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NEIGHBOURHOOD OPERATIONS

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.

This example illustrates a continuous surface that has been created by interpolating sample data points.

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.

Connectivity Analysis

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 techniques are primarily concerned with the proximity of one feature to another. Usually proximity is defined as the ability to identify any feature that is near any other feature based on location, attribute value, or a specific distance. A simple example is identifying all the forest stands that are within 100 metres of a gravel road, but not necessarily adjacent to it. It is important to note that neighbourhood buffering is often categorized as being a proximity analysis capability. Depending on the particular GIS software package, the data model employed, and the operational architecture of the software it may be difficult to distinguish proximity analysis and buffering.

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.

Network analysis is a widely used analysis technique. Network analysis techniques can be characterized by their use of feature networks. Feature networks are almost entirely comprised of linear features. Hydrographic hierarchies and transportation networks are prime examples. Two example network analysis techniques are the allocation of values to selected features within the network to determine capacity zones, and the determination of shortest path between connected points or nodes within the network based on attribute values. This is often referred to as route optimization. Attribute values may be as simple as minimal distance, or more complex involving a model using several attributes defining rate of flow, impedance, and cost.

Three dimensional analysis involves a range of different capabilities. The most utilized is the generation of perspective surfaces. Perspective surfaces are usually represented by a wire frame diagram reflecting profiles of the landscape, e.g. every 100 metres. These profiles viewed together, with the removal of hidden lines, provide a three dimensional view. As previously identified, most GIS software packages offer 3-D capabilities in a separate module. Several other functions are normally available.

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.


While the primitive analytical functions have been presented the reader should be aware that a wide range of more specific and detailed capabilities do exist.

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.