Heatmaps turn individual points into hotspots or clusters, allowing viewers to explore
spatial distributions of events, such as areas of high and low population density or incidents of crime. Figure 12.39 shows an interactive heatmap of
bike theft locations in London between January and July 2020. The underlying
data are coordinate locations for each reported bike theft,
Leaflet.heat plugin transforms
into areas of various densities. Red shows areas of highest density, or areas
where bike theft appeared most often. When you zoom in, areas are re-calculated
into more distinct clusters.
You can adapt the code we used for this London heatmap to create your own.
- Visit the GitHub repository with our code, make sure you are logged in, and click Use this template button to make a personal copy of this repo.
- Modify map’s title and description inside
- Place your point coordinates data inside
data.csv. Do not insert any column headers. Instead of the traditional order, you must write them in latitude,longitude (or y,x) order, one pair per line, like this:
51.506585,-0.139387 51.505467,-0.14655 51.507758,-0.141284
- Depending on your data density, you might want to tweak radius and blur parameters
- Edit the following chunk of code to set your map’s default position and zoom level:
If for some reason you cannot see clusters, make sure your point data is represented
latitude,longitude order, not the other way around. If you have few points,
try increasing the value of
radius property of