NEW GEOGRAPHY-The ongoing pandemic is reshaping the geography of our planet, helping some areas, and hurting others.
In the West, the clear winners have been the sprawling suburbs and exurbs, while dense cores have been dealt a powerful blow. The pandemic also has accelerated class differences and inequality, with poor and working-class people around the world paying the dearest price.
These conclusions are based on data we have repeatedly updated. Despite some variations, our earlier conclusions hold up: the virus wreaked the most havoc in areas of high urban density. This first became evident in the alarming pre-lockdown fatalities that occurred in New York City and the suburban commuting shed from which many of the employees in the huge Manhattan business district are drawn. Similar patterns have been seen in Europe and Asia as well.
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Mapping land use in Mexico City and other urban areas can be a key tool to help cities manage their resources and improve quality of life. Photo by Nitin Badjatia/Unsplash
Remote sensing has revolutionized how we measure and understand the Earth. We can now track deforestation across the globe, predict end-of-season crop yields and identify wildfires in near real-time. But exploration into its possibilities for urban areas has only just begun. Conventional land cover maps, which categorize the surface of the Earth into groupings like “forest” or “water” or “tundra,” often lump urban areas into a single category, like “urban” or “built-up.” This is useful for mapping urban extent generally, but does not capture the complexity of urban areas. Mapping land
This technical note describes the data sources and methodology underpinning a computer system for the automated generation of land use/land cover (LULC) maps of urban areas based on medium-resolution (10–30m/pixel) satellite imagery. The system and maps deploy the LULC taxonomy of the Atlas of Urban Expansion 2016 Edition: open, nonresidential, roads, and four types of residential space. We used supervised machine learning techniques to apply this taxonomy at scale. Distinguishing between recognizable, clearly defined types of land use within a built-up area, rather than merely delineating artificial land cover, enables a huge variety of potential applications for policy, planning, and research. We demonstrate the training and application of machine-learning-based algorithms to characterize LULC over a large spatial and temporal range in a way that avoids many of the onerous constraints and expenses of the traditional LULC mapping process: manual identification and classification o