A broad gap exists between “God’s eye” transit maps from above that experts draw and how domestic workers map their commutes in Bogotá and Medellín, Colombia, through a street-level approach. Based on fieldwork conducted in both cities between 2017 and 2018, including interviews, participant observation, and social cartography, this translational article brings domestic workers’ understanding of the city they traverse daily vis-à-vis how experts conceive modern and rational public transportation systems. Delving into the literature on cartography, the Right to the City (RtC), and feminist geography, the study analyzes this gap and finds how it limits an effective RtC for this massive group of female commuters. It further provides public policy recommendations to address the gap and ensure RtC for all.

There are now an estimated 114 million forcibly displaced people worldwide, some 88% of whom are in low- and middle-income countries. For governments and international organizations to design effective policies and responses, they require comparable and accessible socioeconomic data on those affected by forced displacement, including host communities. Such data is required to understand needs, as well as interactions between complex drivers of displacement and barriers to durable solutions. However, high-quality data of this kind takes time to collect and is costly. Can the ever-increasing volume of open data and evolving innovative techniques accelerate and enhance its generation? Are there applications of alternative data sources, advanced statistics, and machine-learning that could be adapted for forced displacement settings, considering their specific legal and ethical dimensions? As a catalytic bridge between the World Bank and UNHCR, the Joint Data Center on Forced Displacement convened a workshop to answer these questions. This paper summarizes the emergent messages from the workshop and recommendations for future areas of focus and ways forward for the community of practice on socioeconomic data on forced displacement. Three recommended areas of future focus are: enhancing and optimizing household survey sampling approaches; estimating forced displacement socioeconomic indicators from alternative data sources; and amplifying data accessibility and discoverability. Three key features of the recommended approach are: strong complementarity with the existing data-collection-to-use-pipeline; data responsibility built-in and tailored to forced displacement contexts; and iterative assessment of operational relevance to ensure continuous focus on improving outcomes for those affected by forced displacement.