The problem in visualising a big collection of geocoordinated police shooting incidents is to effectively transmit information without overloading the viewer. First, I tried using the Folium library to create separate markers on a map to represent each incidence. But as the dataset expanded, the computing cost of making markers for each data point increased.

I decided to use a HeatMap as a more effective solution to this problem. With the help of the HeatMap, incident concentration may be shown more succinctly and the distribution of events on the map can be seen more clearly. I improved the heatmap’s interpretability by controlling its size and intensity using settings like blur and radius.

Alongside the HeatMap, I also used a MarkerCluster layer to further refine the visualisation but later decided not to. Although the general legibility of the map is increased by the MarkerCluster, which clusters nearby incidences together. The decision to exclude MarkerCluster was made to maintain simplicity and reduce the processing time, especially when dealing with a substantial dataset. The HeatMap alone provides a more concise representation of incident concentration while addressing the computational challenges associated with handling a large number of individual markers. Within these clusters, individual markers stay accessible, enabling users to zoom in and examine individual instances.

The outcome of this thorough process was an engaging and educational map. While the MarkerCluster layer allowed for the examination of individual episodes within clusters, the HeatMap offered a visual summary of incidence concentration. This visualisation method takes into account both specific occurrences and large patterns in the dataset, providing a nuanced perspective on the geographical distribution of police shooting incidents while maintaining efficiency and detail.