Two centuries of settlement and urban development in the United States

GEOWEALTH-US: Spatial wealth inequality data for the United States, 1960-2020

Wealth inequality has been sharply rising in the United States and across many other high-income countries. Due to a lack of data, we know little about how this trend has unfolded across locations within countries. Examining the subnational geography of wealth is crucial because, from one generation to the next, it shapes the distribution of opportunity, disadvantage, and power across individuals and communities. By employing machine-learning-based imputation to link national historical surveys conducted by the U.S. Federal Reserve to population survey microdata, the data presented in this article addresses this gap. The Geographic Wealth Inequality Database ("GEOWEALTH-US") provides the first estimates of the level and distribution of wealth at various geographical scales within the United States from 1960 to 2020. The GEOWEALTH-US database enables new lines of investigation into the contribution of spatial wealth disparities to major societal challenges including wealth concentration, income inequality, social mobility, housing unaffordability, and political polarization.

Population genomic structure of a widespread, urban-dwelling mammal: The eastern grey squirrel (Sciurus carolinensis)

Urbanization is a persistent and widespread driver of global environmental change, potentially shaping evolutionary processes due to genetic drift and reduced gene flow in cities induced by habitat fragmentation and small population sizes. We tested this prediction for the eastern grey squirrel (Sciurus carolinensis), a common and conspicuous forest-dwelling rodent, by obtaining 44K SNPs using reduced representation sequencing (ddRAD) for 403 individuals sampled across the species' native range in eastern North America. We observed moderate levels of genetic diversity, low levels of inbreeding, and only a modest signal of isolation-by-distance. Clustering and migration analyses show that estimated levels of migration and genetic connectivity were higher than expected across cities and forested areas, specifically within the eastern portion of the species' range dominated by urbanization, and genetic connectivity was less than expected within the western range where the landscape is fragmented by agriculture. Landscape genetic methods revealed greater gene flow among individual squirrels in forested regions, which likely provide abundant food and shelter for squirrels. Although gene flow appears to be higher in areas with more tree cover, only slight discontinuities in gene flow suggest eastern grey squirrels have maintained connected populations across urban areas in all but the most heavily fragmented agricultural landscapes. Our results suggest urbanization shapes biological evolution in wildlife species depending strongly on the composition and habitability of the landscape matrix surrounding urban areas.

Characteristics and formation mechanism of Land use conflicts in northern Anhui: A Case study of Funan county

The rapid development of global urbanization and industrialization not only promotes a significant improvement in the level of socio-economic development, but also exacerbates the complexity and vulnerability of regional land resource utilization, resulting in frequent land use conflicts and seriously constraining the sustainable development of regional socio-economic and ecological environment. Taking Funan County as an example, based on interpretation data of Landsat TM/ETM remote sensing image data from 1980 to 2020, this paper analyses the temporal and spatial evolution characteristics of land use conflict in Funan County from 1980 to 2020 using the ArcGIS spatial analysis method, land use conflict measurement model, geographically weighted regression and geographical detector and then deeply analyses the main factors affecting land use conflict in Funan County and its driving mechanisms. In descending order, land use types undergoing the most change include cultivated land, urban and rural construction land, grassland, forestland and water area. The results of land use change are mainly the occupation of cultivated land by construction land, water area and forestland. Overall land use conflict in Funan County is serious with approximately 80 % of land use in the county in conflict, the severe land use conflict is mostly concentrated in urban and township built-up areas, and there is an increase trend year by year. Land use conflict is the result of multiple factors. Policy, economic development, and the social population and natural environment are the key driving factors behind land use conflict, which have a significant impact on the direction, location, scale and rate of land use transfer.Accurately identifying regional land use changes and conflicts and exploring the driving mechanism behind land use conflicts are of great significance for achieving the sustainable development of regional social economies and ecological environments.

Application of advanced trend analysis techniques with clustering approach for analysing rainfall trend and identification of homogenous rainfall regions in Delhi metropolitan city

In the era of global urbanization, the cities across the world are experiencing significant change in the climate pattern. However, analysing the trend and pattern of rainfall over the urban areas has a number of challenges such as availability of long-term data as well as the uneven distribution of rain-gauge stations. In this research, the rainfall regionalization approach has been applied along with the advanced statistical techniques for analysing the trend and pattern of rainfall in the Delhi metropolitan city. Fuzzy C-means and K-means clustering techniques have been applied for the identification of homogeneous rainfall regions while innovative trend analysis (ITA) along with the family of Mann-Kendall (MK) tests has been applied for the trend analysis of rainfall. The result shows that in all rain-gauge stations of Delhi, an increasing trend in rainfall has been recorded during 1991-2018. But the rate of increase was low as the trend slope of ITA and Sen's slope in MK tests are low, which varies between 0.03 and 0.05 and 0.01 and 0.16, respectively. Furthermore, none of the rain-gauge stations have experienced a monotonic trend in rainfall as the null hypothesis has not been rejected (p value > 0.05) for any stations. Furthermore, the study shows that ITA has a better performance than the family of MK tests. The findings of this study may be utilized for the urban flood mitigation and solving other issues related to water resources in Delhi and other cities.

Hydro-bio-geo-socio-chemical interactions and the sustainability of residential landscapes

Residential landscapes are essential to the sustainability of large areas of the United States. However, spatial and temporal variation across multiple domains complicates developing policies to balance these systems' environmental, economic, and equity dimensions. We conducted multidisciplinary studies in the Baltimore, MD, USA, metropolitan area to identify locations (hotspots) or times (hot moments) with a disproportionate influence on nitrogen export, a widespread environmental concern. Results showed high variation in the inherent vulnerability/sensitivity of individual parcels to cause environmental damage and in the knowledge and practices of individual managers. To the extent that hotspots are the result of management choices by homeowners, there are straightforward approaches to improve outcomes, e.g. fertilizer restrictions and incentives to reduce fertilizer use. If, however, hotspots arise from the configuration and inherent characteristics of parcels and neighborhoods, efforts to improve outcomes may involve more intensive and complex interventions, such as conversion to alternative ecosystem types.

A 1.2 Billion Pixel Human-Labeled Dataset for Data-Driven Classification of Coastal Environments

The world's coastlines are spatially highly variable, coupled-human-natural systems that comprise a nested hierarchy of component landforms, ecosystems, and human interventions, each interacting over a range of space and time scales. Understanding and predicting coastline dynamics necessitates frequent observation from imaging sensors on remote sensing platforms. Machine Learning models that carry out supervised (i.e., human-guided) pixel-based classification, or image segmentation, have transformative applications in spatio-temporal mapping of dynamic environments, including transient coastal landforms, sediments, habitats, waterbodies, and water flows. However, these models require large and well-documented training and testing datasets consisting of labeled imagery. We describe "Coast Train," a multi-labeler dataset of orthomosaic and satellite images of coastal environments and corresponding labels. These data include imagery that are diverse in space and time, and contain 1.2 billion labeled pixels, representing over 3.6 million hectares. We use a human-in-the-loop tool especially designed for rapid and reproducible Earth surface image segmentation. Our approach permits image labeling by multiple labelers, in turn enabling quantification of pixel-level agreement over individual and collections of images.

Spatiotemporally Explicit Mapping of Built Environment Stocks Reveals Two Centuries of Urban Development in a Fairytale City, Odense, Denmark

The urban built environment stocks such as buildings and infrastructure provide essential services to urban residents, and their spatiotemporal dynamics are key to the circular and low-carbon transition of cities. However, spatiotemporally explicit characterization of urban built environment stocks remains hitherto limited, and previous studies on fine-grained mapping of built environment stocks often focus on an urban area without consideration of temporal dynamics. Here, we combined the emerging geospatial data and historical maps to quantify the spatially and temporally refined stocks of buildings and infrastructure and developed a novel indexing method to track the construction, demolition, and renovation for each building across various historical snapshots, with a case study of Odense, Denmark, from 1810 to 2018. We show that built environment stock in Odense increased from 80 t/cap in 1810 to 279 t/cap in 2018. Their dynamics appear overall in line with urban development of Odense over the past two centuries and well reflect the combined effects of industrialization, infrastructure development, socioeconomic characteristics, and policy interventions. Such spatiotemporally explicit stock mapping offers a physical and resource perspective for measuring urbanization and provides the public and government insight into urban spatial planning and related resource, waste, and climate strategies.

Gridded land use data for the conterminous United States 1940-2015

Multiple aspects of our society are reflected in how we have transformed land through time. However, limited availability of historical-spatial data at fine granularity have hindered our ability to advance our understanding of the ways in which land was developed over the long-term. Using a proprietary, national housing and property database, which is a result of large-scale, industry-fuelled data harmonization efforts, we created publicly available sequences of gridded surfaces that describe built land use progression in the conterminous United States at fine spatial (i.e., 250 m × 250 m) and temporal resolution (i.e., 1 year - 5 years) between the years 1940 and 2015. There are six land use classes represented in the data product: agricultural, commercial, industrial, residential-owned, residential-income, and recreational facilities, as well as complimentary uncertainty layers informing the users about quantifiable components of data uncertainty. The datasets are part of the Historical Settlement Data Compilation for the U.S. (HISDAC-US) and enable the creation of new knowledge of long-term land use dynamics, opening novel avenues of inquiry across multiple fields of study.

Creeping disaster along the U.S. coastline: Understanding exposure to sea level rise and hurricanes through historical development

Current estimates of U.S. property at risk of coastal hazards and sea level rise (SLR) are staggering—evaluated at over a trillion U.S. dollars. Despite being enormous in the aggregate, potential losses due to SLR depend on mitigation, adaptation, and exposure and are highly uneven in their distribution across coastal cities. We provide the first analysis of how changes in exposure (how and when) have unfolded over more than a century of coastal urban development in the United States. We do so by leveraging new historical settlement layers from the Historical Settlement Data Compilation for the U.S. (HISDAC-US) to examine building patterns within and between the SLR zones of the conterminous United States since the early twentieth century. Our analysis reveals that SLR zones developed faster and continue to have higher structure density than non-coastal, urban, and inland areas. These patterns are particularly prominent in locations affected by hurricanes. However, density levels in historically less-developed coastal areas are now quickly converging on early settled SLR zones, many of which have reached building saturation. These “saturation effects” suggest that adaptation polices targeting existing buildings and developed areas are likely to grow in importance relative to the protection of previously undeveloped land.

Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review

This paper is based on reviewing the literature in the past 10 years on the drivers of land use and land cover change (LULCC) in urban areas. It combines quantitative and qualitative keyword analysis of papers drawn out from the Scopus database. The analysis is primarily based on the number of mentions of keywords in the titles and abstracts of the papers, in addition to the number of keywords appearing in the papers. On the basis of content analysis, a three-level structural categorization of the driving factors was developed. These are presented in a schematic diagram, where the contextual factors are shown as influencing economic and financial factors and policy and regulation, which in turn influences transportation investments and availability, and industrial and residential location choices. Transportation availability was seen as the most frequent factor identified in the literature. This research contends that LULCC is mostly determined by interactions among these four themes in a three-level structure, and on this basis, a model is presented that illustrates LULCC drivers based on local circumstances across the globe.

Towards the automated large-scale reconstruction of past road networks from historical maps

Transportation infrastructure, such as road or railroad networks, represent a fundamental component of our civilization. For sustainable planning and informed decision making, a thorough understanding of the long-term evolution of transportation infrastructure such as road networks is crucial. However, spatially explicit, multi-temporal road network data covering large spatial extents are scarce and rarely available prior to the 2000s. Herein, we propose a framework that employs increasingly available scanned and georeferenced historical map series to reconstruct past road networks, by integrating abundant, contemporary road network data and color information extracted from historical maps. Specifically, our method uses contemporary road segments as analytical units and extracts historical roads by inferring their existence in historical map series based on image processing and clustering techniques. We tested our method on over 300,000 road segments representing more than 50,000 km of the road network in the United States, extending across three study areas that cover 42 historical topographic map sheets dated between 1890 and 1950. We evaluated our approach by comparison to other historical datasets and against manually created reference data, achieving F-1 scores of up to 0.95, and showed that the extracted road network statistics are highly plausible over time, i.e., following general growth patterns. We demonstrated that contemporary geospatial data integrated with information extracted from historical map series open up new avenues for the quantitative analysis of long-term urbanization processes and landscape changes far beyond the era of operational remote sensing and digital cartography.

Grain Production Space Reconstruction and Its Influencing Factors in the Loess Plateau

Grain production space, ecological service space and urban–rural development space are the classifications of land systems from the perspective of the dominant function of the land system. Grain production space reconstruction concentrates on the principal contradictions of land system changes, and is the key to exploring the transformation of land system. Therefore, the pathways, process and influencing factors of grain production space reconstruction in the Loess Plateau of Chian from 1980 to 2018 was explored from three dimensions of quantity–quality–spatial pattern in this study. Results showed that the quantity of grain production space showed a slight downward trend with a net decrease of 9156 km² between 1980 and 2018, but its total quality showed a fluctuating growth trend under rain-fed conditions. Due to the intensification of human activities, grain production space was gradually fragmented, and the distribution tended to be decentralized, and the shape gradually became regular. Meanwhile, both the quantity and quality gravity center of grain production space moved to the northwest by 8.32 km and 86.03 km, respectively. The reconstruction of grain production space in the Loess Plateau was mainly realized through four pathways: Grain for Green, Urban Expansion, Deforestation and Reclamation, and Land Consolidation. The grain production space was mainly reconstructed through the pathway of Grain for Green after 2000. The four reconstruction pathways were the result of a combination of natural environment and socio-economic factors, but influencing factors had different strengths and directions for each reconstruction pathway. From the perspective of social economy–land use–ecological environment coupling, in order to maintain the sustainable development of the land systems, it is necessary to reduce the trade-offs of the functions of land systems as much as possible and strive to coordinate the relationship among grain production, ecological protection and high-quality development.

Global Harmonization of Urbanization Measures: Proceed with Care

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.

Trends in United States Human Footprint Revealed by New Spatial Metrics of Urbanization and Per Capita Land Change

Accelerations in population growth and urban expansion are transforming landscapes worldwide and represent a major sustainability challenge. In the United States, land conversion to impervious surfaces has outpaced population increases, yet there are few spatial metrics of urbanization and per capita land change available nationwide for assessing local to regional trends in human footprint. We quantified changes (2000–2010) in housing density, imperviousness, per capita land consumption, and land-use efficiency for block groups of the contiguous U.S. and examined national patterns and variation in these metrics along the urban–rural gradient and by megaregion. Growth in housing (+13.6%) and impervious development (+10.7%) resulted in losses of rural lands, primarily due to exurbanization and suburbanization. Mean per capita consumption increased in all density classes but was over 8.5 times greater in rural lands than in exurban, suburban, and urban areas. Urban and suburban areas had significantly lower mean consumption, yet change was unsustainable in 60% of these areas. Megaregions across the sprawling Sun Belt, spanning from Arizona to North Carolina, grew most unsustainably, especially compared to regions in the Pacific Northwest and Front Range. This work establishes 21st-century benchmarks that decision-makers can use to track local and regional per capita land change and sustainable growth in the U.S.; however, these metrics of the form, extent, rate, and efficiency of urbanization can be applied anywhere concurrent built-up area and population data are available over time. Our web mapping application allows anyone to explore spatial and temporal trends in human footprint and download metrics, and it is designed to be easily updatable with future releases of validated developed land cover, protected areas, and decennial Census data.

Disentangling the trend in the warming of urban areas into global and local factors

Large cities account for a significant share of national population and wealth, and exert high pressure on local and regional resources, exacerbating socioenvironmental risks. The replacement of natural landscapes with higher heat capacity materials because of urbanization and anthropogenic waste heat are some of the factors contributing to local climate change caused by the urban heat island (UHI) effect. Because of synergistic effects, local climate change can exacerbate the impacts of global warming in cities. Disentangling the contributions to warming in cities from global and local drivers can help to understand their relative importance and guide local adaptation policies. The canopy UHI intensity is commonly approximated by the difference between temperatures within cities and the surrounding areas. We present a complementary approach that applies the concept of common trends to extract the global contributions to observed warming in cities and to obtain a residual warming trend caused by local and regional factors. Once the effects of global drivers are removed, common features appear in cities' temperatures in the eastern part of the United States. Most cities experienced higher warming than that attributable to global climate change, and some shared a period of rapid warming during urban sprawl in the mid-20th century in the United States.

Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems (e.g., the dynamics of the wildland-urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multitemporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values > 0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

Risky Development: Increasing Exposure to Natural Hazards in the United States

Losses from natural hazards are escalating dramatically, with more properties and critical infrastructure affected each year. Although the magnitude, intensity, and/or frequency of certain hazards has increased, development contributes to this unsustainable trend, as disasters emerge when natural disturbances meet vulnerable assets and populations. To diagnose development patterns leading to increased exposure in the conterminous United States (CONUS), we identified earthquake, flood, hurricane, tornado, and wildfire hazard hotspots, and overlaid them with land use information from the Historical Settlement Data Compilation data set. Our results show that 57% of structures (homes, schools, hospitals, office buildings, etc.) are located in hazard hotspots, which represent only a third of CONUS area, and ∼1.5 million buildings lie in hotspots for two or more hazards. These critical levels of exposure are the legacy of decades of sustained growth and point to our inability, lack of knowledge, or unwillingness to limit development in hazardous zones. Development in these areas is still growing more rapidly than the baseline rates for the nation, portending larger future losses even if the effects of climate change are not considered.

Fine-grained, spatiotemporal datasets measuring 200 years of land development in the United States

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. 250m 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).

A century of decoupling size and structure of urban spaces in the United States

Most cities in the United States of America are thought to have followed similar development trajectories to evolve into their present form. However, data on spatial development of cities are limited prior to 1970. Here we leverage a compilation of high-resolution spatial land use and building data to examine the evolving size and form (shape and structure) of US metropolitan areas since the early twentieth century. Our analysis of building patterns over 100 years reveals strong regularities in the development of the size and density of cities and their surroundings, regardless of timing or location of development. At the same time, we find that trajectories regarding shape and structure are harder to codify and more complex. We conclude that these discrepant developments of urban size- and form-related characteristics are driven, in part, by the long-term decoupling of these two sets of attributes over time.

The changing geography of social mobility in the United States

New evidence shows that intergenerational social mobility-the rate at which children born into poverty climb the income ladder-varies considerably across the United States. Is this current geography of opportunity something new or does it reflect a continuation of long-term trends? We answer this question by constructing data on the levels and determinants of social mobility across American regions over the 20th century. We find that the changing geography of opportunity-generating economic activity restructures the landscape of intergenerational mobility, but factors associated with specific regional structures of interpersonal and racial inequality that have "deep roots" generate persistence. This is evident in the sharp decline in social mobility in the Midwest as economic activity has shifted away from it and the consistently low levels of opportunity in the South even as economic activity has shifted toward it. We conclude that the long-term geography of social mobility can be understood through the deep roots and changing economic fortunes of places.