Understanding urbanization: A study of census and satellite-derived urban classes in the United States, 1990-2010

Encouraging online consumers into making better food choices: The power of nature exposure on healthy food choices

BACKGROUND

online environments can influence food desire and choices. We tested if online calming nature and stressful street environments can affect desire for healthy and unhealthy foods.

METHOD

we asked 238 participants (40 ± 14 yrs) to rate their desire (100 mm VAS) for 7 low calorie nutrient rich foods (Healthy) and 7 high calorie nutrient poor foods (Unhealthy), and perceived stress (state anxiety in STAI), before and after imagining themselves in a control, nature park, or busy street condition.

RESULTS

participants who imagined themselves being in a nature park had a significant higher desire for Healthy foods, than participants in the busy street condition (p < 0.05). Participants in the busy street condition decreased their desire for Healthy foods after they imagined themselves in a busy street (p < 0.05)). However, perceived stress did not impact the association between condition and desire for low calorie foods nor high calorie foods.

CONCLUSION

this study suggests that online environments can have an impact on healthy food desires, which could be of importance for the increased number of food choices which are made in online environments.

Comparing built-up area datasets to assess urban exposure to coastal hazards in Europe

Information on urban land use, beyond the urban-rural dichotomy, can improve the assessment of potential impacts of coastal hazards by refining estimates of damages and supporting adaptation planning. However, the lack of a consistent definition of "urban" in previous studies has led to exposure estimates that vary considerably. Here, we explore the sensitivity of exposed population and built-up area in four settlement types, defined by four different built-up area datasets. We find large differences in the exposed population of up to 65% (127 million people) in the "Urban" class. The exposure estimates are highly sensitive to the density thresholds used to distinguish the settlement types, with a difference in exposed urban population of up to 53.5 million people when the threshold varies by 10%. We attribute the high sensitivity of the exposure estimates to the varying definitions of built-up area of the underlying datasets. We argue that the definition of urban land is crucial for coastal impact assessments and make recommendations for the use of the analyzed datasets.

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.

Complex climate-mediated effects of urbanization on plant reproductive phenology and frost risk

Urbanization can affect the timing of plant reproduction (i.e. flowering and fruiting) and associated ecosystem processes. However, our knowledge of how plant phenology responds to urbanization and its associated environmental changes is limited. Herbaria represent an important, but underutilized source of data for investigating this question. We harnessed phenological data from herbarium specimens representing 200 plant species collected across 120 yr from the eastern US to investigate the spatiotemporal effects of urbanization on flowering and fruiting phenology and frost risk (i.e. time between the last frost date and flowering). Effects of urbanization on plant reproductive phenology varied significantly in direction and magnitude across species ranges. Increased urbanization led to earlier flowering in colder and wetter regions and delayed fruiting in regions with wetter spring conditions. Frost risk was elevated with increased urbanization in regions with colder and wetter spring conditions. Our study demonstrates that predictions of phenological change and its associated impacts must account for both climatic and human effects, which are context dependent and do not necessarily coincide. We must move beyond phenological models that only incorporate temperature variables and consider multiple environmental factors and their interactions when estimating plant phenology, especially at larger spatial and taxonomic scales.

Exploring spatial feedbacks between adaptation policies and internal migration patterns due to sea-level rise

Climate change-induced sea-level rise will lead to an increase in internal migration, whose intensity and spatial patterns will depend on the amount of sea-level rise; future socioeconomic development; and adaptation strategies pursued to reduce exposure and vulnerability to sea-level rise. To explore spatial feedbacks between these drivers, we combine sea-level rise projections, socioeconomic projections, and assumptions on adaptation policies in a spatially-explicit model ('CONCLUDE'). Using the Mediterranean region as a case study, we find up to 20 million sea-level rise-related internal migrants by 2100 if no adaptation policies are implemented, with approximately three times higher migration in southern and eastern Mediterranean countries compared to northern Mediterranean countries. We show that adaptation policies can reduce the number of internal migrants by a factor of 1.4 to 9, depending on the type of strategies pursued; the implementation of hard protection measures may even lead to migration towards protected coastlines. Overall, spatial migration patterns are robust across all scenarios, with out-migration from a narrow coastal strip and in-migration widely spread across urban settings. However, the type of migration (e.g. proactive/reactive, managed/autonomous) depends on future socioeconomic developments that drive adaptive capacity, calling for decision-making that goes well beyond coastal issues.

Neonatal and perinatal mortality in the urban continuum: a geospatial analysis of the household survey, satellite imagery and travel time data in Tanzania

INTRODUCTION

Recent studies suggest that the urban advantage of lower neonatal mortality in urban compared with rural areas may be reversing, but methodological challenges include misclassification of neonatal deaths and stillbirths, and oversimplification of the variation in urban environments. We address these challenges and assess the association between urban residence and neonatal/perinatal mortality in Tanzania.

METHODS

The Tanzania Demographic and Health Survey (DHS) 2015-2016 was used to assess birth outcomes for 8915 pregnancies among 6156 women of reproductive age, by urban or rural categorisation in the DHS and based on satellite imagery. The coordinates of 527 DHS clusters were spatially overlaid with the 2015 Global Human Settlement Layer, showing the degree of urbanisation based on built environment and population density. A three-category urbanicity measure (core urban, semi-urban and rural) was defined and compared with the binary DHS measure. Travel time to the nearest hospital was modelled using least-cost path algorithm for each cluster. Bivariate and multilevel multivariable logistic regression models were constructed to explore associations between urbanicity and neonatal/perinatal deaths.

RESULTS

Both neonatal and perinatal mortality rates were highest in core urban and lowest in rural clusters. Bivariate models showed higher odds of neonatal death (OR=1.85; 95% CI 1.12 to 3.08) and perinatal death (OR=1.60; 95% CI 1.12 to 2.30) in core urban compared with rural clusters. In multivariable models, these associations had the same direction and size, but were no longer statistically significant. Travel time to the nearest hospital was not associated with neonatal or perinatal mortality.

CONCLUSION

Addressing high rates of neonatal and perinatal mortality in densely populated urban areas is critical for Tanzania to meet national and global reduction targets. Urban populations are diverse, and certain neighbourhoods or subgroups may be disproportionately affected by poor birth outcomes. Research must capture, understand and minimise risks specific to urban settings.

Urbanization and physical activity in the global Prospective Urban and Rural Epidemiology study

Urbanization may influence physical activity (PA) levels, although little evidence is available for low- and middle- income countries where urbanization is occurring fastest. We evaluated associations between urbanization and total PA, as well as work-, leisure-, home-, and transport-specific PA, for 138,206 adults living in 698 communities across 22 countries within the Prospective Urban and Rural Epidemiology (PURE) study. The 1-week long-form International PA Questionnaire was administered at baseline (2003-2015). We used satellite-derived population density and impervious surface area estimates to quantify baseline urbanization levels for study communities, as well as change measures for 5- and 10-years prior to PA surveys. We used generalized linear mixed effects models to examine associations between urbanization measures and PA levels, controlling for individual, household and community factors. Higher community baseline levels of population density (- 12.4% per IQR, 95% CI - 16.0, - 8.7) and impervious surface area (- 29.2% per IQR, 95% CI - 37.5, - 19.7), as well as the rate of change in 5-year population density (- 17.2% per IQR, 95% CI - 25.7, - 7.7), were associated with lower total PA levels. Important differences in the associations between urbanization and PA were observed between PA domains, country-income levels, urban/rural status, and sex. These findings provide new information on the complex associations between urbanization and PA.

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.

Impact of urbanisation and environmental factors on spatial distribution of COVID-19 cases during the early phase of epidemic in Singapore

Geographical weighted regression (GWR) can be used to explore the COVID-19 transmission pattern between cases. This study aimed to explore the influence from environmental and urbanisation factors, and the spatial relationship between epidemiologically-linked, unlinked and imported cases during the early phase of the epidemic in Singapore. Spatial relationships were evaluated with GWR modelling. Community COVID-19 cases with residential location reported from 21st January 2020 till 17th March 2020 were considered for analyses. Temperature, relative humidity, population density and urbanisation are the variables used as exploratory variables for analysis. ArcGIS was used to process the data and perform geospatial analyses. During the early phase of COVID-19 epidemic in Singapore, significant but weak correlation of temperature with COVID-19 incidence (significance 0.5–1.5) was observed in several sub-zones of Singapore. Correlations between humidity and incidence could not be established. Across sub-zones, high residential population density and high levels of urbanisation were associated with COVID-19 incidence. The incidence of COVID-19 case types (linked, unlinked and imported) within sub-zones varied differently, especially those in the western and north-eastern regions of Singapore. Areas with both high residential population density and high levels of urbanisation are potential risk factors for COVID-19 transmission. These findings provide further insights for directing appropriate resources to enhance infection prevention and control strategies to contain COVID-19 transmission.

Housing unit and urbanization estimates for the continental U.S. in consistent tract boundaries, 1940-2019

Subcounty housing unit counts are important for studying geo-historical patterns of (sub)urbanization, land-use change, and residential loss and gain. The most commonly used subcounty geographical unit for social research in the United States is the census tract. However, the changing geometries and historically incomplete coverage of tracts present significant obstacles for longitudinal analysis that existing datasets do not sufficiently address. Overcoming these barriers, we provide housing unit estimates in consistent 2010 tract boundaries for every census year from 1940 to 2010 plus 2019 for the entire continental US. Moreover, we develop an "urbanization year" indicator that denotes if and when tracts became "urbanized" during this timeframe. We produce these data by blending existing interpolation techniques with a novel procedure we call "maximum reabsorption." Conducting out-of-sample validation, we find that our hybrid approach generally produces more reliable estimates than existing alternatives. The final dataset, Historical Housing Unit and Urbanization Database 2010 (HHUUD10), has myriad potential uses for research involving housing, population, and land-use change, as well as (sub)urbanization.

Built environment as a risk factor for adult overweight and obesity: Evidence from a longitudinal geospatial analysis in Indonesia

Indonesia has nearly doubled its urban population in the past three decades. In this period, the prevalence of overweight and obesity in Indonesia has also nearly doubled. We examined 1993-2014 panel data from the Indonesian Family Life Survey (IFLS) to determine the extent to which the increase in one's built environment contributed to a corresponding increase in adult overweight and obesity during this period. We estimated longitudinal regression models for body mass index (BMI) and being overweight or obese using novel matched geospatial measures of built-up land area. Living in a more built-up area was associated with greater BMI and risk of being overweight or obese. The contribution of the built environment was estimated to be small but statistically significant even after accounting for individuals' initial BMI. We discuss the findings considering the evidence on nutritional and technological transitions affecting food consumption patterns and physical activity levels in urban and rural areas.

Urban Environments Aid Invasion of Brown Widows (Theridiidae: Latrodectus geometricus) in North America, Constraining Regions of Overlap and Mitigating Potential Impact on Native Widows

Urbanization is a major cause of biotic homogenization and habitat fragmentation for native communities. However, the role of urbanization on the success of biological invasions on a continental scale has yet to be explored. Urbanization may facilitate the establishment success of invasive species by minimizing niche differentiation between native and invaded ranges. In such cases, we might expect anthropogenic variables to have stronger influence on the geographic distribution of invasive compared to native populations. In this study, we use ecological niche modeling to define the distribution of non-native brown widow spider (Latrodectus geometricus) and three native black widows (L. hespersus, L. mactans, L. variolus) in North America and gauge the importance of urbanization on the geographic ranges of widows at a continental scale. We also quantify the geographic overlap of L. geometricus with each native widow to assess potential species and regions at risk of ecological impact. Consistent with our hypothesis, we find that the distribution of L. geometricus is strongly constrained to urban environments, while native widow distributions are more strongly driven by climatic factors. These results show that urbanization plays a significant role in facilitating the success of invasion, weakening the significance of climate on the realized niche in its invaded range.

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.

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.

Secondary Traumatic Stress in Emergency Services Systems (STRESS) Project: Quantifying and Predicting Compassion Fatigue in Emergency Medical Services Personnel

Introduction: Compassion fatigue (CF) is defined as the acute or gradual loss of benevolence that occurs after exposure to critical incident stress. Colloquially referred to as the "cost of caring," CF can affect an individual's future response to stressful situations and is unhealthy for caregivers.Objective: To identify the prevalence and predictors of CF in EMS professionals.Methods: This was a cross-sectional survey of EMS personnel using one-stage area sampling. Nine EMS agencies recruited based on location and geographic region provided data on service area and call mix. Respondents were surveyed in-person during monthly training. The survey evaluated the relationship between CF and psychosocial factors using the Professional Quality of Life Scale (ProQOL). Parametric and non-parametric tests were used where appropriate for the univariate analysis. Those factors significant in the univariate analysis were included in the multivariable analysis. A logistic regression was conducted to determine predictors of CF while controlling for potential confounders.Results: A total of 686 EMS personnel completed the survey. Altogether, 48% had CF, of which 50.8% were male and 14% were minorities. Compared to those without CF, more than 4 times as many respondents with CF (n = 28[8.6%] v. 7[2.0%]) self-reported as currently in counseling and over a third (n = 109[33.1%]) had considered suicide. Irrespective of the presence of CF, one in two knew another EMS professional who had completed suicide. African-American EMS professionals were 3 times more likely to have CF (OR:3.1;p = 0.009). Mean scores on the ProQOL CF subscale were 10 points higher in those with CF compared to those without (27.1[±4.34] v. 17.04[±2.9]). EMS personnel were 48% more likely to have CF if they knew an EMS provider who completed suicide (p = 0.047). Additionally, those with concomitant traumatic stress syndromes, such as vicarious trauma and burnout, were 4.61 and 3.35 times more likely to have CF, respectively.Conclusions: CF is a considerable concern for EMS professionals and there are several modifiable factors that may reduce the prevalence of this cumulative stress syndrome. Additional research should focus on causal factors and mitigation strategies, as well as the individual and agency impact of CF on the prehospital work environment.

Open and Consistent Geospatial Data on Population Density, Built-Up and Settlements to Analyse Human Presence, Societal Impact and Sustainability: A Review of GHSL Applications

This review analyses peer-reviewed scientific publications and policy documents that use built-up density, population density and settlement typology spatial grids from the Global Human Settlement Layer (GHSL) project to quantify human presence and processes for sustainability. Such open and free grids provide detailed time series spanning 1975–2015 developed with consistent approaches. Improving our knowledge of cities and settlements by measuring their size extent, as well as the societal processes occurring within settlements, is key to understanding their impact on the local, regional and global environment for addressing global sustainability and the integrity of planet Earth. The reviewed papers are grouped around five main topics: Quantifying human presence; assessing settlement growth over time; estimating societal impact, assessing natural hazard risk and impact, and generating indicators for international framework agreements and policy documents. This review calls for continuing to refine and expand the work on societal variables that, when combined with essential variables including those for climate, biodiversity and ocean, can improve our understanding of the societal impact on the biosphere and help to monitor progress towards local, regional and planetary sustainability.

Exploring the dynamics of migration, armed conflict, urbanization, and anthropogenic change in Colombia

Anthropogenic change has been associated with population growth, land use change, and changing economies. However, internal migration patterns and armed conflicts are also key drivers of anthropogenic and demographic processes. To better understand the processes associated with this change, we explore the spatial relationship between forced migration due to armed conflict and changing socioeconomic factors in Colombia, a country which has a recent history of 7 million internal migrants. In addition, we use remote sensing, Google Earth Engine, as well as spatial statistical analyses of demographic data in order to measure anthropogenic change between 1984 and 2013-a socio-politically important period in Colombia's armed conflict. We also analyze spatiotemporal relationships between socioeconomic and anthropogenic changes, which are caused by forced migration. We found that forced migration is significantly and positively related to an increasing rural-urban type of migration which results from armed conflict. Results also show that it is negatively related to interregional displacement. Indeed, anthropogenic change pertaining to different regions have had different correlations with forced migration, and across different time periods. Findings are used to discuss how socioeconomic and political phenomena such as armed conflict can have complex effects on the dynamics of anthropogenic and ecological change as well as movement of humans in countries like Colombia.

Association of the Built Environment With Childhood Psychosocial Stress

IMPORTANCE

Emerging research suggests that factors associated with the built environment, including artificial light, air pollution, and noise, may adversely affect children's mental health, while living near green space may reduce stress. Little is known about the combined roles of these factors on children's stress.

OBJECTIVE

To investigate associations between components of the built environment with personal and home characteristics in a large cohort of children who were assessed for perceived stress.

DESIGN, SETTING, AND PARTICIPANTS

In this cohort study, a total of 2290 Southern California Children's Health Study participants residing in 8 densely populated urban communities responded to detailed questionnaires. Exposures of artificial light at night (ALAN) derived from satellite observations, near-roadway air pollution (NRP) determined from a dispersion model, noise estimated from the US Traffic Noise Model, and green space from satellite observations of the enhanced vegetation index were linked to each participant's geocoded residence.

MAIN OUTCOMES AND MEASURES

Children's stress was assessed at ages 13 to 14 years and 15 to 16 years using the 4-item Perceived Stress Scale (PSS-4), scaled from 0 to 16, with higher scores indicating greater perceived stress. Measurements were conducted in 2010 and 2012, and data were analyzed from February 6 to August 24, 2019. Multivariate mixed-effects models were used to examine multiple exposures; modification and mediation analyses were also conducted.

RESULTS

Among the 2290 children in this study, 1149 were girls (50%); mean (SD) age was 13.5 (0.6) years. Girls had significantly higher perceived stress measured by PSS-4 (mean [SD] score, 5.7 [3.4]) than boys (4.9 [3.2]). With increasing age (from 13.5 [0.6] to 15.3 [0.6] years), the mean PSS-4 score rose from 5.6 (3.3) to 6.0 (3.4) in girls but decreased for boys from 5.0 (3.2) to 4.7 (3.1). Multivariate mixed-effects models examining multiple exposures indicated that exposure to secondhand smoke in the home was associated with a 0.85 (95% CI, 0.46-1.24) increase in the PSS-4 score. Of the factors related to the physical environment, an interquartile range (IQR) increase in ALAN was associated with a 0.57 (95% CI, 0.05-1.09) unit increase in the PSS-4 score together with a 0.16 score increase per IQR increase of near-roadway air pollution (95% CI, 0.02-0.30) and a -0.24 score decrease per IQR increase of the enhanced vegetation index (95% CI, -0.45 to -0.04). Income modified the ALAN effect size estimate; participants in households earning less than $48 000 per year had significantly greater stress per IQR increase in ALAN. Sleep duration partially mediated the associations between stress and both enhanced vegetation index (17%) and ALAN (18%).

CONCLUSIONS AND RELEVANCE

In this cohort study, children's exposure to smoke at home in addition to residential exposure to ALAN and near-roadway air pollution were associated with increased perceived stress among young adolescent children. These associations appeared to be partially mitigated by more residential green space. The findings may support the promotion of increased residential green spaces to reduce pollution associated with the built environment, with possible mental health benefits for children.

Evaluating the influencing factors of urbanization in the Xinjiang Uygur Autonomous Region over the past 27 years based on VIIRS-DNB and DMSP/OLS nightlight imageries

The Xinjiang Uygur Autonomous Region is the core economic area of the “Silk Road Economic Belt”. The urbanization of this region plays a highly important role in economic and cultural communications between China, Central Asia and Europe. However, the influencing factors of urbanization in this region remain unclear. In this study, we presented a new modified thresholding method to extract the urban built-up areas from two nightlight remote sensing data sources, i.e., the DMSP/OLS and VIIRS/DNB nightlight imageries. Then, geographical detectors and hierarchical partitioning analysis were used to test the influences of anthropogenic and geographic environmental factors on urbanization. Our results showed that the relative error between the actual and the extracted urban built-up areas calculated using our method ranged from -0.30 to 0.27 in two biggest sample cities (Urumqi and Karamay) over the last 27 years. These errors were lower than those calculated by using the traditional method (-0.66 ≤ relative error ≤ -0.11). The expansion of urban built-up areas was greater in the northern regions than the southern regions of Xinjiang, as well as was greater in large cities than small and medium-sized cities. The influence of anthropogenic factors on urbanization has continually decreased over the past 27 years, while the influence of geographical environmental factors has increased. Among all influencing factors, fixed asset investment, topographic position index and per capita possession of water resources have the high contributions on urbanization, accounting for 18.75%, 15.62% and 14.18% of the variance of urbanization, respectively. Here, we provided a new method for studying urbanization by using remote sensing data. Our results are helpful for understand the driving factors of urbanization, and they provide guidance for the sustainable economic development of the Xinjiang Uygur Autonomous Region.

Consumption of food in the EU by the degree of urbanization: data visualization and cluster analysis of the EU sample

This paper examines the consumption of one of the COICOP classes – food and non-alcoholic beverages – by the degree of urbanization on the sample of EU countries in three periods – 2005, 2010, and 2015. The share of this class in total consumption of cities, towns, and suburbs and rural areas presents the second largest item of the total consumption of all structures in question. They examined the key variable creates an input to the analysis stated in the paper. First, the data visualization is realized by creating maps of scaled consumption of food and non-alcoholic beverages in cities, towns, and suburbs and rural areas in the three periods – 2005, 2010, and 2015. The spatial distribution of data shows, that higher shares of consumption of food and non-alcoholic beverages are obtained in CEE and southern countries in all structures and all periods. Considering that consumption of food and non-alcoholic beverages is negatively correlated with GDP per capita or household expenditure per capita it is possible to conclude that countries with lower levels of GDP per capita spend more on goods of daily use. Second, based on k-means clustering, cluster analysis is stated. Similarities between EU countries in the consumption of food and non-alcoholic beverages by the degree of urbanization and with respect to socio-economic conditions are investigated. Clusters are made for all three monitored periods. In 2005 and 2010 five clusters were identified, in 2015 their number has been reduced to four. Similarities between EU countries in the consumption of food and non-alcoholic beverages by the degree of urbanization change through time. The delayed effect of the financial crisis may explain observed changes. The obvious relocation of countries is evident when comparing clusters in the period 2010 and 2015. Besides it, the most stabile cluster is the cluster, which contains core EU countries.

Two centuries of settlement and urban development in the United States

Over the past 200 years, the population of the United States grew more than 40-fold. The resulting development of the built environment has had a profound impact on the regional economic, demographic, and environmental structure of North America. Unfortunately, constraints on data availability limit opportunities to study long-term development patterns and how population growth relates to land-use change. Using hundreds of millions of property records, we undertake the finest-resolution analysis to date, in space and time, of urbanization patterns from 1810 to 2015. Temporally consistent metrics reveal distinct long-term urban development patterns characterizing processes such as settlement expansion and densification at fine granularity. Furthermore, we demonstrate that these settlement measures are robust proxies for population throughout the record and thus potential surrogates for estimating population changes at fine scales. These new insights and data vastly expand opportunities to study land use, population change, and urbanization over the past two centuries.

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

While the population of the United States has been predominantly urban for nearly 100 years, periodic transformations of the concepts and measures that define urban places and population have taken place, complicating over-time comparisons. We compare and combine data series of officially-designated urban areas, 1990-2010, at the census block-level within Metropolitan Statistical Areas (MSAs) with a satellite-derived consistent series on built-up area from the Global Human Settlement Layer to create urban classes that characterize urban structure and provide estimates of land and population. We find considerable heterogeneity in urban form across MSAs, even among those of similar population size, indicating the inherent difficulties in urban definitions. Over time, we observe slightly declining population densities and increasing land and population in areas captured only by census definitions or low built-up densities, constrained by the geography of place. Nevertheless, deriving urban proxies from satellite-derived built-up areas is promising for future efforts to create spatio-temporally consistent measures for urban land to guide urban demographic change analysis.

Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns

Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object’s features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas.

Urbanization in India: Population and Urban Classification Grids for 2011

India is the world’s most populous country, yet also one of the least urban. It has long been known that India’s official estimates of urban percentages conflict with estimates derived from alternative conceptions of urbanization. To date, however, the detailed spatial and settlement boundary data needed to analyze and reconcile these differences have not been available. This paper presents gridded estimates of population at a resolution of 1 km along with two spatial renderings of urban areas—one based on the official tabulations of population and settlement types (i.e., statutory towns, outgrowths, and census towns) and the other on remotely-sensed measures of built-up land derived from the Global Human Settlement Layer. We also cross-classified the census data and the remotely-sensed data to construct a hybrid representation of the continuum of urban settlement. In their spatial detail, these materials go well beyond what has previously been available in the public domain, and thereby provide an empirical basis for comparison among competing conceptual models of urbanization.