Category Archives: GIS

21 Jul 2021

Mapping Unrealized Fertility in Sub-Saharan Africa

Many women in DHS Program countries have fewer children than they desire. Our newest StoryMap explores unrealized fertility in sub-Saharan Africa, based on the recently published analysis Comparing Ideal and Completed Family Size: A Focus on Women in Low- and Middle-income Countries with Unrealized Fertility. Unrealized fertility is most common among women in Western and Central Africa where about 60% of women age 40-49 report that they had fewer children than they would have liked.

The StoryMap and paper also explore sex preference, ideal family size, and subnational variation in unrealized fertility.


Featured image © Roger Tete for PMI/USAID

24 Feb 2021

Spatial Anonymization in Household Surveys

Location is an important factor in population and health outcomes. Knowing the geospatial location of household survey clusters allows researchers to analyze the impact of location on peoples’ health, nutrition, and access to health care services. Geospatial data provide a clearer picture of where progress towards the Sustainable Development Goals is and is not being made at subnational levels.

The United Nations’ Inter-Secretariat Working Group on Household Surveys (ISWGHS) was convened to identify priority areas for household surveys to meet new data demands and increase their impact on policy and research over the next decade of the 2030 Agenda for Sustainable Development. Under the ISWGHS, The DHS Program and the World Bank co-led a task force on Spatial Anonymization of Public-Use Household Survey Datasets.

The DHS Program has collected GPS coordinates for household survey clusters since 1996. To ensure respondent confidentiality and prevent positive identification (disclosure) of respondent locations, the GPS position of each urban cluster is displaced by up to two kilometers and up to 5-10 kilometers for rural clusters. This method of geomasking coordinates developed by The DHS Program is straightforward and has been widely accepted by analysts using DHS geospatial data. Nonetheless, there are legitimate concerns that urban points may be overly displaced, reducing the analytical usefulness of the geospatial data, and that some rural points may not be adequately displaced to ensure respondent confidentiality. In response, the spatial anonymization task force convened to explore more sophisticated methods of anonymizing geospatial data.

The task force developed and tested new population-based displacement tools on multiple DHS survey datasets. These tools use an area’s population to determine the minimum distance a cluster’s GPS position must be displaced. These new methods show promise over current spatial anonymization methods to better protect survey respondents while minimizing any adverse impact on analysis and continue to be explored using DHS datasets.

The task force also outlines immediate steps that can be taken to protect respondents. “Even without switching to a new population-based approach [to anonymize geospatial data], we should take steps to verify that we are within an acceptable level of disclosure risk and that our current anonymization objectives are being achieved,” explains Trinadh Dontamsetti, Lead, Geospatial Research. Standards in data protection and security have evolved—the European Union General Data Protection Regulation requires that personal data, including location data, be safeguarded. The task force recommends assessing the risk of disclosure. By quantifying and measuring spatial disclosure risk, the risk can be managed.

Read the “Spatial Anonymization” report for more from the Spatial Anonymization of Public-Use Household Survey Datasets task force. This report was also discussed by stakeholders to prepare a forthcoming paper on “Positioning Household Surveys for the Next Decade.” Both papers will be presented as part of the 52nd Session of the United Nations Statistical Commission: Better Data Better Lives on March 1-3 and 5, 2021.


01 Feb 2021

New Online Course: Health Data Mapping Online Course

The DHS Program is hosting another Health Data Mapping online course on The DHS Program Learning Hub. The 12-week course focuses on the application of geographic information systems (GIS) in public health, specifically using maps for better program and policy decision making. Participants will be introduced to GIS concepts, manage and clean data in Microsoft Excel, and get a hands-on introduction to QGIS, an open-source GIS software package.

This course is for people who:

  • Have little to no GIS experience, but have an interest in learning QGIS and strong data skills.
  • Live and work in a West African country (Benin, Burkina Faso, Cameroon, Cote d’Ivoire, Gambia, Ghana, Guinea, Liberia, Mali, Mauritania, Nigeria, Senegal, Sierra Leone, Togo).
  • Have at least an undergraduate degree in public health, demography, statistics, monitoring & evaluation, or a related subject, and basic training in statistics.
  • Currently work for government ministries, development partners, NGOs, or universities in the field of public health.
  • Can understand and communicate in English—the course will be conducted in English and participants will be expected to give presentations in English.
  • Have experience using Excel and have a computer that can run the latest stable release of QGIS.

The Health Data Mapping online course begins April 12 and ends July 3, 2021. Participants can expect to spend two to four hours a week working independently on self-paced lessons and completing assignments. Course facilitators will give feedback on assignments and answer questions on the course discussion forum and during periodic instructor-led virtual sessions.

The Health Data Mapping online course is ideal for those interested in learning about GIS to improve evidence-based decision making. Explore mapmaking and data analysis resources at The DHS Program Spatial Data Repository, STATcompiler, Spatial Analysis Reports, and video tutorials.

Apply now: the deadline is Monday, February 15, 2021.

18 Nov 2020

The DHS Program Geospatial Team Celebrates GIS Day

November 18, 2020, is GIS Day, a day to celebrate Geographic Information Systems (GIS) technology and their application to DHS data. This GIS Day, we highlight recent work from The DHS Program Geospatial team.

Highlighting Subnational Inequalities That May Hinder COVID-19 Mitigation

The Geospatial team produced a story map using DHS spatial data on handwashing and the average number of people per sleeping room in some countries in Africa and Asia to illuminate subnational variations and inform COVID-19 mitigation strategies.

Health Data Mapping Workshop Facilitated Virtually

In February 2020, The DHS Program released a call for applications for a Health Data Mapping regional workshop, to be held in Hyderabad, Telangana, India in May. When this workshop was canceled due to the COVID-19 pandemic, the Geospatial team quickly pivoted and developed an online course with the same basic objectives. A new call for applications was launched targeting countries in East Africa. Of 175 applicants, 25 were selected according to established criteria, including 15 men and 10 women.

The redesigned course, a semi-synchronous workshop, was conducted from July to September and included self-paced eLearning and instructor-led virtual sessions. Each week, participants completed readings, videos, and assignments. Participants acquired knowledge on data cleaning, converting GPS data in Excel, and joining data in QGIS, an open-source GIS software package. For their capstone assignment, participants created and presented maps using data from DHS surveys.

The DHS Program periodically hosts virtual instructor-led courses like Health Data Mapping on The DHS Program Learning Hub. For future offerings of the Health Data Mapping and other courses, check The DHS Program website for calls for applications.

Two Spatial Analysis Reports Published

Predicting HIV/AIDS at Subnational Levels using DHS Covariates related to HIV uses GPS data collected in DHS surveys to produce spatially interpolated maps to predict indicator values at non-surveyed locations. This report produces fine spatial and lower level estimates of HIV prevalence for seven Global Fund-supported countries in sub-Saharan Africa. A multi-task Gaussian process is used to leverage HIV prevalence data collected in DHS surveys along with HIV-related indicators such as sexually transmitted infection (STI), condom use, and sex partners. The model produces estimates of HIV prevalence along with uncertainty at the second subnational administrative level (ADMIN 2) where health programs are designed and implemented. This geospatial modeling approach was first described in Interpolation of DHS Survey Data at Subnational Administrative Level 2, and summarized in a previous blog post.

Geospatial Covariates: Proxies for Mapping Urban-Related Indicators uses DHS data from three East African countries to explore what makes an enumeration area rural or urban. The DHS Program does not define urbanicity; countries determine official urban and rural classifications themselves. This can impair comparative analyses of the relationship between urbanicity and health outcomes. In this Spatial Analysis Report, researchers sought to determine if geospatial covariates of urbanicity, including intensity of nightlights and travel times to hospitals, can predict urban-correlated health and demographic indicators from DHS surveys.

Spatial Anonymization in Public-Use Household Survey Datasets

The DHS Program Geospatial team is responsible for displacing cluster location data for all DHS surveys to maintain the confidentiality of DHS survey respondents. This anonymized spatial data is publicly available on The DHS Program Spatial Data Repository to meet research needs. The DHS Program Geospatial team is collaborating with other institutions, such as the World Bank and the United Nations to develop a forthcoming set of guidelines on spatial anonymization of public use microdata.

11 Feb 2020

Luminare: Geospatial Modeling for Locally Available Data

This blog post is part of Luminare, our blog series exploring innovative solutions to data collection, quality assurance, biomarker measurement, data use, and further analysis.


Demographic and Health Surveys (DHS) collect nationally representative data and data representative at the first subnational administrative level (ADMIN 1). The 2016 Ethiopia DHS was designed to produce representative estimates for nine regions and two administrative cities. The 2014 Kenya DHS produced estimates for eight regions (formerly provinces). In addition to national-level indicators, STATcompiler also presents subnational data, as shown in the map of childhood stunting at the ADMIN 1 level in Ethiopia and Kenya.

Childhood Stunting by Subnational Level, 2016 Ethiopia DHS & 2014 Kenya DHS

Click the map to explore childhood stunting using STATcompiler.

National and ADMIN 1 data help countries track their progress towards achieving the Sustainable Development Goals, for instance. However, as countries decentralize their health service delivery systems, local health officials increasingly need local data. In Ethiopia, nine regions are further divided into zones and special districts (ADMIN 2). In Kenya, eight regions are further divided into counties.

One option to get data representative at the ADMIN 2 level is to increase the survey sample size, requiring more time and more money. Another option is to produce spatially interpolated maps, which use Bayesian geospatial modeling techniques to predict indicator values at non-surveyed locations.

The DHS Program’s Geospatial team assembled data for 12 geospatial covariates, such as elevation, precipitation, and population density. These covariates are related to and can partially explain variation in health indicators of interest, allowing for more accurate predictions across the map.

Next, the Geospatial team imported georeferenced cluster data points from the 2016 Ethiopia DHS and 2014 Kenya DHS. (Did you know? You can download shapefiles or geodatabases of georeferenced data for most DHS surveys from the Spatial Data Repository.)

Using the geospatial covariates and survey data, the Geospatial team employed a new modeling approach–a stacked ensemble model–which combines multiple models. This increases predictive power and captures the potentially complex interactions and non-linear effects among the geospatial covariates. Three sub-models were fit to the health indicator data using the geospatial covariates as exploratory predictors. The prediction surfaces generated from the sub-models were then used in the final Bayesian geospatial model, producing 5 X 5 km pixel-level mean estimates of health indicators with associated uncertainty.

Childhood Stunting by 5 X 5 km Pixel, 2016 Ethiopia DHS & 2014 Kenya DHS

Modeled surface maps available from the Spatial Data Repository.

Pixel-level estimates were then used to calculate population-weighted averages to aggregate estimates to the ADMIN 2 level. For Ethiopia, this produced estimates of childhood stunting by zone, and in Kenya, estimates by county.

Childhood Stunting by ADMIN 2 level, 2016 Ethiopia DHS & 2014 Kenya DHS

Health system program managers in Ethiopia and Kenya can now use these zonal- and county-specific estimates to make decisions and manage locally administered health programs to address childhood stunting in their areas.

The DHS Program will continue exploring model-based geostatistics as a feasible, reliable, and cost-effective way to produce local data for local needs.

Read the full report, Interpolation of DHS Survey Data at Subnational Administrative Level 2.

Explore available spatially modeled map surfaces of DHS indicators on the Spatial Data Repository.

07 Sep 2017

Providing Geospatial Covariate Data for Use with DHS Datasets

When users of The DHS Program’s survey data request access to our geospatial data, they usually do so with the intention of linking survey cluster location data to outside datasets – such as rainfall measurements, population density, and distance to road networks. These additional data, when coupled with geographic location, are known as spatial covariates and may shed light on the impact of location on health outcomes. However, linking these covariates to geographic data can often be a challenge as multiple sources of these covariate data exist, often with varying quality. It can be difficult for researchers to know which data source will provide the covariate data that will best complement the GPS cluster data they acquire from The DHS Program.

Having recognized both the demand for DHS geospatial data and the subsequent challenge in linking them to spatial covariates, the DHS Geospatial team endeavored to prepare and make a freely available set of standardized geospatial covariate datasets which do away with the need for linking to clusters’ GPS location data. This allows individuals with little to no Geographic Information Systems (GIS) experience to conduct geospatial statistical analysis in software such as STATA, SAS, or SPSS. Even experienced GIS analysts may benefit from these datasets as they no longer have to take the time to source the proper covariate data and link them to cluster GPS data themselves.

After gathering data from users and experts, we identified the covariates that are most commonly used in published literature in conjunction with The DHS Program’s survey data, that included key topic areas. Further, we reached out to users to get a sense of how they would potentially utilize and benefit from a set of spatial covariates prepared in-house. As a result of these two activities, we identified dozens of potential covariates that are used or that users would like to use in conjunction with our geospatial data.

Working closely with our partners at Blue Raster, we then extracted, at each displaced DHS survey cluster, measurements of selected geospatial covariates. These covariates were selected if they: a) had global or regional extent, b) were publicly available, c) had well-documented acquisition or creation processes with detailed metadata, and d) were available for relevant time frames.

We strove to include those covariates that would be in high demand by our users, including rainfall, ITN net coverage, cases of malaria, travel times to nearest cities, urbanization, and more. A detailed methodology used to extract them can be found on the Spatial Data Repository website.

We hope the spatial covariate datasets will prove to be valuable for a wide range of DHS data users. We are continuing to look into ways to further improve the datasets, including the extraction process used to create these files and release similar extracts for other covariates that weren’t addressed in the first round of this activity. User feedback will be critical in helping us understand what is truly desired out of these datasets, so we strongly encourage those who download and use these files to email us with their thoughts, advice, and requests for future covariates.

Photo Caption: GIS participants at the 2017 Regional Health Data Mapping Workshop in Cambodia.

16 Nov 2016

From National to Local: A New Way to Leverage DHS Data

In DHS survey final reports, data are presented on a national or first-level administrative sub-national level. However, this is usually not the level at which program planning and decision making are truly happening. To support more decentralized decision making at lower administrative levels, data need to be presented on a more disaggregated level.

The DHS Program is producing a standard set of spatially modeled map surfaces for each population-based survey for a select list of indicators that provide smaller area estimates of data. Geostatistics are used to predict (interpolate) the indicator value for unsampled areas based on data from sampled data locations. DHS creates standardized modeled map surfaces using DHS survey data along with global covariate datasets. Currently, sets of standard surfaces are available for 16 surveys. Spatial data packages and stand-alone maps are available for download through The DHS Program’s Spatial Data Repository.

How can modeled map surfaces be used?

These new spatially modeled surfaces can help in several ways to improve decision making for many development sectors that include health, population, nutrition, and water and sanitation programs on multiple levels. Users can combine the maps with other resources to support:

  1. Monitoring and evaluation: analysis and evaluation of past initiatives (impact analysis) or understanding existing situations
  2. Program planning: future planning of appropriate programs and policies

Data in the modeled surfaces can be used to evaluate past programs or to better understand existing situations. Such evaluations can help to understand deviations from the norm, attribute cause, or to contribute to impact evaluations, which analyze what would have happened to the population of an area if a program had not been implemented.

Program managers can also use modeled surfaces to plan, target, and develop interventions and programs that aim to improve situations in targeted geographic areas. Interventions can be targeted more precisely, saving money, time, and human resources in the search for the most effective outcomes.

The matrix below shows potential approaches for monitoring and evaluating past and planning future programs using modeled surfaces.

This matrix is by no means comprehensive, and it is expected that map users will come up with many more potential uses after analyzing their particular situation and maps for their country.

To read more, please see the Spatial Analysis Report 14, “Guidance for Use of The DHS Program Modeled Map Surfaces.” The report delivers more in-depth information on what modeled surfaces The DHS Program is creating, as well as an explanation of their creation process. In addition, the report provides guidance on limitations and assumptions.

The DHS Program is looking forward to seeing how groups will use this new data product to enhance their activities. There is enormous potential for innovative uses of these modeled surfaces beyond those discussed in the report. Users are encouraged to submit ideas and case studies to The DHS Program (spatialdata@dhsprogram.com) as only a large community of users who share their experiences will fully expose the maps’ potential.


Aileen Marshall is the Knowledge Management/Monitoring & Evaluation Specialist at The DHS Program. She is responsible for planning, development, implementation and evaluation of the KM strategy, KM activities as well as the project-wide SharePoint site. Additionally, she is involved in measuring and evaluating capacity strengthening activities at DHS and works closely with all teams to ensure knowledge at DHS is captured, stored and shared efficiently among staff. Aileen holds an MA in English Linguistics from the Westfaelische Wilhelms-University in Muenster, Germany, and an MLIS from the University of South Carolina.

Trinadh Dontamsetti is the Health Geographic Analyst for The DHS Program. He contributes to geospatial analysis, mapmaking, and geographic data processing activities. His research interests include geospatial interpolation, tuberculosis, and vector arthropod-borne diseases.

 

Clara R. Burgert is the GIS Coordinator for The DHS Program. She oversees all  geographic data, mapping, and geospatial analysis activities at The DHS Program.  Additionally, she facilitates workshops in partner countries on using maps for better decision making using open source GIS software.

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14 Sep 2016

Reflections from Accra: A Look Back at the Regional Health Data Mapping Workshop

Group of participants and facilitators at the conclusion of the workshop

In August, The DHS Program Geospatial Team was in Accra, Ghana, hosting this year’s Regional Health Data Mapping Workshop to teach participants how to use Geographic Information Systems (GIS) for public health decision-making and program planning. Participants, most of whom had never before used GIS in any capacity, learned the steps necessary to turn data from a table into a thematic map, working both together and independently to create maps and practice presenting them.

The workshop began with a review of Microsoft Excel techniques for cleaning and preparing indicator data to be used in a GIS software (for this workshop, the QGIS platform), which can often have very particular requirements for such data.  Once the data was cleared of errors and special characters, participants learned how to import this indicator data into a GIS and combine it with geographic data – stored in the form of a shapefile, which is a unique version of file type specifically used to store geographic information – merging the indicator data of a particular region or district to the shape of that area in the map.  Participants were then taught how to colorize the map appropriately, showing the difference between areas, emphasizing regions with higher or lower prevalence with intuitive color schemes, and overall making a visually appealing map.

Participants work in QGIS during a hands-on practice session

After completing four days’ worth of exercises and making maps under the guidance of the facilitators, participants had the opportunity to make their own maps from start to finish on the fifth day. Participants independently prepared these maps using their own program data or data from The DHS Program Spatial Data Repository. Each person had three minutes to present their map to the group and receive feedback on what worked well and identify areas for improvement. This allowed the participants to practice speaking about and presenting a map – an intangible but equally important skill.

Map made by one of the participants, using DHS data from Liberia

Map made by one of the participants, using DHS data from Liberia

While the workshop was focused on teaching participants the skills they needed to use GIS as part of their work, it also stressed the notion that participants would take these skills and knowledge gained in Accra back to their home countries and offices and pass on this information to their coworkers. We hope participants found this workshop to be informative, practical, and not least enjoyable!

Stay tuned for our final blog post, where we will be highlighting one participant in particular! Read the previous blog post in this series here.

For those who did not attend this workshop, The DHS Program offers numerous spatial data and GIS resources that can be used to self-teach. If you are interested in participating in future workshops, follow us on social media or sign up for our email alerts.

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27 Jul 2016

Connecting GIS and Public Health: 2016 Regional Health Data Mapping Workshop

Participants from Kenya and Zambia discussing their hand drawn maps.

In August, The DHS Program Geospatial team is hosting this year’s Regional Health Data Mapping Workshop in Accra, Ghana. The workshop will focus on the application of Geographic Information System (GIS) in public health, specifically using maps for better program and policy decisionmaking. This will be a basic workshop that introduces participants to data concepts including management and cleaning of data in Microsoft Excel, introduction to GIS concepts (including GPS data collection), using maps as part of the decisionmaking process, and hands-on introduction to QGIS, an open source GIS software package.

Participant from Zambia working on a 3-D data visualization activity.

We are excited to share mapmaking skills with a new group of participants! From finalizing the venue, selecting participants, and preparing the curriculum, we are working hard to organize everything for the workshop. Selecting the workshop participants was the most difficult part of the process so far but in the end, 20 participants were accepted out of the 600 that applied!

In 2015, we hosted a similar workshop in East Africa and also had over 600 applicants for 20 spots. We believe this continued show of interest indicates how important a skill mapmaking is, and the great need for this skill in the health sector across the world. The workshop curriculum facilitates learning these skills via guided activities, group work, and hands-on software activities where participants get to show off their hard work!

This year’s workshop specifically targets West African countries (Burkina Faso, Ghana, Liberia, Mali, Nigeria, Senegal, Sierra Leone, and Togo) and two others, Chad and Egypt, where The DHS Program has recently done or will soon be doing a household survey.

Applicants didn’t need to be experts in GIS to be selected – in fact, we preferred that they weren’t! We wanted applicants with little to no GIS experience, though certainly sought those with an interest in learning and strong data skills. In our selection, we focused on individuals with current positions within government ministries, development partners, and local universities. We hope that participants use their newfound health mapping skills to improve the use of DHS data and other data for decisionmaking in their home countries, and also to teach others in their home offices.

Participants from Tanzania practicing GPS data collection skills.

Even individuals who do not attend the workshop can still benefit from the learning of those who do and also from self-taught learning through our many mapmaking and data analysis-related resources at The DHS Program. These include the Spatial Data Repository, STATcompiler, Spatial Analysis Reports, and video tutorials.

I will be co-facilitating the workshop, so stay tuned for an upcoming blog post on how it all went!

18 Nov 2015

GIS Day 2015

I love maps! They are a great way to understand and visualize data, especially when you are looking to understand how place might influence certain health behaviors or outcomes. Luckily, I spend my days here at The DHS Program preparing data for maps, making maps, talking about maps, teaching others to make maps, and thinking of new ways to share maps with the whole world.  The good news is that you don’t have to be a Geographic Information System (GIS) professional to appreciate or even make your own maps using DHS data.

gis day 1

STATcompiler is a great tool that can allow you to visualize DHS data in many ways including via maps. These maps can be seen both at the national and sub-national level, and allow for various customizations including colors and number of categories.

gis day 3The Spatial Data Repository (SDR) is also a tool that can be used by non-GIS professionals to view maps and DHS geographic information. The boundaries page allows anyone to visualize the change in sub-national borders between various surveys. This can be very useful for analysts, survey planners, and individuals interested in sub-national trends over time.

For a full view, please visit spatialdata.dhsprogram.com/SAR12 

The gallery page has maps that have been created by The DHS Program and others using DHS data. Maps located in the gallery are tagged in different ways so that you can find the topic or countries you are most interested in. These maps are created for specific reports, presentations, or other activities. Recently, we created a series of interactive maps as an online supplement to the Spatial Analysis Report 12 Report. These maps allow viewers to explore the report data more in depth and click on a country region to see more information for the indicator selected.

You too can participate in the online map gallery: create an original map using at least some DHS Program data (either downloaded from the SDR or data created using other resources), and submit the map to spatialdata@dhsprogram.com. Static maps (JPG, PDF, etc.) or interactive web maps/apps are welcome. We will review your map and if appropriate for inclusion on SDR, we will contact you to get your permission to upload it to the site.

Happy GIS Day 2015! Now go make a map or go look at some maps!

The information provided on this Web site is not official U.S. Government information and does not represent the views or positions of the U.S. Agency for International Development or the U.S. Government.

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