Chapter 10: Health and GIS
Using Overlays to Estimate Risk
One of the powerful features of GIS is the ability to combine different layers of spatial data in
order to see how different geographic features work together across space. In this exercise, you
will conduct a basic overlay analysis.
Imagine that there is a new emerging infectious disease in South America. The disease vector
thrives in environments characterized by a) large areas of low altitudes; b) extremely high
precipitation; c) warm temperatures; d) dense human populations. Your task is to determine the
location where the vector is likely to thrive in order to guide where key preventative measures
could be implemented.
In order to determine high-risk areas, you will use four maps, each containing information about
the risk characteristics. Your first step is to assign a value to each of the grid cells according to
whether it satisfies a risk condition. Use a transparency of the grid to examine the cells and
record the results on the response sheet provided.
Elevation
2
1
0
High risk
Moderate risk
Low risk
all of the cell is lower than 500 meters above sea level
more than half of the cell is lower than 500 meters above sea level
less than half of the cell is lower than 500 meters above sea level
Precipitation
2
High risk
1
Moderate risk
0
Low risk
any portion of the cell receives more than 3,000 mm of
precipitation
any portion of the cell receives more than 2,000 mm of
precipitation (but no portion more than 3,000 mm)
none of the cell contains precipitation greater than 2,000 mm
Temperature
2
1
High risk
Moderate risk
0
Low risk
any portion of the cell has a temperature greater than 25°C
any portion of the cell has a temperature greater than 20°C (but no
portion more than 25°C)
no portion of the cell has a temperature greater than 20°C
Population Density
2
0
High risk
Low risk
any portion of the cell contains a high population density area
no portion of the cell contains a high population density area
After you have filled in the columns in the response sheet, add the values and record the result in
the total column.
Anthamatten and Hazen (2011), An Introduction to the Geography of Health
Response Sheet
ELV
A1
A2
A3
A4
A5
A6
B1
B2
B3
B4
B5
B6
B7
C1
C2
C3
C4
C5
C6
C7
C8
C9
C10
C11
C12
C13
C14
D1
D2
D3
D4
D5
D6
D7
D8
D9
D10
D11
D12
D13
E1
E2
E3
PRC
TMP POP
TOTAL
ELV
PRC
TMP POP
TOTAL
E4
E5
E6
E7
E8
E9
E10
E11
E12
F2
F3
F4
F5
F6
F7
F8
F9
F10
F11
G2
G3
G4
G5
G6
G7
G8
G9
G10
H3
H4
H5
H6
H7
H8
I4
I5
I6
I7
I8
J4
J5
J6
J7
Anthamatten and Hazen (2011), An Introduction to the Geography of Health
Response Sheet
1. Use the map template provided to produce a map of your results by appropriately shading
the legend and cells to match the data you collected. How would you describe the
location and distribution of “high-risk areas”?
2. Which countries would most likely be affected by this emerging infectious disease?
3. Neither the data in this exercise nor the grid used for the analysis is very precise. How
important do you think that precision is for this kind of analysis?
4. If you were conducting a follow-up to the analysis, what kinds of data do you think
would be important to include in a study to model the vector’s distribution?
Anthamatten and Hazen (2011), An Introduction to the Geography of Health
Grid
Anthamatten and Hazen (2011), An Introduction to the Geography of Health
Elevation
Anthamatten and Hazen (2011), An Introduction to the Geography of Health
Precipitation
Anthamatten and Hazen (2011), An Introduction to the Geography of Health
Temperature
Anthamatten and Hazen (2011), An Introduction to the Geography of Health
Population Density
Anthamatten and Hazen (2011), An Introduction to the Geography of Health
Map Template
Anthamatten and Hazen (2011), An Introduction to the Geography of Health
Sources
Centro Internacional de Agricultura Tropical (CIAT), United Nations Environment Program
(UNEP), Center for International Earth Science Information Network (CIESIN), Columbia
University, and the World Bank. (2005) Latin American and Caribbean Population
Database. Version 3. Available: <http://www.na.unep.net/datasets/datalist.php3> or
<http://gisweb.ciat.cgiar.org/population/dataset.htm>
Legates, D. R. and Willmott, C. J. (1990) ‘Mean seasonal and spatial variability in gaugecorrected, global precipitation’, International Journal of Climatology, 10: 111–27.
United States Geological Survey Earth Resources Observation and Science Center. (1996)
Global 30 Arc-Second Elevation (GTOPO30) [Online]. United States Geological Survey.
Available: <http://eros.usgs.gov/#/Find_Data/Products_and_Data_Available/GTOPO30>
(Accessed 03 November 2010).
Anthamatten and Hazen (2011), An Introduction to the Geography of Health