Want to keep learning?

This content is taken from the University of Twente's online course, Geohealth: Improving Public Health through Geographic Information. Join the course to learn more.

Practical example of spatial data use

Case Study PHFI

Cardiometabolic diseases (CMD), including heart disease and metabolic disorders are a major cause of premature mortality and morbidity among South Asians. The ‘Built Environment’ may influence the development of CMDs. While we know much about the causes and individual risk factors for CMDs, the relationship between the environment and CMD and its risk factors has been sparsely investigated. Cardiometabolic risk factors include smoking, low physical activity and obesity. For example, when people do not have good access to parks, they may do less physical exercise and increase their risk for CMDs.

GPS data collection Public Health Foundation of India - Data collection

At the Public Health Foundation of India (PHFI) the Centre of Excellence (CoE) for CArdiometabolic Risk Reduction in South Asia (CARRS) was established. This centre has a specific study group focussing on the use of Geographical Information Systems (CARRS- GIS). The plan was to collect spatial data relating to individuals and their neighbourhoods and link it with the health and demographic data collected as part of the CARRS main study. The spatial data collected included:

  1. Global Positioning System (GPS) locations of participant study households
  2. GPS locations of selected points of interests such as health care facilities and food and alcohol outlets from the study neighbourhoods (1 km around study households)
  3. Land use features such as green spaces and open areas.

Data collection was carried out using trained data collectors who visited each of the study households and captured the GPS location using portable GPS machines (Garmin Montana 650t). The data collectors also identified and located selected points of interests such as health care facilities, food and alcohol outlets from the study neighbourhoods. Land use variables were derived mostly from Google Earth. We digitized many different land use features like the green areas based on Google Earth maps. In case good quality base datasets on land use would have been available for free we would have preferred using them over Google Earth.

We will come back to CARSS in the upcoming steps in this week (1.8, 1.12, 1.14 and 1.18). In these steps we will explain how we mapped our data (Mapping), the analysis that we conducted (Analysis) and how the project is embedded in our organization (People). We hope that via these steps you will get a good overview of our complete case study project.

Share this article:

This article is from the free online course:

Geohealth: Improving Public Health through Geographic Information

University of Twente

Get a taste of this course

Find out what this course is like by previewing some of the course steps before you join: