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Geographic Information System

A geographic information system (GIS) is a system for creating and managing spatial data and associated attributes. In the strictest sense, it is a puter system capable of integrating, storing, editing, analyzing, and displaying geographically-referenced information. In a more generic sense, GIS is a smart map tool that allows users to create interactive queries (user created searches), analyze the spatial information, and edit data.

Geographic information systems technology can be used for scientific investigations, resource management, asset management, development planning, cartography and route planning. For example, a GIS might allow emergency planners to easily calculate emergency response times in the event of a natural disaster, or a GIS might be used to find wetlands that need protection from pollution.

History of development

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35,000 years ago, on the walls of ces near Lascaux, France, Cro-Magnon hunters drew pictures of the animals they hunted. Associated with the animal drawings are track lines and tallies thought to depict migration routes. These early records followed the two-element structure of modern geographic information systems: a graphic file linked to an attribute database.

In the 18th century, modern surveying techniques for topographic mapping were implemented, along with early versions of thematic mapping, e.g. for scientific or census data.

A notable example of this is John Snow's 1854 map depicting a cholera outbreak in London, which provided analysis to narrow the source of the cholera to a contaminated pump, stemming the outbreak.Images of John Snow's maps

The early 20th century saw the development of photo lithography where maps were separated into layers. puter hardware development spurred by nuclear weapon research would lead to general purpose puter mapping applications by the early 1960s.

The year 1967 saw the development of the world's first true operational GIS in Ottawa, Ontario by the federal Department of Energy, Mines and Resources. Developed by Roger Tomlinson, it was called Canadian GIS (CGIS) and was used to store, analyse and manipulate data collected for the Canada Land Inventory (CLI)an initiative to determine the land capability for rural Canada by mapping information about soils, agriculture, recreation, wildlife, waterfowl, forestry, and land use at a scale of 1:250,000. A rating classification factor was also added to permit analysis.

CGIS was the world's first system and was an improvement over mapping applications as it provided capabilities for overlay, measurement, digitizing/scanning, supported a national coordinate system that spanned the continent, coded lines as arcs hing a true embedded topology, and it stored the attribute and locational information in separate files. Its developer, geographer Roger Tomlinson, has bee known as the father of GIS.

CGIS lasted into the 1990s and built the largest digital land resource data base in Canada. It was developed as a mainframe based system in support of federal and provincial resource planning and management. Its strength was continent-wide analysis of plex data sets. The CGIS was never ailable in a mercial form. Its initial development and success stimulated various mercial mapping applications being sold by vendors such as Intergraph. The development of micro-puter hardware spurred vendors such as ESRI, MapInfo and CARIS to successfully incorporate many of the CGIS features, bining the first generation approach to separation of spatial and attribute information with a second generation approach to anizing attribute data into database structures. The 1980s and 1990s industry growth were spurred on by the growing use of GIS on Unix workstations and the personal puter. By the end of the 20th century, the rapid growth in various systems had been consolidated and standardized on relatively few platforms and users were beginning to export the concept of viewing GIS data over the Inter, requiring data format and transfer standards.

Techniques used in GIS

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Relating information from different sources

If you could relate information about the rainfall of your state to aerial photographs of your county, you might be able to tell which wetlands dry up at certain times of the year. A GIS, which can use information from many different sources in many different forms, can help with such analyses. The primary requirement for the source data consists of knowing the locations for the variables. Location may be annotated by x,y, and z coordinates of longitude, latitude, and elevation, or by other geocode systems like ZIP Codes or by highway mile markers. Any variable that can be located spatially can be fed into a GIS. Several puter databases that can be directly entered into a GIS are being produced by government agencies and non-government anizations. Different kinds of data in map form can be entered into a GIS.

A GIS can also convert existing digital information, which may not yet be in map form, into forms it can recognize and use. For example, digital satellite images generated through remote sensing can be analyzed to produce a map-like layer of digital information about vegetative covers. Another fairly developed resource for naming GIS objects is the Getty Thesaurus of Geographic Names (GTGN), which is a structured vocabulary containing around 1,000,000 names and other information about places[1].

Likewise, census or hydrologic tabular data can be converted to map-like form, serving as layers of thematic information in a GIS.

Data representation

GIS data represents real world objects (roads, land use, elevation) with digital data. Real world objects can be divided into two abstractions: discrete objects (a house) and continuous fields (rain fall amount or elevation). There are two broad methods used to store data in a GIS for both abstractions: Raster and Vector.

Raster data type consists of rows and columns of cells where in each cell is stored a single value. Most often, raster data are images (raster images), but besides just color, the value recorded for each cell may be a discrete value, such as land use, a continuous value, such as rainfall, or a null value if no data is ailable. While a raster cell stores a single value, it can be extended by using raster bands to represent RGB (red, green, blue) colors, colormaps (a mapping between a thematic code and RGB value), or an extended attribute table with one row for each unique cell value. The resolution of the raster dataset is its cell width in ground units. For example, in a LIDAR raster image, each cell is a pixel that represents an area of 3 meters by 3 meters. Usually cells represent square areas of the ground, but other shapes can also be used.

Vector data type uses geometries such as points, lines (series of point coordinates), or polygons, also called areas (shapes bounded by lines), to represent objects. Examples include property boundaries for a housing subdivision represented as polygons and well locations represented as points. Vector features can be made to respect spatial integrity through the application of topology rules such as 'polygons must not overlap'. Vector data can also be used to represent continuously varying phenomena. Contour lines and triangulated irregular works (TIN) are used to represent elevation or other continuously changing values. TINs record values at point locations, which are connected by lines to form an irregular mesh of triangles. The face of the triangles represent the terrain surface.

There are advantages and disadvantages to using a raster or vector data model to represent reality. Raster datasets record a value for all points in the area covered which may require more storage space than representing data in a vector format that can store data only where needed. Raster data also allows easy implementation of overlay operations, which are more difficult with vector data. Vector data can be displayed as vector graphics used on traditional maps, whereas raster data will appear as an image that may he a blocky appearance for object boundaries.

Additional non-spatial data can also be stored besides the spatial data represented by the coordinates of a vector geometry or the position of a raster cell. In vector data, the additional data are attributes of the object. For example, a forest inventory polygon may also he an identifier value and information about tree species. In raster data the cell value can store attribute information, but it can also be used as an identifier that can relate to records in another table.

Data capture

Data captureentering information into the systemconsumes much of the time of GIS practitioners. There are a variety of methods used to enter data into a GIS where it is stored in a digital format.

Existing data printed on paper or mylar maps can be digitized or scanned to produce digital data. A digitizer produces vector data as an operator traces points, lines, and polygon boundaries from a map. Scanning a map results in raster data that could be further processed to produce vector data.

Survey data can be directly entered into a GIS from digital data collection systems on survey instruments. Positions from a global positioning system (GPS), another survey tool, can also be directly entered into a GIS.

Remotely sensed data also plays an important role in data collection and consist of sensors attached to a platform. Sensors include cameras, digital scanners and LIDAR, while platforms usually consist of aircraft and satellites.

The majority of digital data currently es from photo interpretation of aerial photographs. Soft copy works

tations are used to digitize features directly from stereo pairs of digital photographs. These systems allow data to be captured in 2 and 3 dimensions, with elevations measured directly from a stereo pair using principles of photogrammetry. Currently, analog aerial photos are scanned before being entered into a soft copy system, but as high quality digital cameras bee cheaper this step will be skipped.

Satellite remote sensing provides another important source of spatial data. Here satellites use different sensor packages to passively measure the reflectance from parts of the electromagic spectrum or radio wes that were sent out from an active sensor such as radar. Remote sensing collects raster data that can be further processed to identify objects and classes of interest, such as land cover.

When data is captured, the user should consider if the data should be captured with either a relative accuracy or absolute accuracy, since this could not only influence how information will be interpreted but also the cost of data capture.

In addition to collecting and entering spatial data, attribute data is also entered into a GIS. For vector data this includes additional information about the objects represented in the system.

After entering data into a GIS, it usually requires editing, to remove errors, or further processing. For vector data it must be made topologically correct before it can be used for some advanced analysis. For example, in a road work, lines must connect with nodes at an intersection. Errors such as undershoots and overshoots must also be removed. For scanned maps, blemishes on the source map may need to be removed from the resulting raster. For example, a fleck of dirt might connect two lines that should not be connected.

Data manipulation

Data restructuring can be performed by a GIS to convert data into different formats. For example, a GIS may be used to convert a satellite image map to a vector structure by generating lines around all cells with the same classification, while determining the cell spatial relationships, such as adjacency or inclusion.

Since digital data are collected and stored in various ways, the two data sources may not be entirely patible. So a GIS must be able to convert geographic data from one structure to another.

Projections, coordinate systems and registration

A property ownership map and a soils map might show data at different scales. Map information in a GIS must be manipulated so that it registers, or fits, with information gathered from other maps. Before the digital data can be analyzed, they may he to undergo other manipulationsprojection and coordinate conversions, for examplethat integrate them into a GIS.

The earth can be represented by various models, each of which may provide a different set of coordinates (e.g., latitude, longitude, elevation) for any given point on the earth's surface. The simplest model is to assume the earth is a perfect sphere. As more measurements of the earth he accumulated, the models of the earth he bee more sophisticated and more accurate. In fact, there are models that apply to different areas of the earth to provide increased accuracy (e.g., North American Datum, 1983 - NAD83 - works well in North America, but not in Europe). See Datum for more information.

Projection is a fundamental ponent of map making. A projection is a mathematical means of transferring information from a model of the Earth, which represents a three-dimensional curved surface, to a two-dimensional mediumpaper or a puter screen. Different projections are used for different types of maps because each projection particularly suits certain uses. For example, a projection that accurately represents the shapes of the continents will distort their relative sizes. See Map projection for more information.

Since much of the information in a GIS es from existing maps, a GIS uses the processing power of the puter to transform digital information, gathered from sources with different projections and/or different coordinate systems, to a mon projection and coordinate system.

Spatial analysis with GIS

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Data modeling

It is difficult to relate wetlands maps to rainfall amounts recorded at different points such as airports, television stations, and high schools. A GIS, however, can be used to depict two- and three-dimensional characteristics of the Earth's surface, subsurface, and atmosphere from information points.

For example, a GIS can quickly generate a map with lines that indicate rainfall amounts.

Such a map can be thought of as a rainfall contour map. Many sophisticated methods can estimate the characteristics of surfaces from a limited number of point measurements. A two-dimensional contour map created from the surface modeling of rainfall point measurements may be overlaid and analyzed with any other map in a GIS covering the same area.

Topological modeling

In the past 35 years, were there any gas stations or factories operating next to the swamp? Any within two miles and uphill from the swamp? A GIS can recognize and analyze the spatial relationships that exist within digitally stored spatial data. These topological relationships allow plex spatial modelling and analysis to be performed. Topological relationships between geometric entities traditionally include adjacency (what adjoins what), containment (what encloses what), and proximity (how close something is to something else).

works

If all the factories near a wetland were accidentally to release chemicals into the river at the same time, how long would it take for a damaging amount of pollutant to enter the wetland reserve? A GIS can simulate the routing of materials along a linear work. Values such as slope, speed limit, or pipe diameter can be incorporated into work modelling in order to represent the flow of the phenomenon more accurately. work modelling is monly employed in transportation planning, hydrology modelling, and infrastructure modelling.

Cartographic modelling

Powerful analysis techniques with raster data. This section is a stub. You can help by adding to it.

Vector overlay

The bination of two separate spatial datasets (points, lines or polygons) to create a new output vector dataset. These overlays are similar to mathematical Venn diagram overlays. A union overlay bines the geographic features and attribute tables of both inputs into a single new output. An intersect overlay defines the area where both inputs overlap and retains a set of attribute fields for each. A symmetric difference overlay defines an output area that includes the total area of both inputs except for the overlapping area.

Data extraction is a GIS process similar to vector overlay, though it can be used in either vector or raster data analysis. Rather than bining the properties and features of both datasets, data extraction involves using a clip or mask to extract the features of one dataset that fall within the spatial extent of another dataset.

In raster data analysis, the overlay of datasets is acplished through a process known as local operation on multiple rasters or map algebra, through a function that bines the values of each raster's matrix. This function may weigh some inputs more than others through use of an index model that reflects the influence of various factors upon a geographic phenomenon.

Spatial Statistics (Geostatistics)

Using geostatistics to predict fields from points. Point pattern analysis. A way of looking at the statistical properties of spatial data. What makes it unique from other kinds of statistics is the use of graph theory and matrix algebra to reduce the number of parameters in the data being analyzed. This is necessary because it is actually the second-order properties of the GIS data that need analyzing.

When we measure any phenomena, our observation methods dictate the accuracy of any subsequent analysis. Whether our study is concerned with the nature of traffic patterns in an urban core, or with the analysis of weather patterns over the Pacific, there will always contain a variable or a degree of precision which escapes our measurement; this is determined directly by the scale and distribution of our data collection, or survey methods. In order to apply statistical relevance to spatial analysis, an 'erage' must be determined so that points, or gradients, outside of any immediate measurement may be included as to their predicted behior. Limitations in statistics and data collection mean that it is impossible to directly measure a contiuum without the inferential methods of analysis, of which, several forms of interpolation are used in order to predict the behior of particles and locations not directly measured.

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 dataset. In paring 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 dataset to form the model, or local, an algorithm is repeated for a small section of terrain.

Digital Elevation Models (DEM), Digital Terrain Models (DTM), Triangulated Irregular works (TIN), Edge finding algorithms, Theissen Polygons, Fourier analysis, Weighted moving erages, Inverse Distance Weighted, Moving erages, Kriging, Spine, Trend surface analysis.

Regionalized variable theory

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