Spatial data is of great concern to population health studies. People and entities do not exist in a vacuum, and as such are influenced by and influence those around them. This can lead to geographically correlated issues where space and place are of huge importance, hence the need for GIS in health studies.

This is especially true as populations are often distributed unequally. Wealthier people tend to be concentrated in one area, and people of certain races in others, with varying densities.

There are many applications for health GIS, such as disease mapping, cluster detection, environmental hazards, or modelling health services.

Disease mapping is the epidemiological study of how a given disease spreads, where it goes to and at what rate. This is important as it can be used to predict which areas would be impacted next or the most, and can play a key role in developing resilience or response policies.

Cluster detection is the use of GIS to identify areas with high instances of a given health condition. This often requires the use of a Geographically Weighted Regression analysis in order to determine correlation of a health outcome with any of a variety of possible factors.

GIS can also be used to monitor environmental hazards. This involves analysis of risk exposure as well as mitigation techniques.

Modelling health services is another important role of GIS. For example, GIS can be used to find the ideal location for emergency services through the use of a service area tool, which creates drive time maps and can be used to estimate time-distance from response areas.

However, the use of GIS in health geography also comes with some limitations. One key issue is that of data. Often, health GIS requires public census data, which is always out of date and frequently incomplete or flawed. Flawed data leads to flawed analyses. Furthermore, people are extremely dynamic, and it is difficult to take all the possible variables into account, which often results in regression analyses that indicate one of a myriad number of possible underlying variables.

The topic of today’s lecture was an introduction to health geography. Considered to be an improvement by some over “medical geography”, health geography is concerned with not simply epidemiology, but also the spatial analysis of access to and quality of healthcare, and has a variety of applications, such as optimal ambulance routing.

Health geography can be divided into three main areas: disease ecology, healthcare delivery, and environment and health.

Disease ecology studies the geographical spread of infectious diseases.

Healthcare delivery analyzes the efficacy of modern healthcare systems and how they deal with and are impacted by geography. For example,this can include average patient distance from hospitals, or travel times from high injury rate areas to treatment centres.

Environment and health is a branch of geography that studies how place can impact one’s health. An example would be how proximity to a coal plant is correlated with issues such as lung disease.

Wednesday was the first day  of in-class presentations on landscape ecology. However, I ended up presenting on Friday.

The topic of my presentation was a paper written in 2019 analyzing the impact of night time light pollution on bat presence probability and landscape habitat connectivity. The study took place in the city of Lille, France and data was gathered through audio recordings of bats echolocating, as well as from a NOAA light raster.

Analysis in QGIS was used to construct bat presence probability based on light pollution and recorded data, with an algorithm to create a probability raster. Linkagemapping Toolkit in ArcGIS Pro was used to map least cost pathways that represent movement corridors for bat movements.

Results showed each species of bat impacted in different ways, with one species being adverse to light pollution, one species being more prevalent with increasing light, and one species preferring small amounts of light. Overall it was deemed that light pollution was a good indicator of bat presence, but still less so than distance to wetlands.

One other presentation I found interesting was the study concerning modelling lynx populations. I was able to see how the Linkage Mapper Toolkit was application to modelling species habitat connectivity in various other landscape types.