spatial-analysis

Download, model, analyze, and visualize street networks and other geospatial features from OpenStreetMap.

😎Awesome GIS is a collection of geospatial related sources, including cartographic tools, geoanalysis tools, developer tools, data, conference & communities, news, massive open online course, some amazing map sites, and more.

Long list of geospatial tools and resources

PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

A cluster computing framework for processing large-scale geospatial data

Repository contains pgRouting library. Development branch is "develop", stable branch is "master"

USC urban data science course series in Python

High-level geospatial data visualization library for Python.

Raster-based Spatial Analytics for Python

Kriging Toolkit for Python

Kuwala is the no-code data platform for BI analysts and engineers enabling you to build powerful analytics workflows. We are set out to bring state-of-the-art data engineering tools you love, such as Airbyte, dbt, or Great Expectations together in one intuitive interface built with React Flow. In addition we provide third-party data into data science models and products with a focus on geospatial data. Currently, the following data connectors are available worldwide: a) High-resolution demographics data b) Point of Interests from Open Street Map c) Google Popular Times

Google Earth Engine for R

OpenHuFu is an open-sourced data federation system to support collaborative queries over multi databases with security guarantee.

Java map matching library for integrating the map into software and services with state-of-the-art online and offline map matching that can be used stand-alone and in the cloud.

The GIS Tools for Hadoop are a collection of GIS tools for spatial analysis of big data.

Spatial Single Cell Analysis in Python

Boost.Geometry - Generic Geometry Library | Requires C++14 since Boost 1.75

Tidy Geospatial Networks in R

The Spatial Framework for Hadoop allows developers and data scientists to use the Hadoop data processing system for spatial data analysis.

Notebooks and libraries for spatial/geo Python explorations