The heterogeneity and change in the urban structure of metropolitan areas in the United States, 1990-2010

Spatially explicit accuracy assessment of deep learning-based, fine-resolution built-up land data in the United States

Geospatial datasets derived from remote sensing data by means of machine learning methods are often based on probabilistic outputs of abstract nature, which are difficult to translate into interpretable measures. For example, the Global Human Settlement Layer GHS-BUILT-S2 product reports the probability of the presence of built-up areas in 2018 in a global 10 m × 10 m grid. However, practitioners typically require interpretable measures such as binary surfaces indicating the presence or absence of built-up areas or estimates of sub-pixel built-up surface fractions. Herein, we assess the relationship between the built-up probability in GHS-BUILT-S2 and reference built-up surface fractions derived from a highly reliable reference database for several regions in the United States. Furthermore, we identify a binarization threshold using an agreement maximization method that creates binary built-up land data from these built-up probabilities. These binary surfaces are input to a spatially explicit, scale-sensitive accuracy assessment which includes the use of a novel, visual-analytical tool which we call focal precision-recall signature plots. Our analysis reveals that a threshold of 0.5 applied to GHS-BUILT-S2 maximizes the agreement with binarized built-up land data derived from the reference built-up area fraction. We find high levels of accuracy (i.e., county-level F-1 scores of almost 0.8 on average) in the derived built-up areas, and consistently high accuracy along the rural-urban gradient in our study area. These results reveal considerable accuracy improvements in human settlement models based on Sentinel-2 data and deep learning, as compared to earlier, Landsat-based versions of the Global Human Settlement Layer.

Global 30 meters spatiotemporal 3D urban expansion dataset from 1990 to 2010

Understanding the spatiotemporal dynamics of global 3D urban expansion over time is becoming increasingly crucial for achieving long-term development goals. In this study, we generated a global dataset of annual urban 3D expansion (1990-2010) using World Settlement Footprint 2015 data, GAIA data, and ALOS AW3D30 data with a three-step technical framework: (1) extracting the global constructed land to generate the research area, (2) neighborhood analysis to calculate the original normalized DSM and slope height of each pixel in the study area, and (3) slope correction for areas with a slope greater than 10° to improve the accuracy of estimated building heights. The cross-validation results indicate that our dataset is reliable in the United States(R² = 0.821), Europe(R² = 0.863), China(R² = 0.796), and across the world(R² = 0.811). As we know, this is the first 30-meter 3D urban expansion dataset across the globe, which can give unique information to understand and address the implications of urbanization on food security, biodiversity, climate change, and public well-being and health.

Perinatal Health Outcomes Across Rural and Nonrural Counties Within a Single Health System Catchment

Background

Perinatal health outcomes are influenced by a variety of socioeconomic, behavioral, and economic factors that reduce access to health services. Despite these observations, rural communities continue to face barriers, including a lack of resources and the fragmentation of health services.

Objective

To evaluate patterns in health outcomes, health behaviors, socioeconomic vulnerability, and sociodemographic characteristics across rural and nonrural counties within a single health system catchment area.

Methods

Socioeconomic vulnerability metrics, health care access as determined by licensed provider metrics, and behavioral data were obtained from FlHealthCHARTS.gov and the County Health Rankings. County-level birth and health data were obtained from the Florida Department of Health. The University of Florida Health Perinatal Catchment Area (UFHPCA) was defined as all Florida counties where ≥5% of all infants were delivered at Shands Hospital between June 2011 and April 2017.

Results

The UFHPCA included 3 nonrural and 10 rural counties that represented more than 64,000 deliveries. Nearly 1 in 3 infants resided in a rural county, and 7 out of 13 counties did not have a licensed obstetrician gynecologist. Maternal smoking rates (range 6.8%-24.8%) were above the statewide rate (6.2%). Except for Alachua County, breastfeeding initiation rates (range 54.9%-81.4%) and access to household computing devices (range 72.8%-86.4%) were below the statewide rate (82.9% and 87.9%, respectively). Finally, we found that childhood poverty rates (range 16.3%-36.9%) were above the statewide rate (18.5%). Furthermore, risk ratios suggested negative health outcomes for residents of counties within the UFHPCA for each measure, except for infant mortality and maternal deaths, which lacked sample sizes to adequately test.

Conclusions

The health burden of the UFHPCA is characterized by rural counties with increased maternal death, neonatal death, and preterm birth, as well as adverse health behaviors that included increased smoking during pregnancy and lower levels of breastfeeding relative to nonrural counties. Understanding perinatal health outcomes across a single health system has potential to not only estimate community needs but also facilitate planning of health care initiatives and interventions in rural and low-resource communities.

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.

Cost-Related Medication Nonadherence and Patient Cost Responsibility for Rural and Urban Cancer Survivors

PURPOSE

The relationship between out-of-pocket spending and cost-related medication nonadherence among older rural- and urban-dwelling cancer survivors is not well understood.

METHODS

This retrospective cohort study used the Surveillance, Epidemiology, and End Results Program, Medicare claims, and the Consumer Assessment of Healthcare Providers and Systems survey linked data resource linked data (2007-2015) to investigate the relationship between cancer survivors' cost responsibility in the year before and after report of delaying or not filling a prescription medication because of cost in the past 6 months (cost-related medication nonadherence). Secondary exposures and outcomes included Medicare spending and utilization. Generalized linear models assessed bidirectional relationships between cost-related medication nonadherence, spending, and utilization. Effects of residence were assessed via interaction terms.

RESULTS

Of 6,591 older cancer survivors, 13% reported cost-related medication nonadherence. Survivors were a median 8 years (interquartile range, 4.5-12.5 years) from their cancer diagnosis, 15% were dually Medicare/Medicaid-eligible, and prostate (40%) and breast (32%) cancer survivors were most prevalent. With every $500 USD increase in patient cost responsibility, risk of cost-related medication nonadherence increased by 3% (risk ratio, 1.03; 95% CI, 1.02 to 1.04). After report of cost-related medication nonadherence, patient cost responsibility was 22% higher (95% CI, 1.11 to 1.32) compared with those not reporting nonadherence, amounting to $523 USD (95% CI, $430 USD to $630 USD). Medicare spending and utilization were also higher before and after report of cost-related nonadherence versus none. For survivors residing in rural (18%) and urban (82%) areas, residence did not modify adherence or cost outcomes.

CONCLUSION

A bidirectional relationship exists between patient cost responsibility and cost-related medication nonadherence. Interventions reducing urban- and rural-dwelling survivor health care costs and cost-related adherence barriers are needed.

A building height dataset across China in 2017 estimated by the spatially-informed approach

As a fundamental aspect of the urban form, building height is a key attribute for reflecting human activities and human-environment interactions in the urban context. However, openly accessible building height maps covering the whole China remain sorely limited, particularly for spatially informed data. Here we developed a 1 km × 1 km resolution building height dataset across China in 2017 using Spatially-informed Gaussian process regression (Si-GPR) and open-access Sentinel-1 data. Building height estimation was performed using the spatially-explicit Gaussian process regression (GPR) in 39 major Chinese cities where the spatially explicit and robust cadastral data are available and the spatially-implicit GPR for the remaining 304 cities, respectively. The cross-validation results indicated that the proposed Si-GPR model overall achieved considerable estimation accuracy (R2 = 0.81, RMSE = 4.22 m) across the entire country. Because of the implementation of local modelling, the spatially-explicit GPR outperformed (R2 = 0.89, RMSE = 2.82 m) the spatially-implicit GPR (R2 = 0.72, RMSE = 6.46 m) for all low-rise, mid-rise, and high-rise buildings. This dataset, with extensive-coverage and high-accuracy, can support further studies on the characteristics, causes, and consequences of urbanization.

Detecting patterns of vertebrate biodiversity across the multidimensional urban landscape

Explicit characterisation of the complexity of urban landscapes is critical for understanding patterns of biodiversity and for detecting the underlying social and ecological processes that shape them. Urban environments exhibit variable heterogeneity and connectivity, influenced by different historical contingencies, that affect community assembly across scales. The multidimensional nature of urban disturbance and co-occurrence of multiple stressors can cause synergistic effects leading to nonlinear responses in populations and communities. Yet, current research design of urban ecology and evolutionary studies typically relies on simple representation of the parameter space that can be observed. Sampling approaches apply simple urban gradients such as linear transects in space or comparisons of urban sites across the urban mosaic accounting for a few variables. This rarely considers multiple dimensions and scales of biodiversity, and proves to be inadequate to explain observed patterns. We apply a multidimensional approach that integrates distinctive social, ecological and built characteristics of urban landscapes, representing variations along dimensions of heterogeneity, connectivity and historical contingency. Measuring species richness and beta diversity across 100 US metropolitan areas at the city and 1-km scales, we show that distinctive signatures of urban biodiversity can result from interactions between socioecological heterogeneity and connectivity, mediated by historical contingency.

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.

Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework

Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies.

Measuring and Monitoring Urban Impacts on Climate Change from Space

As urban areas continue to expand and play a critical role as both contributors to climate change and hotspots of vulnerability to its effects, cities have become battlegrounds for climate change adaptation and mitigation. Large amounts of earth observations from space have been collected over the last five decades and while most of the measurements have not been designed specifically for monitoring urban areas, an increasing number of these observations is being used for understanding the growth rates of cities and their environmental impacts. Here we reviewed the existing tools available from satellite remote sensing to study urban contribution to climate change, which could be used for monitoring the progress of climate change mitigation strategies at the city level. We described earth observations that are suitable for measuring and monitoring urban population, extent, and structure; urban emissions of greenhouse gases and other air pollutants; urban energy consumption; and extent, intensity, and effects on surrounding regions, including nearby water bodies, of urban heat islands. We compared the observations available and obtainable from space with the measurements desirable for monitoring. Despite considerable progress in monitoring urban extent, structure, heat island intensity, and air pollution from space, many limitations and uncertainties still need to be resolved. We emphasize that some important variables, such as population density and urban energy consumption, cannot be suitably measured from space with available observations.

Combining expert and crowd-sourced training data to map urban form and functions for the continental US

Although continental urban areas are relatively small, they are major drivers of environmental change at local, regional and global scales. Moreover, they are especially vulnerable to these changes owing to the concentration of population and their exposure to a range of hydro-meteorological hazards, emphasizing the need for spatially detailed information on urbanized landscapes. These data need to be consistent in content and scale and provide a holistic description of urban layouts to address different user needs. Here, we map the continental United States into Local Climate Zone (LCZ) types at a 100 m spatial resolution using expert and crowd-sourced information. There are 10 urban LCZ types, each associated with a set of relevant variables such that the map represents a valuable database of urban properties. These data are benchmarked against continental-wide existing and novel geographic databases on urban form. We anticipate the dataset provided here will be useful for researchers and practitioners to assess how the configuration, size, and shape of cities impact the important human and environmental outcomes.

Urban Change in the United States, 1990–2010: A Spatial Assessment of Administrative Reclassification

In today’s increasingly urban world, understanding the components of urban population growth is essential. While the demographic components of natural increase and migration have received the overwhelming share of attention to date, this paper addresses the effects of administrative reclassification on urban population growth as derived from census data, which remain largely unstudied. We adopt a spatial approach, using the finest resolution US census data available for three decennial census periods, to estimate the magnitude of reclassification and examine the spatial-temporal variation in reclassification effects. We supplement the census data by using satellite-derived settlement data to further explain reclassification outcomes. We find that while 10% and 7% of the population live in areas that underwent urban/rural reclassification during the 1990–2000 and 2000–2010 time periods, respectively (with smaller fractions of corresponding land), reclassification has a substantial effect on metrics derived to characterize the urbanization process—comprising roughly 44% and 34% of total urban population growth over each period. The estimated magnitude of this effect is sensitive to assumptions regarding the timing of reclassification. The approach also reveals where, how, to what degree, and, in some part, why reclassification is affecting to the process of urbanization on the fine spatial scale, including the impact of underlying demographic processes. This research provides new directions to more effectively study coupled nature–human systems and their interactions.