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Decreases in terrestrial near-surface wind speed (NSWS) were documented in many regions over the past decades. Various drivers have been proposed for this terrestrial stilling, such as weakening of ocean-land pressure gradients related to climate change and increased surface roughness linked to vegetation growth; but none have been robustly established as the cause. A plausible reason for this quandary is that the local impact of urbanization on NSWS has been overlooked. Here, we used homogenized NSWS records from in situ weather stations and a satellite-based dynamic urban-rural classification scheme to quantitatively assess the contribution of urbanization to observed terrestrial stilling during 1980-2016 over the Beijing-Tianjin-Hebei region of China. Results suggested that urbanization contributed approximately 8% to the observed decrease in the regional NSWS in urban areas, implying that urbanization played a minor role in terrestrial stilling, even in this rapidly developing region. The largest NSWS changes related to urbanization occurred in winter, followed by spring, autumn, and summer. Urbanization reduced the days with relatively strong winds but increased the days with light and gentle winds. We found that except for the Japanese 55 year reanalysis (JRA-55) dataset, none of the common reanalysis products reproduced the observed NSWS trends in urban or rural areas. However, this could be because of JRA-55's intrinsic negative bias in NSWS trends over land. Thus, regional terrestrial NSWS trends derived from reanalysis products deserve careful examination.

Since young adults tend to move from rural to urban regions, whereas older adults move from urban to rural regions, we may expect to see increasing differences in population ageing across urban and rural regions. This paper examines whether trends in population ageing across urban and rural NUTS-2 regions of the EU-27 have diverged over the period 2003-13. We use the methodological approach of convergence analysis, quite recently brought to demography from the field of economic research. Unlike classical beta and sigma approaches to convergence, we focus not on any single summary statistic of convergence, but rather analyse the whole cumulative distribution of regions. Such an approach helps to identify which specific group of regions is responsible for the major changes. Our results suggest that, despite expectations, there was no divergence in age structures between urban and rural regions; rather, divergence happened within each of the groups of regions.

Purpose A trend of higher suicide rates in rural and remote areas as well as areas with low socioeconomic status has been shown in previous research. Little is known whether the influence of social deprivation on suicide differs between urban and rural areas. This investigation aims to examine how social deprivation influences suicide mortality and to identify which related factors of deprivation have a higher potential to reduce suicide risk in urban and rural Queensland, Australia. Methods Suicide data from 2004 to 2008 were obtained from the Queensland Suicide Register. Age-standardized suicide rates (15+ years) and rate ratios, with a 95 % confidence interval, for 38 Statistical Subdivisions (SSDs) in Queensland were calculated. The influence of deprivation-related variables on suicide and their rural–urban difference were modelled by log-linear regression analyses through backward elimination. Results Among the 38 SSDs in Queensland, eight had a higher suicide risk while eleven had a lower rate. Working-age males (15–59 years) had the most pronounced geographic variation in suicide rate. In urban areas, suicide rates were positively associated with tenant households in public housing, Aboriginal and Torres Strait Islander people, the unemployment rate and median individual income, but inversely correlated with younger age and households with no internet access. In rural areas, only tenant households in public housing and households with no internet access heightened the risk of suicide, while a negative association was found for younger and older persons, low-skilled workers or labourers, and families with low income and no cars. Conclusions The extent to which social deprivation contributes to suicide mortality varies considerably between rural and urban areas.

This paper provides a study of the changes in land use in urban environments in two cities, Wuhan, China and western Sydney in Australia. Since mixed pixels are a characteristic of medium resolution images such as Landsat, when used for the classification of urban areas, due to changes in urban ground cover within a pixel, Multiple Endmember Spectral Mixture Analysis (MESMA) together with Super-Resolution Mapping (SRM) are employed to derive class fractions to generate classification maps at a higher spatial resolution using an Artificial Neural Network (ANN) predicted Wavelet method. Landsat images over the two cities for a 30-year period, are classified in terms of vegetation, buildings, soil and water. The classifications are then processed using Indifrag software to assess the levels of fragmentation caused by changes in the areas of buildings, vegetation, water and soil over the 30 years. The extents of fragmentation of vegetation, buildings, water and soil for the two cities are compared, while the percentages of vegetation are compared with recommended percentages of green space for urban areas for the benefit of health and well-being of inhabitants. Changes in Ecosystem Service Values (ESVs) resulting from the urbanization have been assessed for Wuhan and Sydney. The UN Sustainable Development Goals (SDG) for urban areas are being assessed by researchers to better understand how to achieve the sustainability of cities.

Background Hypertension prevalence among the overall US adult population has been relatively stable during the last two decades. However, whether this stabilization has occurred across rural-urban communities and across different geographic regions is unknown, particularly among older adults with diabetes who are likely to have concomitant cardiovascular risk factors. Methods This serial cross-sectional analysis used the 5% national sample of Medicare administrative claims data ( n  = 3,516,541) to examine temporal trends (2005–2017) in diagnosed hypertension among older adults with diabetes, across urban-rural communities and US census regions (Northeast, Midwest, South, and West). Joinpoint regression was used to obtain annual percent change (APC) in hypertension prevalence across rural-urban communities and geographic regions, and multivariable adjusted regression was used to assess associations between rural-urban communities and hypertension prevalence. Results The APC in the prevalence of hypertension was higher during 2005–2010, and there was a slowdown in the increase during 2011–2017 across all regions, with significant variations across rural-urban communities within each of the regions. In the regression analysis, in the adjusted model, older adults living in non-core (most rural) areas in the Midwest (PR = 0.988, 95% CI: 0.981–0.995) and West (PR = 0.935, 95% CI: 0.923–0.946) had lower hypertension prevalence than their regional counterparts living in large central metro areas. Conclusions Although the magnitudes of these associations are small, differences in hypertension prevalence across rural-urban areas and geographic regions may have implications for targeted interventions to improve chronic disease prevention and management.

Today, planning an urban–rural interface requires redefining the planner’s role and toolbox. Global challenges such as food security, climate change and population growth have become urgent issues to be addressed, especially for the implications in land use management. Urban–rural linkages, socio-economic interactions and ecological connectivity are the main issues on which the new urban agenda and sustainable development goals focus. Thus, urban and peri-urban agriculture (professional and not professional) in urban–rural interfaces has a crucial role in the maintenance and enhancement of landscape quality, urban green spaces and ecosystem services. The research presented in this article adopts a holistic approach, with a special focus on open spaces, in order to understand the complexity of peri-urban landscapes and to identify homogeneous units. It also defines map-based indices to characterize peri-urban landscape types and identify main functions to maintain and enhance. The method was applied to the peri-urban area of Turin (Italy), and maps of spatial and functional classification at the landscape unit level were generated, as well as a map of critical areas to improve. Despite some minor limitations, the method and tools proposed appear to have a range of applications in the context of global challenges and from a landscape perspective.

Urbanization plays a critical role in changing the urban environment. Most developed countries have almost completed urbanization. However, with more and more people moving to cities, the urban environment in developing countries is undergoing significant changes. Sustainable development cannot be achieved without significant changes in building, managing, and responding to changes in the urban environment. The classified measurement and analysis of the urban environment in developing countries and the real-time understanding of the evolution and characteristics of the urban environment are of great significance for decision-makers to manage and plan cities more effectively and maintain the sustainability of the urban environment. Hence, a method readily applicable for the state-of-the-art computational analysis can help conceive the rapidly changing urban socio-environmental dynamics that can make the policy-making process even more informative and help monitor the changes almost in real-time. Based on easily accessible data from Google Earth, this work develops and proposes a new urban environment classification method focusing on formality and informality. Firstly, the method gives a new model to scrutinize the urban environment based on the buildings and their surroundings. Secondly, the method is suited for the state-of-the-art machine learning processes that make it applicable and scalable for forecasting, analytics, or computational modeling. The paper first demonstrates the model and its applicability based on the urban environment in the developing world. The method divides the urban environment into 16 categories under four classes. Then it is used to draw the urban environment classes maps of the following emerging cities: Nairobi in Kenya, Mumbai in India, Guangzhou in China, Jakarta in Indonesia, Cairo in Egypt, and Lima in Chile. Then, we discuss the characteristics of different urban environments and the differences between the same class in different cities. We also demonstrate the agility of the proposed method by showing how this classification method can be easily augmented with other data such as population per square kilometer to aid the decision-making process. This mapping should help urban designers who are working on analyzing formality and informality in the developing world. Moreover, from the application point of view, this will provide training data sets for future deep learning algorithms and automate them, help establish databases, and significantly reduce the cost of acquiring data for urban environments that change over time. The method can become a necessary tool for decision-makers to plan sustainable urban spaces in the future to design and manage cities more effectively.

Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings.

We conduct a detailed investigation of correlations between real-time expressions of individuals made across the United States and a wide range of emotional, geographic, demographic, and health characteristics. We do so by combining (1) a massive, geo-tagged data set comprising over 80 million words generated in 2011 on the social network service Twitter and (2) annually-surveyed characteristics of all 50 states and close to 400 urban populations. Among many results, we generate taxonomies of states and cities based on their similarities in word use; estimate the happiness levels of states and cities; correlate highly-resolved demographic characteristics with happiness levels; and connect word choice and message length with urban characteristics such as education levels and obesity rates. Our results show how social media may potentially be used to estimate real-time levels and changes in population-scale measures such as obesity rates.

Objectives. To demonstrate how inferences about rural–urban disparities in age-adjusted mortality are affected by the reclassification of rural and urban counties in the United States from 1970 to 2018. Methods. We compared estimates of rural–urban mortality disparities over time, produced through a time-varying classification of rural and urban counties, with counterfactual estimates of rural–urban disparities, assuming no changes in rural–urban classification since 1970. We evaluated mortality rates by decade of reclassification to assess selectivity in reclassification. Results. We found that reclassification amplified rural–urban mortality disparities and accounted for more than 25% of the rural disadvantage observed from 1970 to 2018. Mortality rates were lower in counties that reclassified from rural to urban than in counties that remained rural. Conclusions. Estimates of changing rural–urban mortality differentials are significantly influenced by rural–urban reclassification. On average, counties that have remained classified as rural over time have elevated mortality. Longitudinal research on rural–urban health disparities must consider the methodological and substantive implications of reclassification. Public Health Implications. Attention to rural–urban reclassification is necessary when evaluating or justifying policy interventions focusing on geographic health disparities.