Run spatial data analysis simply using SQL in your PostgreSQL database!! 🚀
PostGeoDa is a PostgreSQL extension for spatial data analysis. PostGeoDa is developed using C/C++ based on the libgeoda library. By utilizing and the database architecture of PostgreSQL and the spatial index in PostGIS, PostGeoDa has the ability to handle real big spatial data.
-- Create Queen contiguity weights
SELECT queen_weights(gid, the_geom) OVER() FROM natregimes;
-- Apply local Moran statistics
SELECT local_moran(hr60, queen_weights) OVER() FROM natregimes;
-- Apply spatial regionalization SKATER
SELECT skater(ARRAY[hr60, dv60, ue60], queen_weights) OVER() FROM natregimes;PostGeoDa is a free and open-sourced library. It is released under the GNU General Public License (GPLv2 or later). PostGeoDa is developed by Xun Li and Luc Anselin.
- PostGeoDa is the first spatial data analysis extension of PostgreSQL to power spatial data services for cloud mapping platforms.
- PostGeoDa offers many features of spatial data analysis that run efficiently in PostgreSQL, so there is no need to spend extra time transferring geometries over the network.
- PostGeoDa has no dependencies. But it is designed to work with PostGIS to handle big spatial data.
- PostGeoDa works seamlessly with the current SQL API frameworks e.g. CARTO SQL API
If you prefer running spatial data analysis in browser, please check out [jsgeoda](https://www.npmjs.com/package/jsgeoda).
If you prefer running spatial data analysis in Python, please check out [pygeoda](https://geodacenter.github.io/pygeoda).
If you prefer running spatial data analysis in R, please check out [rgeoda](https://geodacenter.github.io/rgeoda).
https://xunli.gitbook.io/postgeoda
- 1 Choropleth Mapping
- Basic Mapping
- Rate Mapping
- Spatial Rate Mapping
- 2 Spatial Weights
- Contiguity-Based Weights
- Distance-Based Weights
- Kernel Weights
- 3 Local Spatial Autocorrelation
- Local Moran
- Local Geary
- Local Getis-Ord G
- Local Join Count
- Quantile LISA
- 4 Local Spatial Autocorrelation - Multivariate
- Local Neighbor Match Test
- Multivariate Local Geary
- Bivariate Local Join Count
- Multivariate Local Join Count
- Multivariate Quantile LISA
- 5 Spatial Clustering
- SKATER
- REDCAP
- 6 Cluster Analysis
- HDBSCAN
- Fast K-Medoids