There’s a perpectually uniform heatmap from the colorcet package that ranges between a min of 0 and max of 250 as set by vmin and vmax. There’s quite a lot going on in the below so let’s talk through the moving pieces. Now we will create the small multiple chart. Fortunately, both data sets have the ‘LAD20CD’ and ‘LAD20NM’ columns, which makes this easier than it might have been. They don’t come with their own geometry, so our first job is to merge them onto our existing UK local authority geodataframe, which does have a geometry. But this is just an illustrative example. Note that there are various issues with these data and they do not tell the whole story of coronavirus by any means. They are counts of deaths that occurred within 28 days of (a known case of) coronavirus organised by the death date. The data we’ll be using tell a tragic story. The second is just a heat map in which the two dimensions are space and time. The first we’ll see is to do a series of small multiples, also known as a facet chart, and advance time by one unit in each plot. There are various ways we could approach this. This is really going to combine two things we already have at our fingertips: space and time. We’ll use the lats and longs of collisions in New York: The most basic layer just turns a series of points into a quadtree. We’ll use an example from the geoplot documentation to illustrate them. Given pre-defined geographies such as Local Authority Districts may not be useful for the question you’re thinking about (or worse could be downright misleading), this is a very helpful property. But, if you have point data, quadtree allows you to aggregate them not according to a pre-defined geography. Quadtree is not a replacement for the latter, because the data are already aggregated. Quadtrees are good at illustrating density, and are more flexible than a conventional choropleth: remember that choropleths can be the result of binning point occurrences into geographical regions or of data that are already aggregated to the region level. This plot takes a sequence of point or polygonal geometries as input and builds a choropleth out of their centroids. The important part of the plot below is the gplt.cartogram but, along with other bits to make the plot look better, we’re adding gplt.polyplot to show what the true size of each region is when it is not proportional to another variable.Ī quadtree is a tree data structure that splits a space into increasingly small rectangular fractals. But others, including Blaenau Gwent and Powys, are shown much smaller than their actual areas. Some areas with higer median incomes, such as Monmouthshire and Conwy, almost completely fill their potential region areas. Actually, due to the tendency of the shape of political regions to reflect choices made 100s or 1000s of years previously, and for urban areas to be under separate political arrangements, quite often economic variables are anti-correlated with areas.Ī cartogram of pay in Wales demonstrates this. They can be especially useful when trying to refectly the fact that regions with the largest area do not always have proportionate variable values. The shape of the region is warped or shrunk. Cartogram #Ī cartogram is a thematic map in which the geographic size of regions is altered to be proportional to a variable. This is useful for, for example, plotting streets of different types. Combining Code and Text in Quarto Markdown
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