
Learn about a better way to manage thousands of map markers by using priority-based ranking instead of traditional clustering.
Say you have a map with with lots of map markers.

The map is slow and cluttered. How would you solve this the standard way? Most map providers include a clustering feature that gets you the following result:

While clustering solved the problem of lag and overlap when using using a large number of densely placed markers on a map, it created a massive UX problem.
Clustering hides data.
It forces users to click and zoom and pan more, hoping the cluster they are expanding actually contains what they are looking for. It sacrifices geographical and contextual precision for the sake of performance.
I’ve been building arenarium/maps, a library that overcomes the clustering problems by focusing on the core problem - overlapping markers. It does this by computing the optimal state for each marker based on a user provider rank. State information consists of when to reveal a marker and where to position the marker based on zoom. The result is that each marker is optimally positioned as the user moves around the map to maximize clarity and information density.
Lets examine a similar issue as before, but solve it in a better way.

Managing the same markers with arenarium/maps we get:

By avoiding clustering, we maintain spatial integrity and increase information density. If a marker represents a specific building or an event, it stays on that coordinate. There is no merging or snapping. This leads to a more elegant, professional, and ultimately more useful map interface.
As the user moves around the map, markers transition between three states based on zoom level and rank:

Here is a short gif showcasing a more complex markers. A user moves around a map viewing the markers and click on some of them for more information.
The tool is built to be a plug-and-play solution regardless of your mapping provider. The architecture is split into two core parts:
@arenarium/maps): The npm package that manages state logic and coordinates with the optimization compute API.The website with live demos and full documentation is at arenarium.dev.
What’s your biggest frustration with standard map markers or clustering? Let's talk in the comments.
<small>Images at top of article provided by source</small>
aiMost of us have seen a coding agent fail to complete a task we know it can do. We just don't...
googlecloudWhen building Generative AI applications, developers often encounter a massive bottleneck: sequential...
discussI’ve been thinking about sharing some electronic circuit posts on Dev.to — small circuits, DIY...
agentsWhat nobody tells you about exporting your multi-agent prototype to a local workspace. Every...
agenticarchitectAutonomous agents are genuinely good at answering messy business questions. Give one an LLM and a set...
aiPR volume went up, ticket quality didn't, and the gap got filled with LLMs on both sides of the review: bots reviewing, bots replying, bots occasionally arguing with bots about priorities that only existed in a teammate's head. Our CEO named the actual problem, and it's bigger than code review.
Workflows from the Neura Market marketplace related to this Stable Diffusion resource