines it 2. relationships which can be computed from the coordinates of the objects - e.g. two lines can be examined to see if they cross - the "crosses" relationship can be computed - e.g. areas can be examined to see which one encloses a given point - the "is contained in" relationship can be computed - e.g. areas can be examined to see if they overlap - the "overlaps" relationship 3. relationships which cannot be computed from coordinates - these must be coded in the database during input - e.g. we can compute if two lines cross, but not if the highways they represent intersect (may be an overpass) - some databases allow an entity called a "complex object", composed of "simple objects", e.g. objects representing "house", "lot", "cable", with associated attributes might be grouped together logically as "account" B. EXAMPLES OF SPATIAL RELATIONSHIPS Point-point - "is within", e.g. find all of the customer points within 1 km of this retail store point - "is nearest to", e.g. find the hazardous waste site which is nearest to this groundwater well Point-line - "ends at", e.g. find the intersection at the end of this street - "is nearest to", e.g. find the road nearest to this aircraft crash site Point-area - "is contained in", e.g. find all of the customers located in this ZIP code boundary - "can be seen from", e.g. determine if any of this lake can be seen from this viewpoint Line-line - "crosses", e.g. determine if this road crosses this river - "comes within", e.g. find all of the roads which come within 1 km of this railroad - "flows into", e.g. find out if this stream flows into this river Line-area - "crosses", e.g. find all of the soil types crossed by this railroad - "borders", e.g. find out if this road forms part of the boundary of this airfield Area-area - "overlaps", e.g. identify all overlaps between types of soil on this map and types of land use on this other map - "is nearest to", e.g. find the nearest lake to this forest fire - "is adjacent to", e.g. find out if these two areas share a common boundary C. CODING RELATIONSHIPS AS ATTRIBUTES - in the database model we can visualize relationships as additional attributes Example - "flows into" relationship overhead - Coding relationships as attributes I - option A: - each stream link in a stream network could be given the ID of the downstream link which it flows into - flow could be traced from link to link by following pointers - option B - alternatively the network could be coded as two sets of entities - links and nodes - the links could "point" to their downstream node - the nodes could "point" to the next downstream link Example - "is contained in" relationship overhead - Coding relationships as attributes II - given: - locations of 4 wells, with attributes of depth and flow - wells lie in two different counties with attributes of population - we wish to determine how much flow is available in each county: 1. find the containing county of each well (compute the "is contained in" relationship) - store the result as a new attribute, County, of each well 2. using this revised attribute table, total flow by county and add results to the county table County Population Flow A 20,000 4,500 B 35,000 5,500 D. OBJECT PAIRS - distance is an attribute of a pair of objects - there are other types of information which are similarly attributes of pairs of objects - e.g. flow of commuters between a suburb and downtown - e.g. trade between two countries - e.g. flow of groundwater between a sink and a spring - in some cases these attributes can be attached to an object linking the origin and destination objects - e.g. on a map, trade can be an attribute of an arrow connecting the two countries - thick arrows indicate strong trade - however, such maps quickly become impossibly complex - in general, it is necessary to allow for information which is not an attribute of any one object but of a pair of objects, including: - distance - connectedness - yes or no - flow of goods, trade - number of trips - such attributes cannot necessarily be ascribed to any real object - e.g. commuting flows between a suburb and downtown are not necessarily attributes of any set of links in the transport network - e.g. flow of groundwater between a sink and a spring does not necessarily follow any aquifer or conduit - these are attributes of object pairs - object pairs are important in various kinds of spatial analysis using GIS - attributes of object pairs can be thought of as tables which have one object as rows and the other object as columns with the values in each cell representing the value of the interaction between them - are many different terms for the implementation of this concept - e.g. interaction matrix, turn table, Cartesian product E. CARTOGRAPHIC AND TOPOLOGICAL DATABASES Strict definition of "topological" - if a map is stretched and distorted, some of its properties change, including: - distances - angles - relative proximities - other properties remain constant, including: - adjacencies - most other relationships, such as "is contained in", "crosses" - types of spatial objects - areas remain areas, lines remain lines, points remain points - strictly, topological properties are those which remain unchanged after distortion Usage of "topological" in GIS - a spatial database is often called "topological" if one or more of the following relationships have been computed and stored - connectedness of links at intersections - ordered set of lines (chains) forming each polygon boundary - adjacency relationships between areas - unfortunately the precise meaning of the term has become distorted by use - in general, "topological" implies that certain relationships are stored, making the data more useful for various kinds of spatial analysis - by contrast, a database is called "cartographic" if the above conditions are absent - objects can be manipulated individually - relationships between them are unavailable or are considered unimportant - cartographic databases are less useful for analysis of spatial data - however they are satisfactory for simple mapping of data - many packages designed for mapping only use cartographic database models - a cartographic database can usually be converted to a topological database by computing relationships - the process of "building topology" through planar enforcement F. PLANAR ENFORCEMENT - objects and their attributes are capable of describing the conditions existing on a map or in reality - variation of a single property like soil type or elevation over a mapped area is achieved by including appropriate attributes for entity types - e.g. elevation described by giving attributes to elevation points - e.g. soil type described by giving attributes to areas - in cases like soil type, the objects used to describe spatial variation must obey certain simple rules - e.g. two areas cannot overlap - e.g. every place must be within exactly one area, or on a boundary - these rules are collectively referred to as planar enforcement - a set of objects obeying these rules is said to be planar enforced - planar enforcement is a very important operation in a vector GIS Process - begin with a number of unrelated line segments - imagine a number of limp spaghetti noodles lying on a table - the following elements are now defined (terminology from the US Census Bureau for development of digital spatial database concepts): overhead - Planar enforcement - a 0-cell (or node) is identified wherever two noodles cross or a noodle terminates - i.e. all intersections are calculated - 1-cell (or link, also "chain", "arc", "edge") is identified for each length of noodle between two consecutive 0-cells (nodes) - a 2-cell (or area, also "face", "polygon") is defined for each group of consecutive 1-cells forming an enclosed area that does not contain any 1-cells that are not part of the boundary - note that these definitions relate directly to the ordinary concept of dimensionality - the results are: - 0-cells are either isolated ("points") or adjacent to one or more 1-cells ("nodes") - all 1-cells end in exactly two 0-cells - each line segment (chain) between adjacent 0-cells is assigned to exactly one 1-cell - all 1-cells lie between exactly two 2-cells - every place on the "map" between noodles is assigned to a single 2-cell (the rest of the world is a 2-cell as well, often given the ID zero) Objective - planar enforcement is used to build objects out of digitized lines (hence the phrase "building topology") - it is a consistent and precise approach to the problem of making meaningful objects out of groups of lines - simple rules can be used to correct some digitizing errors: - a very short 1-cell terminating in a 1-valent 0-cell indicates an overshoot diagram - a long 1-cell terminating in a 1-valent 0-cell very close to another 1-cell indicates an undershoot diagram - planar enforcement is often needed when a set of data is being imported from another system - e.g. if the source is a cartographic database and needs to have relationships computed - e.g. if the database models of the two systems are incompatible, data is transferred as unrelated noodles, then objects are rebuilt - planar enforcement must be applied one layer at a time - planar enforcement concepts are built into many systems G. RELATIONSHIPS IN RASTER SYSTEMS - in general, it is easier to work with relationships in vector systems - the concept of object is not as natural for raster systems, which model the world as composed of pixels - however, relationships can be handled in raster systems with simple techniques: overhead - Relationships in raster systems - e.g. a map of county boundaries - in one layer each pixel has a county code attribute which is an ID pointing to an entry in a county attribute table - in a second layer each well location is coded by giving the appropriate pixel an ID pointing to a well attribute table - the "is contained in" relationship can be computed by an overlay operation and stored as an additional column in the well attribute table - only a few raster systems contain this type of capability to extract relationships into attribute tables - most do not handle relationships between spatial objects THE VECTOR OR OBJECT GIS http://www.geo.wvu.edu/~elmes/geog350/unit13.htm Vector data model B. "ARCS" Storing areas C. DATABASE CREATION Building topology Editing Relationship between digitizing and editing Edgematching D. ADDING ATTRIBUTES E. EXAMPLE ANALYSIS USING VECTOR GIS Objective Procedure Result REFERENCES EXAM AND DISCUSSION QUESTIONS NOTES This unit begins a two part introduction to vector GIS. We have placed these units here since we feel this discussion benefits from an understanding of the previous introduction to spatial data concepts in Units 10 to 12. However, with a little revision, it is possible to move this module so that it follows Units 4 and 5 on raster GIS. UNIT 13 - THE VECTOR OR OBJECT GIS Compiled with assistance from Holly Dickinson, State University of New York at Buffalo A. INTRODUCTION Vector data model - based on vectors (as opposed to space-occupancy raster structures) - fundamental primitive is a point - objects are created by connecting points with straight lines - some systems allow points to be connected using arcs of circles - areas are defined by sets of lines - the term polygon is synonymous with area in vector databases because of the use of straight-line connections between points overhead - Example of vector GIS data - very large vector databases have been built for different purposes - vector tends to dominate in transportation, utility, marketing applications - raster and vector both used in resource management applications B. "ARCS" - when planar enforcement is used, area objects in one class or layer cannot overlap and must exhaust the space of a layer - every piece of boundary line is a common boundary between two areas - the stretch of common boundary between two junctions (nodes) has various names - edge is favored by graph theorists, "vertex" for the junctions - chain is the word officially sanctioned by the US National Standard - arc is used by several systems - arcs have attributes which identify the polygons on either side - these are referred to as "left" and "right" by reference to the sequence in which the arc is coded - arcs (chains/edges) are fundamental in vector GIS Storing areas - two ways of storing areas: - polygon storage - every polygon is stored as a sequence of coordinates - although most boundaries are shared between two adjacent areas, all are input and coded twice, once for each adjacent polygon - the two different versions of each internal boundary line may not coincide - difficult to do certain operations, e.g. dissolve boundaries between neighboring areas and merge them - used in some current GISs, many automated mapping packages - arc storage - every arc is stored as a sequence of coordinates - areas are built by linking arcs - only one version of each internal shared boundary is input and stored - used in most current vector-based GISs overhead - Database creation process C. DATABASE CREATION - database creation involves several stages: - input of the spatial data - input of the attribute data - linking spatial and attribute data - spatial data is entered via digitized points and lines, scanned and vectorized lines or directly from other digital sources - once the spatial data has been entered, much work is still needed before it can be used Building topology - once points are entered and geometric lines are created, topology must be "built" - this involves calculating and encoding relationships between the points, lines and areas - this information may be automatically coded into tables of information in the database overhead - Example of "built" topology Editing - during this topology generation process, problems such as overshoots, undershoots and spikes are either flagged for editing by the user or corrected automatically - automatic editing involves the use of a tolerance value which defines the width of a buffer zone around objects within which adjacent objects should be joined - tolerance value is related to the precision with which locations can be digitized diagram - these edit procedures include such functions as snap, move, delete, split, join, etc. Relationship between digitizing and editing - digitizing and editing are complementary activities - poor digitizing leads to much need for editing - good digitizing can avoid most need for editing - both can be very labor-intensive - the process used to digitize area objects can affect the need for later editing: - in "blind" digitizing all linework is digitized once as "noodles" in any order - it is unlikely that the building and cleaning operations will be able to automatically sort out area objects unambiguously from the resulting jumble diagram - some systems require the user to identify junctions between digitized "noodles" explicitly - usually by touching a special button on the cursor diagram - mistakes in building topology are less likely - some systems require the user to digitize each individual arc/chain separately diagram - much easier to sort out polygons - less need for editing - some systems support the building of topology "on the fly" - the system searches constantly for complete area objects as digitizing proceeds - the user is informed by a sound or by blinking as soon as the object is detected Edgematching - compares and adjusts features along the edges of adjacent map sheets - some edgematches merely move objects into alignment - others "join" the pieces together logically - conceptually they become one object - the user "sees" no interruption - an edgematched database is "seamless" - the sheet edges have disappeared as far as the user is concerned D. ADDING ATTRIBUTES - once the objects have been formed by building topology, attributes can be keyed in or imported from other digital databases - once added to the database, attributes must be linked to the different objects - attributes can be linked by pointing to the appropriate object on the screen and coding its corresponding object ID into the attribute table - unlike many raster GIS systems, attribute data is stored and manipulated in entirely separate ways from the locational data E. EXAMPLE ANALYSIS USING VECTOR GIS - compare with example analysis in Unit 4 (The Raster GIS) Objective - identify areas suitable for logging - an area is suitable if it satisfies the following criteria: - is Jack pine (Black Spruce are not valuable) - is well drained (poorly drained and waterlogged terrain cannot support equipment, logging causes unacceptable environmental damage) - is not within 500 m of a lake or watercourse (erosion may cause deterioration of water quality) Procedure overhead - Vector database - database consists of three layers - note: polygons do not entirely fill the space in each case - hence, areas not included fall in polygon ID 0 overhead - Analysis steps - buffer hydrography out to 500 m - merge buffer and lake - extract Jack pine polygons (species = Jack pine) - extract drained soil polygons (drainage = 2, therefore soil = A) - overlay buffer, Jack pine and soil polygons - build topology - extract polygons not in the buffer but in others (buffer = n, Jack pine = y, drainage = y) Result - loggable area shown in final map REFERENCES Beard, M.V. and N.R. Chrisman, 1988. "Zipping: a locational approach to edgematching," The American Cartographer 15:163-72. Describes a solution to the edgematching problem. Chrisman, N.R., 1990. "Deficiencies of sheets and tiles: building sheetless databases," International Journal of Geographical Information Systems 4:157-67. A more general discussion of building edgematched databases. ESRI, 1990. Understanding GIS: The ARC/INFO Way, ESRI, Redlands, CA. A general introductory tutorial for ARC/INFO, a well-known contemporary GIS. Tomlinson, R.F., H.W. Calkins and D.F. Marble, 1976. Computer Handling of Geographical Data. UNESCO Press, Paris. Excellent semi-technical description of CGIS, an early vector-based system. Spatial Analysis - a Process http://educationally.narod.ru/gis302photoalbum.html http://www.gisdevelopment.net/tutorials/tuman001pf.htm ANALYSIS - What? & Why? The heart of GIS is the analytical capabilities of the system. What distinguish the GIS system from other information system are its spatial analysis functions. Although the data input is, in general, the most time consuming part, it is for data analysis that GIS is used. The analysis functions use the spatial and non-spatial attributes in the database to answer questions about the real world. Geographic analysis facilitates the study of real-world processes by developing and applying models. Such models illuminate the underlying trends in geographic data and thus make new information available. Results of geographic analysis can be communicated with the help of maps, or both. The organization of database into map layers is not simply for reasons of organizational clarity, rather it is to provide rapid access to data elements required for geographic analysis. The objective of geographic analysis is to transform data into useful information to satisfy the requirements or objectives of decision-makers at all levels in terms of detail. An important use of the analysis is the possibility of predicting events in the another location or at another point in time. ANALYSIS - How? Before commencing geographic analysis, one needs to assess the problem and establish an objective. The analysis requires step-by-step procedures to arrive at the conclusions. The range of geographical analysis procedures can be subdivided into the following categories. Database Query. Overlay. Proximity analysis. Network analysis. Digital Terrain Model. Statistical and Tabular Analysis. Spatial Analysis It helps us to: Identify trends on the data. Create new relationships from the data. View complex relationships between data sets. Make better decisions. Geographic Analysis Analysis of problems with some Geographic Aspects. Alternatives are geographic locations or areas. Decisions would affect locations or areas. Geographic relationships are important in decision-making or modelling. Some examples of its application: Nearest Neighbour. Network distances. Planar distances. Mapping Concepts, Features & Properties A map represents geographic features or other spatial phenomena by graphically conveying information about locations and attributes. Locational information describes the position of particular geographic features on the Earth''s surface, as well as the spatial relationship between features, such as the shortest path from a fire station to a library, the proximity of competing businesses, and so on. Attribute information describes characteristics of the geographic features represented, such as the feature type, its name or number and quantitative information such as its area or length. Thus the basic objective of mapping is to provide descriptions of geographic phenomenon spatial and non spatial information map features like Point, Line, & Polygon. Map Features Locational information is usually represented by points for features such as wells and telephone pole locations, lines for features such as streams, pipelines and contour lines and areas for features such as lakes, counties and census tracts. Point feature A point feature represents as single location. It defines a map object too small to show as a line or area feature. A special symbol of label usually depicts a point location. Line feature A line feature is a set of connected, ordered coordinates representing the linear shape of a map object that may be too narrow to display as an area such as a road or feature with no width such as a contour line. Area feature An area feature is a closed figure whose boundary encloses a homogeneous area, such as a state country soil type or lake Mapping Concepts, Features & Properties http://www.gisdevelopment.net/tutorials/tuman002pf.htm Map Characteristics In addition to feature locations and their attributes, the other technical characteristics that define maps and their use includes: Map Scale Map Accuracy Map Extent and Data Base Extent Scale To show a portion of the Earth''s surface on a map, the scale must be sufficiently adjusted to cover the objective. Map scale or the extent of reduction is expressed as a ratio. The unit on the left indicates distance on the map and the number on the right indicates distance on the ground. The following three statements show the same scale. 1 inch = 2.000 feet => 1 inch = 24.000 inches => 1:24.000 The latter is known as a representative fraction (RF) because the amounts on either side of the colon are equivalent: that is 1:24.000 means 1inch equals 24.000 inches or1 foot equals 24.000 feet or 1 meter equals 24.000 meters and so on. Map scale indicates how much the given area has been reduced. For the same size map, features on a small-scale map (1:1,000,0000) will be smaller than those on a large-scale map (1:1,200). A map with less detail is said to be of a smaller scale than one with more detail. Cartographers often divide scales into three different categories. Small-scale maps have scales smaller than 1 : 1,000,000 and are used for maps of wide areas where not much detail is required. Medium-scale maps have scales between 1 : 75,000 and 1 : 1,000,000. Large-scale maps have scales larger than 1 : 75,000. They are used in applications where detailed map features are required. So each scale represents a different tradeoff. With a small-scale map, you''ll be able to show a large area without much detail. On a large-scale map, you''ll be able to show a lot of detail but not for a large area. The small-scale map can show a large area because it reduces the area so much that the large-scale map can only show a portion of one street, but in such detail that you can see shapes of the houses. To convert this statement to a representative fraction, the units of measure on both the sides being compared must be the same. For this example, both measurements will be in meters. To do this: Convert 1.6 inches into meters 1.6 inches x 0.0254 meters/inch = 0.04 meters Let us suppose that 0.04 units on the map = 10,000 units on the ground Then, you can now state the scale as a representative fraction (RF): 0.04:10,000 Though it is a valid statement of scale, most cartographers may find it clumsy. Traditionally, the first number in the representative fraction is made equal to 1: 0.04 / 0.04 = 1 units on the map = 10,000 / 0.04 units on the ground 1 unit on the map = 250,000 units on the ground Scale in Digital Maps With digital maps, the traditional concept of scale in terms of distance does not apply because digital maps do not remain fixed in size. They can be displayed or plotted at any possible magnification. Yet we still speak of the scale of a digital map. In digital mapping, the term scale is used to indicate the scale of the materials from which the map was made. For example, if a digital map is said to have a scale of 1:100,000, it was made from a 1:100,000-scale paper map. However, a digital map''s scale still allows you to make some educated guesses about its contents because, generally, digital maps retain the same accuracy and characteristics as their source maps. So it is still true that a large-scale digital map will usually be more accurate and less general than a small-scale digital map. Because the display size of a computer-based map is not fixed, users are often tempted to blow up maps to very large sizes. For example, a 1:100,000-scale map can easily be plotted at a size of 1:24,000 or even 1:2,000-but it usually is not a good idea to do so. It encourages the user to make measurements that the underlying data does not support. You cannot measure positions to the nearest foot if your map is only accurate to the nearest mile. You will end up looking for information that does not exist. Map Resolution Map resolution refers to how accurately the location and shape of map features can be depicted for a given map scale. Scale affects resolution. In a larger-scale map, the resolution of features more closely matches real-world features because the extent of reduction from ground to map is less. As map scale decrease, the map resolution diminishes because features must be smoothed and simplified, or not shown at all. Map Accuracy Many factors besides resolution, influence how accurately features can be depicted, including the quality of source data, the map scale, your drafting skill and the width of lines drawn on the ground. A fine drafting pen will draw line''s 1/100 of an inch wide. Such a line represents a corridor on the ground, which is almost 53 feet wide. In addition to this, human drafting errors will occur and can be compounded by the quality of your source maps and materials. A map accurate for one purpose is often inaccurate for others since accuracy is determined by the needs of the project as much as it is by the map itself. Some measurements of a map''s accuracy are discussed below. Absolute accuracy of a map refers to the relationship between a geographic position on a map (a street corner, for instance) and its real-world position measured on the surface of the earth. Absolute accuracy is primarily important for complex data requirements such as those for surveying and engineering-based applications. Relative accuracy refers to the displacement between two points on a map (both distance and angle), compared to the displacement of those same points in the real world. Relative accuracy is often more important and easier to obtain than absolute accuracy because users rarely need to know absolute positions. More often, they need to find a position relative to some known landmark, which is what relative accuracy provides. Users with simple data requirements generally need only relative accuracy. Attribute accuracy refers to the precision of the attribute database linked to the map''s features. For example, if the map shows road classifications, are they correct? If it shows street addresses, how accurate are they? Attribute accuracy is most important to users with complex data requirements. A map''s Currency refers to how up-to-date it is. Currency is usually expressed in terms of a revision date, but this information is not always easy to find. A map is Complete if it includes all the features a user would expect it to contain. For example, does a street map contain all the streets? Completeness and currency usually are related because a map becomes less complete as it gets older. The most important issue to remember about map accuracy is that the more accurate the map, the more it costs in time and money to develop. For example, digital maps with coordinate accuracy of about 100 feet can be purchased inexpensively. If 1-foot accuracy is required, a custom survey is often the only way to get it, which drives up data-acquisition costs by many orders of magnitude and can significantly delay project implementation - by months or even years. Therefore, too much accuracy can be as detrimental to the success of a GIS project as too little. Rather than focusing on the project''s benefits, a sponsoring organization may focus on the costs that result from a level of accuracy not justified for the project. Project support inevitably erodes when its original objectives are forgotten in a flurry of cost analyses. A far better strategy is to start the project with whatever data is readily available and sufficient to support initial objectives. Once the GIS is up and running, producing useful results, project scope can be expanded. The quality of its data can be improved as required. Even though no maps are entirely accurate, they are still useful for decision-making and analysis. How ever, it is important to consider map accuracy to ensure that your data is not used inappropriately. Any number of factors can cause error. Note these sources can have at cumulative effect. E = f(f) + f(1) + f(e) + f(d) + f(a) + f(m) + f(rms) + f(mp) + u Where, f = flattening the round Earth onto a two - dimensional surface (transformation from spherical to planar geometry) I = accurately measuring location on Earth (correct project and datum information) c = cartographic interpretation (correct interpretation of features) d = drafting error (accuracy in tracing of features and width of drafting pen) a = analog to digital conversion (digitizing board calibration) m = media stability (warping and stretching, folding. Wrinkling of map) p = digitizing processor error (accuracy of cursor placement) rms = Root Mean Square (registration accuracy of ties) mp = machine precision (coordinate rounding by computer in storing and transforming) u = additional unexplained source error Map Extent The aerial extent of map is the area on the Earth''s surface represented on the map. It is the limit of the area covered, usually defined by rectangle just large enough to include all mapped features. The size of the study area depends on the map scale. The smaller the scale the larger the area covered. Database Extent A critical first step in building a geographic database is defining its extent. The aerial extent of a database is the limit of the area of interest for your GIS project. This usually includes the areas directly affected by your organization''s responsibility (such as assigned administrative units) as well as surrounding areas that either influence or are influenced by relevant activities in the administrative area. Data Automation Map features are logically organized into a set of layers or themes of information. A base map can be organized into layers such as streams, soils, wells or boundaries. Map data, regardless of how a spatial database will be applied, is collected, automated and updated as series of adjacent map sheets or aerial photograph. Here each sheet is mounted on the digitizer and digitized, one sheet at a time. In order to be able to combine these smaller sheets into larger units or study areas, the co-ordinates of coverage must be transformed into a single common co-ordinate system. Once in a common co-ordinate system, attributes are associated with features. Then as needed map sheets for layer are edge matched and joined into a single coverage for your study area. Types of Information in a Digital Map Any digital map is capable of storing much more information than a paper map of the same area, but it''s generally not clear at first glance just what sort of information the map includes. For example, more information is usually available in a digital map than what you see on-screen. And evaluating a given data set simply by looking at the screen can be difficult: What part of the image is contained in the data and what part is created by the GIS program''s interpretation of the data? You must understand the types of data in your map so you can use it appropriately. Three general types of information can be included in digital maps: Geographic information, which provides the position and shapes of specific geographic features. Attribute information, which provides additional non-graphic information about each feature. Display information, which describes how the features will appear on the screen. Some digital maps do not contain all three types of information. For example, raster maps usually do not include attribute information, and many vector data sources do not include display information. Geographic Information The geographic information in a digital map provides the position and shape of each map feature. For example, a road map''s geographic information is the location of each road on the map. In a vector map, a feature''s position is normally expressed as sets of X, Y pairs or X, Y, Z triples, using the coordinate system defined for the map (see the discussion of coordinate systems, below). Most vector geographic information systems support three fundamental geometric objects: Point: A single pair of coordinates. Line: Two or more points in a specific sequence. Polygon: An area enclosed by a line. Some systems also support more complex entities, such as regions, circles, ellipses, arcs, and curves. Attribute Information Attribute data describes specific map features but is not inherently graphic. For example, an attribute associated with a road might be its name or the date it was last paved. Attributes are often stored in database files kept separately from the graphic portion of the map. Attributes pertain only to vector maps; they are seldom associated with raster images. GIS software packages maintain internal links tying each graphical map entity to its attribute information. The nature of these links varies widely across systems. In some, the link is implicit, and the user has no control over it. Other systems have explicit links that the user can modify. Links in these systems take the form of database keys. Each map feature has a key value stored with it; the key identifies the specific database record that contains the feature''s attribute information. Display Information The display information in a digital-map data set describes how the map is to be displayed or plotted. Common display information includes feature colours, line widths and line types (solid, dashed, dotted, single, or double); how the names of roads and other features are shown on the map; and whether or not lakes, parks, or other area features are colour coded. However, many users do not consider the quality of display information when they evaluate a data set. Yet map display strongly affects the information you and your audience can obtain from the map - no matter how simple or complex the project. A technically flawless, but unattractive or hard-to-read map will not achieve the goal of conveying information easily to the user. Cartographic Appeal Clearly, how a map looks - especially if it is being used in a presentation - determines its effectiveness. Appropriate color choices, linetypes, and so on add the professional look you want and make the map easier to interpret. Since display information often is not included in the source data set or is filtered out by conversion software, you may need to add it yourself or purchase the map from a vendor who does it for you. Map display information should convey the meaning of its underlying attribute data. Various enhancements will increase a map''s usefulness and cartographic appeal. Feature Colors and Linetypes. Colors and line representations should be chosen to make the map''s meaning clear. For example, using double-line roads can be quite helpful. Many G Geographical Data Sets http://www.gisdevelopment.net/tutorials/tuman003pf.htm http://educationally.narod.ru/gis303photoalbum.html Geographic Data Types Although the two terms, data and information, are often used indiscriminately, they both have a specific meaning. Data can be described as different observations, which are collected and stored. Information is that data, which is useful in answering queries or solving a problem. Digitizing a large number of maps provides a large amount of data after hours of painstaking works, but the data can only render useful information if it is used in analysis. Spatial and Non-spatial data Geographic data are organised in a geographic database. This database can be considered as a collection of spatially referenced data that acts as a model of reality. There are two important components of this geographic database: its geographic position and its attributes or properties. In other words, spatial data (where is it?) and attribute data (what is it?) Attribute Data The attributes refer to the properties of spatial entities. They are often referred to as non-spatial data since they do not in themselves represent location information. District Name Area Population Noida 395 sq. Km. 6,75,341 Ghaziabad 385 sq. Km. 2,57,086 Mirzapur 119 sq. Km. 1,72,952 Spatial data Geographic position refers to the fact that each feature has a location that must be specified in a unique way. To specify the position in an absolute way a coordinate system is used. For small areas, the simplest coordinate system is the regular square grid. For larger areas, certain approved cartographic projections are commonly used. Internationally there are many different coordinate systems in use. Geographic object can be shown by FOUR type of representation viz., points, lines, areas, and continuous surfaces. Point Data Points are the simplest type of spatial data. They are-zero dimensional objects with only a position in space but no length. Line Data Lines (also termed segments or arcs) are one-dimensional spatial objects. Besides having a position in space, they also have a length. Area Data Areas (also termed polygons) are two-dimensional spatial objects with not only a position in space and a length but also a width (in other words they have an area). Continuous Surface Continuous surfaces are three-dimensional spatial objects with not only a position in space, a length and a width, but also a depth or height (in other words they have a volume). These spatial objects have not been discussed further because most GIS do not include real volumetric spatial data. vector formats Common Vector File Formats Format Name Software Platform Internal or Transfer Developer Comments Arc Export ARC/INFO* Transfer Environmental Systems Research Institute, Inc. (ESRI) Transfers data across ARC/INFO* platforms. ARC/INFO* Coverages ARC/INFO* Internal ESRI AutoCAD Drawing Files (DWG) AutoCAD* Internal Autodesk Autodesk Data Interchange File (DXF™) Many Transfer Autodesk Widely used graphics transfer standard. Digital Line graphs (DLG) Many Transfer United States Geological Survey (USGS) Used to publish USGS digital maps. Hewlett-Packard Graphic Language (HPGL) Many Internal Hewlett-Packard Used to control HP plotters. MapInfo Data Transfer Files (MIF/MID) MapInfo* Transfer MapInfo Corp. MapInfo Map Files MapInfo* Internal MapInfo Corp. MicroStation Design Files (DGN) MicroStation* Internal Bentley Systems, Inc. Spatial Data Transfer System (SDTS) Many (in the future) Transfer US Government New US standard for vector and raster geographic data. Topologically Integrated Geographic Encoding and Referencing (TIGER) Many Transfer US Census Bureau Used to publish US Census Bureau maps. Vector Product Format (VPF) Military mapping systems Both US Defense Mapping Agency Used to publish Digital Chart of the World. Some common raster formats are described below Format Name Software Platform Internal or Transfer Developer Comments Arc Digitized Raster Graphics (ADRG) Military mapping systems Both US Defense Mapping Agency Band Interleaved by Line (BIL) Man Both Common remote-sensing standard. Band Interleaved by Pixel (BIP) Many Both Common remote-sensing standard. Band Sequential (BSQ) Many Both Common remote-sensing standard. Digital Elevation Model for (DEM) Many Transfer United States Geological Survey (USGS) USGS standard format digital terrain models. PC Paintbrush Exchange (PCX) PC Paintbrush Both Zsoft Widely used raster format. Spatial Data Transfer Standard (SDTS) Many (in the future) Transfer US Federal Government New US standard for both raster and vector geographic data; raster version still under development. Tagged Image File Format (TIFF) PageMaker Both Aldus Widely used raster format. Global Positioning System http://www.gisdevelopment.net/tutorials/tuman004pf.htm Global Positioning System Introduction The Global Positioning System (GPS) is a burgeoning technology, which provides unequalled accuracy and flexibility of positioning for navigation, surveying and GIS data capture. The GPS NAVSTAR (Navigation Satellite timing and Ranging Global Positioning System) is a satellite-based navigation, timing and positioning system. The GPS provides continuous three-dimensional positioning 24 hrs a day throughout the world. The technology seems to be beneficiary to the GPS user community in terms of obtaining accurate data upto about100 meters for navigation, metre-level for mapping, and down to millimetre level for geodetic positioning. The GPS technology has tremendous amount of applications in GIS data collection, surveying, and mapping. Geopositioning -- Basic Concepts By positioning we understand the determination of stationary or moving objects. These can be determined as follows: In relation to a well-defined coordinate system, usually by three coordinate values and In relation to other point, taking one point as the origin of a local coordinate system. The first mode of positioning is known as point positioning, the second as relative positioning. If the object to be positioned is stationary, we term it as static positioning. When the object is moving, we call it kinematic positioning. Usually, the static positioning is used in surveying and the kinematic position in navigation. GPS - Components and Basic Facts The GPS uses satellites and computers to compute positions anywhere on earth. The GPS is based on satellite ranging. That means the position on the earth is determined by measuring the distance from a group of satellites in space. The basic principle behind GPS are really simple, even though the system employs some of the most high-tech equipment ever developed. In order to understand GPS basics, the system can be categorised into FIVE logical Steps Triangulation from the satellite is the basis of the system. To triangulate, the GPS measures the distance using the travel time of the radio message. To measure travel time, the GPS need a very accurate clock. Once the distance to a satellite is known, then we need to know where the satellite is in space. As the GPS signal travels through the ionosphere and the earth''s atmosphere, the signal is delayed. To compute a positions in three dimensions. We need to have four satellite measurements. The GPS uses a trigonometric approach to calculate the positions, The GPS satellites are so high up that their orbits are very predictable and each of the satellites is equipped with a very accurate atomic clock. Components of a GPS The GPS is divided into three major components The Control Segment The Space Segments The User Segment The Control Segment The Control Segment consists of five monitoring stations (Colorado Springs, Ascesion Island, Diego Garcia, Hawaii, and Kwajalein Island). Three of the stations (Ascension, Diego Garcia, and Kwajalein) serve as uplink installations, capable of transmitting data to the satellites, including new ephemerides (satellite positions as a function of time), clock corrections, and other broadcast message data, while Colorado Springs serves as the master control station. The Control Segment is the sole responsibility of the DoD who undertakes construction, launching, maintenance, and virtually constant performance monitoring of all GPS satellites. The DOD monitoring stations track all GPS signals for use in controlling the satellites and predicting their orbits. Meteorological data also are collected at the monitoring stations, permitting the most accurate evaluation of tropospheric delays of GPS signals. Satellite tracking data from the monitoring stations are transmitted to the master control station for processing. This processing involves the computation of satellite ephemerides and satellite clock corrections. The master station controls orbital corrections, when any satellite strays too far from its assigned position, and necessary repositioning to compensate for unhealthy (not fully functioning) satellites. The Space Segment The Space Segment consists of the Constellation of NAVASTAR earth orbiting satellites. The current Defence Department plan calls for a full constellation of 24 Block II satellites (21 operational and 3 in-orbit spares). The satellites are arrayed in 6 orbital planes, inclined 55 degrees to the equator. They orbit at altitudes of about 12000, miles each, with orbital periods of 12 sidereal hours (i.e., determined by or from the stars), or approximately one half of the earth''s periods, approximately 12 hours of 3-D position fixes. The next block of satellites is called Block IIR, and they will provide improved reliability and have a capacity of ranging between satellites, which will increase the orbital accuracy. Each satellite contains four precise atomic clocks (Rubidium and Cesium standards) and has a microprocessor on board for limited self-monitoring and data processing. The satellites are equipped with thrusters which can be used to maintain or modify their orbits. The User Segment The user segment is a total user and supplier community, both civilian and military. The User Segment consists of all earth-based GPS receivers. Receivers vary greatly in size and complexity, though the basic design is rather simple. The typical receiver is composed of an antenna and preamplifier, radio signal microprocessor, control and display device, data recording unit, and power supply. The GPS receiver decodes the timing signals from the ''visible'' satellites (four or more) and, having calculated their distances, computes its own latitude, longitude, elevation, and time. This is a continuous process and generally the position is updated on a second-by-second basis, output to the receiver display device and, if the receiver display device and, if the receiver provides data capture capabilities, stored by the receiver-logging unit. GPS Positioning Types Absolute Positioning The mode of positioning relies upon a single receiver station. It is also referred to as ''stand-alone'' GPS, because, unlike differential positioning, ranging is carried out strictly between the satellite and the receiver station, not on a ground-based reference station that assists with the computation of error corrections. As a result, the positions derived in absolute mode are subject to the unmitigated errors inherent in satellite positioning. Overall accuracy of absolute positioning is considered to be no greater than 50 meters at best by Ackroyd and Lorimer and to be + 100 meter accuracy by the U.S. Army Corps of Engineers. Differential Positioning Relative or Differential GPS carries the triangulation principles one step further, with a second receiver at a known reference point. To further facilitate determination of a point''s position, relative to the known earth surface point, this configuration demands collection of an error-correcting message from the reference receiver. Differential-mode positioning relies upon an established control point. The reference station is placed on the control point, a triangulated position, the control point coordinate. This allows for a correction factor to be calculated and applied to other roving GPS units used in the same area and in the same time series. Inaccuracies in the control point''s coordinate are directly additive to errors inherent in the satellite positioning process. Error corrections derived by the reference station vary rapidly, as the factors propagating position errors are not static over time. This error correction allows for a considerable amount of error of error to be negated, potentially as much as 90 percent Accuracy of GPS? There are four basic levels of accuracy - or types of solutions - you can obtain with your real-time GPS mining system: Autonomous Accuracy 15 - 100 meters Differential GPS (DGPS) Accuracy 0.5 - 5 meters Real-Time Kinematic Float (RTK Float) Accuracy 20cm - 1 meter Real-Time Kinematic Fixed (RTK Fixed) Accuracy 1cm - 5 cm GPS satellites broadcast on three different frequencies, and each frequency (or career wave) has some information or codes on it. You can think of it as three different radio stations broadcasting several different programs. The table below lists the signals and the contents: L1 Career L2 Career L3 Career 19 cm wavelength 24 cm wavelength Data not available 1575.42 M Hz 1227.6 M Hz C/A Code P Code Navigation Navigation Message P Code : Reserved for direct use only by the military C/A Code : Used for rougher positioning For Single frequency use only L1 career is used For Double frequency, L1/L2/L3 career is used The navigation message (usually referred to as the ephemeris) tells us where the satellites are located, in a special coordinate system called WGS-84. If you know where the satellites are at any given time, then you can compute your location here on earth. http://www.gisdevelopment.net/tutorials/tuman004pf.htm Global Positioning System Different types of answers given by a GPS. Autonomous Positions Uses……….. C/A code only Requires….. Only one receiver Data from at least four satellites Provides…… An accuracy range of about 15 - 100 meters This solution is designed for people who just need an approximate location on the earth, such as a boat at sea or a hiker in the mountains. Real-Time Differential GPS (DGPS) Positions Uses……….. C/A code only Requires….. Two receivers A radio link between the two receivers Reference receiver at a known location broadcasts RTCM (Radio Technical Commission for Maritime Services) corrections. Rover receiver applies corrections for improved GPS positions Data from at least four satellites - the same four at both the references and rover (common satellites) Provides…… An accuracy range of about 0.5 - 5 meters depending upon the quality of receiver and antennae used. This solution gives much better results because here we have a known position at a reference receiver. However it must have a radio link between the reference receiver and the roving (moving) receiver. Real Time Kinematic (RTK) Float Positions Uses……….. C/A code and career waves. Requires….. Two receivers Reference receiver at a known location tracks satellites and then broadcasts this satellite data over a radio link in a format called CMR. (CMR is a Trimbleв - defined format) Rover receiver receives data from both the satellites and the reference station. A radio link between the two receivers. Data from atleast four common satellites. Provides…… An accuracy range of about 20 cm to 1 meters. This solution uses more of the satellite signal than the autonomous or DGPS solution. The CMR data is carrier phase data. The float solution is actually an intermediate step towards the most precise answer, which we''ll discuss next. Real Time Kinematic (RTK) Fixed Solutions Uses……….. C/A code and career waves. Requires….. Two receivers Reference receiver at a known location tracks satellites and then broadcasts CMR data over a radio link. Rover receiver receives data from both the satellites and the reference station. A radio link between the two receivers. Initialization, which is achieved most easily with dual-frequency receivers. Data from at least five common satellites to initialize on-the-fly (in motion) Tracking at least four common satellites after initializing. Provides…… An accuracy range of about 1 - 5 cm. We noticed that with each increasing level of precision, there are more requirements. The most important unique requirement for the RTK fixed solution is something called an initialization. Here it is not feasible to explain what''s happening in an initialization, but it is relevant to mention that initialization is necessary to work at centi-meter level accuracy. Dual frequency receivers can perform this process automatically. If the receiver looses the initialization - which can happen if it fails to track enough satellites - then your working accuracy will drop to the float solutions status temporarily. Remember, however both of these solutions require a radio link to your reference receiver. If, for any reason, you loose your radio link, you will drop back to the autonomous level - the least precise - until the radio link is regained. Factors that affect GPS There are a number of potential error sources that affect either the GPS signal directly or your ability to produce optimal results: Number of satellites - minimum number required: You must track atleast four common satellites - the same four satellites - at both the reference receiver and rover for either DGPS or RTK solutions. Also to achieve centimeter -level accuracy, remember you must have a fifth satellite for on-the fly RTK initialization. This extra satellite adds a check on the internal calculation. Any additional satellites beyond five provide even more checks, which is always useful. Multipath - reflection of GPS signals near the antennae: Multipath is simply reflection of signals similar to the phenomenon of ghosting on our television screen. GPS signals may be reflected by surfaces near the antennae, causing error in the travel time and therefore error in the GPS positions. Ionosphere - change in the travel time of the signal: Before GPS signals reach your antenna on the earth, they pass through a zone of charged particles called the ionosphere, which changes the speed of the signal. If your reference and rover receivers are relatively close together, the effect of ionosphere tends to be minimal. And if you are working with the lower range of GPS precisions, the ionosphere is not a major consideration. However if your rover is working too far from the reference station, you may experience problems, particularly with initializing your RTK fixed solution. Troposphere - change in the travel time of the signal: Troposphere is essentially the weather zone of our atmosphere, and droplets of water vapour in it can effect the speed of the signals. The vertical component of your GPS answer (your elevation) is particularly sensitive to the troposphere. Satellite Geometry - general distribution of the satellites: Satellite Geometry - or the distribution of satellites in the sky - effects the computation of your position. This is often referred to as Position Dilution of Precision (PDOP). PDOP is expressed as a number, where lower numbers are preferable to higher numbers. The best results are obtained when PDOP is less than about 7. PDOP is determined by your geographic location, the time of day you are working, and any site obstruction, which might block satellites. You can use planning software to help you determine when you''ll have the most satellites in a particular area. When satellites are spread out, PDOP is Low (good). When satellites are closer together, PDOP is High (weak). Satellite Health - Availability of Signal: While the satellite system is robust and dependable, it is possible for the satellites to occassionally be unhealthy. A satellite broadcasts its health status, based on information from the U.S. Department of Defense. Your receivers have safeguards to protect against using data from unhealthy satellites. Signal Strength - Quality of Signal : The strength of the satellite signal depends on obstructions and the elevation of the satellites above the horizon. To the extent it is possible, obstructions between your GPS antennae and the sky should be avoided. Also watch out for satellites which are close to the horizon, because the signals are weaker. Distance from the Reference Receiver : The effective range of a rover from a reference station depends primarily on the type of accuracy you aere trying to achieve. For the highest real time accuracy (RTK fixed), roveres should be within about 10-15 Km (about 6-9 miles) of the reference station. As the range exceeds this recommended limit, you may failto initialize and be restricted to RTK float solutions (decimeter accuracy). Radio Frequency (RF) Interference: RF interference may sometimes be a problem both for your GPS reception and your radio system. Some sources of RF interference include: Radio towers Transmitters Satellite dishes Generators One should be particularly careful of sources which transmit either near the GPS frequencies (1227 and 1575 MHz) or near harmonics (multiples) of these frequencies. One should also be aware of the RF generated by his own machines. Loss of Radio Transmission from Base: If, for any reason, there is an interruption in the radio link between a reference receiver and a rover, then your rover is left with an autonomous position. It is very important to set up a network of radios and repeaters, which can provide the uninterrupted radio link needed for the best GPS results. Some Interesting Links : Factors Affecting GPS Use Summary of Surveying with GPS - Prepared for CET318 - Control Surveying Old Dominion University John D. Love GPS : Intermidiate Stuff Discusses Basic Factors Affecting GPS Accuracy Factors Affecting GPS A Farmers Guide to Precision Farming Factors Affecting GPS GPS, DGPS, and Backup Systems Reference Station Why? We have already discussed that there are different levels of accuracy in GPS positions. For any level except autonomous (which can have a large amount of error in it), you must have a reference receiver, which is stationary, and a rover, which can be mobile or stationary. The GPS reference station normally operates continuously, 24 hours a day. The coordinates of this station must be known before you can begin using GPS on any of your machines. First a proper site for the reference station is to be selected, then a GPS survey is performed to obtain the known coordinates. This is usually done as part of the installation, either by the installation team or other qualified personnel. Once it is installed, the GPS reference station can perform two functions simultaneously: Receive data from the satellites Broadcast GPS data to the rovers in the mine One reference station can support unlimited rovers. The primary constraint may be distance, because your accuracy may suffer if you''re working too far from the reference station. This maximum distance will vary with your accuracy requirements and environment. Selecting the Reference Station Some of the features of a good reference site are: Clear View to the Sky Proximity to your Working Areas This is both a GPS issue and a radio issue. Remember, RTK is generally limited to about 10-15 Km (6-9 miles) for reliable initializations, due primarily ot potential errors from the ionosphere. Therefore, you should select a reference site that is within about 10-15 Km of where your rovers expect to work. Absence of RF Interference Try to place the reference station away from sources of radio interference, which arise from radio towers, transmitters, television or other satellite dishes, high-voltage power lines,and any other obvious source of interference. Minimal Sources of Multipath Multipath at your reference site can cause inaccurate answers or interfere with your rover''s ability to initialize. Continuous AC / DC Power Source Stable Monumentation One should have a stable survey monument or other similarly well-defined physical point at the reference station. Without this, we will have to survey and compute new coordinates to the reference station anytime you move the GPS antennae. Stable Antennae Mount Not only the monument should be stable, but also the GPS antennae itself should be secure and stable to minimize the movement. Accessibility of the station Reference Station Equipment: GPS receiver GPS antenna Radio and antenna, Power supply, & Cables Some Interesting Links : Differential GPS An artice on DGPS Reference Station by Mercantor Inc. Radios We have seen that each GPS rover must receive information from the reference station to achieve accurate positions. To maintain constant communication between your reference station and rover, you need these items at the reference station and at each rover: Radio Radio Antenna Cables The radios are cabled directly into the GPS receiver. Power may be provided to the radio through the GPS receiver. At the reference site, GPS data is broadcast through the radio. At the rover site, the reference GPS data is received by the radio and routed into the rover receiver, where it is processed together with rover''s GPS data/ The rover radio can also draw power from the GPS receiver. Repeater Radios: If, for any reason, the reference station transmission cannot reach your rovers, then you must use one or more repeaters. A repeater relays the data from your reference or another repeater. The maximum number of repeaters you can use depends on your type of radio. Repeaters differ from your reference and rover radios in two important ways: they must have their own source of power, and they can be moved as the needs change. The radios draw very low power, but they require uninterrupted power. Because repeaters may need to be moved to accommodate your needs, batteries or compact solar power units are normally used. Frequency and Bandwidth: Most radios used in GPS fall within one of the following frequency ranges: 150-174 MHz (VHF) 406-512 MHz (UHF) 902-928 MHz (spread spectrum) The lower-frequency radios (150-174 MHZ) tend to have more power, due to design and legal issues (not Physics), However, the bandwidth, which determines the amount of data you can transmit, is narrower in these lower ranges (also due to design, not physics). In the nominal 450 MHz and 900 MHz ranges, the bandwidth is wider. This has positive effects both on the amount of data transmitted and on the number of repeaters possible within the radio network. Radio Range To guarantee steady, uninterrupted transmission over the radio, one should be aware of some of the factors that affect the radio''s effective range. Antenna Height: raising the radio antenna is the easiest and most effective way to increase range. Antenna design: radiating patterns vary, depending on the antenna design. For best performance, be sure you understand how yoour antenna transmits signals. Cable length and type: radio signals suffer loss in cables, so keep the length to a minimum. If you must use long cables, use low-loss cables. Output power: doubling output power does not double your effective range. Be sure one understands the relationship between power and gain before the best system is decided. Obstructions: Buildings, walls and even the machines can block or interrupt radio transmission. The repeaters should be carefully used to help minimize the effect of obstructions. Grounding The radio antenna may be a target for lightning. To avoid damage, you may wish to ground your reference station antenna. Some Interesting Links : GPS Radio The most efficient GPS Transmission GPS Applications One of the most significant and unique features of the Global Positioning Systems is the fact that the positioning signal is available to users in any position worldwide at any time. With a fully operational GPS system, it can be generated to a large community of likely to grow as there are multiple applications, ranging from surveying, mapping, and navigation to GIS data capture. The GPS will soon be a part of the overall utility of technology. There are countless GPs applications, a few important ones are covered in the following passage. Surveying and Mapping The high precision of GPS carrier phase measurements, together with appropriate adjustment algorithms, provide an adequate tool for a variety of tasks for surveying and mapping. Using DGPs methods, accurate and timely mapping of almost anything can be carried GPS - Global Position System links, remote setting and GIS http://educationally.narod.ru/linksgisgpssatellite.html GPS Applications One of the most significant and unique features of the Global Positioning Systems is the fact that the positioning signal is available to users in any position worldwide at any time. With a fully operational GPS system, it can be generated to a large community of likely to grow as there are multiple applications, ranging from surveying, mapping, and navigation to GIS data capture. The GPS will soon be a part of the overall utility of technology. There are countless GPs applications, a few important ones are covered in the following passage. Surveying and Mapping The high precision of GPS carrier phase measurements, together with appropriate adjustment algorithms, provide an adequate tool for a variety of tasks for surveying and mapping. Using DGPs methods, accurate and timely mapping of almost anything can be carried out. The GPS is used to map cut blocks, road alignments, and environmental hazards such as landslides, forest fires, and oil spills. Applications, such as cadastral mapping, needing a high degree of accuracy also can be carried out using high grade GPS receivers. Continuous kinematic techniques can be used for topographic surveys and accurate linear mapping. Navigation Navigation using GPS can save countless hours in the field. Any feature, even if it is under water, can be located up to one hundred meters simply by scaling coordinates from a map, entering waypoints, and going directly to the site. Examples include road intersections, corner posts, plot canters, accident sites, geological formations, and so on. GPS navigation in helicopters, in vehicles, or in a ship can provide an easy means of navigation with substantial savings. Remote Sensing and GIS It is also possible to integrate GPS positioning into remote-sensing methods such as photogrammetry and aerial scanning, magnetometry, and video technology. Using DGPS or kinematic techniques, depending upon the accuracy required, real time or post-processing will provide positions for the sensor which can be projected to the ground, instead of having ground control projected to an image. GPS are becoming very effective tools for GIS data capture. The GIS user community benefits from the use of GPS for locational data capture in various GIS applications. The GPS can easily be linked to a laptop computer in the field, and, with appropriate software, users can also have all their data on a common base with every little distortion. Thus GPS can help in several aspects of construction of accurate and timely GIS databases. Geodesy Geodetic mapping and other control surveys can be carried out effectively using high-grade GPs equipment. Especially when helicopters were used or when the line of sight is not possible, GPS can set new standards of accuracy and productivity. Military The GPS was primarily developed for real time military positioning. Military applications include airborne, marine, and land navigation. Future of GPS Technology Barring significant new complications due to S/A (Selective Availability) from DOD, the GPS industry is likely to continue to develop in the civilian community. There are currently more than 50 manufacturers of GPs receivers, with the trend continuing to be towards smaller, less expensive, and more easily operated devices. While highly accurate, portable (hand-held) receivers are already available, current speculation envisions inexpensive and equally accurate ''wristwatch locators'' and navigational guidance systems for automobiles. However, there is one future trend that will be very relevant to the GIS user community, namely, community base stations and regional receive networks, as GPS management and technological innovations that will make GPS surveying easier and more accurate. Projection System Projection System Maps are flat, but the surfaces they represent are curved. Transforming, three-dimensional space onto a two dimensional map is called "projection". This process inevitably distorts at least one of the following properties: Shape, Area, Distance, Direction, and often more. It is known that a globe is a true representation of the earth, which is divided into various sectors by the lines of latitudes and longitudes. This network is called ''graticule''. A map projection denotes the preparation of the graticule on a flat surface. Theoretically map projection might be defined as "a systematic drawing of parallels of latitude and meridians of longitudes on a plane surface for the whole earth or a part of it on a certain scale so that any point on the earth surface may correspond to that on the drawing." Necessity of Map Projection An ordinary globe is rendered useless for reference to a small country. It is not possible to make a globe on a very large scale. Say, if anyone wants to make a globe on a scale of one inch to a mile, the radius will be 330 ft. It is difficult to make and handle such a globe and uncomfortable to carry it in the field for reference. Not only topographical maps of different scales but also atlas and wall maps would not have been possibly made without the use of certain projections. So a globe is least useful or helpful in the field of practical purposes. Moreover it is neither easy to compare different regions over the globe in detail, nor convenient to measure distances over it. Therefore for different types of maps different projections have been evolved in accordance with the scale and purpose of the map. Selection of Map Projection There is no ideal map projection, but representation for a given purpose can be achieved. The selection of projection is made on the basis of the following: The location and the extension of the feature of the globe. The shape of the boundary to be projected. The deformations or distortions of a map to be minimized. The mathematical model to be applied to preserve some identity of graphical features. Based on these characteristics the utility of the projection is ascertained. http://www.gisdevelopment.net/tutorials/tuman007pf.htm Remote Sensing Remote Sensing An Introduction Remote Sensing is the science and art of acquiring information (spectral, spatial, temporal) about material objects, area, or phenomenon, without coming into physical contact with the objects, or area, or phenomenon under investigation. Without direct contact, some means of transferring information through space must be utilised. In remote sensing, information transfer is accomplished by use of electromagnetic radiation (EMR). EMR is a form of energy that reveals its presence by the observable effects it produces when it strikes the matter. EMR is considered to span the spectrum of wavelengths from 10-10 mm to cosmic rays up to 1010 mm, the broadcast wavelengths, which extend from 0.30-15 mm. Types In respect to the type of Energy Resources: Passive Remote Sensing: Makes use of sensors that detect the reflected or emitted electro-magnetic radiation from natural sources. Active remote Sensing: Makes use of sensors that detect reflected responses from objects that are irradiated from artificially-generated energy sources, such as radar. In respect to Wavelength Regions: Remote Sensing is classified into three types in respect to the wavelength regions Visible and Reflective Infrared Remote Sensing. Thermal Infrared Remote Sensing. Microwave Remote Sensing. Remote Sensing Satellites A satellite with remote sensors to observe the earth is called a remote-sensing satellite, or earth observation satellite. Remote-Sensing Satellites are characterised by their altitude, orbit and sensor. TRIOS Series (1960-1965) The Television and Infrared Observation Satelites. NOAA It is the first generation of National Oceanic and Atmospheric Administration satellites and was as the first operation operational remote sensing satellite system. The third generation NOAA satellites are also successfully used for vegetation monitoring, apart from meteorological monitoring. It is equipped with Advanced Very High Resolution Radiometer (AVHRR) sensors, and is established at an altitude of 850 km. In polar orbit. GMS Geo-synchronous meteorological satellite. It is established at an altitude of 36,000 km, and its main purpose is meteorological observations Landsat is established at an altitude of 700 Kms is a polar orbit and is used mainly for land area observation. Other remote sensing satellite series in operations are: SPOT, MOS, JERS, ESR, RADARSAT, IRS etc. kkkk Overview of GIS http://www.gisdevelopment.net/tutorials/tuman006pf.htm Introduction Geographic Information System (GIS) is a computer based information system used to digitally represent and analyse the geographic features present on the Earth'' surface and the events (non-spatial attributes linked to the geography under study) that taking place on it. The meaning to represent digitally is to convert analog (smooth line) into a digital form. "Every object present on the Earth can be geo-referenced", is the fundamental key of associating any database to GIS. Here, term ''database'' is a collection of information about things and their relationship to each other, and ''geo-referencing'' refers to the location of a layer or coverage in space defined by the co-ordinate referencing system. Work on GIS began in late 1950s, but first GIS software came only in late 1970s from the lab of the ESRI. Canada was the pioneer in the development of GIS as a result of innovations dating back to early 1960s. Much of the credit for the early development of GIS goes to Roger Tomilson. Evolution of GIS has transformed and revolutionized the the ways in which planners, engineers, managers etc. conduct the database management and analysis. Answers GIS can give Till now GIS has been described in two ways: Through formal definitions, and Through technology''s ability to carry out spatial operations, linking data sets together. However there is another way to describe GIS by listing the type of questions the technology can (or should be able to) answer. Location, Condition, Trends, patterns, Modelling, Aspatial questions, Spatial questions. There are five type of questions that a sophisticated GIS can answer: Location What is at………….? The first of these questions seeks to find out what exists at a particular location. A location can be described in many ways, using, for example place name, post code, or geographic reference such as longitude/latitude or x/y. Condition Where is it………….? The second question is the converse of the first and requires spatial data to answer. Instead of identifying what exists at a given location, one may wish to find location(s) where certain conditions are satisfied (e.g., an unforested section of at-least 2000 square meters in size, within 100 meters of road, and with soils suitable for supporting buildings) Trends What has changed since…………..? The third question might involve both the first two and seeks to find the differences (e.g. in land use or elevation) over time. Patterns What spatial patterns exists…………..? This question is more sophisticated. One might ask this question to determine whether landslides are mostly occurring near streams. It might be just as important to know how many anomalies there are that do not fit the pattern and where they are located. Modelling What if……………..? "What if…" questions are posed to determine what happens, for example, if a new road is added to a network or if a toxic substance seeps into the local ground water supply. Answering this type of question requires both geographic and other information (as well as specific models). GIS permits spatial operation. Aspatial Questions "What''s the average number of people working with GIS in each location?" is an aspatial question - the answer to which does not require the stored value of latitude and longitude; nor does it describe where the places are in relation with each other. Spatial Questions " How many people work with GIS in the major centres of Delhi" OR " Which centres lie within 10 Kms. of each other? ", OR " What is the shortest route passing through all these centres". These are spatial questions that can only be answered using latitude and longitude data and other information such as the radius of earth. Geographic Information Systems can answer such questions. Need of GIS? Many professionals, such as foresters, urban planners, and geologists, have recognized the importance of spatial dimensions in organising & analysing information. Whether a discipline is concerned with the very practical aspects of business, or is concerned with purely academic research, geographic information system can introduce a perspective, which can provide valuable insights as 70% of the information has geographic location as it''s denominator making spatial analysis an essential tool. Ability to assimilate divergent sources of data both spatial and non-spatial (attribute data). Visualization Impact Analytical Capability Sharing of Information Components of GIS GIS constitutes of five key components: Hardware Software Data People Method Hardware It consists of the computer system on which the GIS software will run. The choice of hardware system range from 300MHz Personal Computers to Super Computers having capability in Tera FLOPS. The computer forms the backbone of the GIS hardware, which gets it''s input through the Scanner or a digitizer board. Scanner converts a picture into a digital image for further processing. The output of scanner can be stored in many formats e.g. TIFF, BMP, JPG etc. A digitizer board is flat board used for vectorisation of a given map objects. Printers and plotters are the most common output devices for a GIS hardware setup. Software GIS software provides the functions and tools needed to store, analyze, and display geographic information. GIS softwares in use are MapInfo, ARC/Info, AutoCAD Map, etc. The software available can be said to be application specific. When the low cost GIS work is to be carried out desktop MapInfo is the suitable option. It is easy to use and supports many GIS feature. If the user intends to carry out extensive analysis on GIS, ARC/Info is the preferred option. For the people using AutoCAD and willing to step into GIS, AutoCAD Map is a good option. Data Geographic data and related tabular data can be collected in-house or purchased from a commercial data provider. The digital map forms the basic data input for GIS. Tabular data related to the map objects can also be attached to the digital data. A GIS will integrate spatial data with other data resources and can even use a DBMS, used by most organization to maintain their data, to manage spatial data. People GIS users range from technical specialists who design and maintain the system to those who use it to help them perform their everyday work. The people who useGIS can be broadly classified into two classes. The CAD/GIS operator, whose work is to vectorise the map objects. The use of this vectorised data to perform query, analysis or any other work is the responsibility of a GIS engineer/user. Method And above all a successful GIS operates according to a well-designed plan and business rules, which are the models and operating practices unique to each organization. There are various techniques used for map creation and further usage for any project. The map creation can either be automated raster to vector creator or it can be manually vectorised using the scanned images. The source of these digital maps can be either map prepared by any survey agency or satellite imagery. Some Interesting Links : Components of GIS A working GIS integrates five key components - ESRI Components of GIS by Semcor Information Systems and Services GIS Applications Computerized mapping and spatial analysis have been developed simultaneously in several related fields. The present status would not have been achieved without close interaction between various fields such as utility networks, cadastral mapping, topographic mapping, thematic cartography, surveying and photogrammetery remote sensing, image processing, computer science, rural and urban planning, earth science, and geography. The GIS technology is rapidly becoming a standard tool for management of natural resources. The effective use of large spatial data volumes is dependent upon the existence of an efficient geographic handling and processing system to transform this data into usable information. The GIS technology is used to assist decision-makers by indicating various alternatives in development and conservation planning and by modelling the potential outcomes of a series of scenarios. It should be noted that any task begins and ends with the real world. Data are collected about the real world. Of necessity, the product is an abstraction; it is not possible (and not desired) to handle every last detail. After the data are analysed, information is compiled for decision-makers. Based on this information, actions are taken and plans implemented in the real world. Major areas of application Different streams of planning Urban planning, housing, transportation planning architectural conservation, urban design, landscape. Street Network Based Application It is an addressed matched application, vehicle routing and scheduling: location and site selection and disaster planning. Natural Resource Based Application Management and environmental impact analysis of wild and scenic recreational resources, flood plain, wetlands, acquifers, forests, and wildlife. View Shed Analysis Hazardous or toxic factories siting and ground water modelling. Wild life habitat study and migrational route planning. Land Parcel Based Zoning, sub-division plans review, land acquisition, environment impact analysis, nature quality management and maintenance etc. Facilities Management Can locate underground pipes and cables for maintenance, planning, tracking energy use. ppp Tutorials http://www.spatialhydrology.com/tutorial.html NCGIA CORE CURRICULUM 1990 Version http://www.geog.ubc.ca/courses/klink/gis.notes/ncgia/toc.html US National Spatial Data http://gos2.geodata.gov/wps/portal/gos ESRI network http://www.geographynetwork.com/ Map room http://www.maproom.psu.edu/dcw/ Data Sources http://www.innovativegis.com/basis/primer/sources.html GIS and Skanning technology http://www.gisdevelopment.net/technology/gis/techgi0002b.htm GIS Analysis Functions http://educationally.narod.ru/gis312photoalbum.html Measurement. Distances, lengths, perimeters, areas ... Used to accumulate values over an area being navigated; Parameters to define: ... GIS does not always provide exact answers to problems, but by identifying trends based on geography Geomorphometric analysis of surface landscape features http://www.blm.gov/nstc/ecosysmod/surfland.html GIS ANALYSIS PROCEDURES http://www.mtc.ca.gov/planning/smart_growth/stars/Appendix_G_GIS_Analysis_Procedures.pdf Create a Network Dataset..................................................................... G-2 Figure G2. Network Dataset Wizard ...................................................................... G-2 Figure G3. Connectivity Policy.............................................................................. G-2 Figure G4. Elevation of Path Segments.................................................................. G-3 Figure G5. Network Dataset Turn Table ................................................................ G-3 Figure G6. Creating Network Impedances ............................................................. G-4 Figure G7. Impedance Evaluators.......................................................................... G-4 Figure G8. Direction Settings ................................................................................ G-5 Figure G9. Summary of Network Settings ............................................................. G-5 Figure G10. Adding Pedestrian Paths ....................................................................... G-7 Figure G11. Calculate Locations .............................................................................. G-8 Figure G12. Create Service Area Layer .................................................................... G-9 Figure G13. Load Network Facilities...................................................................... G-10 Figure G14. Setting Service Area Parameters......................................................... G-11 Figure G15. Service Area Polygon Settings............................................................ G-12 Figure G16. Generalized Service Area Polygons .................................................... G-13 Figure G17. Detailed Service Area Polygons.......................................................... Working with ArcGIS Spatial Analyst http://www.tessellations.us/cd_spatial_analyst.htm Glossary of GIS and Remote Sensing Terms http://fwie.fw.vt.edu/tws-gis/glossary.htm Raster and vector GIS measurement tutorial http://www.slideshare.net/poerslide/gis-tutorial-purnawan-1232066 GIS Data http://educationally.narod.ru/gis315photoalbum.html http://educationally.narod.ru/gis30photoalbum.html -http://educationally.narod.ru/gis39photoalbum.html http://educationally.narod.ru/gis311photoalbum.html -http://educationally.narod.ru/gis315photoalbum.html all about data SPATIAL ANALYSIS and GIS 2001 ESRI USER CONFERENCE http://www.csiss.org/learning_resources/content/good_sa/#SECTION%201 What is Spatial Analysis? Basic GIS data models GIS function descriptions 2. Spatial Statistics Spatial interpolation 3. Spatial Interaction Models 4. Spatial Dependence 5. Spatial Decision Support Spatial search Districting Understanding the Effective Distance Algorithm http://www.innovativegis.com/basis/Supplements/Ed_letter_Jun01/E_distance2.htm Free Geography Tools http://freegeographytools.com/2008/additional-erdas-viewfinder-utilities GEOGRAPHICAL DATA ANALYSIS http://educationally.narod.ru/gis327photoalbum.html Query Lenguages http://www.spatial.maine.edu/~max/NCGIA90-12.pdf 12 Queries About GIS http://webby.lst.se/strategis/english/12_queries_about_gis.htm What is a GIS? Which SW should we select? Why GIS? Is it difficult to use GIS? Is GIS profitable? How to make decisions supported by GIS? How to implement GIS? How to get data? Which are the impacts on the organisation when utilising GIS? Which are the trends in the GIS world? Which are the requirements on GIS cooperation? How do we proceed? Queries a GIS Can Answer http://www.igis.edu.pk/querygis.htm Queries a GIS Can Answer: WHAT exists here e.g. forest attributes municipal ownership WHERE are specific conditions e.g. all forest stands over 10 metres high, all houses owned by person x WHAT has changed(over time) e.g. areas harvested between then and now houses increased in price by > 50% HOW are patterns related e.g. harvested watersheds and stream water quality, traffic accidents and road surfaces WHAT IF .. (modelling) e.g. climate warmed by 2 degrees (habitats), avalanche took out section of road (network) Fundamental Raster GIS Procedures tutorial - PROJECT MAKING http://www.gsd.harvard.edu/gis/manual/raster/index.htm Like vector-relational GIS, raster GIS provides procedures for deriving new information by transforming or making associations of information from existing layers. The formal logic of rasters is known as Map Algebra. When using ArcGIS to work with raster data and procedures there are several housekeeping issues that arise from all of the derived intermediate data that one generates. The first half of this tutorial provides a basic introduction to Raster modeling procedures and the ideosyncracies of the ArcGIS interface. The second half of this tutorial provides examples of the fundamental tools of raster GIS procedures. More in-depth treatment of these topics can be found in the following documents Raster GIS Fundamentals http://www.gsd.harvard.edu/pbcote/courses/gsd6322/08/raster/ GIS Analysis Functions http://maps.unomaha.edu/Peterson/gis/notes/GISAnal1.html Outline Spatial Data Functions Format Transformations Geometric Transformations Projection Transformations Conflation Edge-matching Editing Functions Line Coordinate Thinning Attribute Data Functions Retrieval Classification Verification Integrated Analysis of Spatial and Attribute Data Overlay Neighborhood Function Point-in-Polygon and Line-In-Polygon Topographic Functions Thiessen Polygon Interpolation Cartographic Modeling Connectivity Functions Output Functions Raster GIS http://msdis.missouri.edu/presentations/intro_to_gis/pdf/Raster.pdf GIS Analysis Functions http://maps.unomaha.edu/Peterson/gis/notes/GISAnal1.html Spatial Data Functions Format Transformations Geometric Transformations Projection Transformations Conflation Edge-matching Editing Functions Line Coordinate Thinning Attribute Data Functions Retrieval Classification Verification Integrated Analysis of Spatial and Attribute Data Overlay Neighborhood Function Point-in-Polygon and Line-In-Polygon Topographic Functions Thiessen Polygon Interpolation Cartographic Modeling Connectivity Functions Output Functions GIS Analysis Functions http://maps.unomaha.edu/Peterson/gis/notes/GISAnal2.html Spatial Data Functions Attribute Functions Integrated Analysis of Spatial and Attribute Data Cartographic Modeling Entirely in the Raster Domain Ability to form a logical sequence Map Algebra Connectivity Functions Contiguity Measures Proximity Function Network Functions Sets of Constraints Spread Functions Seek or Stream Functions Intervisibility Functions Output Functions Map Annotation Line Styles Various Graphic Systems Overlay analysis http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=Overlay_analysis One of the most basic questions asked of a GIS is "what''s on top of what?" For example: What landuse is on top of what soil type? What parcels are within the 100-year floodplain? ("within" is just another way of asking "on top of"). What roads are within what counties? What wells are within abandoned military bases? http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=Overlay_analysis Overlay analysis http://resources.esri.com/help/9.3/ArcGISengine/java/Gp_ToolRef/geoprocessing/overlay_analysis.htm Spatial Interpolation http://skagit.meas.ncsu.edu/~helena/gmslab/viz/sinter.html next http://educationally.narod.ru/linksgis35.html