Tracking and localizing SDG progress with geospatial data
Geospatial data is critical for monitoring progress on the SDGs, localizing SDG monitoring and policy tools, and informing the development of policies upstream.
The SDGs are highly dependent on geospatial information and Earth observations (EO) as the primary data for relating people to their location and place, and to measure where progress is, or is not being made. According to the UN GGIM, approximately 20% of the SDG indicators can be interpreted and measured either through direct use of geospatial data itself or through integration with other statistical data.
Furthermore, the geospatial dimension enables analysis and identification of trends and patterns, making the SDGs actionable at more fine-grained levels. In transforming these data and statistics into information, policymakers can develop targeted policy action by generating knowledge and insights.
At the SDG Transformation Center, we seek to produce new original geospatial indicators for the SDGs that can fill in critical data gaps and better inform the current state of a set of Sustainable Development Goals at local and national scales in a more timely and granular fashion.
Our work on geospatial data and tools falls into three categories:
The SDG Transformation Center, with the support of its partners, acts as a think tank to delineate and produce new original indicators for the SDGs, which in turn are integrated into the SDSN’s Sustainable Development Reports. The outputs are both ready-to-use national and localized indicators derived from the geospatial data sources and the methodology by which they’re developed, allowing national and local authorities to continue running these calculations yearly in order to track the advance made towards SDGs.
Insights and data generated by the work on SDG Policies and Financing and the work on SDG Pathways and Report Cards, which will be published on a dedicated web platform.
Visualization portals and storytelling tools that are leveraged to communicate the results and findings in an accessible way. We leverage the strength of Esri’s tools to display findings, create story maps and localize information and actions.
Learn more about the state of geospatial data for the SDGs here.
SDG Index
Artificial Neural Networks
Leave no One Behind
Localizing the SDG Index with machine learning and satellite imagery
Using artificial neural networks and high-resolution satellite imagery, we built on existing methodologies to downscale SDG Index scores from national to subnational levels. This model helps to identify aggregation biases in national SDG indicators, revealing that nearly 43% of the global population may be overlooked when only national averages are considered.
SDG 11
Transformation 5
Urban Sprawl
Land Cover
Land Use Efficiency (SDG 11.3.1 LUE)
Land consumption is the uptake of land by urbanized land uses, which often involves conversion of land from non-urban to urban functions. This indicator focuses on the measures of the total increase in built-up areas within the urban area over time. This indicator can help identify when urban areas become too dense and/or when they become too sparsely populated.
SDG 12
SDG 15
Transformation 4
Carbon emissions by deforestation driver
Human-induced land use change (LUC), driven by activities such as forestry, logging, and the production of agricultural commodities significantly impacts the Global Commons. The expansion of the agricultural frontier is identified as the predominant direct cause of deforestation globally. To achieve global climate targets, improving our understanding of deforestation drivers is urgently needed.
This dataset is the result of data processing performed to estimate the extent to which commodities and other agricultural products have replaced forests, while mapping the CO2 emission impact making use of the best available spatially explicit data. Results are reported globally for 52 products at national level, as well as agroecological and thermal zones (FAO & IIASA) and a 50km cell vector grid.
SDG 11
Transformation 5
OpenStreetMap
Accessibility
Walkability
The 15-minute city: % of urban populations in walking distance to points of interest
This is an assessment of pedestrian accessibility in the world's main urban centers, aggregated at country and city level. Indicators include the average walking time to different categories of destinations, as well as the proportion of inhabitants that can access each category of services within a 15-minute walk.
This measure is particularly useful for assessing spatial justice in cities, usually represented by underprivileged communities which are pushed to live in deteriorated urban areas receiving a minor share of public investments and thus low levels of accessibility.
This data informs on indicators 11.2.1 “Urban access to public transportation” and 11.7.1 “Urban access to public spaces”.
SDG 9
Transformation 3
Transformation 4
Infrastructure
Human impact
Microsoft Bing
Global Road Density Index
The Global Road Density Index measures road length density in 10x10km and 50x50km cell grids worldwide. Roads are fetched from the Microsoft Road Detection project, which locates roads by using deep learning algorithms and merges them with OpenStreetMap road data, making it the most complete and up-to-date data currently available publicly.
This dataset is a by-product of the Rural Access Index, but can also be used to determine areas where human presence isn’t identified. This is particular useful for developing indicators related to SDG 15 Life on land.
SDG 9
Transformation 3
Infrastructure
Accessibility
Rural Access Index (RAI)
The Rural Access Index (RAI) is a measure of access, developed by the World Bank in 2006. It was adopted as Sustainable Development Goal (SDG) indicator 9.1.1 in 2015, to measure the accessibility of rural populations. It is currently the only indicator for the SDGs that directly measures rural access.
The RAI measures the proportion of the rural population that lives within 2 km of an all-season road. An all-season road is one that is motorable all year, but may be temporarily unavailable during inclement weather.
Learn more about SDSN's reports, publications and data visualizations in our library
Showing 3 of 3
