AUTHOR: Amber L. Iler, Research Scientist, ISCIENCES, L.L.C
In 1989, the United Nations Development Programme selected July 11 as the annual day to raise awareness about global population issues. This year, the United Nations Population Fund (UNFPA) has selected the theme “Everyone Counts” to underscore the importance of data for development. It is their hope that this year’s celebration of World Population Day will “foster an understanding of why reliable, disaggregated data is so crucial to progress and encourage people to participate in the census and other data collection efforts.”[1] Much of the work we do at ISciences depends upon these types of data, and we recognize that knowing where people are on Earth is essential to understanding the interaction between humans and their environment. This knowledge can be especially meaningful when faced with assessing vulnerability to natural or man-made disasters such as earthquakes, hurricanes, war, or famine.
In honor of World Population Day this Sunday, I took a look at the 2 arc-minute (i.e. disaggregated) Population Density 2007 dataset previously examined by our Global Data Hound, thinking that it might exemplify the value of knowing where people are. I focused on how this LandScan-based dataset can help us calculate the population affected by a natural disaster such as the 2010 earthquake in Haiti: In January of this year, a Magnitude 7.0 earthquake hit Haiti approximately 25 km west of the capital, Port-au-Prince, resulting in the deaths of over 200,000 people.[2] Port-au-Prince is the largest city in Haiti with an official population of 704,776 as of the 2003 census,[3] but this fact alone only accounts for part of the story…
Calculating the impact of a natural disaster
[Time from launch of GDV: Under 30 seconds. Click count: 7.]
The Population Density 2007 dataset can be used to estimate how many people were within 35 km of the epicenter of the earthquake. First, open the Population Density map in TerraViva! Global Data Viewer (GDV). This map can be found under MapLibrary > Population > Population Density 2007 (ORNL).
Next, we want to zoom in on Port-au-Prince, which can be done using the Gazetteer. On the GDV toolbar you’ll find a blue icon with a white “G” inside it. Clicking on this will bring up the Gazetteer where you can select CitiesPlaces under Geographic Entity to locate any major city on your map. Begin typing “Port” in the Name field and once Port-au-Prince appears in the list, click on it, and a blinking red crosshair will appear over the city in the map window. Zoom in on this area of the map to get a better look at the population density in Haiti.
Now we will use the spatial query tool to select the Population Density map as the analysis theme and build a query searching within a 35 km buffer distance of the epicenter. The USGS reports the epicenter of the earthquake as 18.457, -72.533.[4] To center your map on this position, select MapLibrary > Map Options > Set Map Projection. A dialogue box will open and you will see a checkbox option called “Automatic Projection Parameter Setting,” which is selected by default. By deselecting this option, several fields will appear allowing you to type in the epicenter coordinates and then reproject your map to this new centerpoint.
Population of Haiti within 35 km of the epicenter of the 2010 earthquake in January. Click to enlarge.
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The spatial query tool is selected by right clicking on the Population Density map at the centerpoint, then by selecting Quick Query > Other. A window will pop up asking for the radius for the query in kilometers. Type “35″ and “Enter” and the query circle will appear on the map and the query statistics in a separate table as shown above.
The statistics for this query show that over 3 million people may have been in that area and that in the densest regions, just outside of Port-au-Prince, there may have been as many as 54,300 people in a square kilometer. The LandScan Pop Density dataset from ORNL estimates “ambient” population density – where people are likely to be at noon. Though the earthquake took place late in the afternoon on a Tuesday,[4] the population density was probably fairly similar to the estimated distribution at noon. With such a high concentration of people in this region, even under the best circumstances many would have surely been affected. As a contrast, I repeated this query centered on nearby Hispaniola in the Dominican Republic: The results show the same sized region would have only included 278,196 people.
The importance of population data
As this example shows, having specific, accurate demographic data is critical to identify where vulnerabilites to natural disasters may lie, as well as for the planning of emergency and other services. Having aggregated data on a per-country level is useful, but by collecting and analyzing disaggregated population data, important trends may be revealed.
This year, the UNFPA is highlighting the importance of data for development for World Population Day and posing the question, “What striking situation does research reveal in your country?”[5] Here at ISciences, we believe if you want to really understand your world, you first need to make sure everyone is counted.
Notes
[1] https://www.unfpa.org/public/sitemap/wpd, retrieved July 6, 2010.
[2] http://en.wikipedia.org/wiki/2010_Haiti_earthquake, retrieved July 6, 2010.
[3] http://en.wikipedia.org/wiki/Port-au-Prince, retrieved July 6, 2010.
[4] http://earthquake.usgs.gov/earthquakes/eqinthenews/2010/us2010rja6/, retrieved July 6, 2010.
[5] https://www.unfpa.org/public/site/global/lang/en/world-population-day, retrieved July 6, 2010.