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Abstract Rural America is often depicted as a distressed and left‐behind place, with limited opportunities for the children growing up there. This paper addresses this topic by examining the dynamics of rural places over the past four decades and how these changes impact the economic mobility of children raised in poor rural households. Employing a place‐based framework, we utilise sequence analysis to identify dominant trajectories of change for more than 8000 rural communities. Our analysis reveals highly diverse community trajectories that connect deindustrialisation and racial inequality to elevated and rising poverty rates in certain places, while also documenting more favourable poverty trends elsewhere. These diverging local outcomes shed new light on the conflicting narratives surrounding rural America. We then demonstrate that, among children from poorer households, exposure to community poverty is predictive of adult economic mobility, patterns which are partly mediated by family stability and child poverty. Our finding that poorchildrenface additional disadvantages when they also grow up in poorplacessuggests a potential role for place‐based policies and redistribution to help ameliorate these disparities. 

Abstract Understanding changes in the built environment is vital for sustainable urban development and disaster preparedness. Recent years have seen the emergence of a variety of global, continent-level, and nation-wide datasets related to the current state and the evolution of the built environment, human settlements or building stocks. However, such datasets may face limitations like incomplete coverage, sparse building information, coarse resolution, and limited timeframes. This study addresses these challenges by integrating three spatial datasets to create an extensive, attribute-rich sequence of settlement layers spanning 200 years for the contiguous U.S. This integration process involves complex data processing, merging property-level real estate, parcel, and remote sensing-based building footprint data, and creating gridded multi-temporal settlement layers. This effort unveils the latest edition (Version 2) of the Historical Settlement Data Compilation for the U.S. (HISDAC-US), which includes the latest land use and structural information as of the year 2021. It enables detailed research on urban form and structure, helps assess and map the built environment’s risk to natural hazards, assists in population modeling, supports land use analysis, and aids health studies. 

We address three basic issues regarding the long-term dynamics of inequality in society. First, we consider the interpretation of residence sizes in socioeconomic terms by comparing statistical patterns extracted from the Global Dynamics of Inequality (GINI) Project database with those from the 21st-century United States. Second, we examine the degree to which the size and productivity of human networks is systematically related to inequality. Finally, we investigate relationships between productivity and productivity growth distributions for patterns of inequality and development across preindustrial societies. We find that across preindustrial societies residence size distributions provide a reasonable proxy for the distribution of productivity (income, a flow of physical and social resources to the group) and a minimum estimator for the distribution of wealth (a stock of such resources accumulated over time); that scale and productivity affect levels of inequality but account for only a small fraction of the observed variance across societies; and that inequality growth is independent of productivity growth, on average and over time. These findings have important implications for efforts to promote more equitable economic development in the present. 

The most destructive and deadly wildfires in US history were also fast. Using satellite data, we analyzed the daily growth rates of more than 60,000 fires from 2001 to 2020 across the contiguous US. Nearly half of the ecoregions experienced destructive fast fires that grew more than 1620 hectares in 1 day. These fires accounted for 78% of structures destroyed and 61% of suppression costs ($18.9 billion). From 2001 to 2020, the average peak daily growth rate for these fires more than doubled (+249% relative to 2001) in the Western US. Nearly 3 million structures were within 4 kilometers of a fast fire during this period across the US. Given recent devastating wildfires, understanding fast fires is crucial for improving firefighting strategies and community preparedness. 

The scaling relations between city attributes and population are emergent and ubiquitous aspects of urban growth. Quantifying these relations and understanding their theoretical foundation, however, is difficult due to the challenge of defining city boundaries and a lack of historical data to study city dynamics over time and space. To address this issue, we analyze scaling between city infrastructure and population across 857 metropolitan areas in the conterminous United States over an unprecedented 115 years (1900–2015) using dasymetrically refined historical population estimates, historical urban road network models, and multi-temporal settlement data to define dynamic city boundaries. We demonstrate that urban scaling exponents closely match theoretical models over a century. Despite some close quantitative agreement with theory, the empirical scaling relations unexpectedly vary across regions. Our analysis of scaling coefficients, meanwhile, reveals that contemporary cities use more developed land and kilometers of road than cities of similar population in 1900, which has serious implications for urban development and impacts on the local environment. Overall, our results provide a new way to study urban systems based on novel, geohistorical data. 

Fine scale data collection on vulnerability metrics is necessary for just policy outcomes. Those most likely to be disproportionately affected by specific climate risks should be identified early so that the needs of vulnerable communities (especially historically marginalized communities) can be addressed and mitigated in accordance with climate justice principles. While there is a growing body of event-specific and place-based studies, systematic studies on coastal populations at risk have typically not applied equity principles and have often ignored attributes such as race and ethnic composition, age structure, urban/rural classification, and housing tenure. Additionally, assumptions about future population trends depend on understanding past spatial patterns of change, as well as demographic and socioeconomic characteristics of the populations at risk, especially considering increasing coastal hazards. Yet, with few exceptions, research on coastal vulnerability has not analyzed changes in exposure over time and has not systematically addressed implications for communities of color over time. This paper seeks to fill these gaps. In this paper, using an equity lens and spatial demographic methods with the finest-resolution data available (census blocks), we estimate the extent of exposure and population change from 1990 to 2020 in the low elevation coastal zone in the continental United States. We find that the population of the LECZ has increased during this period, primarily by the growth of the urban population which has risen from about 22 million to 31 million persons. From 2000 to 2020, the urban population consistently grew at higher rates inside the LECZ than outside of it, reversing the pattern from the decade prior. We also examine changes in the population by race and Hispanic origin, urban and rural status, and a set of more expansive vulnerability themes. Our estimates, tabulated by counties and states, reveal the concentration and characteristics of exposure and changes to it over the past 30 years. Key findings include: residents of the LECZ are much older than average; Black residents are overrepresented in renter-occupied housing units in the urban LECZ; and from 2000 to 2020, Hispanic population growth was much higher in urban LECZ areas than urban areas elsewhere. These systematic insights into the demographic attributes of the populations most at risk of sea-level rise and associated coastal hazards can be used to ensure adaptation, mitigation, and disaster-related policies are tailored to the specific needs of these communities and actors at local, regional, and national scales. It also showcases how spatial methods can be used to understand demographic change and be put in place for future estimates of population in non-traditional units (e.g., coastal zones or other environmentally-vulnerable areas). 

Abstract. Multi-temporal measurements quantifying the changes to the Earth's surface are critical for understanding many natural, anthropogenic, and social processes. Researchers typically use remotely sensed Earth observation data to quantify and characterize such changes in land use and land cover (LULC). However, such data sources are limited in their availability prior to the 1980s. While an observational window of 40 to 50 years is sufficient to study most recent LULC changes, processes such as urbanization, land development, and the evolution of urban and coupled nature–human systems often operate over longer time periods covering several decades or even centuries. Thus, to quantify and better understand such processes, alternative historical–geospatial data sources are required that extend farther back in time. However, such data are rare, and processing is labor-intensive, often involving manual work. To overcome the resulting lack in quantitative knowledge of urban systems and the built environment prior to the 1980s, we leverage cadastral data with rich thematic property attribution, such as building usage and construction year. We scraped, harmonized, and processed over 12 000 000 building footprints including construction years to create a multi-faceted series of gridded surfaces, describing the evolution of human settlements in Spain from 1900 to 2020, at 100 m spatial and 5-year temporal resolution. These surfaces include measures of building density, built-up intensity, and built-up land use. We evaluated our data against a variety of data sources including remotely sensed human settlement data and land cover data, model-based historical land use depictions, and historical maps and historical aerial imagery and find high levels of agreement. This new data product, the Historical Settlement Data Compilation for Spain (HISDAC-ES), is publicly available (https://doi.org/10.6084/m9.figshare.22009643, Uhl et al., 2023a) and represents a rich source for quantitative, long-term analyses of the built environment and related processes over large spatial and temporal extents and at fine resolutions.