pysal

SPAtial GrapHs: nETworks, Topology, & Inference

Geographic Data Science'17

Spatial Analysis with R and Python - SEAI 2022 Labs

Supervised machine learning for predicting and interpreting dynamic drivers of plantation forest productivity in northern Tasmania, Australia

Work in progress to the development of methods for road network simplification #GSoC2022

Spatio-temporal hotspot analysis of traffic accidents in Saudi Arabia. The analysis is conducted using Python PySAL library on traffic accidents data from the years 2015 to 2018. The results are presented using Plotly library.

Introdução ao uso da biblioteca PySal para análises exploratórias espaciais.

Python implementation of the max-p-compact-regions problem for spatial regionalization with compactness constraints (Feng et al., 2022, Transactions in GIS)

Detection of spatio temporal hotspots of traffic accidents in Saudi Arabia for the years 2015 to 2018. The data is aggregated nonetheless using Hijri calendar years 1437, 1438, 1439. Each Hijri year consists of 12 months and approx. 355 days.

IPython Notebook for spatial data analysis of electric vehicle charging stations in Ontario, Canada.

Conducting geodemographic classification for ethnic groups in NYC using K-means algorithm available in sklearn.cluster module.

Geospatial analysis and spatial regression of suspected non-fatal opioid overdoses in Toronto shelters, 2018–2025

This repo serves as an announcement platform for the PySAL Code of Conduct Committee

Calculating global and local spatial autocorrelation of income noted per each polish county in 2022 based on Moran's I and LISA statistics. Calculations were conducted using the following packages: pySAL, splot.esda, geopandas.

Multi-temporal environmental health exposure assessment for Khordha and Cuttack districts, Odisha. Sentinel-2 LULC, PCA-weighted composite index (heat, green access, blue proximity, air quality, industrial exposure), spatial statistics, scenario-based green-blue intervention modelling, and ML-driven urban typologies.

01.12.18 - Notebook provides basic introduction to spatial data science in python for UMN Day of Data 2018

Multiscale GWR insurance risk pricing model for LA County. Full spatial regression progression: OLS -> Spatial Error -> GWR -> MGWR, with per-variable bandwidth analysis, significance-masked coefficient maps, LISA clustering, and an interactive Folium dashboard. FEMA, Census ACS, NIFC & OSM data.

Work in progress for the project of panel data econometrics models in spreg from PySAL.

🗺️ Análise Exploratória de Dados Espaciais (AEDE) da difusão do Pix no Brasil. Código e dados da monografia em Economia (UFPE).

k-nearest neighbor network creator for the generation of statistics from geospatial data.