Analysis in GIS

GIS-based landscape classification and mapping of European Russia http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V91-3WK3DMS-1&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1032840987&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=6178041f718fe53ca063a0d81df0aa2b Abstract The landscape approach is widely recognised today as a powerful method of multidisciplinary environmental research. Integrating data both on natural geoecosystems and socio-economic impacts and their relationships, it offers an ideal frame of territorial sampling for evaluating, mapping and modelling environmental status and dynamics. This study is intended to compile a broad-scale environmental frame of European Russia, and to improve existing landscape classifications using GIS techniques. It also suggests a simple and efficient method of validation for broad-scale landscape maps by small-area �maplets'' generated from high-resolution remote sensing data. DIGITAL ORTHOPHOTOS FOR MAPPING AND INTERPRETATION IN HYBRID GIS- http://libraries.maine.edu/Spatial/gisweb/spatdb/egis/eg94209.html Satallite data methodology and methods of GIS http://www.fao.org/DOCREP/004/Y0785E/y0785e05.htm classification of interpretation methods of GIS: local or global; exact or approximate; gradual or abruled;gradual value between identical and completely different, ... deterministic or stochastic Interpolation is the process by which a surface is created, usually a raster dataset, through the input of data collected at a number of sample points. There are several forms of interpolation, each which treats the data differently, depending on the properties of the data set. In comparing interpolation methods, the first consideration should be whether or not the source data will change (exact or approximate). Next is whether the method is subjective, a human interpretation, or objective. Then there is the nature of transitions between points: are they abrupt or gradual. Finally, there is whether a method is global (it uses the entire data set to form the model), or local where an algorithm is repeated for a small section of terrain. http://74.125.155.132/search?q=cache:3YJaO5AQVVoJ:en.wikipedia.org/wiki/Geographic_information_system+exact+or+approximate+method+gis&cd=8&hl=en&ct=clnk&gl=us http://en.wikipedia.org/wiki/Interpolation http://en.wikipedia.org/wiki/Geostatistics Objectives of the project http://www.fao.org/DOCREP/004/Y0785E/y0785e04.htm A classification of interpretation methods in gis http://books.google.com/books?id=kAq-DdhRGwgC&pg=PA256&lpg=PA256&dq=classification+of+interpretation+methods+in+gis&source=bl&ots=737Moe5DkU&sig=WUr2mvwRbfL0f8G798RBJ2UDGHI&hl=en&ei=9Y7GSs3MNYPOsQPs2tWhBQ&sa=X&oi=book_result&ct=result&resnum=10# GIS Analysis Methods http://arachnid.colgate.edu/webguild/project/gis.htm Geomorphometric landscape analysis using a semi-automated GIS-approach http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VHC-4P5RM68-1&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1032855068&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=e4c2b484cfa39904386f9ef19867e248 This paper presents LANDFORM, a customized GIS application for semi-automated classification of landform elements, based on topographic attributes like curvature or elevation percentile. These parameters are derived from a Digital Elevation Model (DEM) and used as thresholds for the classification of landform elements like crests, flats, depressions and slopes. With a new method, slopes were further subdivided into upper, mid and lower slopes at significant breakpoints along slope profiles. The paper discusses the results of a fuzzy set algorithm used to compare the similarity between the map generated by LANDFORM and the visual photo-interpretation conducted by a soil expert over the same area. The classification results can be used in applications related to precision agriculture, land degradation studies, and spatial modelling applications where landscape morphometry is identified as an influential factor in the processes under study. An integrated GIS and location-allocation approach to public facilities planning http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V9K-3VV039J-Y&_user=10&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1032856952&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=8b0c5ef687ca47a8ce2d10193e0d4157 Public facilities planning normally applies a planning standard, such as how many hectares of open space are required for a certain number of people in a district. But planning standards only specify the area required and seldom specify where the public facility should be located. A location-allocation model that attempts to find the best sites for facilities is a more useful tool for public facilities planning. Although its application has been limited by the availability of data, even this has changed with the availability of land information systems in many cities. Accordingly, this paper discusses the integration of GIS and a location-allocation model for public facilities planning, using open space planning in Hong Kong as an example. It demonstrates how such a spatial decision support system can provide a set of solution spaces on which decision makers can focus their discussions and make collective choices. It can also enable decision makers to have a better view of the problem and to test different scenarios by varying the objectives, constraints, and parameters of the models. Uncertainty Propagation in GIS http://www.ncgia.ucsb.edu/giscc/units/u098/u098_f.html NCGIA Core Curriculum in Geographic Information Science URL: "http://www.ncgia.ucsb.edu/giscc/units/u098/u098_f.html" Advanced Organizer Unit Topics this unit outlines an introduction to the problem of uncertainty propagation in GIS the definition and identification of a stochastic error model for quantitative spatial attributes a description of common error propagation techniques applications of the theory how the results of an uncertainty analysis may be used to improve the accuracy of GIS products Intended Learning Outcomes after reading this unit, you should be able to present an overview of the main areas where error propagation within GIS is currently of concern describe how errors in spatial attributes can be defined using statistical terminology discuss the principles of common error propagation techniques and their pro?s and con''s have an idea about how the theory of error propagation in GIS may be applied in practice have sufficient clues and references to dig into this problem more thoroughly if interested The Arc/Info method http://www.ciesin.org/docs/005-331/005-331.html spatial operations Many computer programs, such as spreadsheets (e.g., Lotus 1-2-3), statistics packages (e.g., SAS; Minitab), or drafting packages (e.g., AutoCAD) can handle simple geographic or spatial data. Why, then, are they not usually thought of as a GIS? The generally accepted answer is that a GIS is only a GIS if it permits spatial operations on the data. As an example, consider the table below. aspatial queries Asking "What''s the average number of people working with GIS in each location" is an aspatial query--the answer doesn''t require the stored value of latitude and longitude: nor does it describe where the places are in relation to each other. spatial queries "How many people work in GIS in the major centers of Western Europe" "Which centers lie within 1,000 miles of each other?" "What''s the shortest route passing through all these centers?" These are spatial queries that can only be answered using latitude and longitude data and other information, such as the radius of the earth. A geographic information system can readily answer such questions. data linkage A GIS typically links data from different sets. As an example, suppose you need to know what percentage of each country''s total food production is grown for export. You''ve located the data you need, but your total food production for each country is stored in one computer file, and the food export data is contained in a separate file. You must combine these files to solve the problem. Once the files are combined, it''s a simple process to have the computer perform the arithmetic to produce your answer. If this seems trivial--hardly needing a GIS--consider the different ways in which data sets may need to be linked. exact matching Exact matching occurs when you have information in one computer file about many geographic features (e.g., counties) and additional information in another file about the same set of features. The operation to bring them together is easy, achieved by using a key common to both files--in this case, the county name. So, the record in each file with the same county name is extracted and the two are joined and stored in another file. hierarchical matching Some types of information, however, are collected in more detail or more frequently than other types of information. For example, finance and unemployment data covering large areas is collected frequently. On the other hand, population data is collected for small areas, but at less frequent intervals. If the smaller areas nest (i.e., fit exactly) within the larger ones, then the solution for matching these data is to use hierarchical matching. Group the small areas together until they cover the same area as the larger area, total their data, and then perform an exact match. fuzzy matching On many occasions, the boundaries of the smaller areas do not match those of the larger ones. This is especially true when dealing with environmental data. For example, crop boundaries, usually defined by field edges, rarely match the boundary between types of soil. If you want to determine the most productive soil for a particular crop, you need to overlay the two data sets and compute crop productivity for each and every soil type. In principle, this is like laying one map over another and noting the combinations of soil and crop productivity. (Lesson 8 describes this overlay process more thoroughly.) A GIS can perform all these operations because it uses geography, or space, as the common key between the data sets. Information is linked only if it relates to the same geographic area. Why is data linkage so important? Consider a situation where you have two data sets for the same area, such as yearly income by county and average cost of housing. Each data set might be analyzed and mapped individually. Alternatively, they can be combined to produce one valid combination. If, however, you have 20 data sets for the county, you have over one million possible combinations. Although not all combinations are meaningful (e.g., unemployment and soil type), you can answer many more questions than if the data sets are kept separate. Combining them adds value to the database. To do this, you need a GIS. Questions a GIS can answer So far, a GIS has been described in two ways: 1) through formal definitions, and 2) through its ability to carry out spatial operations, linking data sets using location as the common key. You can, however, also distinguish a GIS by listing the types of questions it can (or should be able to) answer. For any application there are five generic 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 any ways using, for example, a place name, a post or zip code, or a geographic reference, such as latitude and longitude. condition Where is it? The second question is the converse of the first and requires a spatial analysis to answer. Instead of identifying what exists at a given location, you want to find a location where certain conditions are satisfied (e.g., an unforested section of land at least 2,000 square meters in size, within 100 meters of a road, and with soils suitable for supporting buildings). trends What has changed since...? The third question might involve both of the first two and seeks to find the differences within an area over time. patterns What spatial patterns exist? This question is more sophisticated. You might ask this question to determine whether cancer is a major cause of death among residents near a nuclear power station. Just as important, you might want to know how any anomalies there are that don''t fit the pattern and where they are located. modeling 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 groundwater supply. Answering this type of question requires geographic as well as other information. Sample GIS applications Applications for GIS technology developed around the world. Many of the early applications in Europe built land registration systems and environmental databases. However, Britain''s largest GIS expenditure in the 1980s was for developing utility systems and creating a comprehensive topographic database for the country. Canada developed an important forestry application to plan the volume of timber to cut, identify access to the timber, and report the results to the Provincial governments. Applications in China and Japan emphasized monitoring and modeling possible environmental changes. In the United States, the U.S. Bureau of Census and the U.S. Geological Survey used GIS technology for their Topologically Integrated Geographic Encoding and Referencing (TIGER) project. They produced a computerized description of the U.S. transportation network--at a cost of about $170 million--to facilitate taking and reporting the 1990 census. Today, the number and variety of applications for GIS are impressive. The amount of geographic data that has been gathered is staggering and includes volumes of satellite imagery collected from space. Local governments use GIS for planning and zoning, property assessment and land records, parcel mapping, public safety, and environmental planning. Resource managers rely on GIS for fish and wildlife planning; management of forested, agricultural, and coastal lands; and energy and mineral resource management. GIS supports the daily activities of automated mapping and facilities management with applications for electricity, water, sewer, gas, telecommunications, and cable television utilities, using capabilities such as load management, trouble call analysis, voltage drop, basemap generation and maintenance, line system analysis, siting, network pressure and flow analysis, leak detection, and inventory. Demographers use GIS for target market analysis, facility siting, address matching and geocoding, as well as product profiles, forecasting, and planning. GIS also has an increasing role in supporting education and research in the classroom, the computer lab, the research institute, and the public library. The most important point to note is that these diverse applications are carried out using similar software and techniques--a GIS is truly a general-purpose tool. Appendix E contains several maps illustrating a few of the many GIS applications. Several components constiture a GIS: The user becomes part of the GIS whenever complicated analyses, such as spatial analyses and modeling, have to be carried out. These usually require skill in selecting and using tools from the GIS toolbox and intimate knowledge of the data being used. At present and for years to come general-purpose GIS will rely on users to know what they are doing--pressing a button is not enough. what a GIS is not A GIS is not simply a computer system for making maps, although it can create maps at different scales, in different projections, and with different colors. A GIS is an analytical tool. The major advantage of a GIS is that it allows you to identify the spatial relationships between map features. A GIS does not store a map in any conventional sense: nor does it store a particular image or view of a geographic area. Instead, a GIS stores the data from which you can draw a desired view to suit a particular purpose. A GIS links spatial data with geographic information about a particular feature on a map. The information is stored as attributes of the graphically represented feature. For example, the centerline that represents a road on a map doesn''t tell you much about the road except its location. To find out the road''s width or pavement type, you must query the database. Using the information stored in the database, you could create a display symbolizing the roads according to the type of information that needs to be shown. A GIS also uses the stored feature attributes to compute new information about map features; for example, to calculate the length of a particular road segment or to determine the total area of a particular soil type. geographic database In short, a GIS doesn''t hold maps or pictures--it holds a database. The database concept is central to a GIS and is the main difference between a GIS and drafting or computer mapping systems, which can only produce good graphic output. All contemporary geographic information systems incorporate a database management system. If you want to go beyond just making pictures, you need to know three things about every feature stored in the computer: what it is, where it is, and how it relates to other features (e.g., which roads link to form a network). Database systems provide the means of storing a wide range of such information and updating it without the need to rewrite programs. In ARC/INFO, ARC handles where the features are, while the INFO component handles the feature descriptions and how each feature is related to others. Essentially, a GIS gives you the ability to associate information with a feature on a map and to create new relationships that can determine the suitability of various sites for development, evaluate environmental impacts, calculate harvest volumes, identify the best location for a new facility, and so on. Geostatistics http://en.wikipedia.org/wiki/Geostatistics Geostatistics is a branch of statistics focusing on spatiotemporal datasets. Developed originally to predict probable distributions for mining operations, it is currently applied in diverse disciplines including petroleum geology, hydrogeology, hydrology, meteorology, oceanography, geochemistry, geometallurgy, geography, forestry, environmental control, landscape ecology, and agriculture (esp. in precision farming). Geostatistics is applied in varied branches of geography, particularly those involving the spread of disease (epidemiology), the practice of commerce and military planning (logistics), and the development of efficient spatial networks. Geostatistics are incorporated in tools such as geographic information systems (GIS) and digital elevation models. see all GEO contents in links The Arc/Info method and GIS http://www.ciesin.columbia.edu/gsasearch/search?q=cache:Nl6djDz_ACAJ:www.ciesin.columbia.edu/docs/005-331/005-331.html+gis&access=p&output=xml_no_dtd&ie=UTF-8&client=default_web&site=CIESIN&proxystylesheet=default_web&oe=ISO-8859-1 see exact method (matching) in http://www.ciesin.columbia.edu/docs/005-331/pg4a.gif deterministic method - http://landscapemodelling.net/gif/he2008.gif http://spatial-analyst.net/wiki/images/thumb/0/0b/Fig_soil_variation_and_RK.jpg/450px-Fig_soil_variation_and_RK.jpg - stochastic method Stochastic method of interpretation in GIS http://spatial-analyst.net/wiki/index.php?title=Regression-kriging Regression-kriging (RK) is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as terrain parameters, remote sensing imagery and thematic maps) with kriging of the regression residuals. It is mathematically equivalent to interpolation method variously called Universal Kriging and Kriging with External Drift, where auxiliary predictors are used directly to solve the kriging weights. Such algorithms will play more and more important role in geostatistics because the number of possible covariates is today increasing dramatically (Pebesma, 2006). DEMs are now available from a number of sources. Detailed and accurate images of topography can now be ordered from remote sensing systems such as SPOT and ASTER; SPOT5 offers the High Resolution Stereoscopic (HRS) scanner, which can be used to produce DEMs at resolutions of up to 5 m (Toutin, 2006). Finer differences in elevation can also be obtained with airborne laser-scanners. The cost of data is either free or dropping in price as technology advances. NASA recorded most of the world''s topography in the Shuttle Radar Topographic Mission in 2000 (Rabus et al., 2003). From summer of 2004, these data has been available (e.g. via USGS ftp) for almost whole globe at resolution of about 90 m (for the North American continent at resolution of about 30 m). Likewise, MODIS multispectral images are freely available for ftp download at resolutions of 250 m. A large free repository of Landsat images is also available for ftp download via the Global Land Cover Facility (GLCF). Read more about globally available auxiliary maps/images in this article. A step-by-step instructions to run the regression-kriging with your own data set in various software packages you can find here. Create Thiessen Polygons (Analysis) (ArcInfo only) http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=create_thiessen_polygons_(analysis) Dynamic Segmentation and Thiessen Polygons http://proceedings.esri.com/library/userconf/proc95/to150/p114.html Abstract Managers of many river and reservoir systems employ a specialized coordinate system for the identification of locations on the water. The location of a point in this coordinate system requires the specification of two numbers; a river mile, as measured along a curving center line, and a linear distance from either the left or right river bank. Typically, both of these coordinates are estimates that are made from boats in the water. GIS can be used to improve the estimation of these river mile coordinates. River miles are ``defined'''' by points on navigation charts along the center line. Because of the ``fractal coastline'''' phenomenon, one cannot simply calculate the linear distance along the centerline, since the linear distance will keep increasing as the centerline vector is broken into more segments. The calibrateroutes command, available as a part of Dynamic Segmentation, permits the establishment of a route system along the centerline, but will not allow the identification or location of points off the centerline yet still in the water. We have developed a procedure involving the construction of Thiessen polygons around events along the centerline route which permits the construction of a coverage where polygons specify particular rivermile increments. The intersection of this polygon coverage with a point coverage of sample locations, for example, allows the accurate assignment of river mile locations to those samples. Variants of this technique may also be useful for interstate highway applications, where a similar type of coordinate system may be in use. Thiessen Polygons http://www.bbc.co.uk/dna/h2g2/A901937 Also known as ''Voronoi networks1'' and ''Delaunay triangulations2'', Thiessen polygons were independently discovered in several fields of study, including climatology and geography. They are named after a climatologist who used them to perform a transformation from point climate stations to watersheds. Thiessen polygons can be used to describe the area of influence of a point in a set of points. If you take a set of points and connect each point to its nearest neighbour, you have what''s called a triangulated irregular network (TIN). If you bisect3 each connecting line segment perpendicularly4 and create closed polygons with the perpendicular bisectors, the result will be a set of Thiessen polygons. The area contained in each polygon is closer to the point on which the polygon is based than to any other point in the dataset. What Are They Used For? If you have a set of features or events that are represented as points and you wish to determine the area of influence of each individual event or feature, you can create a set of Thiessen polygons based on the points. how GPS works http://www.innovativegis.com/basis/pfprimer/Topic7/TOPIC7.html#GPS: Intermediate Stuff Who uses GPS? http://www8.garmin.com/aboutGPS/applications.html GPS has a variety of applications on land, at sea and in the air. Basically, GPS is usable everywhere except where it''s impossible to receive the signal such as inside most buildings, in caves and other subterranean locations, and underwater. The most common airborne applications are for navigation by general aviation and commercial aircraft. At sea, GPS is also typically used for navigation by recreational boaters, commercial fishermen, and professional mariners. Land-based applications are more diverse. The scientific community uses GPS for its precision timing capability and position information. Surveyors use GPS for an increasing portion of their work. GPS offers cost savings by drastically reducing setup time at the survey site and providing incredible accuracy. Basic survey units, costing thousands of dollars, can offer accuracies down to one meter. More expensive systems are available that can provide accuracies to within a centimeter. Recreational uses of GPS are almost as varied as the number of recreational sports available. GPS is popular among hikers, hunters, snowmobilers, mountain bikers, and cross-country skiers, just to name a few. Anyone who needs to keep track of where he or she is, to find his or her way to a specified location, or know what direction and how fast he or she is going can utilize the benefits of the global positioning system. GPS is now commonplace in automobiles as well. Some basic systems are in place and provide emergency roadside assistance at the push of a button (by transmitting your current position to a dispatch center). More sophisticated systems that show your position on a street map are also available. Currently these systems allow a driver to keep track of where he or she is and suggest the best route to follow to reach a designated location. GPS and Racing Applications http://www.biggerhammer.net/offshorepage/features/gps/raceapps.html GPS receivers have been showing up in race boats for several years. My first opportunity to climb into the cockpit of a race boat brought me face to face with a Trimble GPS in Stuart Hayim''s Recovery. Neither Stuart nor Joey knew how to run the gadget, so I became the instant GPS instructor. With some assistance from Ted Sabarese (who usually sports about five Trimbles in his boat), the crew of Recovery became educated in the ways of electronic navigation and their love/hate relationship with GPS took off. From there on, I started to notice how many boats were sporting the little antennas somewhere on the hull. During the 1995 season, many boats showed up at races with GPS installed. Installation is the easy part for the racers - just find someone to rig the boat. The hard part is learning how to use the thing. Compared to a compass, it may seem a bit daunting. After all, you have to push buttons and remember to turn it on. But GPS really isn''t as complex as it seems, it just takes some time to get used to the jargon and button-pushing. And that''s the same whether you''re running a 46'' Skater at 140 mph on Lake X or a 20'' Bayliner at 20 mph on Lake Travis. Why put a GPS on a race boat? Well, if you''re out in front, there is no one to follow to the first turn buoy. And for a lot of racers, the race is spent mostly cruising along and hunting for some sign of the next turn. So, GPS can guide you to the turns and help shave seconds off lap times with better navigation. It reduces the likelihood that you will wander off course or go the wrong way back to the crane. And it makes a great conversation piece. GPS doesn''t replace a compass, it is a complement to the traditional navigation systems for boating. This applies in racing, too. No one would give up their compass and map and go with a GPS. As many racers have discovered, GPS isn''t always reliable. It also is finicky. Some units stare blankly back at the driver or navigator. Others come and go, with no predictability. Why are they so frustrating, you might ask? Well, it has a lot to do with how GPS receivers are designed to work. The signals from the satellites are transmitted at very low wattage, and consequently the receivers are sensitive. Put a very high output electronic ignition system close to a receiver, and you have the potential for problems. The unit may work one day, crap out the next. In addition, life is hard on race boats. They take a beating every time they go out on the water. High speed runs over rough water provide a jarring ride for the equipment. Ever notice how things seem to loosen up when you bounce around a lot on the lake? Well, same thing, only worse, for a race boat. The best way to prevent electronic problems is to properly rig the GPS receiver. There are several options, including rigging a separate battery or dedicated circuit for the receiver. Proper antenna placement, away from the ignition system and engines, will lessen the likelihood of interference. Filters are available from a number of outfitters for eliminating the RF signals that interfere with the satellite signals. Of course, learning the basics of running the GPS is important. Receivers are getting easier to set up and run, but they aren''t as intuitive as a five point harness. Programming way points (turn buoys) and setting up the correct number of laps is important if you want to not get lost or quit a lap too soon. So learn to use the receiver and it will probably never let you down. Just in case, though, keep the course map taped to the dash, count the laps, and use the compass as a backup. And getting a lesson in GPS from a pro doesn''t hurt either. I always enjoy the chance to walk someone through the setup. Earth Observation and Satellite Imagery http://www.ga.gov.au/remote-sensing/basics/ Earth Observation and Satellite Imagery Contact Earth Observation Client Services Basics Earth Observation Gallery About Earth Observation and Satellite Imagery Satellite Reception Areas Other Earth Observation Organisations Our Capabilities How to Get Satellite Imagery and Data Satellites and Sensors An Overview of Remote Sensing http://www.ucalgary.ca/UofC/faculties/SS/GEOG/Virtual/remoteintro.html Remote Sensing is an extensive science, drawing from many areas for support and development. It depends greatly on the support of governments and private industries worldwide. Satellite and digital imagery play an important role in remote sensing; providing information about the land studied. Remote Sensing Systems offer four basic components to measure and record data about an area from a distance. These components include the energy source, the transmission path, the target and the satellite sensor. The energy source, electromagnetic energy, is very important. It is the crucial medium required to transmit information from the target to the sensor. Remote sensing provides important coverage, mapping and classification of landcover features, such as vegetation, soil, water and forests (diagram of spectral reflectance curves for vegetation, soil and water). The Kananaskis Valley has provided an environment for remote sensing studies, using satellite and digital imagery (from Landsat, SPOT and CASI). The degree of accuracy achieved in classification depends on the quality of the images and the degree of knowledge possessed by the researcher, of the native types of species in the areas. Topographic data and a Digital Elevation Model also increase the classification accuracies. Correlations can then be drawn between drainage, surficial deposits and topographic features, in order to show the relationships that occur between forest, vegetation and soils. This provides important information for land classification and land-use management. Remote sensing is an interesting and exploratory science, as it provides images of areas in a fast and cost-efficient manner, and attempts to demonstrate the "what is happening right now" in a study area. While airphotos and fieldwork remain critical as sources of information, the cost and time to carry out these methods sometimes may not be feasible for the study. Satellite and digital imagery acquired recently, provide more overall detail to assist the researcher in the classification process. Literature reviews and map interpretation are methods that can also be used for interpretation processes. The benefits of remote sensing continue to arise. It can be used to access hard to reach areas for fieldwork, and provides a more detailed, permanent and objective survey that offers a different perspective. Airphotos are still favoured and easily accessible sources of information for classification. Remote Sensing of the Global Environment http://www.geo.mtu.edu/rs/ What is remote sensing? Remote sensing is the science and art of obtaining information about a phenomenoa without being in contact with it. Remote sensing deals with the detection and measurement of phenomena with devices sensitive to electromagnetic energy such as: Light (cameras and scanners) Heat (thermal scanners) Radio Waves (radar) How is remote sensing useful? It provides a unique perspective from which to observe large regions. Sensors can measure energy at wavelengths which are beyond the range of human vision (ultra-violet, infrared, microwave). Global monitoring is possible from nearly any site on earth. Background Information Electromagnetic Spectrum Energy Interactions Digital Imaging Browse Gallery of Satellite Images Lake Superior Temperature Maps of the Great Lakes AVHRR Examples Keweenaw Peninsula SSM/I Imagery of Lake Superior Mississippi River Flood Yellowstone Fires Surface Temperature Map of Lake Superior Three Dimensional Images of Guatemalan Volcanoes -------------------------------------------------------------------------------- Other Satellite Images on the Internet Note: These sites are not at Michigan Tech. You may have trouble with network traffic and image availability. If you get impatient, click on the stop sign at the top of the page. That will cancel your request. If you go to these sites, you need to use the Back button on the bottom of the frame to get back to this page. SSEC Realtime Data Check the Weather Global Sea Surface Temperature Map: Updated Weekly MODIS Airborne Simulator NASA SeasWiFS Project DMSP Satellite Data Space Shuttle Imaging Radar -------------------------------------------------------------------------------- Other Interesting Earth Science Sites Historical & Technical Perspectives of Remote Sensing http://rst.gsfc.nasa.gov/Intro/Part2_1.html The Introduction, replete with images and illustrations, is designed to cover the meaning embodied in the concept of "remote sensing", some of the underlying principles (mainly those associated with the physics of electromagnetic radiation [other related topics are deferred until Sections 8 and 9]), a survey of the chief satellite programs that have depended on remote sensors to gather information about the Earth, and some specialized topics . Emphasis is placed on the Landsat series of satellites that, starting in 1972, have provided a continuous record of the Earth''s land (and some ocean) surfaces using the multispectral approach. In this Introduction, and most of the Sections that complete the Tutorial, as well as several of the Appendices, each page will be individually summarized at the top and all illustrations will have captions accessible by clicking at the lower right of each display. The page you are now on once again defines the term "remote sensing", develops a brief discussion of implications, and places limits on its meaning. It also draws distinctions between what are the usual areas of application (confined to measurements at selected wavelengths in the electromagnetic spectrum) and what can more conventionally be called geophysical applications which measure particles and fields. -------------------------------------------------------------------------------- INTRODUCTION: Theoretical and Technical Perspectives of Remote Sensing; Special Applications The bulk of this Section is devoted to a wide range of topics that are embedded in the idea of remote sensing. The main theme is tied to the use of satellites as orbiting platforms that mount sensors devoted to compiling the data resulting from some mode(s) of remote sensing. Emphasis is placed on satellites launched and maintained by NASA but those of other countries are considered. Here is a pictorial showing most of the earth-orbiting NASA satellites now active Earth Observatory http://earthobservatory.nasa.gov/Features/RemoteSensing/ The technology of modern remote sensing began with the invention of the camera more than 150 years ago. Although the first, rather primitive photographs were taken as "stills" on the ground, the idea and practice of looking down at the Earth''s surface emerged in the 1840s when pictures were taken from cameras secured to tethered balloons for purposes of topographic mapping. Perhaps the most novel platform at the end of the last century is the famed pigeon fleet that operated as a novelty in Europe. By the first World War, cameras mounted on airplanes provided aerial views of fairly large surface areas that proved invaluable in military reconnaissance. From then until the early 1960s, the aerial photograph remained the single standard tool for depicting the surface from a vertical or oblique perspective The philosophical underpinnings of remote sensing http://www.abdn.ac.uk/~geo402/rs.htm The philosophy behind remote sensing can perhaps be divided up into three sections, first, what the technology is; second, why the technology has been developed, and how it has developed; and third, what the technology is used for, and why it is of benefit. Firstly, what is remote sensing? A standard definition is provided by Lillesand and Kiefer (1994), who describe remote sensing as: "the science and art of obtaining information about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investigation" (p.1). Using our eyes to read or look at any object is also a form of remote sensing. However, remote sensing includes not only what is visual, but also what can�t be seen with the eyes, including sound and heat (although these can also be turned into a visual representation as well). Secondly, why has remote sensing been developed? Remote sensing has a very long history dating back to the end of the 19th century when cameras were first made airborne using balloons and kites. The advent of aircraft further enhanced the opportunities to take photographs from the air, and perhaps World War 1 saw the first major use of remote sensing as a method of data and information acquisition. The history of remote sensing appears to show a logical route as to why remote sensing was developed, and continues to be developed. Initially, still photography would have provided (and still does) a fascinating and exciting way of capturing moments in time, a record of something that happened that looked more realistic than a drawing or painting, and that could be captured much quicker than by drawing or painting. With an insight to perspective, and a possible interest in landscape photography, a photographer would soon realise that a different and perhaps more revealing view of a particular landscape (whether built or natural) could be gained by taking a photograph from a vantage point, such as an incline or building. It is, therefore, not surprising that airborne photography was soon embraced, initially perhaps through simple experimentation, but later because it was realised that the airborne perspective gave a completely different view to that which was available from the ground. Arguably, this different view that remote sensing affords of the Earth was historically, and is now the main driving force behind the continued development of remote sensing. Today, remote sensing is carried out using airborne and spaceborne methods using satellite technology. Furthermore, remote sensing not only uses film photography, but also digital camera, scanner and video, as well as radar and thermal sensors. Whereas in the past remote sensing was limited to what could be seen in the visual part of the electromagnetic spectrum, the parts of the spectrum which can not be seen with the naked human eye can now be utilised through special filters, photographic films and other types of sensor. The advantage of a different view of the Earth has, therefore, been further enhanced by new information about the environment available outwith the visible spectrum. Remote sensing was then initially developed through experimentation. However, the unique view of the Earth which it offered was soon applied to practical and real applications, the most notable of which is probably aerial reconnaissance during the First World War. Aerial photography allowed the positions of the opposing armies to be monitored over wide areas, relatively quickly, and more safely than a ground based survey. Aerial photographs would also have allowed rapid and relatively accurate updating of military maps and strategic positions. Today, the benefits of remote sensing are heavily utilised in environmental management which frequently has a requirement for rapid, accurate and up-to-date data collection. Remote sensing has many advantages over ground-based survey in that large tracts of land can be surveyed at any one time, and areas of land (or sea) that are otherwise inaccessible can be monitored. The advent of satellite technology, and multispectral sensors has further enhanced this capability, with the ability to capture images of very large areas of land in one pass, and by collecting data about an environment that would normally not be visible to the human eye. Remote sensing has come to be a very important method of data collection about the environment, and this is largely due to the unique view that it provides us of the Earth. However, remote sensing has not yet totally replaced ground-based survey methods and this is largely because some limitations with the technology still exist. These include potential limitations with spatial, spectral and temporal resolutions of the various sensors, problems with all weather capability (not all sensors can �see� through cloud), costs of data collection and data purchase, and problems with data analysis and interpretation. While remote sensing is a useful technique, it can still only supplement ground surveys. However, the insight that it provides into the environment, its features and processes is extremely valuable indeed Goes 39um Channel Tutorial http://rammb.cira.colostate.edu/training/tutorials/goes_39um/default.asp Introduction Basic Radiation Science Energy Sources Emmission and Reflection 3.9 & 10.7 um Channel Comparisons Temperature Responsivity Sub-pixel Response Noise Diffraction Imagery Presentation Imagery Applications Currently Developed Night-time Fog, Stratus & Cirrus Super-cooled Clouds Fog, Ice & Water Clouds Over Snow Winter Storms Earth- & Sea-surface Temperatures Thin Cirrus & Multi-layered Clouds Urban Heat "Islands" Fire Detection Under Investigation Day-time Reflectivity Visibility Contaminates Sun Glint Cumulus Bands at Night Convective Cloud Phases Volcanic Ash Cloud Monitoring (NEW!) Glossary GOES 3.9 um Channel Imagery Applications http://rammb.cira.colostate.edu/training/tutorials/goes_39um/imagery_applications.asp Applications Currently Available Night-time Fog, Stratus & Cirrus Super-cooled Clouds Fog, Ice & Water Clouds Over Snow Winter Storms Land- and Sea-surface Temperatures Thin Cirrus & Multi-layered Clouds Urban Heat "Islands" Fire Detection Applications Currently under Development Day-time Reflectivity Visibility Contaminates Sun Glint Cumulus Bands at Night Convective Cloud Phases Volcanic Ash Cloud Monitoring Remote sensing satellites GOES 3.9 um Channel Imagery Applications Geo Data Institute http://www.geodata.soton.ac.uk/Booklet.html GIS ... A New Experience For Your Data! `Geographic Information Systems, usually abbreviated to GIS, is a term ... normally used to describe computer facilities, which are used to handle data referenced to the spatial domain, with the capability to inter-relate datasets, to carry out functions to assist in their analysis and the presentation of the results ...'' Chorley Report, 1987. Geographic Information Systems (GIS) have become a major IT development area in teaching, research, government, the public services, commerce and industry. The relative rapidity of this development means that the new technology is now involving many groups that had not previously been strongly committed to computer databases or computer graphics. There is therefore an immediate requirement to provide these people with an awareness of the concepts and capabilities of GIS followed by more in-depth training to provide the required skills. The Information Technology Training Initiative (ITTI) launched by the Information Systems Committee of the University Funding Council provides a context for the development of awareness and support materials for use in both the academic and non-academic sectors. The materials are aimed mainly at: New users, with an emphasis on new groups of users, not just new members of well-established groups; Staff of computing/IT service providers with responsibility for supporting the introduction of GIS training or operational use; Those charged with evaluating, selecting or implementing GIS in a group or department that had not previously used this technology. This is the first in a series of products to promote the awareness of GIS and consists of a simple `Storyboard'' - a non-interactive demonstration - plus this booklet. They aim to provide an initial general awareness of GIS principles and applications. Topics covered include: What is GIS ? How is the real world represented within a GIS ? What functionality is available within a GIS ? What are the benefits of using GIS ? What are the applications of GIS ? What Is GIS ? An understanding of what Geographic Information Systems represent may be helped by considering the component parts of the term separately. Geographic... This term is used because GIS tend to deal primarily with `geographic'' or `spatial'' features. These are objects which can be referenced or related to a specific location in space. The objects may be physical, cultural or economic in nature. Features on a map for instance are pictorial representations of spatial objects in the real world. Symbols, colours and line styles are used to represent the different spatial features on the two-dimensional map. Computer technology has been able to assist in this mapping process through the development of automated cartography and computer aided design. Computer programs can now accomplish in minutes and hours tasks which previously took days or weeks for cartographers and draughtsmen to complete. Information... This represents the large volumes of data which are usually handled within a GIS. All real world objects have their own particular set of characteristics or descriptive attributes. This non-spatial alphanumeric data plus locational information needs to be stored and managed for all spatial features of interest. Historically maintained as paper files, computer technology has enabled much more efficient handling and management of information within automated database management systems. Systems... This term is used to represent the systems approach taken by GIS, whereby complex environments are broken down into their component parts for ease of understanding and handling but are considered to form an integrated whole. Computer technology has aided and even necessitated this approach so that most information systems are now computer based. Computer systems are becoming vital for the storage and manipulation of the increasing volumes of data, the handling of complex spatial algorithms and the integration of data of different scales, projections and formats. All of which are essential to GIS. Geographic Information Systems... Geographic Information Systems are thus usually computer based with an emphasis on preserving and utilising the inherent characteristics of spatial data, by handling both components of spatial data: the physical location in space and the set of characteristics associated with that location. GIS tend to handle the two elements of spatial features separately, the spatial relationships being represented by graphical display and the attribute information being stored within a database. The GIS thus needs the ability to relate the attribute information to the spatial locality. How Is The Real World Represented Within A GIS ? The real world is far too complex to model in its entirety within any information system, so only specific areas of interest should be selected for inclusion within a given GIS application. Once a particular application area has been chosen the next task is to select those features which are relevant to the application and to capture information about their locations and characteristics. The GIS, being computer based, needs to have all of this information in digital form. It is thus necessary to consider how each real world feature can best be modelled within the computer system. There are essentially only five different types of spatial object, also known as entity, feature or facility, which can be represented within a GIS. Point... An object that occurs at one physical location in space and which has only one reference coordinate. Examples include trees, pylons, rainfall gauges, health clinics and hotels. Line... An object which spans between points and thus requires at least two reference coordinates, its start and end, to define its spatial location. Examples include roads, rivers, pipes and cables. Area... An object which has area and is defined by a continuous closed boundary. A number of coordinates are required to define its boundary. Area features are also known as polygons. Examples include fields, counties, lakes, planning sites, health districts and enumeration districts. Surface... A feature which requires three dimensions to define it. Thus a series of spatially distributed x,y coordinates are necessary to define a surface, each with a vertical z value. The z value may represent physical terrain, population density or rainfall, for example. Network... A feature defined by a series of line segments connected to form a continuous branching system of links. This structure enables the calculation of optimal routes through road networks or the simulation of flow through rivers or pipes. It is the first three features which are the most commonly used but occasionally it is necessary to model more complex entities which require the use of surface or network facilities. The above features can then be represented within a GIS in one of two quite different ways: vector or raster format. Vector... Positional data in the form of x,y coordinates. Each feature has a coordinate or string of coordinates to represent a particular location within a specific spatial referencing system. Spatial objects are thus defined by points and lines, in a similar way to conventional paper maps and drawings. Examples of data in vector format include site plans, ordnance survey maps and Computer-Aided Design (CAD) drawings. Raster... Data expressed as a matrix or array of grid cells or pixels. Each coordinate or value is represented by a cell in the regular array of cells. The position of spatial objects can thus only be defined to the nearest cell. Examples of data in raster format include scanned aerial photographs, satellite images and scanned documents or maps. The two structures are each appropriate to different data sets and applications: Vector A vector structure can provide a flexible and accurate representation of an object due to the fine resolution obtainable with coordinate points. Vector structures also tend to incorporate the topology and other spatial relationships between the individual entities and are therefore ideally suited to representing linked networks such as pipe or road systems. It is very accurate for the measurement of areas or lengths and ideal where there is a requirement for cartographic-quality pen plots. Computer data storage is very economical but certain operations such as overlay analysis and proximity calculations have high computational requirements, which result either in slow operations or high hardware specification requirements. Manipulation and analysis of digital images, which are essentially raster, is not feasible. Raster A raster structure provides information at a much lower resolution since data can only be located to the nearest grid cell. Computer storage tends not to be economic, although data compression techniques are improving the situation. Operations such as overlay, buffering and neighbourhood analysis are, however, more efficiently accomplished with a raster structure. Raster structures are ideal where the source data is raster-based, such as satellite or scanned photogrammetric data, and particularly where the data also need to be output to a raster device. Traditionally, commercial GIS were based on one format or the other and were not designed to handle both. Vector-based GIS tended to arise from CAD or automated cartography systems whilst raster systems grew from image processing technology. Most GIS software today will enable conversions between the two formats or will at least allow users to display vector data over the top of raster data, provided that the latter is geo-referenced first. Layers... Regardless of the way the data are structured, all GIS separate the different types of information into data `layers''. This means for instance that all the water features are held on one layer and all the roads on another. This allows for separate display and processing when necessary but does not prevent cross referencing between data layers during query and analysis. A number of data layers are thus built up into a sandwich within the GIS. Layers are referenced to a common spatial domain so that they can be scaled and overlain in such a way that any given reference point can be located on any of the layers and the data value extracted. What Functionality Is Available Within A GIS ? GIS have the ability to perform numerous tasks utilising both the spatial and attribute information stored within them. It is these often very sophisticated functions which are the real strength of a GIS. This functionality can be subdivided into four main groups. Data Acquisition And Input... This is the collection of data in both digital and analogue form and its transformation into an appropriate standardised format for entry into the GIS. Data sources include paper maps, satellite images, aerial photographs, field notes and other paper records. These need to be converted into a digital form, if they are not already, before the GIS can make use of them. Procedures such as digitising, scanning and manual keyboarding are involved in this conversion process. Data Storage And Management... The data are stored and indexed within a database system which facilitates shared access to the data, and which maintains the reliability, security and integrity of the data by controlling access to it and supervising updates. Database management thus tends to be at the heart of a GIS ensuring controlled and coordinated data retrieval and analysis. Ideally the data set should be structured in such a way as to be independent of the applications which access it. Data Manipulation And Analysis... Geographic analysis requires a close association between the spatial elements and their attribute data. Previously these have been held and managed separately within automated cartography packages and databases respectively. GIS provides the technology to perform more sophisticated analysis which makes use of the links between the two. Queries to a GIS can thus be graphics driven or data driven. Graphics-driven queries involve spatial based searches for objects and retrieval of the associated attribute data, or point and query functions to select displayed features and retrieve the associated information. Data-driven queries involve the use of data values selectively to display the matching spatial features or the use of attribute values to determine shading pattern or colour coding of the relevant spatial elements. Operations to retrieve, summarise, selectively display and analyse both the alphanumeric and graphical data are usually embedded within a GIS. Examples of the types of spatial analysis usually available within a GIS include: Choropleth mapping Area features are classified according to their attributes. A legend acts as a look-up table with each range of attribute values being associated with a particular colour or shading pattern - eg. shading enumeration districts according to population density. Buffer generation Boundaries are created around points, lines or areas at an equal distance in all directions. The result being circular, corridor or polygon shaped features, respectively, which represent areas at set distances from the original object - eg. creation of buffers around an airport to calculate zones of different noise intensity. Polygon overlay Area features on one data layer are overlaid onto those of other data layers in order to calculate areas which have a certain combination of attributes or lack certain values - eg. overlaying soil, drainage and slope data layers will provide information on the land most suitable for crop production. Contouring The interpolation of lines of the same elevation from spot height data - eg. the calculation of height contours from which to generate slope and elevation data for land surfaces or any other statistical surfaces. Terrain analysis The creation of three-dimensional views or digital terrain models (DTM) from height data - eg. a three-dimensional view of a landscape can be used to assess the visual impact of an afforestation programme. Network analysis Tracing through a network of connected line features in order to simulate flows of traffic or water. Often used to calculate shortest or quickest paths between two points in a network - eg. use of a network to calculate optimum routes for emergency vehicles. Area and length calculations The calculation of the area of polygon features or the length of linear features. Often these simple calculations can be constrained to include only those features with attributes satisfying certain selection criteria - eg. calculation of the area of parkland within a city which needs to be maintained or calculation of the length of pipeline within a sewage n Geo Data Institute http://www.geodata.soton.ac.uk/Booklet.html What Are The Benefits Of Using GIS ? There are a number of advantages of implementing a particular application on a GIS, as opposed to developing it manually or on any other computer system. The first in the following list is the most tangible benefit and the reasoning many of the larger organisations give for investing in GIS technology. Further advantages of GIS however are less tangible and are therefore frequently left out of formal cost-benefit analyses but they are none the less important and in the long term are probably more important. Existing Tasks More Efficiently... A GIS has the major advantage of replacing paper maps and documents which have traditionally been duplicated throughout an organisation and which require frequent updating and replacing. Tangible cost benefits can thus be achieved by removing the need for such paper documents and converting to GIS technology. GIS based maps can quickly be updated, edited, printed or duplicated whereas traditional maps can take days of careful manual labour to achieve the same. New Tasks Not Previously Possible... A GIS provides the technology to perform tasks not previously possible because they were too time-consuming or not physically practical before automation. Multiple map overlays, generation of buffer corridors, surface interpolation and visualisation of terrain models are examples of such sophisticated analysis. Data Management... A GIS provides all the advantages of controlled information management such as sharing data between multiple users, reducing data duplication and increasing security, accuracy, integrity and validity of data. Shared access to a central database is much more efficient than providing numerous copies of the same data for everyone to view and alter as they wish. It is then that inconsistencies in the data arise and errors can develop and therefore the data become less reliable and useful. Scenario Modelling... A GIS provides capabilities to undertake modelling scenarios and test `what if ?'' type queries. This is an extremely powerful tool for planners whereby different potential outcomes resulting from changes to the input parameters can be tested quickly and efficiently. The potential for better informed decision-making is thus greatly increased. Value-Added Processing... A GIS provides the potential to create new information from existing data, through selection and combination analysis techniques. Individual data layers can be combined in numerous ways to produce further information layers. The combination of slope, drainage, soils and vegetation data layers for instance could provide a graded map for erosion potential or crop suitability depending on the manner in which the various layers are combined. Information Provision... A GIS tends to be based around the realisation that information is a valuable resource to be utilised to its full potential. GIS provide very effective data management, retrieval and analysis tools, but perhaps the greatest strength of GIS is the capability to visualise spatial features and relationships. Knowledge of the location and characteristics of objects as well as their relationships to each other is crucial for effective management, planning and investment decisions. Increasingly it is becoming a statutory requirement for organisations to provide accurate and up to date information for a wide variety of purposes, thus the demand for GIS and other automated data handling systems is likely to increase. What Are The Applications Of GIS ? The applications or uses for a GIS are endless, wherever spatial features need to be modelled and analysed. Some common examples are listed below. Environmental Resource Management... Environmental applications lend themselves very well to GIS because they often require the integration of numerous different data sets during the analysis, since environmental systems tend to be complex and composed of inter-related sub-systems. Particular applications include river channel maintenance, coastal defence, forestry and national park management. Emergency Planning And Routing... The provision of optimum locations for emergency service centres can also be aided by GIS analysis of the various parameters such as access to roads, population density and various health indicators. Network analysis can be utilised to define optimum routes, such as shortest or fastest, for the routing of emergency service vehicles. Provision Of Health, Educational Or Retail Services... Consideration of the spatial distribution of different sectors of the population, their health and socio-economic characteristics and the accessibility to transport routes plus the location of existing facilities are required prior to the effective location of new facilities or the allocation of new services. Facility Management For The Utilities... The utility industries tend to have vast numbers of facilities to manage in order to provide large customer regions with an efficient and reliable service. Gas, water, electricity and sewage utilities for instance own a lot of land, buildings, cables, pipes and other physical facilities which need monitoring, maintaining and managing in order to provide an effective service. Highway Maintenance And Accident Monitoring... Roads and motorways need to be maintained and monitored for accident trouble spots. GIS are ideal for representing the spatial relationships between sections and storing the associated information tied to each section of road. Maintenance records can also be incorporated into the GIS and so provide up to date displays of the state of the road network and the sections which require immediate maintenance. Market Analysis... The spatial distribution of the population and particularly the different age groups and socio-economic sectors are essential information to the market analyst attempting to discover the most suitable place to launch a new product or sell a particular brand. The effectiveness of any given marketing stratey can also be modelled and evaluated. Population Analysis And Prediction... The spatial distribution of the population and the predicted level of a population are essential information to planners and developers when deciding what type of facilities need to be constructed now in order to best suit the needs of the future population. Census data thus provide an important input to GIS. GIS essentially enable the relationships between various spatial features to be visualised and analysed which in turn encourages a better understanding of the interactions between the various features. GIS also enable the data to be manipulated and analysed quickly and flexibly in a single system which is an extremely powerful capability. Other Important Issues To Consider GIS are complex systems and can revolutionise the way organisations manage their information and approach certain spatially dominated applications, however the implementation of a GIS involves much more than hardware and software choices. There are five major groups of issues to consider. Hardware... The type of machines, number of terminals, networking requirements, digitising and printing needs all have to be considered for successful implementation of a GIS in an organisation. Final choices will depend on the budget available, the number and location of potential end users and the type of GIS to be installed. Software... The required operating system, database, GIS package and other supporting software also need to be decided. There is no `best'' GIS software, the most appropriate choice depends on an individual organisation, its needs, the number and type of users, the type of applications involved and the budget at its disposal. GIS implementation can range from a PC based system for individual use, to departmental workstations and up to corporate systems based on a mainframe which serve an entire organisation. There is also the choice of a tool-box or menu-driven system. The former offers a high degree of flexibility in the implementation of a system so that it can be customised to an individual organisation''s requirements and applications, however the effort involved in getting the GIS up and running is usually enormous. The latter choice is usually much more user-friendly and can be operational very quickly, however the choice of functions and interfaces is limited. Increasingly, however, commercial GIS will offer a basic menu-driven system but with the option of using a command or macro language to create tailored functions, interfaces or full applications. Data... The accuracy, source, ownership, copyright, confidentiality, security, standards and formats of data are important issues to consider. GIS technology allows the integration of data from a variety of sources, scales and formats for visualisation and analysis purposes, however the output from any GIS can only be as reliable and relevant as the information entered into it. Care is thus needed during data acquisition and input in order to maintain accurate and reliable data sets. This is a particularly important consideration since data acquisition and conversion usually represents the major component in the cost of implementing a GIS. Also as organisations become more open and begin to share their information it is vital to ensure the security and confidentiality within the database to safeguard the interests of the data owners. Applications... It is important to consider whether GIS technology is the most appropriate and relevant to any particular proposed application. Not all types of application benefit from GIS treatment. Projects which require the spatial analysis of a number of different data sets, require repeated access and query facilities, involve frequent updates and meet the demands of a number of personnel are valid for GIS implementation since the benefits of automation are enormous. However where an application is only relevant to a limited number of people, involves limited data layers and has only a one-off use, then the applicability of GIS technology is less than certain. Data acquisition and conversion tend to involve a considerable proportion of the total time taken to reach full GIS implementation and the costs involved with this stage may well not be worth it for the latter type of project. Organisation... The implementation of a GIS can have profound implications and ramifications within an organisation. In particular, corporate scale GIS will require previously disparate departments to cooperate and share information, will necessitate retraining of some staff and the employment of new ones, and will involve technology probably unfamiliar to most employees. Successful GIS implementation within an organisation appears to be dependent on a high level of staff awareness, involvement, training and support. The management should be supportive in this and provide sufficient time and resources. As with the incorporation of any IT development there will probably be resistance to change and a reluctance to assist from some of the staff. Experience seems to suggest that careful management is required at all stages of GIS implementation in order for the various organisational changes to occur smoothly. GIS in Summary A GIS is usually a computer-based system which provides facilities for data capture, storage, manipulation, analysis and presentation. The emphasis is on preserving and utilising the inherent characteristics of spatial data. Spatial data comprise both a physical location in space plus a set of characteristics about that specific location. GIS recognise this fact and also that the different components of spatial information cannot be efficiently managed in the same manner. A graphical representation is most appropriate for visualising spatial relationships, whilst some kind of database is more appropriate for storing and analysing attribute information. A true GIS, therefore, needs the ability to relate the attribute information to the spatial locality. A brief introduction to GIS and Archaeology http://ads.ahds.ac.uk/project/goodguides/gis/sect21.html What is GIS http://www.gis.com/whatisgis/index.html GIS allows us to view, understand, question, interpret, and visualize data in many ways that reveal relationships, patterns, and trends in the form of maps, globes, reports, and charts. A GIS helps you answer questions and solve problems by looking at your data in a way that is quickly understood and easily shared. GIS technology can be integrated into any enterprise information system framework. Trend surface analysis http://www.kgs.ku.edu/Tis/surf3/s3trend1.html The two general classes of techniques for estimating a regular grid of points on a surface from scattered observations are methods called "global fit" and "local fit." As the name suggests, global-fit procedures calculate a single function describing a surface that covers the entire map area. The function is evaluated to obtain values at the grid nodes. In contrast, local-fit procedures estimate the surface at successive nodes in the grid using only a selection of the nearest data points. Trend surface analysis is the most widely used global surface-fitting procedure. The mapped data are approximated by a polynomial expansion of the geographic coordinates of the control points, and the coefficients of the polynomial function are found by the method of least squares, insuring that the sum of the squared deviations from the trend surface is a minimum. Each original observation is considered to be the sum of a deterministic polynomial function of the geographic coordinates plus a random error. The polynomial can be expanded to any desired degree, although there are computational limits because of rounding error. The unknown coefficients are found by solving a set of simultaneous linear equations which include the sums of powers and cross products of the X, Y, and Z values. Once the coefficients have been estimated, the polynomial function can be evaluated at any point within the map area. It is a simple matter to create a grid matrix of values by substituting the coordinates of the grid nodes into the polynomial and calculating an estimate of the surface for each node. Because of the least-squares fitting procedure, no other polynomial equation of the same degree can provide a better approximation of the data. http://educationally.narod.ru/gistrendphotoalbum.html Simple regression and trend surface modelling http://www.spatialanalysisonline.com/output/html/Simpleregressionandtrendsurfacemodelling.html Trend-surface analysis of morphometric parameters http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6V7D-4G0DF31-4&_user=5719616&_rdoc=1&_fmt=&_orig=search&_sort=d&_docanchor=&view=c&_acct=C000068207&_version=1&_urlVersion=0&_userid=5719616&md5=c866628cc5d1f6ddca09e90448c1ae04 CREATING DATA, Constructing a Triessen Polygon Net, and EDITING SPATIAL DATA in ArcMAP http://www.umsl.edu/~loiselleb/GIS2002/CreateData2.pdf When new data is entered into a GIS (e.g., by scanning, digitizing, or creation of an event theme) it does not possess the desired topological characteristics. Data also contains a variety of errors, especially digitized data. Some of the common errors are: Arcs that do not connect (undershoots) Arcs that are too long (overshoots) Polygons that are not closed Poorly digitized arcs Missing arcs Missing polygon labels, or too many polygon labels In addition to creating maps and spatial analysis, ArcMap allows for creating and editing geographic and tabular data. With ArcMAP, you can edit shapefiles, coverages, and geodatabases. ArcMAP uses CAD-based (computer-aided drawing) editing tools that enable the user to construct features relatively quickly and easily. Creating an accurate data set generally involves an iterative process as follows: 1. enter data 2. construct topology 3. check for errors 4. fix errors 5. repeat steps 2-4 until there are no more errors. This iterative procedure can generate a large number of different coverages. To ensure data integrity and to be able to replicate the steps used to create the data, the coverage created at each step is (usually) given a new name. Surfacewater Modeling System http://www.crwr.utexas.edu/gis/gishyd98/byu/sms/Sms.htm A MARINE FISHERIES RESOURCE GIS http://www.fao.org/docrep/003/w0615e/W0615E02.HTM Thiessen Polygon Application http://www.hunter-gis.com/WS_Solutions/TK_Thiessen/TK_Thiessen.htm Thiessen polygons, also referred to as the Dirichlet Tessellation or the Voronoi Diagram, define the indivdual ''regions of influence'' around each of a set of points. Thiessen Polygons divide a plane, assigning the area to point in the set such that any location within a particular polygon is nearer to that polygon''s point than to any other point. Mathematically, a Thiessen is defined by the perpendicular bisectors of the lines between all points One of the most widespread uses of Thiessen Polygons is the delineation of the marketshed of retail or service nodes (eg. malls, stores, hospitals); Walter Christaller''s Central Place Theory is the classic application of this locational tool within that field. Thiessen Polygons are used to predict the values at surrounding points from a single point observation, as in the fields of hydrology/hydrogeology (water sampling) and climatology (rain gauges). Thiessen Polygons have also been utilized to generate the center-line street network for a series of roads that had been digitized as polygons. This application provides three programs for the computation of Thiessen Polygons: Windows Program: SDF File Preparation for MapGuide Author Permits off-line generation of an SDF file containing Thiessen Polygons generated for a set of points read from an ASCII file. The SDF file may then be added as a layer to an MWF via the MapGuide Author. CGI Program: Server-Side Computation The CGI program provides for Server-Side computation of Thiessen Polygons for points submitted as an input parameter, or obtained by the program as a user-defined relational database query. The SDF file is output to a directory, where it may be accessed as an MWF layer in order to send the results back to the client. ActiveX Control: Client-Side Computation The ActiveX Control supports Internet Explorer applications to generate Thiessen Polygons on the client-side. The control accepts a set of MapGuide Viewer API objects, computes the solution, and permits access to the results through set of public function calls via JavaScript or VBScript. The results may then be portrayed through the MapGuide Viewer as a Redline Layer. The application also includes an SDF Viewer that enables the graphic display of one or more SDF files. The SDF Viewer permits immediate viewing of SDF files without the need for authoring maps or conversion to another format. A more efficient way of determining proximity is with �Thiessen polygons�, also known as �Voronoi tessellations�. A Thiessen polygon for a library represents an area in which any location it contains is closest to that library. Therefore, a Thiessen polygon layer for all libraries in the county represents a set of areas containing points that are nearest to each library (Figure 1). Given an address, we can select the area it falls in to determine the closest library. This kind of query can be performed very efficiently and doesn�t require any complex calculations. The �What�s In My Neighborhood� function can be found in the Real Estate Inquiry on the County website. Select a parcel by searching for a house number or by zooming in to an area and clicking on a parcel with the Identify tool. This will show basic information about the selected parcel and a list of buttons that provide more information, including the new �Neighborhood� button. It will soon also be available directly on the main page of the County website. More information about Voronio tessellations may be found on Wikipedia at http://en.wikipedia.org/wiki/Voronoi. Create Thiessen Polygons http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=create_thiessen_polygons_(analysis) Thiessen polygons have the unique property that each polygon contains only one input point, and any location within a polygon is closer to its associated point than to the point of any other polygon. Dynamic Segmentation and Thiessen Polygons http://proceedings.esri.com/library/userconf/proc95/to150/p114.html Edited Guide Entry -Thiessen Polygons http://www.bbc.co.uk/dna/h2g2/A901937 Also known as ''Voronoi networks1'' and ''Delaunay triangulations2'', Thiessen polygons were independently discovered in several fields of study, including climatology and geography. They are named after a climatologist who used them to perform a transformation from point climate stations to watersheds. Thiessen polygons can be used to describe the area of influence of a point in a set of points. If you take a set of points and connect each point to its nearest neighbour, you have what''s called a triangulated irregular network (TIN). If you bisect3 each connecting line segment perpendicularly4 and create closed polygons with the perpendicular bisectors, the result will be a set of Thiessen polygons. The area contained in each polygon is closer to the point on which the polygon is based than to any other point in the dataset. Moving averages: An Integrated Streams http://www.crwr.utexas.edu/gis/gishydro07/InstreamFlows/StreamClassification.htm Spatial Analysis and Surface Modeling, filters http://www.innovativegis.com/basis/Papers/Other/SModeling/GIS_00_SM.htm Methods of Generating Surfaces In Environmental GIS Applications http://proceedings.esri.com/library/userconf/proc95/to100/p089.html Exploring the structure of space: towards geo-computational theory http://www.geovista.psu.edu/sites/geocomp99/Gc99/029/gc029.htm Space is a concept which is central to our understanding of the world. Indeed, in recent years it seems to have taken pride of place in many more fields of enquiry than might be thought strictly sensible. �Spaces� of various sorts seem to be everywhere these days: geographic space, urban space, architectural space, virtual space, cyberspace (that old chestnut), body space, mental space, cognitive space, �the space of flows�, geographic space, psychological space, dream space, symbolic space... the list is potentially endless. The sheer multiplicity of spaces in contemporary academic discourse is overwhelming (see for example Benko and Strohmayer, 1997, Soja, 1989). Such widespread use is in danger of making the term meaningless. At the very least, it requires those of us who want to use the concept to define what we mean. In this paper I use a recent concept from geographic modelling - proximal space - as the starting point for an exploration of the properties of space, in particular its effects on certain kinds of dynamic processes. Two possible representations of such a proximal model of space are introduced: graphs and cellular automata (CA), along with a brief consideration of some of the analytical techniques which have previously been used to investigate these representations. A modelling approach which allows these two powerful representations to be combined in a natural way is then proposed: graph-CA models. In some respects such a model is not new and is similar to discrete component models (Zeigler, 1976), however, this perspective does allow us to investigate questions about the relationships between spatial structures and spatial processes in new ways. A possible investigative approach is then proposed. In direct analogy with physicists� explorations of CA rule-space (Wolfram, 1983, 1984b, Langton, 1990), it is proposed that spatial theorists (geographers, architects, urbanists) might explore graph-CA cell space. This in turn may constitute the beginnings of a research program to answer the call for a specifically geo-computation made by Couclelis (1998). 2. The proximal model of space It is clear that a wide variety of spatial concepts may be brought to bear when building spatial models to investigate spatial processes (Couclelis, 1992a, 1992b). Particular attention has often been paid to a long-standing division rooted in natural science and philosophy, between absolute and relative models of space. Rather than explore this division further, I want to use a recently introduced notion of space: proximal space. In the proximal conception of space, the fundamental element is the neighbourhood (Couclelis, 1997). A neighbourhood is defined by relations of nearness between spatial elements. Nearness depends on both (spatial) adjacency and (functional) influence. Proximal models are useful because they allow non-contiguous neighbourhoods, based on relations of influence between elements. They also allow the integration of functional and spatial relations, and of fuzzy concepts and phenomena. That proximal space has been developed in the context of a discussion of cellular automata models for urban and regional planning is significant (Takeyama, 1995, Takeyama and Couclelis, 1997, Couclelis, 1997) and strongly influences the methodology proposed here for developing the idea. Allowing that proximal conceptions of space represent a useful innovation, on what bases might proximal spatial models be constructed? Various metrics are possible: a Voronoi approach is one such, wherein regions are associated with objects according to which is nearest, and the resulting spatial partition produces spatial elements (Gold, 1992). Extensions of the basic Voronoi concept of proximity polygons can be based on different metric systems (Okabe et al., 1994). This is illustrated in figure 1. Ideas from urban morphology (see for example Atkin, 1974a, 1974b, 1975, Hillier and Hanson, 1984, Kr�ger, 1979a, 1979b, Krafta, 1994, 1996) represent another rich set of ideas about how proximal models might be constructed. The proximal model approach is informed by pragmatism, and an interest in tackling problems, not by the abstract ideas of philosophers, so that Gould�s (1997: 128) remark seems relevant: "...we start with the idea that this strange no-thing [space] is structured by other things, which we relate in various ways to each other, and which we measure as various distances to each other as the fancy takes us according to our purpose of utility, curiosity, or ambition." 3D terrain visualization http://www.gisdevelopment.net/technology/ip/pdf/ma03065.pdf DEM based terrain modelling http://www.esri.com/news/arcuser/0799/demdemo.html Modeling Two-Dimensional Data in a 3D Scene The TIN just constructed can be used in ArcView 3D Analyst to create a 3D Scene. Aspect-Slope Map http://blogs.esri.com/Support/blogs/mappingcenter/archive/2008/05/22/aspect-slope-map.aspx Introduction to 3D GIS http://geog.hku.hk/gislab/PSDAS_materials/Exercise1_web.pdf HERAKLES PROJECT: Classical Boiotia through the GIS http://www.uam.es/proyectosinv/sterea/beocia/boiotia_gis.htm GIS Maps : altitude, slope, aspect in 3D analysis http://www.eol.ucar.edu/projects/cases/maps.html Advanced Data Visualization, Spatio-Temporal Modeling Interface http://research.cens.ucla.edu/projects/2006/Terrestrial/EMISSARY/default.htm Spatial Decision http://www.crg.cs.nott.ac.uk/research/projects/GIS/ New Tools Produce Classic Cartographic Effects: cartographic hillshading techniques http://www.esri.com/news/arcuser/0701/althillshade.html Curvature and morphometric analysis http://www.spatialanalysisonline.com/output/html/Curvatureandmorphometricanalysis.html Open Source Software Tools for Soil Scientists http://casoilresource.lawr.ucdavis.edu/drupal/book/export/html/95 Textural Analysis of Aerial Photography to Characterize Large Scale Land Cover Change http://proceedings.esri.com/library/userconf/proc97/proc97/to650/pap643/p643.htm features at a 3x3 pixel scale (150m objects) - we do not see areas, and 121x121 (6km objects) gives areas.DTM large skale. GIS: A Modeling Tool to Answer Critical Area Questions http://proceedings.esri.com/library/userconf/proc01/professional/papers/pap845/p845.htm DTM EXTRACTION FROM MIDDLE-RESOLUTION SATELLITE IMAGERY http://www.photogrammetry.ethz.ch/general/persons/fabio/aster_barcellona.pdf Visibility analysis http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=Visibility_analysis Visible-not-visible A line of sight is a line between two points that shows the parts of the surface along the line that are visible to or hidden from an observer. Creating a line of sight allows you to determine whether a given point is visible from another point. If the terrain hides the target point, you can see where the obstruction is and what else is visible or hidden along the line of sight. The visible segments are shown in green, and the hidden segments are shown in red. A black dot at the beginning of the line represents the observer location. A blue dot represents the point of obstruction from the observer to the target. A red dot at the end of the line represents the target location. a viewshed analysis http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=Performing_a_viewshed_analysis Viewshed is useful when you want to know how visible objects might be�for example, From which locations on the landscape will the water towers be visible if they are placed in this location? or What will the view be from this road? In the example below, the viewshed from an observation tower is identified. The elevation raster displays the height of the land (darker locations represent lower elevations), and the observation tower is marked as a green triangle. The height of the observation tower can be specified in the analysis. Cells in green are visible from the observation tower, while cells in red are not GIS Viewshed Analysis http://en.wikipedia.org/wiki/GIS_Viewshed_Analysis A viewshed is an area that is visible from a specific location based on elevation values of a DEM. Viewshed analyses are a common function of most GIS software. The analysis uses the elevation value of each cell of the DEM to determine visibility to or from a particular cell. The location of this particular cell varies depending on the needs of the analysis. For example, a Viewshed analysis is commonly used to locate communication towers or determining the view from a road. Viewsheds can be calculated using an individual point such as a tower or multiple points such as a line representing a road. When analyzing a line segment, each of the vertices along the line is calculated to determine its visible area. The process can also be reversed. For example, when locating a landfill, the analysis can determine from where the landfill is visible to keep it hidden from view Network analysis A viewshed is an area that is visible from a specific location based on elevation values of a DEM. Viewshed analyses are a common function of most GIS software. The analysis uses the elevation value of each cell of the DEM to determine visibility to or from a particular cell. The location of this particular cell varies depending on the needs of the analysis. For example, a Viewshed analysis is commonly used to locate communication towers or determining the view from a road. Viewsheds can be calculated using an individual point such as a tower or multiple points such as a line representing a road. When analyzing a line segment, each of the vertices along the line is calculated to determine its visible area. The process can also be reversed. For example, when locating a landfill, the analysis can determine from where the landfill is visible to keep it hidden from view Network analysis work flow http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?TopicName=Network_analysis_work_flow Network analysis layers are composite layers in ArcMap used to store inputs, parameters, and results of network analysis. A network analysis layer acts as an in-memory workspace for each type of input as well as the result, all of which are stored as in-memory feature classes. The analysis parameters are stored as properties of the analysis layer the shortest path problem http://bmf.hu/conferences/HUCI2003/petrik.pdf Using location-allocation models for regional planning in GIS http://www.gisdevelopment.net/application/nrm/overview/nrm01.htm Spatial_analysis http://en.wikipedia.org/wiki/Spatial_analysis Spatial Analysis and GIS http://www.dpi.inpe.br/gilberto/tutorials/spatial_analysis/spatial_analysis_primer.pdf Quantitative Spatial Analysis http://www.learn.colostate.edu/courses/STAT/STAT523.dot reduce large data sets to smaller amounts of more meaningful information explore data to suggest hypotheses or examine the distribution of data. Exploratory data analysis techniques are used explore spatial patterns, test hypotheses about these patterns and examine the role of randomness in their gemeration. QM FOR SPATIAL DATA ANALYSIS not independent, but it is assumed that features that are close together in geographycal space are in some way related. Modifiable area unit problem. Descriptive Statistics and Exploratory Data Analysis http://www.gs.washington.edu/academics/courses/akey/56008/lecture/lecture2.pdf Descriptive Statistics and Exploratory Data Analysis http://www.utcomchatt.org/docs/Descriptive_Statistics_1142008.pdf Predictive modelling http://libraries.maine.edu/Spatial/gisweb/spatdb/amfm/am94059.html Predictive models, predictive statistics http://mpa.itc.it/research/6_en.html scatterplot of paired observation that show the relationship between the dependet variable and the independent variable (altitude), regression analysis and subsequent model Prescriptive statistics http://innovativegis.com/basis/courses/carleton/Projects/Example_projects.htm what if? Cluster analysis http://www.statsoft.com/TEXTBOOK/stcluan.html http://educationally.narod.ru/clusteranalysisphotoalbum.html General Purpose Statistical Significance Testing Area of Application Joining (Tree Clustering) Hierarchical Tree Distance Measures Amalgamation or Linkage Rules Two-way Joining Introductory Overview Two-way Joining k-Means Clustering Example Computations Interpretation of results EM (Expectation Maximization) Clustering Introductory Overview The EM Algorithm Finding the Right Number of Clusters in k-Means and EM Clustering: v-Fold Cross-Validation the chi-squared distribution and Overlay Analysis (Spatial Correlation) http://www.gis.usu.edu/~tl009/Chi-Square.htm http://www.colby.edu/biology/BI17x/freq.html Chi-square analysis has been used successfully for years in analyzing a wide variety of datasets. In the realm of GIS, an analyst might want to see if there is a correlation between the location of high-end retail stores and income, the distribution of species and rainfall, or even the effect that churches have on the crime rate. The basics of Chi-square analysis consist of the creation of a cross-tabulation or contingency table. This table is made up of i rows and j columns dependent upon the # of factors a given variable has. For example, we have a report that contains data on vegatation type and elevation for an area in the Lincoln National Forest surrounding High Rolls. The vegetation is broken down into four different types: Creosote, Pinyon-Juniper , Ponderosa Pine, and Douglas Fir. The second variable consists of elevation which has been separated into 4 categories : 2,001-4,000 ft. , 4,001-6,000 ft., 6,001-8,000 ft., and 8,001-10,000 ft. For this analysis we need to come up with two hypotheses which we are going to test. In statistics there are primarily two types of hypotheses, the null hypothesis and the alternative hypothesis.The standard hypotheses in chi-square analysis are: 1. Ho: The two variables are unrelated (null) 2. Ha: The two variables are related (alternative) In this case we are going to test to see if there is a significant correlation between elevation and vegatation type. We can write this as follows: 1. Ho: Vegatation Type and Elevation are unrelated (null) 2. Ha: Vegatation Type and Elevation are related (alternative) At this point we also want to choose our significance level. The significance level is the point at which you state that a correlation or relationship is significant. This value lies between 0 and 1, with values closer to zero corresponding to graeter significance. Therefore a smaller level of significance (i.e. 0.05, 0.01) means that our conclusion is right between 95 and 99% of the time. Likewise larger levels of significance (i.e. .15,.25) mean that 15 -25% of the time our conclusion might be wrong. For our analysis we chose a significance level of 0.05 The table created by this dataset would be termed a 4 by 5 contingency table or cross-tabulation. Below is an example of this table without values added, along with the row and column designations across the table . To fill in this table we need to find the observed count of occurences for each factor of a variable, yet this is only half of the data we need for this type of analysis. First, we will place the observed counts from the NFS report into the table below. The second set of data we need is the expected count for each observation. You can find the expected counts by multiplying the row total by the column total and dividing it by the table total. The equation for the expected count looks like this n1+ * n+1/n++ = the expected count for cell containing 34 in the table above. The table below contains both the observed and expected values for the data. Once we have both the observed and expected counts we can compute the chi-square statistic. The formula for the chi-square statistic is (observed-expected)2/expected for each cell. Therefore the equation for Pinyon-Juniper and 6,001-8,000 would be (34-21.5)2/ 21.5 = (12.5)2/21.5 = 156.25/21.5 = 7.26. This procedure needs to be performed for each combination of vegetation type and elevation in the table. Once all of the cells have been calculated, the values need to be added up to find the chi-square statistic. For this cross-tabulation the chi-square statistic is 283.96. Before we use this statistic we need to find out the degrees of freedom for this cross tabulation. To find this we take the (row total-1)(column total-1)=df. In this case the df= (4-1)(4-1) =(3)(3)=9. Therefore we have X2 = 283.96 and df=9. To see whether or not this value is significant we need to find the p-value by looing at a chi-squared distribution table. A p-value is the probability (assuming Ho is true) of observing a value of the test statistic that is contradictory to the null hypothesis as the actual statistic computed from the sample data. This means that the probability of getting a chi-squared value different from the one we calculated is less than 5% (.05) 1%(.01) etc. Looking at a chi-squared distribution table I found that the p-value was <0.001. Click here for SurfStat statistical tables. Therefore I can reject the null hypothesis which stated that there was no relationship between elevation and vegatation. By rejecting the null, we accept the alternative hypothesis of a relationship between vegatation and elevation. Given that our p-value was so small we are overwhelmingly certain of some type of relationship. CAUTION: The fact that we have shown that there is a correlation between vegetation and elevation does NOT mean that we have found out anything about WHY this is so. In our analysis we might state our assumptions as to why this is so, but we would need to perform other analyses to show causation. GIS Analysis: Spatial Statistics for Public Health http://distance.jhsph.edu/more/webEventsMedia/ScottGIS-secB-09-26-08/GIS-sec11B_6.pdf Ordnance Survey http://www.ordnancesurvey.co.uk/oswebsite/gisfiles/section4/ Water on the web - Internet Map Server - The Query Function http://waterontheweb.org/under/gis/query.html GIS in geology:ROCK WORKS software