By Kyle Wiebe
The 2030 Agenda aims to ensure that no one is left behind in pursuit of a more just and sustainable world. To fulfill this commitment, it is necessary to first ask the question, “Who is currently being left behind?” While the question may seem simple, empirically answering it can be technically and methodologically difficult. This is because the data used to monitor and evaluate the impact of the SDGs come largely from National Statistical Offices (NSOs)—national agencies responsible for the conceptualization, collection, and dissemination of statistics. NSOs typically operate using standardized approaches (e.g., censuses and surveys), and many populations experiencing marginalization—such as those experiencing homelessness or undocumented individuals—can fall through the gaps. If NSOs are unable to collect information on those left behind, they also don’t know where those being left behind are. In this way, it is not just a question of who is being left behind, but where they are located.
To improve data collection regarding those left behind, there is a push among the global statistical community to utilize georeferenced data—data that can be ascribed to a particular location. This type of data is also referred to as geospatial data, spatial data, or geographical information. The benefits of georeferenced data are obvious; you cannot implement policy to improve housing conditions if you cannot locate where inadequate housing exists. Therefore, data are most effective when information relates to location. However, bridging the gap between information and location can be difficult for NSOs for several reasons. This article explores efforts to improve the availability of geospatial data by NSOs and showcases how other organizations are filling data gaps.
To promote the availability of georeferenced data among NSOs, the Partnership in Statistics for Development in the 21st Century (PARIS21) and Statistics Sweden released a comprehensive, eight-step guide for NSOs to integrate their statistics and geospatial data seamlessly across all geographic scales (PARIS 21, 2021). The guide was motivated by the realization that the NSOs of many low- and middle-income countries rely on outdated technology or lack the capacity required to develop the geospatial data necessary for effective and efficient decision making. The eight steps comprise a sequential process to guide NSOs from identifying the groups that need to be mapped and setting up a basic framework for geographies, all the way to the final step of making sure data is interoperable. In short, it supports countries in operationalizing the complex process of developing georeferenced data. By providing this guide, PARIS21 hopes to help achieve the 2030 Agenda by making available the data necessary to determine not only who is being left behind, but where they are. You cannot implement policy to improve housing conditions if you cannot locate where inadequate housing exists.
One significant barrier to NSOs’ ability to identify populations being left behind is the statistical method of enumerating households. To administer surveys, NSOs need a method to send and receive them. Traditionally this is done by mailing physical copies of the surveys to households, which excludes people who do not have an address. The World Bank estimates that in 2018 nearly 30% of the urban population lived in informal settlements and potentially without an address. Therefore, even if NSOs can georeference their data, these individuals would never be accounted for.
Alternative approaches are arising to fill the data gaps left by this surveying method. IDEAMAPS was founded in 2020 with the aim of developing informed slum maps—georeferenced data about slums and their inhabitants. Inhabitants of slums are often underrepresented in both general information and georeferenced data due to their informal status, often unrecognized by governments. By combining community-based field mapping with digitized imagery and machine learning, IDEAMAPS has developed a “deprived area map” of slums that provides key georeferenced data for evidence-based decision making.
The need to identify those left behind is not just a problem of informal settlements in low- and middle-income countries. In Canada, the method of enumeration used by Statistics Canada to conduct the census excludes the population experiencing homelessness. In Winnipeg, Canada, community organizations have initiated a way to address this gap, by conducting a Street Census—a point-in-time count of Winnipeg’s population experiencing homelessness. The survey asks questions similar to the national census and helps to identify service needs
While surveying is one example where georeferenced data is important, surveys are typically conducted annually or even less frequently in the case of censuses. To achieve the SDGs, there is a need for georeferenced data that is up-to-date and available in real time. As part of its strategy for the prevention and control of snakebite envenoming, the World Health Organization launched the Snakebite Information and Data Platform. Using geospatial software built by Esri, the platform allows users to both identify the potential location of venomous snakes and upload geo-tagged sightings of venomous snakes. The availability of this data to the public not only helps educate individuals on the presence and identity of venomous snakes, but it also provides resources on how and where to treat potentially fatal bites. The tool puts geospatial data into the immediate service of those who may find themselves in desperate need.
The World Bank estimates that in 2018 nearly 30% of the urban population lived in informal settlements and potentially without an address.
Geospatial data can do more than just provide real-time information; it can also be used to analyze data for decision making. One of the earliest instances of geospatial data was compiled in 1854 by English physician John Snow. During this time, London was experiencing a cholera outbreak, and it was believed that pollution was causing the disease to spread. By mapping outbreak locations, Snow began to see patterns emerge and determined that the clusters of outbreaks were related to drinking water sources. This type of mapping, first conducted over 150 years ago, has become commonplace in how we present and analyze data, and it is once again proving important as we endure the most severe pandemic of our time.
Using the same principle, in 2020, the government of Ontario launched the COVID-19 School Dashboard to track cases in schools across the province. The tool also tracks the percentage of low-income households and the number of immigrant families near the schools to help identify disparities in socio-economic exposure to COVID-19. This type of platform has become widely available since COVID-19 began, due to growing demand to identify cases and locate potential areas of transmission. Access to localized data has helped governments to make evidence-based decisions that have slowed the spread of the virus. One only needs to imagine how difficult it would be to plan for a pandemic if we could not locate outbreaks to understand the importance of georeferenced data.
These case studies highlight the varied uses of geospatial data. While there are many applications, no single actor or agency is responsible for georeferencing data. Instead, data projects must pursue a marriage between information and location to identify and account for those who are left behind. Only when we know where those who are marginalized are located can we make the evidence-based decisions required to make sure they are no longer left behind.
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