Sponsored Research

The CGA leads and co-leads sponsored research projects that blend spatial thinking, data infrastructure, and domain-focused inquiry, to address challenges across the various domains of climate, migration, health, ecosystems, and historical mapping.

Current and recent work creation of development of, and participation in, a virtual center for spatiotemporal research (Spatiotemporal Innovation Center), predictive analytics for climate-induced migration (CLIMB), large-scale environmental mapping (e.g. mangroves), COVID-19 mortality impact modeling, real-time pandemic response platforms, GPU-enabled geospatial analysis, ecosystem composition via imaging spectrometry, and digital humanities efforts such as the Temporal Gazetteer and China Historical GIS.

These projects illustrate CGA’s capacity to design and build platforms, apply spatial methods to topical domains, and support collaborative scholarship.

Spatiotemporal Innovation Center

Spatiotemporal Innovation Center Everything happens in space and time. From natural disasters to human catastrophes; from public health to energy sustainability; from urban growth to food security; 21st-century challenges call for a deep understanding of...

CLIMB: Climate-Induced Migration in Africa and Beyond: Big Data and Predictive Analytics

About the CLIMB project Climate change (interlinked with humanitarian crises and other economic and health factors) could lead to internal resettlement, international migration, and other (new) forms of human mobility. However, the empirical link between...

Mangrove Forests Mapping

Coastal mangrove forests provide important ecosystem goods and services, including carbon sequestration, biodiversity conservation, and hazard mitigation. However, they are being destroyed at an alarming rate by human activities. To characterize mangrove...

COVID-19 Impact on Mortality of Various Causes in the United States

This project analyzed CDC published mortality data of a dozen major causes since 1999, and applied the Exponential Smoothing (ETS) algorithm to simulate the 2020 mortality rates per cause, per month and per state, assuming there was no COVID-19 pandemic...

RAPID: Building a Spatiotemporal Platform for Rapid Response to COVID 19

Sponsored by NSF, this project is to build a comprehensive data repository of virus cases, associated social and natural information from different resources for sustainable archive; share data with the research communities through smart data discovery...

Geospatial data science tools and data on Harvard's High Performance Computing Cluster (HHPCC)

In collaboration with OmniSci Technologies and the NSF Spatiotemporal Innovation Center, and in close coordination with Harvard Research Computing (FASRC), the CGA has deployed the OmniSci GPU database and PostGIS, on Harvard's large computation cluster....