![]() ![]() ![]() For example, in the first case, an area will be labelled as "male" if there are more males than females living in that particular LSOA. Our strategy to turn these into a single categorical variable for each case is to compare the counts for each area and assign that of the largest case. The way these are presented in its raw form is as tabulated counts of each of the possible categories. In particular, we are going to use tables QS104EW (Gender) and KS103EW (marital status). It is not possible to compare their different values in a quantitative way (religion A is not double or half of religion B) but instead they represent qualitative differences. For example, religion or country of origin are categorical variables. Remember a categorical variable is one that comprises only a limited number of potential values, and these are not comparable with each other across a numerical scale. Although most of the Census variables are continuous, we will transform them to create categorical characteristics. In order to explore additional dimensions of deprivation, and to have categorical data to display with "unique values" choropleths, we will use some of the Census data pack. In order to create maps with a base layer that provides context, we will be using a raster file derived from OS VectorMap District (Backdrop Raster) and available for download on this link.Īs usual, let us set the paths to the folders containing the files before anything so we can then focus on data analysis exclusively (keep in mind the specific paths will probably be different for your computer): For that we will revisit the Census Data Pack ( link) we used previously. This dataset can be most easily downloaded from the CDRC data store ( link) and, since it already comes both in tabular as well as spatial data format (shapefile), it does not need merging or joining to additional geometries.Īlthough all the elements of the IMD, including the ranks and the scores themselves, are in the IMD dataset, we will also be combining them with additional data from the Census, to explore how deprivation is related to other socio-demographic characteristics of the area. For this tutorial, we will use the recently released 2015 Index of Multiple Deprivation (IMD) for England and Wales.
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