A Spatial Approach to Location Quotients
The intent of this post is not simply to uncover where the highest density of people belonging to a particular ethnic group are, but rather to use the ‘location quotient’ (LQ) technique to compare the ethnic density in any one area to the overall ethnic density in Southwark, thus providing a relative insight into where the density of particular groups is more, less or as dense as expected.
Location Quotients tend to work with areal units, characterising different areas subject to a larger region and providing a basic insight into where functions are clustered. Because the Southwark patient register data is address geocoded, we would be losing some spatial information if we choose to aggregate the data, not to mention the question of which areal aggregation is best. More info on how to create location quotients here.
A Location Quotient has 3 possible interpretations; if it is around 1 then the ethnic population in that area is at the level we would expect given what we observe nationally. If the LQ is less than 1 then that area has a lesser population of a particular ethnic group that what we would expect based upon national figures. Finally, in the LQ value is over 1 this suggests a concentration of the ethnic group in the area which is greater than we would expect given nationally observed levels. A LQ is quite simply a rate-ratio.
Instead of the standrad areal approach, the maps here use a density estimation approach in which disaggregate point data is transformed into a representation of the continuous density function of the point distribution. The LQ can then be computed for each cell based on the density of that cell with respect to the total density of the surface. This creates a smoothed LQ surface which is readily interpretable in the same manner as above. The Kernel Density Estimation used to create the ethnic and total population density surfaces should be parameterised in the same way; these examples use a 250m bandwidth and a 25m cells size, which is largely empirically redundant, based on the input dataset’s spatial resolution, but creates a more aesthetically appealing mapped representation. Naturally, the procedure works well for clustered data, in Southwarks case for the African and Muslim groups.
