Minimize GeoJSON, Shapefile, and KML Sizes Without Compromising Detail > 자유게시판

When working with map data files such as shapefiles, GeoJSON, or Keyhole Markup Language, storage footprint can become a bottleneck, especially when delivering map data via HTTP. Large files slow down loading times, drive up hosting costs, and can even cause browser freezes. The good news is that you can significantly reduce file size without losing rendering fidelity or attribute completeness by following a few practical optimization techniques.

First, apply generalizing vector shapes. High-detail polygons with excessive coordinate nodes can often be streamlined with minimal visual impact, especially at smaller zoom levels. Software such as Open-source GIS platform, MapShaper, or the generalization tools in GDAL allow you to apply algorithms such as Douglas Peucker to eliminate redundant coordinates. Adjust the simplification parameter that trades precision for efficiency—usually a value between 0.00005 to 0.005 decimal degrees works well for global datasets.

Also, evaluate converting your data to more efficient formats. Text-based vector data is human readable but inefficient. Using TopoJSON encoding can shrink data by over two-thirds because it eliminates duplicate geometry. For SHP files, prune irrelevant columns. Delete unused columns that aren’t used in styling or analysis. You can also convert text fields to integers or categories where possible—for example, swap "California" for "CA".

File compression offers major gains. Use gzip or brotli to optimize delivery before delivering via web servers. Most modern web servers support this out-of-the-box, and clients handle decompression seamlessly. A large KML dataset can shrink to under 5 MB with compression, making it render instantly.

When handling dense point clouds, implement point aggregation. Don’t plot every coordinate, group nearby points into aggregated markers that break down when the user zooms in. This reduces the number of features rendered at once and boosts frame rates.

Finally, validate your data. Redundant records, پاسپورت لایه باز topological errors, or null values can increase file weight and produce visual glitches. Leverage tools such as GDAL’s ogrinfo or built-in validation tool to repair errors before publishing.

Through a combination of vector generalization, format conversion, field removal, Brotli, and data cleaning, you can often cut data volume by 70–90% without any noticeable detail loss. The delivers faster maps, cheaper hosting, and a smoother experience for your users.