Neighbourhood operations

Neighbourhood functions evaluate the characteristics of an area surrounding a feature’s location. A neighbourhood function “scans” the neighbourhood of the given feature(s), and performs a computation on it(them).

• Proximity computations:

  • Buffer zone generation (or buffering) is one of the best-known neighbourhood functions. It determines a spatial envelope (buffer) around a given feature or features. The buffer created may have a fixed width or a variable width that depends on characteristics of the area.

  • Thiessen Polygon generation

• Computation of diffusion

• Flow computation

For instance, our target might be a medical clinic. Its neighbourhood could be defined as:

• an area within a radius of 2 km distance as the crow flies; or

• an area within 2 km travelling distance; or

• all roads within 500 m travelling distance; or

• all other clinics within 10 minutes travelling time;

• all residential areas for which the clinic is the closest clinic.

Finally, in the third step we indicate what it is we want to discover about the phenomena that exist or occur in the neighbourhood. This might simply be its spatial extent, but it might also be statistical information such as:

• how many people live in the area;

• what is their average household income;

• are any high-risk industries located in the neighbourhood.

These are typical questions in an urban setting. When our interest is more in natural phenomena, different examples of locations, neighbourhoods and neighbourhood characteristics arise.

In the explanation of  overlay operators, the guiding principle was to compare or combine the characteristic value of a location from two data layers and to do so for all locations. This is what map algebra, for instance, gives us: cell by cell calculations with the results stored in a new raster.

There is another guiding principle in spatial analysis that can be equally useful. The principle in this case is to find out the characteristics of the vicinity, here called neighbourhood, of a location. After all, many suitability questions, for instance, depend not only on what is at a location but also on what is near the location. Thus, the GIS must allow us “to look around locally”. To perform neighbourhood analysis, we must:

1. state which target locations are of interest to us and define their spatial extent;

2. define how to determine the neighbourhood for each target; and

3. define which characteristic(s) must be computed for each neighbourhood. 

Since raster data are the more commonly used in this case, neighbourhood characteristics often are obtained via statistical summary functions that compute values such as the average, minimum, maximum and standard deviation of the cells in the identified neighbourhood.

To select target locations, one can use the selection techniques. To obtain characteristics from an eventually-to-be identified neighbourhood, the same techniques apply. So what remains to be discussed here is the proper determination of a neighbourhood. One way of determining a neighbourhood around a target location is by making use of the geometric distance function. Geometric distance does not take into account direction, but certain phenomena can only be studied by doing so. Think of the spreading of pollution by rivers, groundwater flow or prevailing weather systems.

Diffusion functions are based on the assumption that the phenomenon in question spreads in all directions, though not necessarily equally easily in each direction. Hence it uses local terrain characteristics to compute local resistances to diffusion.