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A large number of Censuses and surveys around the globe only measure ‘migrations’ crossing particular politico‐administrative boundaries, most commonly ‘major’ areas like states. These moves, in turn, are often assumed to be representative of all long‐distance or, in some settings, urban–urban moves. While important because such boundaries signal relevant policy environments, little research has tested these assumptions and, more broadly, the implications of examining mobility using an inter/intrastate classification schema versus other substantively‐relevant approaches. Because these examinations have been particularly absent in developing nations, we compare the dynamics and correlates of mobility across inter/intrastate, distance‐ and rural/urban‐based classification schemata in Mexico, a nation with heterogeneous mobility similar to other large middle‐income countries and overall good data availability. We use 2000, 2010 and 2020 Census long‐form data to examine the changing dynamics of mobility patterns and correlates across the classification schemata. While we find that interstate mobility does cover a large majority of long‐distance and many urban–urban moves, we find that the correlates of interstate movement vary considerably from urban–urban movement in particular. We also find that excluding intrametropolitan from other types of moves may be a sensible strategy to better characterize some processes, an issue of increasing relevance in a more urban world and where city‐regions span across major administrative areas. Given these findings, for a better understanding socioeconomic patterns and trends, we recommend that studies of internal migrationavoidintra/interstate schema, consider separating intrametropolitan moves, and combine distance‐based and rural‐urban‐metropolitan approaches.

 

By 2050, two-thirds of the world’s population is expected to be living in cities and towns, a marked increase from today’s level of 55 percent. If the general trend is unmistakable, efforts to measure it precisely have been beset with difficulties: the criteria defining urban areas, cities and towns differ from one country to the next and can also change over time for any given country. The past decade has seen great progress toward the long-awaited goal of scientifically comparable urbanization measures, thanks to the combined efforts of multiple disciplines. These efforts have been organized around what is termed the “statistical urbanization” concept, whereby urban areas are defined by population density, contiguity and total population size. Data derived from remote-sensing methods can now supply a variety of spatial proxies for urban areas defined in this way. However, it remains to be understood how such proxies complement, or depart from, meaningful country-specific alternatives. In this paper, we investigate finely resolved population census and satellite-derived data for the United States, Mexico and India, three countries with widely varying conceptions of urban places and long histories of debate and refinement of their national criteria. At the extremes of the urban–rural continuum, we find evidence of generally good agreement between the national and remote sensing-derived measures (albeit with variation by country), but identify significant disagreements in the middle ranges where today’s urban policies are often focused. 

Abstract. The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth's surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250 m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c). 

Abstract Urban nature—such as greenness and parks—can alleviate distress and provide space for safe recreation during the COVID-19 pandemic. However, nature is often less available in low-income populations and communities of colour—the same communities hardest hit by COVID-19. In analyses of two datasets, we quantified inequity in greenness and park proximity across all urbanized areas in the United States and linked greenness and park access to COVID-19 case rates for ZIP codes in 17 states. Areas with majority persons of colour had both higher case rates and less greenness. Furthermore, when controlling for sociodemographic variables, an increase of 0.1 in the Normalized Difference Vegetation Index was associated with a 4.1% decrease in COVID-19 incidence rates (95% confidence interval: 0.9–6.8%). Across the United States, block groups with lower income and majority persons of colour are less green and have fewer parks. Our results demonstrate that the communities most impacted by COVID-19 also have the least nature nearby. Given that urban nature is associated with both human health and biodiversity, these results have far-reaching implications both during and beyond the pandemic. 

In May 2020, the New York City (NYC) Mayor’s Office of Climate Resiliency (MOCR) began convening bi-weekly discussions, called the Rapid Research and Assessment (RRA) Series, between City staff and external experts in science, policy, design, engineering, communications, and planning. The goal was to rapidly develop authoritative, actionable information to help integrate resiliency into the City’s COVID response efforts. The situation in NYC is not uncommon. Extreme events often require government officials, practitioners, and citizens to call upon multiple forms of scientific and technical assistance from rapid data collection to expert elicitation, each spanning more or less involved engagement. We compare the RRA to similar rapid assessment efforts and reflect on the nature of the RRA and similar efforts to exchange and co-produce knowledge. The RRA took up topics on social cohesion, risk communication, resilient and healthy buildings, and engagement, in many cases strengthening confidence in what was already known but also refining the existing knowledge in ways that can be helpful as the pandemic unfolds. Researchers also learned from each other ways to be supportive of the City of New York and MOCR in the future. The RRA network will continue to deepen, continue to co-produce actionable climate knowledge, and continue to value organizational sensemaking as a usable climate service, particularly in highly uncertain times. Given the complex, rare, and, in many cases, unfamiliar context of COVID-19, we argue that organizational sensemaking is a usable climate service.