Geo Spatial Data Analytics on Spark

Kitware's system for 3D building reconstruction for the IARPA CORE3D program

Extract and Visualize location from any file

Convert Shapefile to the Network and find number of shortest paths

Sparklyr extension package providing geospatial analytics capabilities

Go implementation of turfjs

open source tools for interaction with IBM PAIRS:

Utilites and functions for easy usage of S2 objects (Google geometries abstractions)

Geospatial data analytics: Affects of community gardens on housing prices in New York City

PostGIS on Greenplum demo

A Python client for Radiant Earth Foundation platform

This project aims to recommend effective deployment locations for police emergency response cars.

Advanced geospatial analysis for golang, android and ios.

Your data aggregrated into nicely shaped polygons for visual and tabular output.

The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

The Radiant Earth Foundation Platform base web app

Utilities related to GIS, such as geo-processing scripts.

This repo is python client for geoSearch system of APIs to search and process satellite dataset

This repository contains the Geo-Data Science challenge presented in the final part of the DTU Data Science course 42577 & 42184: Data Science for Mobility / Introduction to Business Analytics

Training materials in Python for geoscientists