Land Use Mix (Regions)


This tool calculates a measure of the heterogeneity/homogeneity of land uses within each neighbourhood catchment, by calculating an entropy measure, which uses the areas of only land uses of interest falling within the neighbourhood polygon.


Opening up the Land Use Mix tool in the Walkability tools provides the following box for parameter input

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The parameters you need to include in the Land Use Mix tool are:

    •  Neighbourhoods: Polygon spatial data set for which attributes are being calculated.
    •  Land Use Polygon Data Set: Polygon data set of land uses
    •  Classification Attribute: An attribute within the land use polygons which may need to be aggregated to a more meaningful classification schema
    •  Classification Categories Dataset: A tabular data set containing two attributes of interest, one of which must be named the same as “Classification attribute” in the land use polygon data set, and another which is a reclassification “Classification categories attribute”.  Most likely uploaded as a csv file by the user.
    •  Classification Categories Attribute: An attribute in the “Classification categories dataset”, which act as a reclassification of the “Classification attribute” in the land use polygon data set.
    •  Classification Categories Values: Attributes in the Land Use dataset describing each of the land uses – you can check as many as you would like to include.

Once you have put your parameters into the tool, click Add and Run to execute the tool.


Once you have executed the tool, it should appear in your Data panel entitled: Output: walkability-009-lum… . You will also get a dialogue box pop up which will allow you to display the outputs in table form (shown below)

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This table contains the calculated connectivity measures:

    •  LUM_[Land Use] – the square metres for each land use within each neighbourhood polygon.
    •  LandUseMixMeasure – the entropy measure of land use mix
    •  LandUseMixMeasure_ZScore  – the connectivity score normalised to a Z score by the following formula

\(Z_{i} = {X_{i} – \overline{X}\over s}\)

where \(X_{i}\) is the non normalised score of observation \(i\), \(\overline{X}\) is the sample mean and \(s\) is the sample standard deviation. The Z score will tell you how much higher or lower than the rest of the neighbourhoods that a single neighbourhood is (where the mean is 0)

You can also visualise the outputs as neighbourhoods. To do this, click on the spanner symbol next to the connectivity output in your Data panel, and select Display on Map. Once you have selected the colours you want for the neighbourhoods, click Updated and Display. This should cause the neighbourhoods to appear on your map (with a row in you Visualise panel as well). Hovering over a neighbourhood will bring up its connectivity attributes (shown below).