Heatmaps in Unreal Engine

Does Unreal Engine have built-in heatmaps?

Unreal Engine does not provide one universal, ready-made heatmap system for gameplay or movement analytics. Instead, it provides the building blocks needed to create one: materials, textures, render targets, decals, debug drawing, Niagara, navigation data, and runtime mesh generation.

That means a heatmap in Unreal Engine is usually a project-specific feature. A simple prototype can be only a translucent material with a gradient texture. A production analytics tool usually needs a data model, editor baking, runtime controls, validation tools, and a clear definition of what each color actually means.

Common use cases

• Player analytics: visited areas, dead zones, interaction points, and level flow.
• Crowd simulation: occupancy, density, bottlenecks, queue formation, and evacuation pressure.
• AI debugging: path usage, navigation failures, cover selection, or decision frequency.
• Industrial simulation: operator movement, logistics routes, workstation load, and safety zones.
• Performance analysis: expensive actors, overdraw, collision hotspots, or streamed area usage.

The correct implementation depends on whether the heatmap is a design aid, an editor-only debug view, a runtime UI feature, or an exported analytical result.

Data sources

Heatmap values can come from many sources inside or outside Unreal Engine:

• runtime actor locations sampled during play,
• recorded player or occupant trajectories,
• AI path requests and path following events,
• simulation frames generated by Mass, custom crowd systems, or external tools,
• collision, overlap, interaction, or trigger events,
• imported CSV, JSON, database, or telemetry data.

Before building the visual layer, define whether samples represent actor origins, capsule centers, feet positions, projected floor positions, or already aggregated analytical values.

Implementation options

Material or texture overlay

The simplest option is to render values into a texture or render target and use a material to map the values to colors. This works well for flat surfaces, minimaps, dashboards, and cases where the heatmap can be represented as a 2D image.

Decals

Decals can project colored regions onto scene geometry. They are convenient for localized runtime markers, but they can become harder to manage when the heatmap needs precise numerical sampling, multi-floor separation, or large analytical surfaces.

Niagara

Niagara can visualize many points or soft blobs in world space. It is useful for live debug views and expressive visual effects, especially when exact tile-level analytics are less important than readable visual feedback.

Procedural mesh overlay

A procedural overlay gives the most control over geometry, height, UVs, and material values. It is a good fit for NavMesh-based or grid-based analytics where the heatmap should follow walkable space and stay separate from the visible level mesh.

External analysis with Unreal playback

For heavier analytics, data can be processed outside Unreal Engine and then imported back as a texture, data asset, or time series. This is useful when the same dataset must also be used in BI tools, reports, or offline validation pipelines.

Choosing a spatial model

The spatial model determines what a heatmap cell means. A world-space grid is easy to understand, but it may include walls, props, voids, or unreachable space. A mesh-based solution can follow level geometry, but it may be difficult to aggregate cleanly. A NavMesh-based solution follows the space where agents can actually move.

Typical choices are:

• 2D grid for simple top-down maps and flat layouts.
• 3D voxel grid for volumetric data, flying agents, or vertical gameplay.
• Surface mesh for visual fidelity on custom geometry.
• NavMesh polygons for movement, AI, and crowd analytics.
• Hybrid grid over NavMesh for readable tiles with walkable-area awareness.

Metrics

Heatmaps are easy to misread when the metric is vague. The same color can mean a completely different thing depending on the data behind it.

• Occupancy counts how many actors or samples are present in an area.
• Density divides occupancy by surface area, usually in actors per square meter.
• Dwell time measures how long actors remain in an area.
• Traffic volume counts how often actors pass through an area.
• Event intensity counts interactions, collisions, failures, or other gameplay events.

Movement analytics often benefits from storing both raw counts and normalized density. Counts show traffic volume, while density can reveal narrow areas that become overloaded even with fewer people.

Time and aggregation

A heatmap can describe a single moment or a whole simulation. Common aggregation modes include:

• current frame or current simulation time,
• sliding time window,
• cumulative values up to the current time,
• cumulative values over the whole run,
• average, maximum, or percentile values over time.

Color normalization matters as much as the metric. Normalizing by the current frame makes weak patterns visible, but the meaning of colors changes during playback. Normalizing by a global or fixed maximum keeps the scale stable and is better for comparison.

Visual design

A useful heatmap should be readable without hiding the scene. Runtime controls usually include opacity, gradient selection, minimum visible value, value range, smoothing, and the active visualization mode.

Smoothing can make noisy data easier to read, but it can also hide local peaks. For analytical tools, it is helpful to support both raw values and a smoothed view. For multi-floor buildings, floors or height layers should be separated clearly so that a busy area on one floor is not mixed with an empty area above or below it.

Example integration: NavMesh-based movement heatmap

One practical approach for pedestrian or occupant analytics is to build the heatmap over Recast NavMesh. This is especially useful when tracked samples represent character capsule centers, because NavMesh already describes where those agents can move.

In this type of integration, the editor first converts NavMesh into an analytical grid. Each tile stores its grid coordinates, effective walkable area, representative height, normal, surface slot, and links to the original NavMesh polygons. Surface slots allow several walkable layers to exist above the same XY position, which is important for floors, ramps, stairs, and bridges.

Recorded trajectories are then projected onto navigation and assigned to analytical tiles. The bake can store per-frame occupancy, cumulative counts, density values, global maxima, and a finer kernel-density raster around individual occupants for smoother visualization.

Possible asset structure

• HeatMapSurfaceAsset stores the analytical surface derived from NavMesh.
• HeatMapBakeAsset stores time-based values generated from trajectories or simulation frames.

Runtime visualization

A runtime manager can render a ProceduralMeshComponent overlay above the analytical surface and feed values into a transient R32_FLOAT texture. A material samples that texture and maps the values through a color gradient. The same system can switch between walkable area, occupancy, density, and occupant-radius density views.

The overlay may support opacity, a minimum visible value, bilinear sampling, optional smoothing, local peak preservation, tile subdivision, and a small surface offset to avoid z-fighting.

Debugging and validation

A heatmap should show where the data is strong and where the data may be missing. Useful debug views include tile IDs, source polygons, surface slots, representative heights, raw sample counts, normalized values, and failed mapping reasons.

For NavMesh-based movement heatmaps, the most important validation question is whether the heatmap and the simulation use the same movement constraints. If the tracked trajectories represent character capsule centers, the NavMesh agent radius and height should match the tracked agents.