Spatial Analysis and GIS Functions

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Spatial Analysis in ArcView (executable video adapted from ESRI educational material)

Spatial analysis involves data queries performed on geo-referenced information to answer complex questions, like "How many people live within one mile of a hazardous waste site in the United Kingdom?"

From Simple Questions to Analytic Questions

Once we have a functioning GIS containing our geographic information, we can begin to ask simple questions such as:

• How far is it between two places? (measuring distances)

• Where is land zoned for industrial use?

And analytical questions such as:

• Where are all the sites suitable for building new houses (or new healthcare facilities)?

• If we build a new highway here, how will traffic be affected?

“Spatial Analysis” – not just a map. With GIS, users can turn data into information, ask
questions and interact with the system

GIS provides both simple point-and-click query capabilities and sophisticated analysis tools to provide timely information to managers and analysts alike. GIS technology really comes into its own when used to analyse geographic data to look for patterns and trends and to undertake "what if" scenarios. By applying the latest GIS to public health research, it is possible to confirm an existing hypothesis about the cause of a certain disease, to identify previously unstudied, preventable causes of a disease and to determine people at risk (risk assessment). Modern GIS have many powerful analytical tools, but two are especially important: proximity analysis and overlay analysis.

Proximity Analysis

• How many houses lie within 100 m of this water main?

• What is the total number of patients within 10 km of this healthcare facility?

• What proportion of identified cases lies within 500 m of a suspected well (as source of infection)?

To answer such questions, GIS technology uses a process called buffering to determine the proximity relationship between features.

Overlay Analysis

The integration of different data layers involves a process called overlay. At its simplest, this could be a visual operation, but analytical operations require one or more data layers to be joined physically. This overlay, or spatial join, can for example link land-use and environmental data to population and disease data.

Let's consider this example: we want to assess the risk to people living in a certain area regarding their exposure to some carcinogenic pesticide that had been sprayed and polluted the soil. We can use GIS to calculate the distance from the residential parcels to the likely sources of pollution.

But since forests reduce drift from aerial spraying of pesticides by capturing some of the spray in their foliage, we should also overlay data about land use (e.g., forests, homes, etc.) with data about sources of pollution (e.g., sprayed areas, cranberry bogs, etc.) to see where forests come between pollution and homes (protecting homes from pollution). GIS could be then used to identify the areas with the greatest risk of exposure by selecting residential land-use polygons that intersect or are adjacent to pesticide sources.

Data Linkage for Analysis

Multiple databases can be linked and related

Analysis requires data linkage, within the same dataset and/or in a second dataset. GIS uses geography, or space, as the common key element between datasets. Information is linked only if it relates to the same geographic area. GIS can communicate with conventional DBMS.

Spatio-Temporal Analysis

By adding a temporal (time) dimension to spatial data and analysis, we can track changes that might occur regarding some variable/condition within the same location with time. Also the variable/condition we are studying might change locations with time, or extend beyond the original location to involve additional ones.

Spatial Analytic Techniques for Medical Geographers

(Albert and Gesler, 2000)

See also:

• Diggle P. Spatial Statistics for Environmental Epidemiology. Medical Statistics Unit, Lancaster University, UK. 2000

• The GeoMed Advisor: A Disease Clustering Advisor: The advisor will ask you a series of questions about your data and the kind of clustering you wish to detect, then suggest an appropriate test (Source: UMICH.EDU, US)

Visualisation

For many types of geographic operation the end result is best visualised as a map or graph. Maps are very efficient at storing and communicating geographic information. While cartographers have created maps for millennia, GIS provides new and exciting tools to extend the art and science of cartography. Map displays can be integrated with reports, three-dimensional views, photographic images, and other output such as multimedia.

GIS combines various display methods, all of them very useful in supporting the decision maker

In the Health Sector, GIS Can Answer the Following Questions and Serve the Following Functions:

Some Real-Life Examples:

Site location and client distance

Modelling future trends

Clusters and comparison of data

Environmental monitoring and modelling - toxic plume

Health Geomatics
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This page was last modified October 14, 2002
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