Search

Climate change can contribute to the increase in the prevalence of cardiovascular (CV) diseases, the leading cause of global deaths. Accordingly, many big cities are interested in increasing their residents’ resilience against heat waves. With these issues in mind, the aim of our study was to identify salient features of urban areas and to analyze the effect of such features on population vulnerability to heat (VtoH) as it relates to CV health. This approach was developed and tested in the city of Milan, Italy, considering the summer periods (May–September) of 2017–2022. Milan was divided into 86 districts and 11 features were considered. K-means was applied for clustering, and both spatial and non-spatial regression were used to study the VtoH, defined as the percentage of CV emergencies on a given heat day and on the day after, compared to the total number of CV emergencies. Socio-urban features were spatially non-stationary and three different clusters of districts were identified. In the whole city, regression analysis depicted a spatial relationship between the focal features and the VtoH, with the model estimating a significant effect in five variables: mean summer temperature, density of drinking water fountains and percentages of elderly, female and graduate residents. Three additional features were found to be significant in only some of the cities’ clusters. Our spatial analysis of CV health emergencies applied to the entire geographical area, rather than at the patient level, represents a relatively underexplored approach in public health-related research. The results of our study and future research taking this approach can inform solutions to equitably protect cities’ residents, which is important in the context of ongoing urbanization and climate change. [ABSTRACT FROM AUTHOR]

Food insecurity and transportation inequity are known to disproportionately impact the welfare of marginalized communities. Our study analyzed census data in Harris County, Texas comparing food insecurity rate and components of the Quality Affordable Transportation Index to explore their relationship. Mapping, geographically weighted regression (GWR), and linear regression methods were used to identify relationships between food insecurity and transportation. Poor walkability is more commonly associated with food insecure areas than is poor public transit in Harris County. GWR helped to identify an area of strong correlation between walkability and food insecurity in southwestern Harris County that may benefit from further study. [ABSTRACT FROM AUTHOR]

Urban agglomerations (UAs), which play a significant role in socioeconomic development and urbanization, are confronted with mounting ecological stress and a profound global imbalance in ecosystem services (ES). Understanding the conflict and coordination of knowledge about ES supply and demand (ESSD) can facilitate more efficacious guidance on the ecological sustainable development of UAs. Nevertheless, the characteristics of the conflict and coordination relationship between ESSD in Chinese UAs remain unclear, and further investigation into the interactive coercive relationship between ESSD is warranted. Consequently, we employed spatial regression and coupled coordination models to elucidate the conflict and coordination relationship between ESSD, utilizing multi-source data on Chinese UAs from 2000 to 2020. We found that ES supply in the UAs decreased, while ES demand increased. Furthermore, the coupling coordination degree between ESSD demonstrated an increase trend. The overall coupling coordination degrees between ESSD in UAs were 0.260, 0.285, and 0.311 in 2000, 2010, and 2020, respectively. The central UAs were identified as stress areas, whereas the peripheral areas were classified as non-stress areas. This study offered valuable insights into the interactive relationship between ESSD in UAs and provided a basis for formulating differentiated policies for the sustainable development of ecosystems and human activities. [ABSTRACT FROM AUTHOR]

There are urgent calls for new approaches to map the global urban conditions of complexity, diffuseness, diversity, and connectivity. However, existing methods mostly focus on mapping urbanized areas as bio physical entities. Here, based on the continuum of urbanity framework, we developed an approach for cross-scale urbanity mapping from town to city and urban megaregion with different spatial resolutions using the Google Earth Engine. This approach was developed based on multi-source remote sensing data, Points of Interest – Open Street Map (POIs-OSM) big data, and the random forest regression model. This approach is scale-independent and revealed significant spatial variations in urbanity, underscoring differences in urbanization patterns across megaregions and between urban and rural areas. Urbanity was observed transcending traditional urban boundaries, diffusing into rural settlements within non-urban locales. The finding of urbanity in rural communities far from urban areas challenges the gradient theory of urban-rural development and distribution. By mapping livelihoods, lifestyles, and connectivity simultaneously, urbanity maps present a more comprehensive characterization of the complexity, diffuseness, diversity, and connectivity of urbanized areas than that by land cover or population density alone. It helps enhance the understanding of urbanization beyond biophysical form. This approach can provide a multifaceted understanding of urbanization, and thereby insights on urban and regional sustainability.

Abstract Brazilian agriculture is characterized by the prevalence of small farms and regions with a high degree of rurality and dominance of the agricultural sector in the economy. These two characteristics affect the diversity of agricultural production in the country. Specifically, the article aims to analyze the effects of size farm and farmland use on agricultural diversification and the effects of demand and technology adopted by farmers. The database encompasses 4298 Brazilian municipalities from 1996 to 2017 (the last three agricultural censuses). Empirically, we consider spillover effects by estimating spatial models at the municipal level using panel data, highlighting the importance of location and neighborhood. The study’s findings indicate a tendency toward local concentration of agricultural production in the country, despite the balance between municipalities with diversified and concentrated production. The results showed a significant effect of small farms and the municipalities’ rurality degree on the agricultural output diversification. The study provides insights into the discussion on measures to strengthen support for small properties and regions that diversify crops to ensure economic efficiency and food security.

In primary‐care‐centric models of care provision, specialist co‐location with primary care physicians (PCPs) can potentially improve care coordination and continuity. This study asks whether the co‐location of specialists with referring PCPs can reinforce racial, ethnic, and class inequities in spatial access to care. Given a US healthcare policy context wherein audiologist services are only reimbursed if they are medical practitioner‐referred, audiologists are hypothesized to co‐locate with PCPs. Using spatial cluster analysis and spatial regression approaches, this study quantifies the tendency for PCPs and audiologists to co‐locate and analyzes the consequences for spatial access disparities in the Chicago, Illinois metropolitan region. Audiologists and PCPs co‐cluster significantly across Chicagoland. The spatial lag model confirms racial, ethnic, and class disparities in network travel distance to audiology services in the core counties of the region. The results suggest that, for audiology services, health policies and the resultant interdependence across the hierarchy of care manifest spatially, possibly reinforcing service access disparities within segregated city regions. Key points: Healthcare policies governing interprofessional practice have a spatial dimension.Provider co‐location across the hierarchy of care can reinforce access disparities.Racial, ethnic, and class disparities in access to hearing care shape service use. [ABSTRACT FROM AUTHOR]

Regression models for spatially varying data use spatial random effects to reflect spatial correlation structure. Such random effects, however, may interfere with the covariate effect estimates and make them unreliable. This problem, known as spatial confounding, is complex and has only been studied for models with linear covariate effects. However, as illustrated by a forestry example in which we assess the effect of soil, climate, and topography variables on tree health, the covariate effects of interest are in practice often unknown and nonlinear. We consider, for the first time, spatial confounding in spatial models with nonlinear effects implemented in the generalised additive models (GAMs) framework. We show that spatial+, a recently developed method for alleviating confounding in the linear case, can be adapted to this setting. In practice, spatial+ can then be used both as a diagnostic tool for investigating whether covariate effect estimates are affected by spatial confounding and for correcting the estimates for the resulting bias when it is present. Supplementary materials accompanying this paper appear online. [ABSTRACT FROM AUTHOR]

Exploring the low-carbon transition in China can offer profound guidance for governments to develop relevant environmental policies and regulations within the context of the 2060 carbon neutrality target. Previous studies have extensively explored the promotion of low-carbon development in China, yet no studies have completely explained the mechanisms of the low-carbon transition in China from the perspective of per capita carbon emissions (PCEs). Based on the statistics and carbon emissions data of 367 prefecture level cities in China from 2000 to 2020, this study employed markov chain, kernel density analysis, hotspots analysis, and spatial regression models to reveal the spatiotemporal distribution patterns, future trends, and driving factors of PCEs in China. The results showed that China's PCEs in 2000, 2010, and 2020 were 0.72 ton/persons, 1.72 ton/persons, and 1.91 ton/persons, respectively, exhibiting a continuous upward trend, with evident regional heterogeneity. PCEs in northern China and the eastern coastal region were higher than those of southern China and the central and southwestern regions. The PCEs in China showed obvious spatial clustering, with hot spots mainly concentrated in Inner Mongolia and Xinjiang, while cold spots were mainly in some provinces in southern China. The transition of PCEs in China exhibited a strong stability and a 'club convergence' phenomenon. A regression analysis revealed that the urbanization level and latitude had negative effects on PCEs, while the regional economic development level, average elevation, average slope, and longitude showed positive effects on PCEs. These findings have important implications for the promotion of the low-carbon transition and the effective achievement of the "dual carbon" goal. [ABSTRACT FROM AUTHOR]

Using a novel subnational-level dataset of Thailand, the results show a strong spatial association between poverty and educational attainment in Thailand. Provinces with more educated populations are more likely to have lower poverty incidence. In particular, the findings of this study suggest that attainment of tertiary education can significantly affect poverty reduction, while the negligible effects of primary and secondary education could be due to the disparities in education quality. Therefore, the 9-year compulsory education provided by the Thai government might no longer be enough to reduce poverty, and tertiary education might play a more critical role in poverty alleviation. [ABSTRACT FROM AUTHOR]

Brazilian agriculture is characterized by the prevalence of small farms and regions with a high degree of rurality and dominance of the agricultural sector in the economy. These two characteristics affect the diversity of agricultural production in the country. Specifically, the article aims to analyze the effects of size farm and farmland use on agricultural diversification and the effects of demand and technology adopted by farmers. The database encompasses 4298 Brazilian municipalities from 1996 to 2017 (the last three agricultural censuses). Empirically, we consider spillover effects by estimating spatial models at the municipal level using panel data, highlighting the importance of location and neighborhood. The study's findings indicate a tendency toward local concentration of agricultural production in the country, despite the balance between municipalities with diversified and concentrated production. The results showed a significant effect of small farms and the municipalities' rurality degree on the agricultural output diversification. The study provides insights into the discussion on measures to strengthen support for small properties and regions that diversify crops to ensure economic efficiency and food security. [ABSTRACT FROM AUTHOR]

Determining bike-sharing usage patterns and their explanatory factors on demand is essential for the effective and efficient operation of bike-sharing systems (BSSs). Most BSSs provide different passes that vary with the period of use. However, studies investigating the differences in usage patterns are rare compared to studies conducted at the system level, even though explanatory factors depending on the type of pass may cause different characteristics in terms of usage patterns. This study explores the differences in the usage patterns of BSSs and the impact of explanatory factors on the demand depending on the type of pass. Various machine learning techniques, including clustering, regression, and classification, are used, in addition to basic statistical analysis. As observed, long-term season passes of over six months are mainly used for transportation (especially commuting), whereas one-day or short-term season passes seem to be used more for leisure than for other purposes. Furthermore, differences in the purpose of bike rentals seem to cause differences in usage patterns and variations in demand over time and space. This study improves ther understanding of the usage patterns that appear differently for each pass type, and provides insights into the efficient operation of BSSs in urban areas. [ABSTRACT FROM AUTHOR]

This study examines the impact of spatial characteristics on gendered retail consumption in Seoul, South Korea, providing empirical evidence for gender-sensitive urban planning. Gender-sensitive urban planning integrates gender perspectives into all stages of urban development, aiming to address the diverse needs and experiences of all genders spatially. While existing research has predominantly focused on gender differences in labor participation, this study shifts the focus to retail consumption, which is a critical aspect of daily life. Our research analyzes the spatial attributes of urban spaces at the neighborhood scale and their influence on aggregated retail consumption by gender. The aggregated retail sales by census output area (jipgyegu) represent the aggregated retail consumption. Utilizing spatial regression methods, this study identifies significant spatial autocorrelations and clustering patterns in retail sales data. The findings reveal that traditional markets, less-developed commercial areas, and specific retail sector (retailing, medical, and educational services) densities positively impact SMW (subtraction of men's retail sales from women's retail sales), while city center areas, developed commercial districts, special tourism zones, and specific retail sector (restaurants and entertainment) densities have negative impacts. [ABSTRACT FROM AUTHOR]

This study examined how two different types of food environments—food deserts and food swamps—were associated with geographic disparities in obesity and diabetes in North Carolina using geographic information systems and spatial regression. To better identify the association between food swamps and health disparities, this study incorporated socioeconomic dimensions into the food swamp measure and considered dollar stores and gas stations with convenience stores as unhealthy food retailers, which were often overlooked in previous studies. It found that food deserts were concentrated in eastern North Carolina (ENC) and western North Carolina and food swamps in ENC, while obesity and diabetes hot spots were clustered in ENC. The results indicated that obesity and diabetes remained associated with food swamps even after spatial autocorrelation was controlled for, but the associations with food deserts—statistically significant in a nonspatial regression—became no longer significant when food swamps and spatial autocorrelation were controlled for. These results demonstrate that a food swamp might better explain health disparities in North Carolina than a food desert. Identifying ENC as a region containing both food deserts and food swamps illustrates the need for state and local governments to focus their efforts in this region to mitigate food insecurity and health disparities. [ABSTRACT FROM AUTHOR]

A major driving force behind China's low-carbon, environmentally friendly development of agriculture and the accomplishment of the "dual-carbon" goal is the digital transformation of rural areas. In this study, on the basis of clarifying the degree of rural digitization and agricultural carbon emissions efficiency in China from 2010 to 2021, the degree of coupled coordination and the spatiotemporal pattern characteristics between the two are examined using the coupled coordination model. Then, the influencing factors are analyzed in depth using the spatial Durbin model. Our findings reveal that, first, in terms of the degree of rural digitization, the index increases overall and the spatial imbalance is obvious, with a spatial distribution pattern of "high in the east and low in the west". Regarding the efficiency of agricultural carbon emissions, there is an overall "N-shaped" change, which is mainly influenced by technological progress in agricultural production, and the regional annual averages are, in descending order, the Western, Eastern, Northeastern, and Central regions, with obvious regional differences. Second, the coupling coordination index shows a fluctuating upward trend, from "extreme disorder" to "high-level coordination". Furthermore, there are obvious regional characteristics. The regional growth rates are, in descending order, the Western, Eastern, Central, and Northeastern regions. Third, coupling coordination is jointly influenced by a variety of factors, including government input, educational level, industrial structure, energy use, urbanization rate, living standards, driving temporal patterns, and regional differences. This study not only helps to clarify the relationship between the two, offering a reference for the realization of the "dual-carbon" goal, but also broadens the concepts of the low-carbon and environmentally friendly development of agriculture. [ABSTRACT FROM AUTHOR]

Globally, natural habitats have suffered tremendous damage from human activities, a phenomenon that is increasingly evident in basin regions. The management of natural habitats in basin regions is dependent on understanding of the various impacts of human activities on these ecosystems. Despite the various studies that have been conducted on the effects of human activities on habitats in basin regions, there is still a lot of doubt regarding the impact of these activities on the quality of basin ecosystems. To fill this gap, this study employs a series of spatial analysis methods and logistic regression modeling to delve into the spatial and temporal patterns of human activities and habitat quality in the Yangtze River Basin (YRB) as well as the differences in the impacts of human activities on habitat quality in the sub-basins of the YRB. The findings indicate a 0.408% decline in the overall environmental quality of the YRB area from 2000 to 2020, accompanied by a 15.396% surge in human activities. Notably, the southeastern Qilian Mountains and the mountainous regions in the northwestern sector of the Sichuan Basin emerge as pivotal areas for habitat quality restoration. Conversely, the southwestern Qilian Mountains and the urban clusters in the Yangtze River Delta (YRD) face significant habitat quality deterioration. Spatial regression analyses reveal a noteworthy trend: the burgeoning human activities in the Yangtze River region pose a substantial threat to habitat recovery efforts. Further differential analyses focusing on the upper, middle, and lower basin segments underscore that human activities exert the most pronounced impact on habitat quality within the lower basin region, while the upper basin experiences the least influence. The implications of this study are manifold. It furnishes valuable policy insights for the comprehensive management and targeted preservation of habitats across the YRB. By delineating areas of habitat restoration and degradation and highlighting the differential impacts of human activities across basin segments, this research lays a solid foundation for informed decision making in habitat conservation and ecosystem management within the YRB. [ABSTRACT FROM AUTHOR]

The recent COVID-19 pandemic has provided a renewed impetus for empirical research on slow and active modes of transportation, specifically bicycling and walking. Changes in modal choice appear to be sensitive to the actual quality of the environment, the attractive land use and built environment conditions, and the ultimate destination choice. This study examines and models the influence of cyclists’ health concerns during the pandemic on their spatial destination and route choices. Using a large real-time dataset on the individual daily mobility of cyclists in the province of Utrecht, the Netherlands, collected through GPS-linked sensors on bikes (VGI, or volunteered geographical information), the analysis employs spatial regression models, Shapley decomposition techniques, and spatial autocorrelation methods to unveil the backgrounds of changes in spatial behavior. The results reveal that the perceived wellbeing benefits of bicycling in green areas during the pandemic have significantly influenced cyclists’ choice behavior, in particular route and destination choice.

Conceptually, environmental sustainability involves maintaining crucial environmental functions while considering both present and future development. However, existing methods for expressing environmental sustainability are mainly derived from a steady state with minimal spatial explicitness. Furthermore, the environmental impact of certain events may exhibit a lag, particularly in basins. Here, we propose a framework that employs a space-time cube to articulate environmental sustainability. This cube can visualize the environment's evolution over time, identify hot and cold spots in space, and concurrently determine underlying influencing factors via spatial regression analysis. Unlike traditional methods, the space-time cube incorporates not only spatial dimensions but also temporal dimensions. We applied this framework to China's upper Han River basin, using the Remote Sensing Ecological Index (RSEI) as an indicator of environmental sustainability. It enabled us to chart the basin's ecological trajectory with spatial and temporal explicitness from 1990 to 2020. Our findings reveal that climate change (represented by temperature and precipitation changes) and human activities (represented by nighttime light) were the main factors driving changes in environmental sustainability from 2000 to 2020 in the basin. Therefore, our proposed spatial-temporal integration framework proves to be an efficient tool in articulating environmental sustainability.

Contemporary development policy concentrates predominantly on reducing noticeable economic differences in a spatial system, and an important role in this respect is played by EU Cohesion Policy. Owing to the considerable scale of financial exposure of Cohesion Policy, the assessment of effectiveness of the implemented measures and their greater reliance on evidence are of major significance. Despite numerous attempts to empirically verify the effects of EU funds spending, the problem remains unresolved, and the results of recent studies lead frequently to ambiguous conclusions.

Responsible consumption and production is the 12th of the seventeen SDGs which is difficult for developing countries to achieve due to high waste production. Indonesia is the second largest producer of food waste in the world. Garbage is solid waste generated from community activities. Population density is an indicator to estimate the amount of waste generated in an area. The choice of West Java Province as the research area is based on the fact that this Province has the second highest population density in Indonesia. This study aimed to determine the predictors/factors that influence waste production in the districts/cities of West Java Province. The data used in this study are total waste as a response variable and GRDP (gross domestic product), total spending per capita, average length of schooling, literacy rate, number of MSMEs (micro, small, and medium enterprises), and several recreational and tourism places, the number of people's markets, and the number of restaurants as predictors. The methods used in this research are spatial autoregressive regression/SAR, spatial Lag-X/SLX, and spatial Durbin/SDM. The results of this study show that the SAR is the best model with the lowest BIC (74.442) and pseudo-R-squared (0.7934). Factors that significantly affect total waste production are literacy levels, the number of MSMEs, the number of traditional markets, and the number of recreational and tourist places.

Objective To investigate the current registration rate of tuberculosis in each county and district of Linyi City, Shandong Province, China and its spatial local relationship with related influencing factors. Methods Related data were collected for the incidence rate of tuberculosis and related influencing factors in 12 counties and districts of Linyi City from January to December 2019. Geoda 4.02 software was used to analyze spatial autocorrelation; GWR 4.09 software was used to construct an ordinary least squares regression (OLS) model and a geographic weighted regression (GWR) model; ArcGIS 10.2 software was used to plot the spatial distribution map of influencing factors based on the GWR model. Results In 2019, there were 3 261 cases of tuberculosis in Linyi City, with an overall incidence rate of 32.39/100 000. There was a significant difference in the incidence rate of tuberculosis between different counties, sexes, and age groups (χ2=71.257-1 532.464, P

The aim of this research is to design a spatial regression model with an inverse distance weighting matrix formed from the crime rates in the provinces of Bali and East Java, based on influencing factors, and to explore the interconnection between crime rates in one district/city with others. This study utilizes crime rate data obtained from the 2023 publications of the Statistics Indonesia (BPS) of Bali and East Java, covering 47 districts/cities. Spatial regression with inverse distance matrix is employed to analyze the factors influencing crime rates in both provinces, including spatial autocorrelation. The research findings indicate interconnection among districts/cities in the two provinces. The Spatial Error Model (SEM) shows that factors such as average length of schooling, population density, gender ratio, and GDP per capita significantly influence crime rates. This model has an AIC value of 525.06 and a pseudo-R2 value of 64.10%. [ABSTRACT FROM AUTHOR]

Exposure to ambient air pollution has serious adverse impacts on human health. Yet air pollution does not affect all individuals in the same way. Existing evidence of environmental inequality in low- and middle-income countries (LMIC) is limited and contradictory, despite 91% of premature death due to air pollution in LMIC. This study aims to estimate the association between community socioeconomic status (CSES) and ambient air pollution in eastern China. The study comprised 19,622 individuals. CSES was measured by income, occupation and education. Air pollution was measured by 4-year-average ambient levels of PM2.5, PM10, NO2, and CO. Spatial autoregressive models and U-test was applied. Subsample analyses were conducted based on participants’ rural/urban location and hukou status. Air pollutant exposure had an inverted U-shaped correlation with CSES. Before (after) the inflection point, increasing CSES by 1% increased(decreased) community exposure to PM2.5 by 0.527% (0.379%), PM10 by 0.460%(0.215%), NO2 by 0.584%(0.288%), and CO by 0.582% (0.382%). All results remained robust in sensitivity analysis. Subsample analysis showed that compared to rural (urban) residents, the increment of air pollution exposure concentration for migrants was 4.042 (4.556) μg/m3 for PM2.5, 5.839 (10.624) μg/m3 for PM10, 3.212 (5.719) μg/m3 for NO2 and 0.205(0.208) mg/m3 for CO. Our study finds moderate SES communities facing the highest level of exposure. Our results aid policymakers to understand the locality-specific patterns of environmental pollution and to design intervention strategies to improve the environment, especially for economically vulnerable groups, such as migrants. [ABSTRACT FROM AUTHOR]

An early industrial town's spatial segregation is studied using empirical data concerning the Russian population of the town of Vyborg. Several hypotheses for explaining segregation are considered using spatial analysis. The spatial data are derived from historical maps and demographic data from various tax records. Socioeconomic segregation is studied as a possible cause of ethnic segregation. The main drivers of spatial segregation were the explicit policies of segregation enforced by both the Russian military administration and the town's civilian administration. While the effects of segregation gradually diminished due to social diffusion, the impact of policy decisions driving segregation in the 18th and early 19th centuries was still visible in the population's later 19th-century segregation. Yet neither the different preferences of Russians and others nor the income differences between areas explains the distribution of Russians. Segregation based on the membership of a guild was insignificant, with a few exceptions. Other factors such as discrimination, prejudice, and differences in housing market information probably contributed to segregation, but they cannot be studied with the data used. [ABSTRACT FROM AUTHOR]

Background and Aims: Airborne diseases due to climate change pose significant public health challenges in Bangladesh. Little was known about the spatio‐temporal pattern of airborne diseases at the district level in the country. Therefore, this study aimed to investigate the spatio‐temporal pattern and associated meteorological factors of airborne diseases in Bangladesh using exploratory analysis and spatial regression models. Methods: This study used district‐level reported cases of airborne diseases (meningococcal, measles, mumps, influenza, tuberculosis, and encephalitis) and meteorological data (temperature, relative humidity, wind speed, and precipitation) from 2017 to 2020. Geospatial mapping and spatial error regression models were utilized to analyze the data. Results: From 2017 to 2020, a total of 315 meningococcal, 5159 measles, 1341 mumps, 346 influenza, 4664 tuberculosis, and 229 encephalitis cases were reported in Bangladesh. Among airborne diseases, measles demonstrated the highest prevalence, featuring a higher incidence rate in the coastal Bangladeshi districts of Lakshmipur, Patuakhali, and Cox's Bazar, as well as in Maulvibazar and Bandarban districts from 2017 to 2020. In contrast, tuberculosis (TB) emerged as the second most prevalent disease, with a higher incidence rate observed in districts such as Khagrachhari, Rajshahi, Tangail, Bogra, and Sherpur. The spatial error regression model revealed that among climate variables, mean (β = 9.56, standard error [SE]: 3.48) and maximum temperature (β = 1.19, SE: 0.40) were significant risk factors for airborne diseases in Bangladesh. Maximum temperature positively influenced measles (β = 2.74, SE: 1.39), whereas mean temperature positively influenced both meningococcal (β = 5.57, SE: 2.50) and mumps (β = 11.99, SE: 3.13) diseases. Conclusion: The findings from the study provide insights for planning early warning, prevention, and control strategies to combat airborne diseases in Bangladesh and similar endemic countries. Preventive measures and enhanced monitoring should be taken in some high‐risk districts for airborne diseases in the country. [ABSTRACT FROM AUTHOR]

The spatial Durbin model (SDM) is a spatial regression model which shows the existence of spatial dependency on the response variable and predictor variables. However, SDM modelling may sometimes involve problems associated with e.g. the existence of spatial outliers. One way to overcome outliers in the SDM model is to use robust regression in the form of the robust spatial Durbin model (RSDM). This study aims to estimate the parameters of RSDM based on data on tuberculosis (TB) cases recorded in 2020 in the South Sulawesi Province in Indonesia and to identify the factors that affect the number of TB cases in the region. The MM-Estimator robust regression estimation method was used. It is a combination of a method involving a high breakdown value for the S-estimator and a high efficiency of the M-estimator. The results of the analysis show that RSDM can overcome outliers in spatial regression models. This is reflected in the value of the mean square error (MSE) of the RSDM, which is 6,461.734, i.e. smaller than the value of the SDM model, and the adjusted R^2 value of 99.52%, which is greater than that of the SDM model. The factors that influence the number of TB cases in the South Sulawesi Province are population density, the percentage of households leading a healthy lifestyle, the percentage of residents with Bacillus Calmette-Guérin (BCG) immunisation, and the percentage of those suffering from malnutrition. [ABSTRACT FROM AUTHOR]

This paper presents a modelling framework which can detect the simultaneous presence of two different types of spatial process. The first is the variation from a global mean resulting from a geographical unit's 'vertical' position within a nested hierarchical structure such as the county and region where it is situated. The second is the variation at the smaller scale of individual units due to the 'horizontal' influence of nearby locations. The former is captured using a multi-level modelling structure while the latter is accounted for by an autoregressive component at the lowest level of the hierarchy. Such a model not only estimates spatially-varying parameters according to geographical scale, but also the relative contribution of each process to the overall spatial variation. As a demonstration, the study considers the association of a selection of socio-economic attributes with voting behaviour in the 2019 UK general election. It finds evidence of the presence of both types of spatial effects, and describes how they suggest different associations between census profile and voting behaviour in different parts of England and Wales. [ABSTRACT FROM AUTHOR]

Approaching by spatial regression method, this study examines the impact of fiscal decentralisation on economic growth in 63 provinces/cities of Vietnam in the period 2010–2020. Findings suggest that, in general, revenue decentralisation and expenditure decentralisation not only have a positive impact on the economic growth of provinces/cities but also have spillover effects on other locations in improving GRDP per capita. The above findings provide some important policy implications for improving fiscal decentralisation to promote economic growth in Vietnam. [ABSTRACT FROM AUTHOR]

Energy intensity convergence can be used to assess the effectiveness of policies in reducing energy intensity. This study analyzes the energy intensity convergence in Indonesia based on panel data of 33 provinces from 2010 to 2018. Spatial econometrics techniques are used in the estimation of beta convergence to measure the spatial dependence of energy intensity. Empirical results show that there is evidence of both absolute and conditional beta convergences with no evidence of sigma convergence. From the results, this study found that other variables, such as provincial income, the role of manufacturing industries, the role of international trade, FDI, and population density, might encourage energy intensity convergence. From the estimation results, several policy recommendations are derived to increase energy efficiency: First, using a more efficient industrial technology. Second, attracting foreign investment to non-industrial sectors. Third, developing exports from sectors that use less energy to increase energy efficiency. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemich as significantly affectedva nous count ries worl dwide, including Indonesia. This study specifically examines the spatial distribution pattern of COVID-19 cases among sub-districts in DKI Jakarta and its neighboring areas. The study investigates the impact of spatial characteristics such as building density, population density, road network connectivity, and accessibility, as well as infrastructure completeness. A spatial regression model was employed to analyze the influence and pattern of COVID-19 case distribution among sub-districts. Spatial modeling indicates that geo graphic 1ocation has an effect on the data, often referred to as the autocorrelation effect. Moran's Index was used to test the relationship between district locations and the number and growth rate of cases. The study findings reveal a positive spatial autocorrelation in the growth rate pattern of COVID-19 cases among sub-districts and dusters in DKI Jakarta and its surrounding areas. The spatial regression model, specifically the Spatial Autoregressive Model (SAR), identifies road connectivity, number of health centers, building density, and population density as spatial variables that significantly influence the rate of COVID-19 cases. [ABSTRACT FROM AUTHOR]

Objective: Diabetes mellitus is the leading cause of death worldwide, and multiple risk factors associated with diabetes mortality. Methods: Employing spatial statistics, we characterized the spatial distribution and patterns of diabetes mortality, and revealed the spatial relationship between diabetes mortality and 11 socioeconomic and environmental risk factors at the country level, from 1990 to 2019. Results: Globally, significantly high rates of diabetes mortality were primarily clustered in countries with limited land areas or located on islands, such as Fiji, Kiribati, Eswatini, and Trinidad and Tobago. Countries with weaker economic independence are more likely to have higher diabetes mortality rates. In addition, the impact of socioeconomic and environmental factors was significant at the country level, involving health expenditure, number of physicians, household and ambient air pollution, smoking, and alcohol consumption. Notably, the spatial relationship between diabetes mortality and ambient air pollution, as well as alcohol consumption, showed negative correlations. Countries with high diabetes mortality rates generally had lower levels of ambient air pollution and alcohol consumption. Conclusion: The study highlights the spatial clustering of diabetes mortality and its substantial variation. While many risk factors can influence diabetes mortality, it’s also essential to consider the level of these factors at the country level. Tailoring appropriate interventions based on specific national circumstances holds the potential to more effectively mitigate the burden of diabetes mortality. [ABSTRACT FROM AUTHOR]

The growing global interest in developing environment-friendly and sustainable transportation solutions is motivating mass adoption of Electric Vehicles (EVs). This increasing EV penetration is anticipated to result in a growing electricity demand to address the EV charging requirements. Therefore, precise demand modeling is essential to enable optimal sizing of the electricity generation and distribution networks as well as optimal placement of the EV charging infrastructure. Furthermore, microscopic modeling of EV traffic patterns and trip-wise energy requirements is essential to enable effective charging coordination and demand distribution for on-the-move EVs. However, microscopic EV demand modeling is typically hindered by the scarcity of open-access data that integrates EV charging and driving patterns. Accordingly, this work proposes a methodology for microscopic modeling of the trip-wise electricity demand of mobile EVs in the spatial and temporal domains, using both multiple linear regression and spatial autoregressive models. Secondary open-access data is extracted, wrangled, and pre-processed from a number of data sources to test and validate the proposed methodology on a case study of Dubai - UAE, acknowledging the growing EV adoption rates in the city. The proposed models are benchmarked against baseline models to confirm their superior performance.

Urban agglomerations (UAs), which play a significant role in socioeconomic development and urbanization, are confronted with mounting ecological stress and a profound global imbalance in ecosystem services (ES). Understanding the conflict and coordination of knowledge about ES supply and demand (ESSD) can facilitate more efficacious guidance on the ecological sustainable development of UAs. Nevertheless, the characteristics of the conflict and coordination relationship between ESSD in Chinese UAs remain unclear, and further investigation into the interactive coercive relationship between ESSD is warranted. Consequently, we employed spatial regression and coupled coordination models to elucidate the conflict and coordination relationship between ESSD, utilizing multi-source data on Chinese UAs from 2000 to 2020. We found that ES supply in the UAs decreased, while ES demand increased. Furthermore, the coupling coordination degree between ESSD demonstrated an increase trend. The overall coupling coordination degrees between ESSD in UAs were 0.260, 0.285, and 0.311 in 2000, 2010, and 2020, respectively. The central UAs were identified as stress areas, whereas the peripheral areas were classified as non-stress areas. This study offered valuable insights into the interactive relationship between ESSD in UAs and provided a basis for formulating differentiated policies for the sustainable development of ecosystems and human activities.

Short-term house rentals constitute a growing component of tourist accommodation in several countries and the determination of factors affecting rents is an important consideration in relevant studies. Short-term rentals have shown increasing trends in the city of Athens, Greece; however, this activity has not been adequately studied. In this paper, spatial data of Airbnb rentals in Athens are analyzed in order to indicate the factors which are important for the spatial variation of rents. Factors such as property capacity, host attributes and review characteristics are considered. In addition, several locational attributes are examined. Regression analysis techniques are used to predict the cost per night, according to various explanatory factors, while the results of two models are presented: ordinary least squares (OLS) and geographically weighted regression (GWR). The results of the OLS model indicate several factors determining the rent, including capacity and host characteristics, as well as locational attributes. The GWR model produces more accurate results with a smaller number of independent variables. For the residuals analysis several additional amenities were examined that resulted in a small impact on rents. The unexplained spatial variation of rents may be attributed to neighborhood characteristics, socioeconomic conditions and special characteristics of the rentals. [ABSTRACT FROM AUTHOR]

Macro econometrics has been recently affected by the so-called 'Google Econometrics'. Comparatively less attention has been paid to the subject by the regional and spatial sciences where the Big Data revolution is challenging the conventional econometric techniques with the availability of a variety of non- traditionally collected data (such as, e. g., crowdsourcing, web scraping, etc) which are almost invariably geo-coded. However, these unconventionally collected data represent only what in statistics is known as a "convenience sample" that does not allow any sound probabilistic inference. This paper aims at making aware researchers of the consequence of the unwise use of such data in the applied work and to propose a technique to minimize such the negative effects in the estimation of spatial regression. The method consists of manipulating the data prior their use in an inferential context. [ABSTRACT FROM AUTHOR]

This study set out to identify and analyze the factors that influence the formation of rental prices for residential apartments in the city of Belém. The approach adopted was based on the hedonic price theory, which considers that the rental price of an apartment reflects the implicit prices of its attributes—structural, locational, and neighborhood quality. The sample used consisted of 259 observations, corresponding to the rental advertisements of the representative apartments in each building. The ordinary least squares (OLS), spatial lag model (SLM), and geographically weighted regression (GWR) techniques were used in the statistical analysis in this study. The results of the OLS model showed statistical significance between the attributes analyzed and the rental price of the apartments. In turn, the SLM indicated that the structural attributes have an impact on the rental prices of neighboring apartments, configuring a contagion effect in the real estate market. The GWR model showed that there was no spatial heterogeneity in the effects of the determinants on apartment rental prices throughout the sample. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Planning Education & Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The benefits of the urban tree and tree canopy (UTC) are increasingly crucial in addressing urban sustainability. Yet, increasingly evident from earlier research is the distributional inequities of UTC and active efforts to expand tree plantings. Less is known about the dynamics of UTC loss over time and location. This study aims to understand the dynamics of UTC change, especially canopy loss, and to investigate the drivers of the loss. This study draws on a high–resolution dataset of an urban canopy in Portland, Oregon, USA, assessing changes in UTC from 2014 to 2020. By integrating demographic, biophysical, and policy data with UTC information, we use a spatial autoregressive model to identify the drivers of UTC loss. The results reveal an unexpected spatial distribution of UTC change: less gain in the neighborhoods with the least UTC, and greater loss in the neighborhoods with moderate UTC. This study identifies four primary drivers of UTC loss: socioeconomic characteristics, urban form, activities on trees, and residential status. Factors such as population density, race, and income have an impact on canopy loss, as well as the building footprint and the number of multifamily housing units; residential statuses, such as the proportion of owner-occupied housing and residential stability, impact canopy loss. [ABSTRACT FROM AUTHOR]

Urban night vitality is a manifestation of a city's diverse life and economic prosperity. However, few existing studies pay attention to urban night vitality. Furthermore, large spatial scale research of urban night vitality remains scarce. To fill these gaps, this empirical study on the urban night vitality of central Shanghai is based on fine-grained mobile phone signaling data and other multisource data. The objective of this study is twofold. First, mobile phone signaling data (with refined spatiotemporal resolutions) is applied to measure urban night vitality on a city-level spatial scale. Second, the spatial lag model is utilized to identify factors that influence urban night vitality. The results indicate that urban vitality presents a stronger commercially driven spatial agglomeration pattern during the night, and the urban night vitality of young people has a more concentrated spatial pattern than that of middle-aged and older people. Furthermore, the spatial agglomeration pattern of urban night vitality diminishes as time passes. The results of the spatial lag model reveal that night businesses and mixed land use are significantly and positively related to urban night vitality. Specifically, bars and consumption levels of stores have the highest relative significance, followed by mixed land use. These findings illuminate the understanding of the spatiotemporal characteristics of urban night vitality, which has universal significance. [ABSTRACT FROM AUTHOR]

Dengue is a viral infection transmitted by the Aedes aegypti mosquito. This study aimed to assess the distribution of cases and deaths from dengue and severe dengue, and its relationship with social vulnerability in Belo Horizonte, State of Minas Gerais, Brazil, from 2010 to 2018. The incidence and lethality rates of dengue and their relationship with sex, age, education, skin color, and social vulnerability were studied using chi-square tests, Ordinary Least Squares (OLS), and Geographically Weighted Regression (GWR) analyses. The number of cases of dengue in Belo Horizonte during the study period was 324,044 dengue cases, with 1,334 cases of severe dengue and 88 deaths. During the past few decades, the incidence rate of both dengue and severe cases varied, with an average incidence rate of respectively 1515.5 and 6.2/100,000 inhabitants. The increase in dengue cases was directly related to areas with higher social vulnerability areas and more working-age people. Also, the disease is more severe in people self-declared as black, elderly, and male. The findings of this study might provide relevant information for health services in the organization of control and prevention policies for this problem, emphasizing the most vulnerable urban areas and categories. [ABSTRACT FROM AUTHOR]

Arctic-boreal lakes emit methane (CH4), a powerful greenhouse gas. Recent studies suggest ebullition might be a dominant methane emission pathway in lakes but its drivers are poorly understood. Various predictors of lake methane ebullition have been proposed but are challenging to evaluate owing to different geographical characteristics, field locations, and sample densities. Here we compare large geospatial data sets of lake area, lake perimeter, permafrost, land cover, temperature, soil organic carbon content, depth, and greenness with remotely sensed methane ebullition estimates for 5,143 Alaskan lakes. We find that lake wetland fraction (LWF), a measure of lake wetland and littoral zone area, is a leading predictor of methane ebullition (adj. R2 = 0.211), followed by lake surface area (adj. R2 = 0.201). LWF is inversely correlated with lake area, thus higher wetland fraction in smaller lakes might explain a commonly cited inverse relationship between lake area and methane ebullition. Lake perimeter (adj. R2 = 0.176) and temperature (adj. R2 = 0.157) are moderate predictors of lake ebullition, and soil organic carbon content, permafrost, lake depth, and greenness are weak predictors. The low adjusted R2 values are typical and informative for methane attribution studies. Our leading model, which uses lake area, temperature, and LWF (adj. R2 = 0.325, n = 5,130) performs slightly better than leading multivariate models from similar studies. Our results suggest landscape-scale geospatial analyses can complement smaller field studies, for attributing Arctic-boreal lake methane emissions to readily available environmental variables. [ABSTRACT FROM AUTHOR]

\nKey policy highlightsBy deploying remotely sensed data together with spatial statistical modeling, we use regression modeling to investigate the relationship between the density of the built environment and two types of crime. We show how the Global Human Settlement Layer (GHSL) data set, which is a measure of building density generated from Sentinel 2A satellite imagery, can be used to create different indexes to describe the built environment for the purpose of analyzing crime patterns for indoor crimes (residential burglary) and open space crimes (street theft). Analysis is at neighborhood level for Stockholm, Sweden. Modeling is then extended to incorporate six planning areas which represent different neighborhood types within the city. Modeling is further extended by adding selected social, economic, demographic and land use variables that have been found to be significant in explaining spatial variation in the two crime categories in Stockholm. Significant associations between the GHSL-based indexes and the two crime rates are observed but results indicate that allowance for differences in neighborhood type should be recognized. Average income and transport hubs were also significant variables in the investigated crime categories. The article provides a practical demonstration and assessment of the use of high-resolution satellite data to examine the association between urban density and two common types of crime and offers reflections about the use of satellite image data in crime analysis. Data Quality and Standardization: Encourage the testing and standardization of methods and measures using remote sensing data, in particular satellite imagery, as a basis for research on urban crime and practical use in urban safety interventions.Open Access to Data: Push for policies that promote open access to remote sensing data in research and practice, especially satellite imagery. This is of particular importance in resource-poor urban environments, especially in countries of the Global South where official statistics are not available or are not regularly updated. Open data, such as the one used in this study, provides evidence, fosters innovation, and is the basis for sustainable urban governance.Evidence-Based Resource Allocation: Highlight the importance of using remote sensing data and methodologies to inform resource allocation decisions in law enforcement agencies. Advocate for policies that prioritize data-driven strategies to address crime concentrations and allocate resources efficiently.Data Quality and Standardization: Encourage the testing and standardization of methods and measures using remote sensing data, in particular satellite imagery, as a basis for research on urban crime and practical use in urban safety interventions.Open Access to Data: Push for policies that promote open access to remote sensing data in research and practice, especially satellite imagery. This is of particular importance in resource-poor urban environments, especially in countries of the Global South where official statistics are not available or are not regularly updated. Open data, such as the one used in this study, provides evidence, fosters innovation, and is the basis for sustainable urban governance.Evidence-Based Resource Allocation: Highlight the importance of using remote sensing data and methodologies to inform resource allocation decisions in law enforcement agencies. Advocate for policies that prioritize data-driven strategies to address crime concentrations and allocate resources efficiently. [ABSTRACT FROM AUTHOR]