GeoTools vs. Other GIS Libraries: A Comparative Overview

• Choose bandwidth (radius).
• For each point, add kernel-weighted contribution to grid cells within bandwidth.
• Normalize by kernel sum.

9. Performance tuning and memory management

• Stream features: use FeatureIterator and FeatureWriter rather than loading whole collections.
• Use spatial indexes for in-memory operations.
• Push operations to spatial databases when possible (PostGIS is highly optimized).
• Process rasters block-by-block and avoid materializing large arrays.
• Use simpler geometries (linearring vs complex multipolygon) where possible; apply TopologyPreservingSimplifier.
• Tune JVM (heap size, GC) for large batch jobs.
• Use Profiler and heap dumps to find hotspots and memory leaks.

10. Testing, debugging, and deployment tips

• Unit test geometry operations with known fixtures; use small CRS-aware examples.
• Visualize intermediate results with GeoTools’ rendering or export GeoJSON for quick inspection in QGIS.
• Use logging at debug level for Geotools modules; capture exception stack traces for CRS and transform errors.
• For production, prefer containerized deployment with fixed GeoTools version to avoid classpath conflicts.
• When upgrading GeoTools, run regression tests—geospatial libraries can change behavior subtly between versions (CRS axis ordering, precision models).

11. Further reading and resources

• GeoTools user guide and Javadocs (project site)
• JTS Topology Suite documentation for advanced geometry ops
• PostGIS documentation for pushing spatial SQL to the database
• Example projects and workshops (GeoServer, GeoMesa) for large-scale geospatial processing

This article provided practical techniques and examples for advanced spatial analysis with GeoTools: CRS handling, geometry/topology, spatial indexing, raster-vector workflows, geoprocessing pipelines, and performance considerations. The code snippets are sketches—adapt to your GeoTools version and project needs.