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Medical Geographic Information Systems Laboratory


Housed on the CDU Campus, the Charles Drew Medical Geographic Information Systems (CDMGIS) Laboratory provides a "virtual center" for the storage, management, analysis, modeling, and mapping of spatially referenced health related data using state of the art Geographic Information Systems Technology (Medical GIS). The CDMGIS laboratory was established with partial funding from the National Center for Research Resources and the National Center on Minority Health and Health Disparities (NCRR# 5G12RR003026-RCMI & NCMHD# 5P20MD000148-EXPORT) to support the integration of Geographic Information Science (GISC) theories and methods within biomedical research. The establishment of a medical GIS laboratory at Charles Drew University builds upon ongoing research activities of the National Institutes of Health (NIH) supported Research Centers in Minority Institutions Biomedical Research Center (RCMI) and constitutes an integral component of a rapidly expanding university research informatics infrastructure.

A primary goal of the center is to facilitate interdisciplinary collaboration by linking the computing facilities, spatial databases and the resident expertise at the CDMGIS laboratory with other research and teaching activities occurring at Charles Drew University, UCLA and other RCMI institutions. Additionally, collaborations are actively sought with community-based organizations, local health and services organizations, government agencies, and academic campuses and institutes across the nation.




Human Studies


  • Custom web interface for geocoded data ( Data analysis service )

    If you have a dataset of any kind that contains geographic information for each datapoint, the CDU MedGIS Lab can create a dynamic website for you that enables you to query and display your data in a variety of ways. Contact Dr. Paul Robinson for more information.

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Last updated: 2012-04-18T13:07:40.040-05:00

Copyright © 2016 by the President and Fellows of Harvard College
The eagle-i Consortium is supported by NIH Grant #5U24RR029825-02 / Copyright 2016