Spatial Data Private Limited - Providing GIS Services And Products, Bangalore, India  
TRC - Tuberculosis Research Centre, Chennai
To create GIS database to determine and visualize village wise spatial distribution of TB infected patients undergoing treatment for Model DOTS Project Area, Thiruvallur
Monitoring the infected patients of Model DOTS project area for progress of treatment, was difficult.
TRC chose a GIS solution that allowed location of Patients on a digital map and linking this with Patient profile.
  • TRC has GIS database to do spatial analysis to determine location related issues affecting the program performance.
  • Planning village wise intervention is quick and easy
  • Monitoring performance and achievement at micro level is enabled.
  • Accessiblity checks to remote locations is quick and easy



Case Study - The Tuberculosis Research Centre  (TRC) is a permanent research institute of Indian Council of Medical Research (ICMR),working on Tuberculosis.

Spatial Data Private Limited has built a GIS for their Model DOTS project area in Thiruvallur District of Tamil Nadu.

TRC has developed its own unique identifiers for Project Villages, and Patients tracked in the program. The GIS data layers developed in this project were joined with the related information on villages and patients that have been captured already.

The essential components for project were creation of map data, conduct GPS survey to capture Patient location village wise in the project area, Collect Socio-Economic data and build the GIS database. Spatial Data Pvt. Ltd. (Spinfo) has worked with TRC to design an appropriate GIS database with digital maps of the project area – Thiruvallur District, Tamil Nadu.

Patient locations in 218 villages in 5 Taluks of Thiruvallur district were located and collected with Patient Unique ID so that this can be associated with the Patient profile data collected by TRC based on the Patient Unique ID to enable spatial analysis.


This GIS enables officials to determine:

  • Patient Locations in remote village areas
  • Accessibility to these areas
  • Cluster of Patients and their response to the treatment
  • Patient distribution and specific patterns