Basic

We have developed the following geospatial datasets designed to support innovative research and learning across a wide range of disciplines. These datasets are freely available to the Harvard community, fostering exploration and advancements in fields...

CGA has developed the following novel geospatial big data analytics applications: Raster Information Extraction System (RINX) Optipath K- Nearest Neighbor Billion Object Platform V2.0 RapidRoute

The following open source GitHub repositories have been developed as a part of the project: Big data/ Data Science projects at CGA RINX V1.0 RINX V2.0 Rapid Route V1.0 Rapid Route V2.0 Twitter Sentiment Geographical Index (TSGI) Geospatial Data Management...

Raster Information Extraction System (RINX) "The PRISM climate data extracted by the CGA allowed us to study associations of precipitation, relative humidity and temperature with lung function in children. The climate data will also allow us to study...

CGA has deployed the following GIS applications on New England Research Cloud (NERC), enabling large-scale geospatial processing within a cloud environment. This setup allows Harvard researchers to access advanced geospatial tools with flexibility and at...

The following big data applications are now available on FASRC, enabling researchers across Harvard to access advanced geospatial tools with flexibility and minimal to no cost. Scripts are also available to assist users in deploying these tools on other...

CGA now supports the Harvard community to perform Geospatial analysis at large scale on the following cloud and cluster computing resources. Harvard's FAS Research Computing (FASRC) Harvard’s FASRC supports complex research by providing state-of-the-art...