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Week 1 summary

In this week we have discussed the topic Geohealth as a combination of Health Care Geography and Spatial Epidemiology.

We have taken the GIS system apart and identified the components of Data, Software, Mapping, Analysis and People. Each of these components plays an important role in the Geohealth application.

We have seen how data splits into “Primary Data”, in this case health related data, and “Secondary data” (environmental data or administrative data). We looked at ways to geo-reference data and identified several future trends (big data).

For software we identified many different types of software with specific functionality ranging from database software, GIS software, statistical software to modelling software. This software can be either commercial or open source.

We have seen that we are using different map types in our work. Sometimes a point map is the most obvious choice. But other types of maps have also been presented like the choropleth map, allocation maps and animations.

For the analysis part we have seen that we can split this group into cluster analysis, correlations studies that link disease to environmental factors and diffusion studies when we focus on spatial epidemiology but for health care geography we can split into accessibility studies and planning health facilities.

The People involved in a GIS system can be divided into the producers of information and the consumers. Producers come from the health and GIScience domain, but the consumers are mainly Government, policy makers and the general public.

You also completed your first in depth topic on health facility planning, accessibility and GIS. The topic covered a number of elements. It started explaining that we can view health services delivery as a problem of distributing scarce health resources in geographic space. An important notion introduced is the accessibility concept which can be seen as a performance indicator that shows us in how far a spatial constellation of health facilities is capable of serving the health needs of a spatially dispersed population. From the conceptual level we then moved to a more operational level by discussing three of the most commonly used GIS-based accessibility measures in the GeoHealth domain. We concluded with an example that illustrated how GIS can be used for strategic spatial planning of health facilities.

What’s next?

We hope that you have enjoyed this first week of our course. It must have been a tough job as this week contained many steps. The next weeks are filled with in-depth topics.

In week 2 we will focus on Machine Learning. In this week you will be introduced to the use of Volunteered Geographic Information in health research. By asking the general public to enter tick bites on a website we are trying to get a more complete picture of ticks in the Netherlands. You will also learn more about the difficulties in using Volunteered Geographic Information in analysis.

The second topic in week 2 focusses on Self-Organizing Maps for studying the diffusion of Measles. During the years, several Measles epidemics have taken place and we would like to know if these epidemics followed the same spatial temporal diffusion pattern. Understanding more about diffusion patterns can help in future to predict the way an epidemic will spread over a country or even over the world.

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This article is from the free online course:

Geohealth: Improving Public Health through Geographic Information

University of Twente

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