Iraklis Karampourniotis

Ms Eng. Rural & Surveying Engineer

Dr. Eng. Candidate

Rural & Surveying Engineering Dept.

Polytechnic Faculty

Aristotle University of Thessaloniki

Abstract

Geographic Information Systems (GIS) are software packages that can be used in a variety of applications with multifaceted results thus being able to cover a very large range of needs. Especially in medicine and health related applications in general, GIS can be used from the simplest visualization of an epidemic spread on a map of a region to the support of decisions through Decision Support Systems (DSS) and disease spread scenarios and how to minimize damages and losses.

Introduction

A wide definition of a Geographic Information System is that it is the combined hardware, software, tools and human resources used to record, manage, analyze and visualize any type of information with or without a spatial component.

Based on the above, a GIS application allows its users to introduce the recorded data into a database and visualize them on maps based on specifications developed by the group of people that will use these maps. Through the same application the user may search for information as it was recorded as well as extracted information based on calculations performed on the raw data while in parallel visualizing the results on a map. The information may or may not have a spatial component. In the case that it does have one then additional data can be extracted such as area being covered, perimeter, location of a specific characteristic etc.

A very interesting and yet simple example is taken from the website of healthmap.org which is being used as a world map for recording and reporting incidents relevant to health. The following image shows a map where incidents of the virus of the Western Nile where reported.

Figure 1. Western Nile Virus reported incidents[1]

Understanding the variety of subjects ranging from epidemiology to access to health facilities requires the understanding of the geography of these subjects and this can only be achieved through the use of Geographic Information Systems (Najafabadi, 2009).

GIS have a very wide range of applications in the field of health such as:

• Visualization, trend and correlation analysis of epidemiological data
• Epidemic expansion monitoring
• Scenario analysis for decision support
• Mapping and correlating health issues with geographic regions
• Use of satellite imagery to extrapolate data and correlate information on atmospheric conditions, temperature, soil conditions etc with medical data and conditions
• Health infrastructure management such as identifying the most prominent location to setup a new hospital
• Publication of health related data such as health infrastructure installation locations, epidemics etc.

All the above items are directly connected to quality in health as they can be crucial in maintaining a high level of awareness with regard to the variety of factors that can affect public and personal health.

GIS tools that can be used for health applications

Buffers: This is one of the most useful GIS tools specific to geographic analysis. It provides the user with the ability to create a zone / buffer around a specific subject / phenomenon. For example the user can create a buffer of specific radius around the location of a chemicals' factory and then combine the resulting buffer with a function that provides as a result the number of cancer patients in relation to the distance from the factory. The user may also opt to acquire the number of cancer patients inside the buffer zone. This way buffering can also be used to identify the rate with which the number of cancer patients increase or decrease in relation to the distance from the factory.

Spatial and Temporal Analysis: GIS provides a multitude of tools to analyze data on a spatial as well as on a temporal basis overlaying temporal information over spatial information. Spatial Analysis tools allow the user / researcher to visualize the data relevant to the subject he/she is working on by using isopleth or choropleth lines, contour lines, ranges and check the spatial correlation of the data (Balaji) as for example the area where a specific virus can be located. This way the user can identify not only the medical but also the spatial and geographic qualities of the research area that can lead to the specific virus appearing.

The use of temporal analysis allows the combination of spatial data with the parameter of time and thus the user can now monitor the spatial and temporal evolution of a phenomenon as well as make projections for the future of the evolution based on trend and correlation analysis.

For example, specific atmospheric conditions combined with specific geographical conditions of an area allows for a pathogen to thrive. Monitoring this pathogen on spatial and temporal level can allow the user to detect the parameters that affect the evolution of the pathogen and thus support the decision process on how to prevent this pathogen from spreading or even appearing again thus enhancing the level of the quality of health in that specific region.

Mapping and Visualization: The most basic ability and capacity of a GIS is the visualization of data in the form of maps. The user can visualize on the maps the relevant data using a variety of thematic mapping techniques.

A very important example is taken from Longley, Goodman, Maguire και Rhind who used a GIS application in order to monitor poliomyelitis in India while being able to track the virus and correlate it with specific geographic characteristics that allowed for the virus to appear. Using this data they were able to create a model and plan to support the effort of fighting the appearance of the virus instead of only trying to cure it (Longley, Goodchild, Maguire, & Rhind, 2005).

Network Analysis: GIS provide the ability to analyze information being provided in the form of a network. For example, the road network can be used to transfer a disease simply by moving diseased animals from one location to the other. Analyzing the network can provide information about the areas that the animals went through and in combination with all other relevant to the disease data such as environmental conditions, atmospheric conditions, geography etc a set of scenarios about the spread of the disease can be created along with the strategies to mitigate the risks and results.

Network analysis can also be used in order to identify areas that would benefit from the installation of a new hospital. Both Roovali & Kiivet and Jordan et al used versions of the same technique (travel time) to identify the covariance and correlation between the use of a hospital in relation to the distance from it and thus leading to a model for the creation of new hospitals (Roovali & Kiivet, 2006), (Jordan, Roderick, Martin, & Barnett, 2004) and thus supporting a higher level of quality in public health.

Statistical Analysis: Statistical Analysis can be used to calculate statistical data and statistical correlations between the data. For example, statistical analysis can be used to correlate the appearance of cancer incidents in relation to the distance from a nuclear power plant. It can also be used to calculate the number of victims that a hospital can care for after a large accident in relation to its distance from the location of the accident.

Querying: GIS provide the ability to create dynamic queries and combine data from a variety of data sources.

Interpolation and Data Extraction: Using modeling techniques "new" data can be created for areas where data does not exists using interpolation.

References

Balaji, L. GIS in Health. UNICEF, India Country Office, Monitoring & Evaluation Section.

Jordan, H., Roderick, P., Martin, D., & Barnett, S. (2004). Distance, rurality, and the need for care: access to health services in South West England. Int. J. Health Geograph .

Longley, P., Goodchild, M., Maguire, D., & Rhind, D. (2005). Geographic Information Systems and Science 2005. Wiley.

Najafabadi, A. T. (2009, October). Application of GIS in Health Sciences. Shiraz E Medical Journal , 10, σσ. 221-230.

Roovali, L., & Kiivet, R. (2006). Geographical variations in hospital use in Estonia. Health & Place , σσ. 195-202.