Spider Maps: Visualizing Clustered Networks

2 min read

This article shares the story behind creating a custom map to visualize research center member locations. The project began when a client needed to replace their Google Maps-based solution with something more cost-effective and easier to maintain. Their manual process of updating location data was becoming unsustainable, especially since the Google Maps embedding service was incurring costs.

Figure 1: Spatial map visualizing multiple categories Each color on this map represents a different membership category within the research community

The challenge

The client faced several limitations with their existing approach:

  • Their Google Maps implementation lacked the specific visualization features needed for their growing database of members
  • Manually updating location markers was becoming time-consuming and error-prone When multiple members worked at the same institution, the map became cluttered with overlapping markers
  • They needed to distinguish between different member types (faculty, students, collaborators) visually
  • The solution needed to seamlessly integrate with their WordPress website

The main issue wasn’t necessarily that Google Maps was prohibitively expensive, but rather that the client wanted a more customizable solution that would give them complete control over their data and visualization techniques without potential usage constraints in the future.

The Solution

After evaluating several options, R with the leaflet mapping framework emerged as a good option. The open-source nature of these tools offered more customization possibilities. To handle the overlapping markers problem, we used the OverlappingMarkerSpiderfier in Leaflet - when clicking on a location with multiple members, the markers expand outward like a spider web, clearly revealing each individual.

Additionally, the map was made downloadable in a self-contained html page, which was then embedded into the website.

The workflow developed for the client looked like this:

The client was particularly pleased with the “spider” feature that elegantly solved their overlapping markers problem. Another benefit was how the map revealed previously unnoticed patterns in their membership distribution - showing regional concentrations, category clusters, and international reach in ways that weren’t apparent from their database.

Link to the map.