Demographic mapping is a way of using GIS (global information system) mapping technology to show data on population characteristics by region or geographic area. Many demographic maps use interactive technology, allowing users to customize the data to their needs, to view changes over time or select regions for comparison.
Maps will typically display one demographic variable or indicator, often using colour coding to indicate the density, frequency or percentage in a given region, allowing quick comparison between regions. Colour coding can also be used to show changes over time (e.g. red for regions where an indicator got worse between two specific years, blue for areas where an improvement was seen and so on). Other information can be overlaid on the map, for example specific sites (e.g. hospitals or distribution centres) and text.
Maps can be static or interactive. Technical skills and software programs like Bespoke, Weave, and StatPlanet are necessary to build interactive maps in particular. However, simpler technology is available such as Microsoft’s MapPoint, which uses wizards to guide you through the customisation stages. There are other cheaper low-tech options for static maps including overlaying information on maps in PowerPoint or customizing Google Maps.
Demographic mapping is most useful when the data you wish to display has a strong regional or geographic dimension, or when regional patterns have changed over time. It can be a visually compelling way to explain decisions regarding the design and targeting of the programme or project, for example in an inception or analytical report, or to display evaluation results.
Data on issues such as poverty and inequality benefit in particular from demographic mapping, as they have many regional determinants, including geographic and agro-climatic factors, access to services and infrastructure etc. Mapping by governments or other organisations can also improve the targeting of expenditure and investment, improve decisions regarding emergency response and humanitarian programmes, and contribute to national and regional policy formulation processes.
In 2007 the Government of Uganda commissioned a report into regional poverty dynamics in the country, and used demographic mapping techniques to display its results. The report examined changes in poverty in Uganda over the period 1992-2002 using household survey data and provided estimates of Ugandan poverty and inequality at the district, county and sub-county levels. Analysis of changes in poverty allowed the Government of Uganda to identify poverty ‘hotspots’ or areas where poverty has increased significantly over the decade, and to target resources and programmes accordingly.
The analysis generated indicators of well-being for all 164 counties and 958 sub-counties in Uganda. The study found that between 1992 and 2002 the vast majority of counties in Uganda saw marked improvements in poverty levels, with important exceptions in the North and east of the country, where in many cases poverty got worse.
Source: (Emwanu, Okiira Okwi, Hoogeveen, Kristjanson & Henninger, 2007)
Advice for CHOOSING this option (tips and traps)
The variables being graphed should never be in raw counts. Raw counts are almost always disproportionate and misleading when mapped geographically. Use proportions like per capita.
Make sure that demographic mapping is the most appropriate way to display your data. Most importantly, does the data have strong regional dimensions that would benefit from display on a map?
The design of the map will depend on what it is being used for:
The variables displayed on the map (the regional data) should be those directly targeted by the programme, or variables that could influence outcomes or effectiveness.
If you are using the map to support the design of a project, does it make more sense to display data by county, administrative region, tribal area or climatic zone?
If you are using the map to display evaluation results, the data and regions displayed should reflect the programme design and key performance indicators.
If the aim is to draw comparisons between one region or country, and another, does comparable data exist? If the definition of indicators varies between regions it will make the map less useful.
Advice for USING this option (tips and traps)
Carefully consider in advance the best way to disseminate your findings. Maps are visually compelling ways to convey data, yet are often relegated to the appendix of an inception or evaluation report. If your programme has a website, interactive maps are a great way to display demographic data and allow users to customise the data for their own ends.
More than a Pretty Picture: Using Poverty Maps to Design Better Policies and Interventions: detailed guidance on the use of Small Area Estimation poverty maps in research and policy making.
Nature, Distribution and Evolution of Poverty and Inequality in Uganda: This report compares maps of poverty rates (expenditure-based metric) with maps of two other well-being indicators: a qualitative measure of well-being and a measure reflecting access to safe drinking water, respectively.
Google Fusion Tables - free online tool for visualing data in map form
How to use MapPoint: These pages demonstrate how to use MapPoint to its full capabilities. Each function and feature is described, along with caveats and potential problems.
Weave: Another free online mapmaker.
Bedi, T., Coudouel, A., & Simler, K., (Eds). The World Bank, (2007). More than a pretty picture : using poverty maps to design better policies and interventions. Retrieved from website: http://siteresources.worldbank.org/INTPGI/Resources/342674-1092157888460/493860-1192739384563/More_Than_a_Pretty_Picture_ebook.pdf
Emwanu, T., Okiira Okwi, P., Hoogeveen, J. G., Kristjanson, P., & Henninger, N. Uganda Bureau of Statistics and the International Livestock Research Institute (ILRI), (2007). Nature, distribution and evolution of poverty and inequality in Uganda.