Achieving the global goals for sustainable development while leaving no one behind requires a continued commitment to evidence-based policy analysis. Fortunately, new datasets are being released which can improve the global community's understanding of what it will take to meet development challenges. Our task is to improve our ability to use these existing and new datasets for more valuable policy insights.

Exploration is facilitated not just by the quantity of data available, but also how easy the data can be combined to test hypotheses. Building a better interface combining various data sources will ease the exploration of relationships that are otherwise difficult to identify and understand, such as how administrative boundaries limit or magnify socio-economic progress, how eco-zones affect economic vulnerability, how distance is related to policy effectiveness, how remote sensing data can be used to close data gaps, and many others.

Using data from USAID's Demographic and Health Surveys (DHS) Program as a foundation, including its expansive and detailed estimates of health and demographic indicators which were recently released as modeled surfaces, this challenge will expand the use of data sources and creative visualizations in understanding the role of policy for sustainable development. It will accomplish this by improving the ease of matching household survey data with other geo-referenced datasets and by presenting this combined data in meaningful and innovative ways.

Part 1

Combine the data

Part 2

Explore

Part 3

Present the results

Use the newly released DHS interpolated surfaces and/or the DHS Program API data based on available household surveys (which include spatial information) to combine DHS data with other georeferenced datasets in an innovative way, to help further explore the progress in the SDGs. Links to the DHS modelled surfaces and the DHS API, together with examples of additional datasets can be found in the section on 'Data', below.

Example 1: combine data from interpolated maps of "population living in households using an improved water source" with poverty mapping, access to electricity, and distance to cities and roads.

Example 2: combine satellite maps of night lights, population density, access to electricity, and data on health and mortality.

Use your combined dataset to explore and analyze the status and trends of SDG indicators you select. A specific focus on Least Developed Countries should be included.

Example 1: illustrate how access to education and access to improved water source are related, with the specific focus on LDC countries and sub-national areas.

Example 2: illustrate how rainfall relates to health and child nutrition over time,  with the specific focus on LDC countries and sub-national areas.