Open-Source Data in the Pursuit of Sustainable Development
How the use of free and openly available data can benefit the most vulnerable and hard to reach communities.
Introduction
Open-source data is an incredibly powerful tool, which allows everyone to access global and national data for free. Recently, open-source data has become more available and accessible, with many organisations and national governments freely providing up-to-date geographic data with more detailed information.
One example of open-source data is from Open Street Map (OSM), who provides free data that is created, and constantly updated, by a community of mappers all around the world.
At Alcis, we work in fragile and conflict-affected states where data, and the infrastructure required to maintain it, is often unreliable. This can result in a basic dataset that may be old, have gaps or missing attributes. Combining open-source data with our proprietary data results in more accurate and sophisticated analysis, as well as reducing the time needed to create or update large foundational datasets.
Open-source data provides a lifeline to those without the time or budget to create their own datasets. Policy and decision-makers can analyse and provide the answers to key questions such as where they should target their funds or focus their development projects by linking humanitarian issues with geospatial information.
Networking the Roads
Using OSM's road data meant we could create a network and carry out an in-depth analysis of distances between different facilities and calculate the time it takes to travel from one to another. We could do this by travelling along the roads, instead of making a general estimation. We can also determine how long it takes to travel to a health facility from a village or identify the closest school to individual households.
At Alcis, we have used OSM roads data for Afghanistan, after it was reviewed and cleaned, to create a road network. Drawing on our in-depth knowledge and experience of Afghanistan, we calculated the average speed of different road types. We could then add a time element as well as the distance.
A national level Road Network Dataset of Afghanistan created from OSM data.
Analysis using distance buffers (left) vs. OSM's roads to form a network dataset for distance from Province and District centres (right). The new generated areas gives a more visual representation of real world distance than using buffers.
A distance and time Analysis can be created for any point data, such as health centres, schools, trading centres, and water points.
Analysing the Network
Given that the size of an urban area provides a proxy indicator to economic power and jobs, different distance ranges were used depending on the physical extent of the District and Province centres.
You can see in the map opposite the different sized service areas, based on a district centre's size.
For the Province centres, these service areas are larger due to their perceived increase in economic output and livelihood opportunities compared to the District centres.
By combining and overlaying the previous layers we can assess areas that have better access to these economic hubs based on the size of urban areas and the travel distances along roads.
On the map opposite, you can see the influence of the larger Provincial centres vs the smaller District centres. Our assumption here is that people are more willing to travel further if there are more opportunities.
This analysis, combined with other datasets, provides valuable insight into a community’s access to jobs, healthcare, education, and other essential services.
What About Where the People Live?
Further analysis, including population data, will show us where the people live. Alcis' 2019 Compound Dataset locates every residential compound in Afghanistan and allows us to identify the households who have access to particular services. An example is shown below.
Travel Time analysis between Health Facilities (Hospitals and Medical Centres) - and individual compounds can be generated using the road network analysis.
Here in Helmand Province, the Health Facility analysis is displayed. The darkest red areas equate to a Travel Time of 30 minutes or less, while the lighter coloured areas equate to 2 hours or more.
By using specialist geospatial tools, this level of analysis helps decision-makers identify the health facilities that are most accessible for any individual family. This in turn allows humanitarian and development projects to target specific communities for different interventions such as vaccinations.
What does this mean?
By using free and openly sourced data (with the option to include other data sources) this level of analysis can be applied in any country to create a wide range of data analysis and intelligence for those who did not have access to it before.
With open-source data, such as OSM, road data can easily be accessed from anywhere. Furthermore, with the ever increasing mapping and data collecting communities, data is continuously improving and being updated.
However, you need to be mindful of the quality of the data. Some road data isn't perfect - there may be roads missing or missing attributes, but in the majority of cases it may be the best option you have!
How can this help the UN Sustainable Development Goals?
To achieve the SDG’s by 2030 access to data is critical. By combining OSM and other open-source data with earth observation and geospatial analysis, we can accelerate the progress of many of the goals and targets, as well as accurately report on their progress.
Target 3.3: By 2030, end the epidemics of AIDS, tuberculosis, malaria, and neglected tropical diseases and combat hepatitis, water-borne diseases, and other communicable diseases.
Road Networks and Open Sourced data can help establish how many compounds are served by a particular Health Centre; how far one needs to travel to reach the right type of facility; or how far they need to travel to reach where vaccinations are being distributed.
Target 4.1: By 2030, ensure that all girls and boys complete free, equitable, and quality primary and secondary education leading to relevant and effective learning outcomes.
We can help determine those who do not have access to an education facility or in what time and distance brackets they are located in, in order to improve children's access to education services.
Target 6.1: By 2030, achieve universal and equitable access to safe and affordable drinking water for all water points.
We can see which communities don't have access to water points or are too far to travel in order to target development to help improve their livelihoods.
