Heat Map
A heat map is a data visualization technique that uses color gradients to represent the intensity or density of a variable across a geographic area. In location intelligence, heat maps reveal concentrations of customers, foot traffic, sales, or demand, making spatial patterns immediately visible.
A heat map—sometimes written as heatmap—is a graphical representation of data where values are depicted by color intensity. In geospatial applications, heat maps overlay a continuous color surface on a geographic map, with warmer colors (reds and oranges) indicating higher values and cooler colors (blues and greens) indicating lower values. This makes it easy to spot clusters, hot spots, and cold spots at a glance.
How It Works
Geographic heat maps are typically generated using kernel density estimationKernel Density EstimationKernel density estimation (KDE) transforms discrete point data into a smooth, continuous density surface by placing a... (KDE), which takes a set of point data—such as customer addresses, transaction locations, or device pings—and produces a smooth, continuous surface representing the estimated density of events per unit area. The algorithm places a kernel function (usually Gaussian) over each point and sums the overlapping kernels to produce a density value at every location on the map. The choice of bandwidth (kernel radius) significantly affects the appearance: narrow bandwidths reveal fine-grained clusters, while wide bandwidths show broader regional trends.
Types of Heat Maps
Point-density heat maps are the most common in location analytics, used to visualize customer concentrations, incident locations, or foot traffic intensity. Gridded heat maps assign values to regular grid cells (squares or hexagons) and are useful for comparing discrete areas. Temporal heat maps add a time dimension, showing how density patterns shift across hours, days, or seasons. Some platforms support 3D heat maps that use height in addition to color to represent value magnitude.
Applications
Retailers use heat maps to visualize customer home locations, revealing the geographic core of their trade area and identifying areas of weak penetration. Site selectionSite SelectionSite selection is the analytical process of evaluating and choosing optimal physical locations for new stores, facili... teams overlay demand heat maps with competitor locations to spot underserved markets. Marketing teams use heat maps to plan out-of-home advertising placement in high-traffic corridors. Urban planners visualize crime hot spots, traffic congestion, and pedestrian activity. Real estate developers assess neighborhood vitality by mapping commercial activity density.
Advantages
Heat maps communicate complex spatial data instantly and intuitively, requiring no technical expertise to interpret. They are supported by virtually every GISGISGeographic Information Systems (GIS) enable users to analyze and visualize spatial data to uncover patterns, relation... and business intelligence platform and can be generated from any dataset with geographic coordinates.
Limitations
Heat maps can oversimplify data by smoothing over important local variation. The visual impression is highly sensitive to color scheme, bandwidth, and opacity settings, which can be manipulated—intentionally or accidentally—to emphasize or suppress patterns. Heat maps are best used for exploratory analysis and communication rather than precise quantitative comparison, where choropleth maps or tabular data may be more appropriate. Heat maps are among the most powerful and accessible tools in the location intelligence toolkit. By transforming raw spatial data into vivid visual patterns, they accelerate insight discovery and make geographic analysis approachable for every stakeholder.
Code-Beispiele
import { DeckGL } from "@deck.gl/react";
import { HeatmapLayer } from "@deck.gl/aggregation-layers";
function HeatMap({ data }) {
const layer = new HeatmapLayer({
id: "heatmap",
data,
getPosition: d => [d.longitude, d.latitude],
getWeight: d => d.value,
radiusPixels: 30,
intensity: 1,
threshold: 0.05,
colorRange: [
[255, 255, 178],
[254, 204, 92],
[253, 141, 60],
[240, 59, 32],
[189, 0, 38]
]
});
return <DeckGL layers={[layer]} />;
}Verwandte Mapular-Lösungen
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