Genomic interval operations on Pandas DataFrames
BBoxDB is a scalable, highly available, and distributed data store for multi-dimensional big data. The software supports operations like multi-dimensional range queries and spatial joins. In addition, data streams are supported.
An example of how to join point to polygon data with geopandas and Python
A fast library for spatial joining and merging data in JavaScript 🚀
RTDL is a research system for writing and running non-graphical ray-tracing programs across Embree, OptiX, and Vulkan.
Beta. How much of California's public wildfire damage-inspection record set can be attributed to a published electric service territory, and how much cannot. 37.9% of records fall inside more than one published boundary. Every rate carries its denominator and a confidence interval; no utility is ranked. Unofficial.
Spatial joining with a map reduce program on top of Apache Spark using the Apache Sedona spatial extension
Adding timing and location to traditional types of data and to build data visualizations.
An advanced multi-scalar geovisualisation of the UK's renewable energy landscape. This project maps the "Renewable Gap" by integrating the Q4 2025 REPD database with 33,000+ LSOA boundaries, featuring a drill-down dashboard to analyze operational reality vs. the planning pipeline.
Polygon & GMR 1 — QGIS plugin: join a polygon layer with an Excel file (GMR). Reads the real column headers of every sheet, matches on a chosen join field, and outputs only the matching polygons with their attribute table enriched with the Excel columns.
Grid-based spatial join algorithms implemented with Apache Spark for scalable geospatial data processing using the RAILS and AREALM datasets.
GIS screening analysis using river proximity and low elevation to identify schools and health facilities requiring closer flood assessment.
An enhanced intersect operation with sjoin in GeoPandas
Vector spatial analysis of vape-store proximity to Christchurch schools using CRS validation, buffers, spatial joins, and nearest distances.
GIS analysis of school distribution, proximity zones and county-level primary and secondary school counts across Kenya.
Point-in-polygon testing and point-to-polygon spatial joins for Standard ML, with an optional R-tree-accelerated join.
Geospatial pipeline converting KML/KMZ service areas into FCC Fabric-matched location datasets with DuckDB, Shapely, and Google Cloud.
End-to-end GIS data engineering pipeline: Natural Earth → GeoParquet QA → DuckDB Spatial analytics → GitHub Pages Leaflet map.
Benchmark spatial joins across GeoPandas, Shapely 2, and PostGIS
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