Written by Heleen Elenbaas for the brokeronline.eu This piece was elected runner-up in the CSDS – The Broker Student Blog Competition on SDGs.
Three years after drafting the Sustainable Development Goals (SDGs), the first steps of progress towards meeting them can be assessed. However, high quality data to report on the SDGs is often lacking, especially for those countries furthest behind in the Global South. The use of ‘new’ spatial data sources has the potential to allay this problem.
In measuring progress towards meeting the SDGs, the national level is considered to be the most significant and appropriate level of reporting. This relies upon survey and census data from National Statistics Offices (NSOs). Although the SDGs are Global Goals and aim for being inclusive, there are large differences between the Global North and South, both in terms of what these goals focus on and how they are measured. Measuring development in the Global North is often based on consistent sources and near real-time data. This differs from the situation in the Global South, where registers are often incomplete, incorrect or non-existent, making reporting on the SDGs challenging for NSOs. In the absence of other data sources, data from national censuses or surveys often provides the only indication of development towards the SDGs.
Yet, data from national censuses has limitations and is therefore not always suitable for reporting on the SDGs. In most countries in the Global South, a census is conducted every ten years. Given that the SDGs cover a period of 15 years, censuses only provide a snapshot once, or twice at most. Additionally, because of the scant updating of census data, indicators often lack a proper baseline. Censuses therefore do not adequately depict change, making it difficult for governments to show development in their countries. Because of these limitations, many actors including 510, the data team of The Netherlands Red Cross, are arguing for a shift towards the increased use of ‘new’, spatial data sources.
The opportunities spatial data provides for SDG monitoring are substantial and can include valuable data sources and methods such as aerial or drone imagery, geotagged household surveys or mobile data collection tools (see for instance this blog or this initiative). The potential of spatial data can be illustrated by, for example, how the drinking water supply is monitored (SDG 6.1.1.) in rural Malawi. Geoinformation can visualize where water sources are located and where people live, and can also be used to calculate the average distance or travel time to the closest water point. In addition, spatial water point data plays an important role in estimating water coverage and can indicate problem areas in respect to water shortage or the spread of water-borne diseases. Since these types of analyses are extremely useful in monitoring the SDGs in the Global South, a pertinent question is how such data can be obtained.
One way spatial data can be collected is through mobile collection tools. The Madzi Alipo project is an excellent example of mobile data collection for monitoring SDG 6.1.1. in Malawi. Madzi Alipo developed an app for in-field data collection on water sources. Additionally, they collate data gathered by other actors in the field. All information is stored in a spatial database, creating a comprehensive and detailed overview of available water points throughout the country. Based on this information, the Madzi Alipo team is able to send mechanics to repair targeted, non-functional water points or to inform those responsible for the maintenance of the water point. An excellent overview of how progress regarding SDG 6.1.1 in Malawi is monitored is presented in this poster.
Unfortunately, field data is often not collected in a harmonized and standardized way between the many organizations involved. Data can contain, for instance, duplicated water point locations or variances in coordinate reference systems, resulting in water point locations shifting up to 50 metres from their actual location. Checking all inconsistencies by sending reporters to water points is labour-intensive and not feasible, however, drone imagery can be used to validate datasets and to correct them. Additionally, algorithms are being developed that can detect houses in aerial photography, demonstrating the potential of such algorithms in automated water point detection.
The several, yet limited initiatives of spatial data creation on SDG 6.1.1. in rural Malawi exist in parallel, but do not achieve their full potential because of little data sharing initiatives between them. The Malawi NSO, for instance, started digitizing every household as a point on the map, but without collaborating with other useful spatial data sources such as OpenStreetMap. Further, governmental information management systems are often closed or provide only limited user access, resulting in barriers to data sharing. Conversely, there are also examples of inter-organizational data infrastructure, such as shared geospatial data platforms. The identification of such issues has led to, among other things, the initiation of the ‘Building data collaboratives for SDGs in Malawi’ project, a collaboration between the Malawi NSO, the data team of the Malawi Red Cross Society and 510, funded by the Global Partnership on Sustainable Development Data.
Without regularly updated, disaggregated and accurate data, the ability to monitor progress towards the SDGs will be constrained. Increased adoption of spatial data from non-governmental sources to enrich national censuses therefore seems a promising addition in reporting on the SDGs. The United Nations (UN) also recognizes the potential of spatial data and has initiated a spatial data hub (the Federated Information System for the SDGs) in cooperation with the Environmental Systems Research Institute (ESRI). With the support of the UN and an increasing number of national stakeholders – who are, in the end, responsible for reporting on the SDGs – acknowledging these opportunities, spatial data faces a bright future in reporting on the SDGs. Further research would benefit this transition by focusing on efficient ways to gather and map data of high quality, as outlined in this article.