Complementing National SDG Platforms

The Sustainable Development Goals (SDGs) are shaping global development, from how national policies and legislation are developed, how recipients of aid report their outcomes, to how all countries will report (and set baselines for) the state of their infrastructure, economy, and practically all elements relating to human development. From establishing the 17 Goals, 169 targets, and 232 indicators the inter-governmental discourse is now changing to how to monitor attainment of the SDGs. One facet of this is the technical one, on the 22nd to the 24th of January, the United Nations Statistics Division convened the National Platforms for SDG Reporting forum.

This convened countries, international organisations, and platform service providers to discuss differing approaches to monitoring the SDGs, technical implementations and innovations to discuss how to set recommendations and guidelines for such platforms. The outputs and presentations are all online too.

Given the nature of the forum, the data powering the platforms is drawn from National Statistical Offices and other data custodian agencies, as a component of their existing data collecting programs. For the inter-governmental process, this is essential – countries produce their own authoritative data and this will always be used as a primary source. But, what about secondary, complementary sources? This is compounded by methodological questions on how to analyse specific indicators.

For example, SDG Indicator 9.1.1 regards understanding the “Share of the rural population who live within 2 km of an all-season road”. Breaking this down:

  • How to differentiate between rural and urban populations – where do peri-urban areas lie?
  • How to calculate 2km from a road – is this in a straight line, what about geographic features, such as rivers and valleys?
  • How to define an “All season road”.

The question on urbanization/rural communities is a very common discussion, so I’ll leave it to one side. The last two points however do not have easy answers – and a lack of documented methodological guidance is severely hampering progression of the SDGs.

In many countries, road inventory is scant (see here), but there isn’t full GIS coverage of the road features, let alone an understanding of the quality. So, in effect, can we use what’s already out there, free and open data, to work out the SDGs?

Right now*, “80% of the world’s user-generated road map is more than 80% complete”. Global population data  – WorldPop and the High Resolution Settlement Layer – can offer insights into where people are globally.

So, in theory, sources like OpenStreetMap and WorldPop can already offer a level of insight that can assist with the methodological development of the goals. They could provide the data foundation for methodological development of the indicators and run in parallel with strengthening activities within the countries – ie. when the most aid dependent countries are capable of producing authoritative data for the indicators, technical platforms and methods will already exist to transform these data into actionable insights and drive data-driven policy development.

The SDGs should leave no-one behind and while attainment of the SDGs should be country-owned and country-led, what actions can the global commons take to work in parallel to support this agenda? An open test bed for SDG methodological analysis, modeled on OSM Analytics perhaps? Authoritative data and the choice of whether to use complementary/user generated/crowdsourced (etc etc) data will rest within the National Statistics/Mapping/Data Agency, there is an opportunity to develop the methods alongside this strengthening process – that way, when the agencies are strengthened, they’ll have a method to use for their indicators, targets, and goals.

*It’s using OpenStreetMap, OSM has a variety of sources, from crowdsourced data to imports. But… something’s there…!


Towards the Next Generation Road Survey

Over the past few weeks, I’ve managed to escape the office and get back to the field. With an impending change, it’s been a very refreshing time to get back into the mix – especially out onto the roads of Zanzibar.

Alongside work with scaling out Ramani Huria and working with (awesome!) colleagues on the signing of an Memorandum of Understanding between Ardhi University, World Bank, and DfID to support the development of curriculum (with ITC Twente) and the sustainability of community mapping in Tanzania for the next five years. I’ve been working on a side project to look at how machine learning can be used to assess road quality.

To do this, the N/LAB team at University of Nottingham and Spatial Info (the spin out of my team that helped build Ramani Huria/Tanzania Open Data Initiative) and I are working with the Zanzibari Department of Roads, under the Ministry of Infrastructure, Communications and Transport to survey all roads in Ugunja Island Zanzibar.

The Department of Roads & Uni Nottingham Team

So far, we’ve worked on getting a surveying vehicle back on the road, initial back and forth with government stakeholders, and working on pulling together the various road data sources (such those from the Government and OpenStreetMap) to work out where to drive and the sequencing of the survey. All of this will support a data collection protocol that merges traditional surveying techniques, with novel ones such as RoadLab Pro.

All of these data streams will then be used as a training dataset to see how machine learning can inform on road quality. But first, we’re getting the traditional survey underway. It’s going to be long road ahead – as long as all the roads in Zanzibar!

Watch this space, the project’s Medium page, and the N/LAB’s blog on using machine learning for automated feature detection from imagery. Get in contact below in the comments as well.

Written in the Al-Minaar Hotel, Stone Town, Zanzibar (-6.16349,39.18690)