Focal Operations With Network Distance Based Neighbourhoods: Implementation, Application and Visualization

In spatial analysis, many operations are performed considering the neighbouring locations of a feature. The standard definition of a neighbourhood is an area confined by geometrical length and direction with respect to its focus.

When allocating a location for a service, the population distribution is often considered. Standard GIS software includes tools for computations with uniform neighbourhoods, usually equal sized circles. These tools can be used for distribution analysis.

Many geographic studies used as basis for city planning decisions use distance as an evaluator. It is a frequent occurrence that the actual distance is approximated using factored straight-line distance. For great distances and large datasets, this is a sufficient means of evaluation, whilst for smaller distances for specific locations, it poses major drawbacks.

For distribution analysis in a network space, the neighbourhood would need to be derived from the local set of network features, creating a unique neighbourhood for each location. The neighbourhood can then be used to overlay other datasets to perform analysis of features within the network space.

This report describes the application of network distance based neighbourhoods to design a tool, Network Location Analysis, for calculating focal statistics for use as a city planning decision support. The tool has been implemented as a workflow of ArcGIS tools scripted as a Python toolbox. The input required by the tool is a population point layer and a vector network dataset. The output is a grid of points with population statistics as attributes and corresponding neighbourhoods generalized as polygons.

The tool has been tested by comparing it to standard focal operations implemented in ArcGIS and by applying it to the dataset used when conducting a study on the location of a new metro station using conventional ArcGIS tools. The results have been analysed and visualized and compared to data used in the study.

The result concludes that Network Location Analysis surpasses conventional ArcGIS tools when conducting analysis on features in a network. It derives an accurate set of sum and proximity statistics for all locations within the processing extent, enabling analysis on the population distribution throughout the area and for specific points.

The output is intuitive, manageable and can be visualized as raster or by displaying the neighbourhoods as polygons and can be used to evaluate population distribution and network connectivity. The drawbacks of the tool are its lack of robustness, its rigidity to input and the inefficient implementation causing execution time to be unpractical.
Source: KTH
Author: Ottenby, Nore

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