30 Days of Maps Day 12 - Time and Space

Moving onto day 12 of the #30DayMapChallenge and today's theme is Time and Space - Map something where time matters. Visualize change over time—urban growth, migration, or environmental shifts. Show the relationship between time and geography.

I kind of covered this topic yesterday, showing the ice cap change over time, so I thought I'd pick something that I've had to deal with in customer projects. Boundary changes add a level of complexity to a reporting project, even when there isn't a map. This is a topic that especially impacts public sector organisations. When Local Authorities or Police Forces merge, or when census areas change it creates complexities for comparing data over time. In today's challenge I've attempted to address this. I've brought in crime data from data.police.uk for all of England for the last few years. This covers every police service, for every month. It's about a 4gb dataset before we even start thinking about shapes etc.

I'm mapping the crime data as a choropleth map at Local Authority level, but from here you can drill down to Lower Super Output Area level, and then down to street level as circles. The complexity comes as between 2022 and 2023 there were a number of Local Authority merges in Cumbria, Yorkshire and Somerset. The LSOAs in 2023 aggregate up into different or new Local Authorities.

Firstly in the map, we handle the changes by showing the appropriate shapes for the local authorities according to the date selected in the slicer, and also have separate lookup data between LSOA and local authority for each year. This means the right shapes are showing based on the date, and allows drill-down to continue functioning.

To show some different techniques, I've added the local authority shapes as WKT in the Power BI dataset - this is the easiest method of handling filtering based on date. Then the for the LSOA level, these are all based on 2021 boundaries, so I've used an Esri Shapefile uploaded into the report, and then for the circle layer, I'm using Longitude and Latitude coordinates, and the number of crimes at that location to size the circle. I've also added a filtered reference layer which only shows when circles are showing, to show the boundary of the LSOA we've drilled into.

If you'd like to see how the report was built, you can download it here.