antvis/l7

Overview

This skill is a comprehensive guide to AntV L7, a WebGL-powered geospatial visualization library for building interactive maps and large-scale spatial dashboards. It covers scene initialization, data parsing, layer creation (points, lines, polygons, heatmaps), interactions, animations, and performance tuning. It explains integrations with AMap (GaodeMap), Mapbox, Maplibre, or standalone L7 Map and offers practical workflows for production use.

How this skill works

The skill walks through the core workflow: initialize a Scene with a chosen map adapter, prepare spatial data (GeoJSON/CSV/JSON), create and style Layers, add interactions and components, then optimize rendering. It inspects common configuration options, parser settings, visual mappings (color, size, shape), and provides layer-specific patterns for point, line, polygon, heatmap, raster and image layers. It also documents animation hooks and performance strategies for large datasets.

When to use it

• When you need WebGL-accelerated interactive maps for thousands to millions of spatial features.
• To visualize points, trajectories, polygons, heatmaps or tiled raster imagery with rich styling.
• When integrating maps with AMap (Gaode), Mapbox, Maplibre, or running standalone L7 map.
• To build location-based dashboards combining multiple layers and interactive components.
• When optimizing rendering and data pipelines for large-scale geospatial datasets.

Best practices

• Always initialize Scene first and choose the map adapter that fits your base map provider.
• Prefer GeoJSON as canonical input; use parsers to convert CSV/JSON to spatial features.
• Apply data aggregation and filtering for large datasets; use GPU-powered layers where possible.
• Start with basic rendering, then progressively add interactions, popups, and animations.
• Use visual mappings (color, size, opacity) to encode data meaningfully and keep styles performant.

Example use cases

• City dashboard combining choropleth polygons, point-of-interest clusters, and heatmaps.
• Real-time vehicle tracking with animated LineLayer trajectories and point markers.
• Population density visualization using HeatmapLayer and aggregated grid tiles.
• Custom tiled satellite imagery with Image or Raster layers over a Maplibre base.
• Spatial analysis pipeline: CSV import → GeoJSON parser → aggregated point layer visualization.