How GIS represents and organizes geographic information
Last modified October 27, 2007
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These four types of geographic information (features, rasters, attributes, and surfaces) are actually managed using three primary GIS data structures:
|Map Layer Types||GIS Datasets|
|Features—points, lines, and polygons||Feature classes|
|Surfaces||Both features and rasters can be used to provide a number of alternative surface representations:
Typically, a GIS is used for handling several different datasets where each holds data about a particular feature collection (for example, roads) that is geographically referenced to the earth's surface.
A GIS database design is based upon a series of data themes, each having a specified geographic representation. For example, individual geographic entities can be represented as features (such as points, lines, and polygons); as imagery using rasters; as surfaces using features, rasters, or TINs; and as descriptive attributes.
In a GIS, homogeneous collections of geographic objects are organized into data themes such as parcels, wells, buildings, ortho imagery, and raster-based digital elevation models (DEMs). Precisely and simply defined geographic datasets are critical for useful geographic information systems, and the layer-based concept of data themes is a critical GIS concept.
A dataset is a collection of homogeneous features. Geographic representations are organized in a series of datasets or layers. Most datasets are collections of simple geographic elements such as a road network, a collection of parcel boundaries, soil types, an elevation surface, satellite imagery for a certain date, well locations, and so on.
In a GIS, spatial data collections are typically organized as feature class datasets or raster-based datasets.
Many data themes are best represented by a single dataset such as for soil types or well locations. Other themes, such as a transportation framework, are represented by multiple datasets (such as a separate feature class each for streets, intersections, bridges, highway ramps, railroads, and so on).
Raster datasets are used to represent georeferenced imagery as well as continuous surfaces such as elevation, slope, and aspect.
|Parcel tax records||Tables|
The concept of a data theme was one of the early notions in GIS. Historically, GIS practitioners thought about how the geographic information in maps could be partitioned into a series of logical information layers—as more than a random collection of objects. They envisioned homogeneous collections of representations that could be managed as layers and that these data layers could be combined through georeferencing. These early GIS users organized information in various data themes that described the distribution of a phenomenon and how each should be portrayed across a geographic extent.
These layers also provided a protocol for collecting the representations. For example, a data theme could be defined that delineated various areas representing the dominant soil type (that is, a layer collection of soil type polygons). Each and every area (the polygons) in a specified extent could be assigned an explicit soil type, and the soil types could be described using properties or attributes of each polygon.
Each GIS will contain multiple themes for a common geographic area. The collection of themes acts as a stack of layers. Each theme can be managed as an information set independent of other themes. Each has its own representation (as a collection of points, lines, polygons, surfaces, rasters, and so on). Because layers are spatially referenced, they overlay one another and can be combined in a common map display. GIS analysis operations, such as polygon overlay, can fuse information between data layers to discover and work with the derived spatial relationships.
The GIS design concept of layer-based data themes has some key implications:
A GIS will use numerous datasets, each containing its specific representation, often from many organizations. A number of alternative file formats and schemas will be used across a range of systems, but users still have the need to share and re-use each other's data.
Therefore, it is important for GIS datasets to be: