How Baidu Maps Turns Massive Geodata into Interactive Visual Stories

After the holiday, Baidu Map’s technical team shares a deep dive into how massive geospatial data is turned into compelling visual stories using web‑based visualization techniques.

1. Baidu Map API and Personalized Maps

Baidu Map itself is a classic visualization product. Its JavaScript API enables developers to display geographic data on maps, and the platform supports custom base‑map configurations to highlight or hide details such as POI or small roads, adapting the style for city‑level outlines, street‑level detail, or dark themes for dashboards.

2. Point Data Visualization

Point data, such as POI locations, are the most common geospatial data. Examples include banks near Xidan displayed as simple dots, colored or sized points to convey additional attributes, and custom POI icons. 3D column charts can also illustrate the distribution of popular overseas destinations.

When point density is high, overlapping occurs. Spatial algorithms, clustering, or statistical aggregation reduce visual clutter. The example below shows point clustering for banks around Xidan.

Grid‑based aggregation (hexagonal bins) can also summarize point density, with colors indicating the amount of data per cell. 3D column charts further visualize the quantity within each grid.

Heatmaps provide another way to view overall distribution, as shown for bank POIs around Xidan.

For massive POI datasets, a heatmap can reveal trends without overwhelming detail, such as user location density where brighter areas indicate higher positioning frequency.

Tag clouds can also be combined with maps to display location‑related textual information, using size and color to reflect attributes while avoiding label overlap.

3. Line Data Visualization

Line data represents connected points, such as roads, rivers, or railways. It is often used to display vehicle trajectories. The example below shows a car’s daily route.

Different brush styles can enrich line appearance, as illustrated by two distinct styles.

When many trajectories overlap, visual clutter occurs. For detailed analysis of a single vehicle, fine‑grained rendering is needed; for large‑scale pattern analysis, simplification, thinning, and aggregation are applied, as shown in the aggregated taxi trajectories in Nanjing.

OD line bundling visualizes commuting flows, reducing visual noise by grouping similar routes.

4. Polygon Data Visualization

Polygon data represents closed areas such as buildings, administrative boundaries, or lakes. Visualization typically uses color gradients to convey attributes like population density or sales volume. An example shows province‑level user distribution using ECharts.

Heatmaps can also illustrate visitor flow in scenic areas, providing travel references.

5. Other Applications

Population migration analysis combines ECharts with maps to show inter‑city movement, while indoor crowd flow analysis helps mall planning.

6. Front‑End Technologies Used

All visualizations are built on web technologies. The stack includes Canvas, WebGL, SVG, node‑canvas for server‑side rendering, and libraries such as ECharts, MapV, and the open‑source Baidu Map visualization library MapV . Canvas is preferred for its pixel‑level control, performance with large datasets, and animation capabilities, outperforming DOM and SVG in many scenarios.

Performance optimizations include reducing API calls, minimizing context state changes, avoiding expensive effects like shadows, using cache pre‑drawing, rendering only changed regions, and leveraging requestAnimationFrame for smooth animations. When data size exceeds browser limits, offline rendering with node‑canvas generates image tiles for both front‑end and IE compatibility.