One of the simplest methods of highlighting possible outliers is to create a histogram of the data, typically using a fine class division, and then to examine the extreme classes. Where this facility is linked to a map of the data, the location of the object(s) may be identified and examined (Figure 5‑9). The upper figure shows the histogram and basic statistics for the attribute OWN_OCC (the number of Owner Occupiers, i.e. property owners) within 86 census districts (test census Output Areas, OAs, for part of Manchester, UK). The districts with the highest data values have been selected on the histogram window, and are simultaneously highlighted in the map window (lower figure). The same approach may be applied for other vector object types, such as point data.

Figure 5‑9 Histogram linkage

Source: UK 2001 Census Test Output Areas (OAs)

Data items that lie at the upper or lower limits of a dataset range may be described as global outliers. This term refers to values that are extreme compared to the dataset as a whole. However, within the dataset there may be values that are “relatively extreme” and these are referred to as local outliers. A local outlier is a value that is markedly different from (spatially) neighboring values. An example of this might be a set of measurements taken along a transect, with a value part of the way along the transect that is very different from those immediately before or after, but still well within the overall range of the data recorded on the entire transect. Some ESDA software packages, such as ArcGIS Geostatistical Analyst, provide tools for displaying local as well as global outliers for selected data types.