The main criteria used to define a GIS is its capability to transform and integrate spatial data.

Coordinate thinning involves the weeding or reduction of coordinate pairs, e.g. X and Y, from arcs. This function is often required when data has been captured with too many vertices for the linear features. This can result in redundant data and large data volumes. The weeding of coordinates is required to reduce this redundancy.

The thinning of coordinates is also required in the map generalization process of linear simplification. Linear simplification is one component of generalization that is required when data from one scale, e.g. 1:20,000, is to be used and integrated with data from another scale, e.g. 1:100,000. Coordinate thinning is often done on features such as contours, hydrography, and forest stand boundaries.

This function is concerned with the registering of a data layer to a common coordinate scheme. This usually involves registering selected data layers to a standard data layer already registered. The term rubber sheeting is often used to describe this function. Rubber sheeting involves stretching one data layer to meet another based on predefined control points of known locations. Two other functions may be categorized under geometric transformations. These involve warping a data layer stored in one data model, either raster or vector, to another data layer stored in the opposite data model. For example, often classified satellite imagery may require warping to fit an existing forest inventory layer, or a poor quality vector layer may require warping to match a more accurate raster layer.

This functionality concerns the transformation of data in geographic coordinates for an existing map projection to another map projection. Most GIS software requires that data layers must be in the same map projection for analysis. Accordingly, if data is acquired in a different projection than the other data layers it must be transformed. Typically 20 or more different map projections are supported in a GIS software offering.

Conflation is formally defined as the procedure of reconciling the positions of corresponding features in different data layers. More commonly this is referred to as sliver removal. Often two layers that contain the same feature, e.g. soils and forest stands both with a specific lake, do not have exactly the same boundaries for that feature, e.g. the lake. This may be caused by a lack of coordination or data prioritization during digitizing or by a number of different manipulation and analysis techniques. When the two layers are combined, e.g. normally in polygon overlay, they will not match precisely and small sliver polygons will be created. Conflation is concerned with the process for removing these slivers and reconciling the common boundary.

There are several approaches for sliver removal. Perhaps the most common is allowing the user to define a priority for data layers in combination with a tolerance value. Considering the soils and forest stand example the user could define a layer that takes precedence, e.g. forest stands, and a size tolerance for slivers. After polygon overlay if a polygon is below the size tolerance it is classified a sliver. To reconcile the situation the arcs of the data layer that has higher priority will be retained and the arcs of the other data layer will be deleted. Another approach is to simply divide the sliver down the centre and collapse the arcs making up the boundary. The important point is that all GIS software must have the capability to resolve slivers. Remember that it is generally much less expensive to reconcile maps manually in the map preparation and digitizing stage than afterwards.

Edge matching is simply the procedure to adjust the position of features that extend across typical map sheet boundaries. Theoretically data from adjacent map sheets should meet precisely at map edges. However, in practice this rarely occurs. Misalignment of features can be caused by several factors including digitizing error, paper shrinkage of source maps, and errors in the original mapping. Edge matching always requires some interactive editing. Accordingly, GIS software differs considerably in the degree of automation provided.

Interactive graphic editing functions involve the addition, deletion, moving, and changing of the geographic position of features. Editing should be possible at any time. Most graphic editing occurs during the data compilation phase of any project. Remember typically 60 to 70 % of the time required to complete any project involves data compilation. Accordingly, the level of sophistication and ease of use of this capability is vitally important and should be rated highly by those evaluating GIS software. Many of the editing that is undertaken involves the cleaning up of topological errors identified earlier. The capability to snap to existing elements, e.g. nodes and arcs, is critical.

The functionality of graphic editing does not differ greatly across GIS software offerings. However, the user interface and ease of use of the editing functions usually does. Editing within a GIS software package should be as easy as using a CAD system. A cumbersome or incomplete graphic editing capability will lead to much frustration by the users of the software.

The critical function for any GIS is the integration of data.

Perhaps the initial GIS analysis that any user undertakes is the retrieval and/or reclassification of data. Retrieval operations occur on both spatial and attribute data. Often data is selected by an attribute subset and viewed graphically. Retrieval involves the selective search, manipulation, and output of data without the requirement to modify the geographic location of the features involved.

Reclassification involves the selection and presentation of a selected layer of data based on the classes or values of a specific attribute, e.g. cover group. It involves looking at an attribute, or a series of attributes, for a single data layer and classifying the data layer based on the range of values of the attribute. Accordingly, features adjacent to one another that have a common value, e.g. cover group, but differ in other characteristics, e.g. tree height, species, will be treated and appear as one class. In raster based GIS software, numerical values are often used to indicate classes. Reclassification is an attribute generalization technique. Typically this function makes use of polygon patterning techniques such as crosshatching and/or color shading for graphic representation.

n a vector based GIS, boundaries between polygons of common reclassed values should be dissolved to create a cleaner map of homogeneous continuity. Raster reclassification intrinsically involves boundary dissolving. The dissolving of map boundaries based on a specific attribute value often results in a new data layer being created. This is often done for visual clarity in the creation of derived maps. Almost all GIS software provides the capability to easily dissolve boundaries based on the results of a reclassification. Some systems allow the user to create a new data layer for the reclassification while others simply dissolve the boundaries during data output.

One can see how the querying capability of the DBMS is a necessity in the reclassification process. The ability and process for displaying the results of reclassification, a map or report, will vary depending on the GIS. In some systems the querying process is independent from data display functions, while in others they are integrated and querying is done in a graphics mode. The exact process for undertaking a reclassification varies greatly from GIS to GIS. Some will store results of the query in query sets independent from the DBMS, while others store the results in a newly created attribute column in the DBMS. The approach varies drastically depending on the architecture of the GIS software.

The capability to overlay multiple data layers in a vertical fashion is the most required and common technique in geographic data processing. In fact, the use of a topological data structure can be traced back to the need for overlaying vector data layers. With the advent of the concepts of mathematical topology polygon overlay has become the most popular geoprocessing tool, and the basis of any functional GIS software package.

Topological overlay is predominantly concerned with overlaying polygon data with polygon data, e.g. soils and forest cover. However, there are requirements for overlaying point, linear, and polygon data in selected combinations, e.g. point in polygon, line in polygon, and polygon on polygon are the most common. Vector and raster based software differ considerably in their approach to topological overlay.

Raster based software is oriented towards arithmetic overlay operations, e.g. the addition, subtraction, division, multiplication of data layers. The nature of the one attribute map approach, typical of the raster data model, usually provides a more flexible and efficient overlay capability. The raster data model affords a strong numerically modelling (quantitative analysis) modelling capability. Most sophisticated spatial modelling is undertaken within the raster domain.

In vector based systems topological overlay is achieved by the creation of a new topological network from two or more existing networks. This requires the rebuilding of topological tables, e.g. arc, node, polygon, and therefore can be time consuming and CPU intensive. The result of a topological overlay in the vector domain is a new topological network that will contain attributes of the original input data layers. In this way selected queries can then be undertaken of the original layer, e.g. soils and forest cover, to determine where specific situations occur, e.g. deciduous forest cover where drainage is poor.

Most GIS software makes use of a consistent logic for the overlay of multiple data layers. The rules of Boolean logic are used to operate on the attributes and spatial properties of geographic features. Boolean algebra uses the operators AND, OR, XOR, NOT to see whether a particular condition is true or false. Boolean logic represents all possible combinations of spatial interaction between different features. The implementation of Boolean operators is often transparent to the user.

Generally, GIS software implements the overlay of different vector data layers by combining the spatial and attribute data files of the layers to create a new data layer. Again, different GIS software utilize varying approaches for the display and reporting of overlay results. Some systems require that topological overlay occur on only two data layers at a time, creating a third layer. This pairwise approach requires the nesting of multiple overlays to generate a final overlay product, if more than two data layers are involved. This can result in numerous intermediate or temporary data layers. Some systems create a complete topological structure at the data verification stage, and the user merely submits a query string for the combined topological data. Other systems allow the user to overlay multiple data layers at one time. Each approach has its drawbacks depending on the application and the nature of the implementation. Determining the most appropriate method is based on the type of application, practical considerations such as data volumes and CPU power, and other considerations such personnel and time requirements. Overall, the flexibility provided to the operator and the level of performance varies widely among GIS software offerings.

The following diagram illustrates a typical overlay requirements where several different layers are spatially joined to created a new topological layer. By combining multiple layers in a topological fashion complex queries can be answered concerning attributes of any layer.

Neighbourhood operations evaluate the characteristics of an area surrounding a specific location. Virtually all GIS software provides some form of neighbourhood analysis. A range of different neighbourhood functions exist. The analysis of topographic features, e.g. the relief of the landscape, is normally categorized as being a neighbourhood operation. This involves a variety of point interpolation techniques including slope and aspect calculations, contour generation, and Thiessen polygons. Interpolation is defined as the method of predicting unknown values using known values of neighbouring locations. Interpolation is utilized most often with point based elevation data.

Elevation data usually takes the form of irregular or regular spaced points. Irregularly space points are stored in a Triangular Irregular Network (TIN). A TIN is a vector topological network of triangular facets generated by joining the irregular points with straight line segments. The TIN structure is utilized when irregular data is available, predominantly in vector based systems. TIN is a vector data model for 3-D data.

An alternative in storing elevation data is the regular point Digital Elevation Model (DEM). The term DEM usually refers to a grid of regularly space elevation points. These points are usually stored with a raster data model. Most GIS software offerings provide three dimensional analysis capabilities in a separate module of the software. Again, they vary considerably with respect to their functionality and the level of integration between the 3-D module and the other more typical analysis functions.

Without doubt the most common neighbourhood function is buffering. Buffering involves the ability to create distance buffers around selected features, be it points, lines, or areas. Buffers are created as polygons because they represent an area around a feature. Buffering is also referred to as corridor or zone generation with the raster data model. Usually, the results of a buffering process are utilized in a topological overlay with another data layer. For example, to determine the volume of timber within a selected distance of a cutline, the user would first buffer the cutline data layer. They would then overlay the resultant buffer data layer, a buffer polygon, with the forest cover data layer in a clipping fashion. This would result in a new data layer that only contained the forest cover within the buffer zone. Since all attributes are maintained in the topological overlay and buffering processes, a map or report could then be generated.

Buffering is typically used with point or linear features. The generation of buffers for selected features is frequently based on a distance from that feature, or on a specific attribute of that feature. For example, some features may have a greater zone of influence due to specific characteristics, e.g. a primary highway would generally have a greater influence than a gravel road. Accordingly, different size buffers can be generated for features within a data layer based on selected attribute values or feature types.

The distinguishing feature of connectivity operations is that they use functions that accumulate values over an area being traversed. Most often these include the analysis of surfaces and networks. Connectivity functions include proximity analysis, network analysis, spread functions, and three dimensional surface analysis such as visibility and perspective viewing. This category of analysis techniques is the least developed in commercial GIS software. Consequently, there is often a great difference in the functionality offered between GIS software offerings. Raster based systems often provide the more sophisticated surface analysis capabilities while vector based systems tend to focus on linear network analysis capabilities. However, this appears to be changing as GIS software becomes more sophisticated, and multi-disciplinary applications require a more comprehensive and integrated functionality. Some GIS offerings provide both vector and raster analysis capabilities. Only in these systems will one fund a full range of connectivity analysis techniques.

Proximity analysis is often used in urban based applications to consider areas of influence, and ownership queries. Proximity to roads and engineering infrastructure is typically important for development planning, tax calculations, and utility billing.

The identification of adjacency is another proximity analysis function. Adjacency is defined as the ability to identify any feature having certain attributes that exhibit adjacency with other selected features having certain attributes. A typical example is the ability to identify all forest stands of a specific type, e.g. specie, adjacent to a gravel road.

These include the following functions :

	user definable vertical exaggeration, viewing azimuth, and elevation angle;
	identification of viewsheds, e.g. seen versus unseen areas;
	the draping of features, e.g. point, lines, and shaded polygons onto the perspective surface;
	generation of shaded relief models simulating illumination;
	generation of cross section profiles;
	presentation of symbology on the 3-D surface; and
	line of sight perspective views from user defined viewpoints.

The overriding theme of all GIS software is that the analytical functions are totally integrated with the DBMS component. This integration provides the necessary foundation for all analysis techniques.