Case 04 · Wemap · Data visualization · 2D & AR · Spatial data

A map that shows everything, helps you decide nothing.

Wemap publishes large location datasets on 2D maps and in AR. As the data grew, the maps only got more crowded. This is a prioritization system that keeps the data rich while guiding people to faster, more confident decisions, across two very different visual spaces.

Role: UI/UX Designer — UX & UI design
Product: Wemap · 2D + AR spatial platform
Scope: 2D + AR · data viz · information density
Status: Shipped

Data visualization2D & ARSpatial dataInformation density

Context

Maps built to display data & asked to guide decisions.

Wemap is a spatial publishing platform for tourism and cultural partners, who use it to manage and display large amounts of location data on 2D maps and in augmented reality.

The maps were designed to show everything a partner had. But as datasets grew, density climbed without clarity following. More points did not mean more help; they meant more to sort through.

The problem

Everything competed for attention, so nothing stood out.

Combining post-navigation feedback, recurring client requests, and support insights, the same three frictions surfaced.

01 · Overload

Too much at once

With many points shown together, the map turned cluttered and hard to read.

02 · No hierarchy

Everything looked equal

All points competed equally for attention, so users could not tell where to focus.

03 · No decision

Stuck at exploration

People could see places, but struggled to compare options and decide where to go next.

The reframe. The map was a display surface. It needed to become a decision surface, without throwing away the density of information partners depend on.

A dense 2D map of central Nîmes with many equally-weighted category pins.

A dense AR view with a row of category pins competing for attention at the bottom of the frame.

Dense views where nothing reads as important.

Environment 1 · In augmented reality

Height instead of clusters.

In AR you cannot collapse markers into clusters like a 2D map: points live in a physical space, and stacking them makes them unreadable. So instead of grouping, I used the vertical axis itself to encode distance and priority.

No clustering

Points sit in real space. Collapsing them breaks spatial logic.

Faster overload

Visual overload hits far quicker in AR than on a flat screen.

User movement

Users walk and rotate. Info must adapt to distance and angle.

Performance

Too many 3D elements hurt frame rate, tracking, and battery.

Vertical ordering by proximity

When points overlap, the closest one sits in front. A distant marker never dominates a nearer one, and height reads as depth.

Distance-based height thresholds

A point's height rises in steps with distance (0–30m, 30–100m, 100–300m), giving an instant read of near, mid, or far without any numbers. Beyond range, points are deprioritized or hidden to hold back overload.

Horizontal collision avoidance

When two points sit at a similar distance and would overlap, a minimal, deterministic horizontal offset keeps each one individually selectable, preserving accuracy.

Diagram: the vertical axis as a distance scale — closer points sit lower, larger, and in front.

The vertical axis becomes the distance scale. Depth is read at a glance. Every point is still visible and selectable.

Before and after of the same AR street: crowded pins versus pins ordered by depth with distance labels.

Before / after the same street, ordered by depth and distance. It also allowed space for titles and tags.

The AR solution beat overload with depth. On a flat 2D map there is no depth to use, so density had to be managed a different way.

Environment 2 · On the 2D map

Filter the field, then highlight what matters.

On a 2D map, clarity comes from two levers: users filter the dataset down to what they care about, and partners choose the few things worth highlighting each given one clear, unmistakable signal.

Crowded view

In dense areas, too many points land on one screen at once.

Visual competition

Without hierarchy, points, labels, and map context all fight equally.

Category filters sit right below the search bar, so users refine the dataset before they ever touch the map. Then, for the points that deserve emphasis, a single subtle highlight in a warm accent chosen because it stays visible in real environments and never clashes with category colors. One signal, used sparingly, does the work a dozen competing cues could not.

When the highlight appears

A city or tourism partner promotes a point
A sponsored recommendation is present
A point belongs to a curated route or theme

How much emphasis, at most

Max 3 highlighted points visible at once in AR
Max 6 highlights per zoom level on the 2D map
Beyond that, the emphasis stops meaning anything

2D map with category filter chips below the search bar, and an AR view with a single warm-highlighted pick.

Category filters below search; warm highlights marking an editorial pick.

In a tourism context, people usually want trusted guidance, not exhaustive choice. Surfacing editorial selections helps a visitor see what is recommended not just what exists.

Outcome

From a visual field to a decision space.

43%

0% → 43% of selections

Point selections came from highlighted locations. Users leaning on prioritization signals instead of trial-and-error browsing.

−37%

16s → 10s

Faster time-to-selection in AR, as depth cues and progressive disclosure cut unnecessary scanning.

Structured spatial clarity

Progressive disclosure and vertical hierarchy made dense AR data legible without hiding meaningful context.

Confident decisions

Selections shifted toward highlighted points. Users went from browsing to choosing.

Less cognitive friction

Distance-based disclosure and field-of-view management cut involuntary scanning in AR.

The takeaway

Density isn't the enemy. Undifferentiated density is.

Two very different spaces, one goal: help people see less and understand more. That's what turned browsing into deciding.