2. Untangling Crowding and Density
- First, population densities decline with incomes, as countries move across stages of economic development. Kinshasa, Mumbai, and Karachi are four to five times more densely populated than Shanghai, Tokyo, or London.
- Second, naïve measures of population density can conflate crowding with livable densities. Kinshasa, Mumbai, and Hong Kong are three cities with similar population densities but very different levels of income.
- 12 m digital elevation and radar intensity data from the German TanDEM-X satellite mission.
- 10 m multispectral Sentinel-2 imagery.
- The human settlement mask of the World Settlement Footprint 2015 (WSF 2015).
- Where available, vector data for building location—for example, data from the Open Street Map initiative.
3. Crowding and COVID-19 Contagion Risk
- The practical inability for keeping people apart, based on a combination of population density and livable floor space that does not allow for 2 m of physical distancing.
- Conditions where, even under lockdown, people might have little option but to cluster (e.g., to access public toilets and water pumps/standpipes).
- First, we use the WorldPop population 2019 raster (see https://www.worldpop.org/ for details on WorldPop data and methodology.) These rasters contain the estimated number of people that live in each pixel, with a resolution of 100 × 100 m. While we used global population datasets, the analysis can be easily adjusted if local sources are available. Population data from local sources may be more accurate than a global population raster. Some cities have developed spatial datasets at considerably high levels of disaggregation, although generally not at the pixel level. The challenge lies in creating a population grid based on these datasets, which usually implies modeling that assigns population to pixels, based on land use and built-up areas. The German Aerospace Center (DLR) has developed a model to generate population grid datasets based on spatially disaggregated population data.
- The second dataset is a layer with the location of key services, such as water kiosks and public toilets, obtained from the Open Street Maps Platform (OSM) (© OpenStreetMap contributors). A schematic outlining the approach is shown in Figure 4.
3.2. Application of the Methodology
3.2.1. Kinshasa, DRC
3.2.2. Dhaka, Bangladesh
3.2.3. Freetown, Sierra Leone
4. Looking Forward—From Crowding to (Livable) Density
4.1. Access to Land, Land Use Planning, and Development Regulations
4.3. Concluding Remarks
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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