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Social and economic factors relate to the prevention and control of infectious diseases. The purpose of this paper was to assess the distribution of COVID-19 morbidity rate in association with social and economic factors and discuss the implications for urban development that help to control infectious diseases. This study was a cross-sectional study. In this study, social and economic factors were classified into three dimensions: built environment, economic activities, and public service status. The method applied in this study was the spatial regression analysis. In the 13 districts in Wuhan, the spatial regression analysis was applied. The results showed that: 1) increasing population density, construction land area proportion, value-added of tertiary industry per unit of land area, total retail sales of consumer goods per unit of land area, public green space density, aged population density were associated with an increased COVID-19 morbidity rate due to the positive characteristics of estimated coefficients of these variables. 2) increasing average building scale, GDP per unit of land area, and hospital density were associated with a decreased COVID-19 morbidity rate due to the negative characteristics of estimated coefficients of these variables. It was concluded that it is possible to control infectious diseases, such as COVID-19, by adjusting social and economic factors. We should guide urban development to improve human health.

Urban Heat Islands (UHIs), Land Surface Temperature (LST), and Land Use Land Cover (LULC) changes are critical environmental concerns that require continuous monitoring and assessment, especially in cities within arid and semi-arid (ASA) climates. Despite the abundance of research in tropical, Mediterranean, and cold climates, there is a significant knowledge gap for cities in the Middle East with ASA climates. This study aimed to examine the effects of LULC change, population, and wind speed on LST in the Mashhad Metropolis, a city with an ASA climate, over a 30-year period. The research underscores the importance of environmental monitoring and assessment in understanding and mitigating the impacts of urbanization and climate change. Our research combines spatial regression models, multi-scale and fine-scale analyses, seasonal and city outskirts considerations, and long-term change assessments. We used Landsat satellite imagery, a crucial tool for environmental monitoring, to identify LULC changes and their impact on LST at three scales. The relationships were analyzed using Ordinary Least Squares (OLS) and Spatial Error Model (SEM) regressions, demonstrating the value of these techniques in environmental assessment. Our findings highlight the role of environmental factors in shaping LST. A decrease in vegetation and instability of water bodies significantly increased LST over the study period. Bare lands and rocky terrains had the most substantial effect on LST. At the same time, built-up areas resulted in Urban Cooling Islands (UCIs) due to their lower temperatures compared to surrounding bare lands. The Normalized Difference Vegetation Index (NDVI) and Dry Bare-Soil Index (DBSI) were the most effective indices impacting LST in ASA regions, and the 30×30 m 2 micro-scale provides more precise results in regression models, underscoring their importance in environmental monitoring. Our study provided a comprehensive understanding of the relationship between LULC changes and LST in an ASA environment, contributing significantly to the literature on environmental change in arid regions and the methodologies for monitoring such changes. Future research should aim to validate and expand additional LST-affecting factors and test our approach and findings in other ASA regions, considering the unique characteristics of these areas and the importance of tailored environmental monitoring and assessment approaches.

(1) Background: Evidence regarding scarlet fever and its relationship with meteorological, including air pollution factors, is not very available. This study aimed to examine the relationship between ambient air pollutants and meteorological factors with scarlet fever occurrence in Beijing, China. (2) Methods: A retrospective ecological study was carried out to distinguish the epidemic characteristics of scarlet fever incidence in Beijing districts from 2013 to 2014. Daily incidence and corresponding air pollutant and meteorological data were used to develop the model. Global Moran’s I statistic and Anselin’s local Moran’s I (LISA) were applied to detect the spatial autocorrelation (spatial dependency) and clusters of scarlet fever incidence. The spatial lag model (SLM) and spatial error model (SEM) including ordinary least squares (OLS) models were then applied to probe the association between scarlet fever incidence and meteorological including air pollution factors. (3) Results: Among the 5491 cases, more than half (62%) were male, and more than one-third (37.8%) were female, with the annual average incidence rate 14.64 per 100,000 population. Spatial autocorrelation analysis exhibited the existence of spatial dependence; therefore, we applied spatial regression models. After comparing the values of R-square, log-likelihood and the Akaike information criterion (AIC) among the three models, the OLS model (R2 = 0.0741, log likelihood = −1819.69, AIC = 3665.38), SLM (R2 = 0.0786, log likelihood = −1819.04, AIC = 3665.08) and SEM (R2 = 0.0743, log likelihood = −1819.67, AIC = 3665.36), identified that the spatial lag model (SLM) was best for model fit for the regression model. There was a positive significant association between nitrogen oxide (p = 0.027), rainfall (p = 0.036) and sunshine hour (p = 0.048), while the relative humidity (p = 0.034) had an adverse association with scarlet fever incidence in SLM. (4) Conclusions: Our findings indicated that meteorological, as well as air pollutant factors may increase the incidence of scarlet fever; these findings may help to guide scarlet fever control programs and targeting the intervention.

Disasters have substantial consequences for population mental health. We used Twitter to (1) extract negative emotions indicating discomfort in New York City (NYC) before, during, and after Superstorm Sandy in 2012. We further aimed to (2) identify whether pre- or peri-disaster discomfort were associated with peri- or post-disaster discomfort, respectively, and to (3) assess geographic variation in discomfort across NYC census tracts over time. Our sample consisted of 1,018,140 geo-located tweets that were analyzed with an advanced sentiment analysis called ”Extracting the Meaning Of Terse Information in a Visualization of Emotion” (EMOTIVE). We calculated discomfort rates for 2137 NYC census tracts, applied spatial regimes regression to find associations of discomfort, and used Moran’s I for spatial cluster detection across NYC boroughs over time. We found increased discomfort, that is, bundled negative emotions after the storm as compared to during the storm. Furthermore, pre- and peri-disaster discomfort was positively associated with post-disaster discomfort; however, this association was different across boroughs, with significant associations only in Manhattan, the Bronx, and Queens. In addition, rates were most prominently spatially clustered in Staten Island lasting pre- to post-disaster. This is the first study that determined significant associations of negative emotional responses found in social media posts over space and time in the context of a natural disaster, which may guide us in identifying those areas and populations mostly in need for care.

Gunshot detection technology (GDT) has been increasingly adopted by law enforcement agencies to tackle the problem of underreporting of crime via 911 calls for service, which undoubtedly affects the quality of crime mapping and spatial analysis. This article investigates the spatial and temporal patterns of gun violence by comparing data collected from GDT and 911 calls in Louisville, Kentucky. We applied hot spot mapping, near repeat diagnosis, and spatial regression approaches to the analysis of gunshot incidents and their associated neighborhood characteristics. We observed significant discrepancies between GDT data and 911 calls for service, which indicate possible underreporting of firearm discharge in 911 call data. The near repeat analysis suggests an increased risk of gunshots in nearby locations following an initial event. Results of spatial regression models validate the hypothesis of spatial dependence in frequencies of gunshot incidents and crime underreporting across neighborhoods in the study area, both of which are positively associated with proportions of African American residents, who are less likely to report a gunshot. This article adds to a growing body of research on GDT and its benefits for law enforcement activity. Findings from this research not only provide new insights into the spatiotemporal aspects of gun violence in urban areas but also shed light on the issue of underreporting of gun violence.

Regional scientists frequently work with regression relationships involving sample data that is spatial in nature. For example, hedonic house-price regressions relate selling prices of houses located at points in space to characteristics of the homes as well as neighborhood characteristics. Migration, commodity, and transportation flow models relate the size of flows between origin and destination regions to the distance between origin and destination as well as characteristics of both origin and destination regions. Regional growth regressions relate growth rates of a region to past period own- and nearby-region resource inputs used in production. Spatial data typically violates the assumption that each observation is independent of other observations made by ordinary regression methods. This has econometric implications for the quality of estimates and inferences drawn from nonspatial regression models. Alternative methods for producing point estimates and drawing inferences for relationships involving spatial data samples comprise the broad topic covered by spatial econometrics. Like any subdiscipline, spatial econometrics has its quirks, many of which reflect influential past literature that has gained attention in both theoretical and applied work. This article asks the question: “What should regional scientists who wish to use regression relationships involving spatial data in an effort to shed light on questions of interest in regional science know about spatial econometric methods?”

Because eigenvector spatial filtering (ESF) provides a relatively simple and successful method to account for spatial autocorrelation in regression, increasingly it has been adopted in various fields. Although ESF can be easily implemented with a stepwise procedure, such as traditional stepwise regression, its computational efficiency can be further improved. Two major computational components in ESF are extracting eigenvectors and identifying a subset of these eigenvectors. This paper focuses on how a subset of eigenvectors can be efficiently and effectively identified. A simulation experiment summarized in this paper shows that, with a well-prepared candidate eigenvector set, ESF can effectively account for spatial autocorrelation and achieve computational efficiency. This paper further proposes a nonlinear equation for constructing an ideal candidate eigenvector set based on the results of the simulation experiment.

Background Obesity rates are recognized to be at epidemic levels throughout much of the world, posing significant threats to both the health and financial security of many nations. The causes of obesity can vary but are often complex and multifactorial, and while many contributing factors can be targeted for intervention, an understanding of where these interventions are needed is necessary in order to implement effective policy. This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants, especially through the use of geographically weighted regression (GWR). Method This paper provides a critical review of previous GWR models of obesogenic processes and then presents a novel application of multiscale (M)GWR using the Phoenix metropolitan area as a case study. Results Though the MGWR model consumes more degrees of freedom than OLS, it consumes far fewer degrees of freedom than GWR, ultimately resulting in a more nuanced analysis that can incorporate spatial context but does not force every relationship to become local a priori . In addition, MGWR yields a lower AIC and AICc value than GWR and is also less prone to issues of multicollinearity. Consequently, MGWR is able to improve our understanding of the factors that influence obesity rates by providing determinant-specific spatial contexts. Conclusion The results show that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants compared to both GWR and ordinary least squares.

Social vulnerability models are becoming increasingly important for hazard mitigation and recovery planning, but it remains unclear how well they explain disaster outcomes. Most studies using indicators and indexes employ them to either describe vulnerability patterns or compare newly devised measures to existing ones. The focus of this article is construct validation, in which we investigate the empirical validity of a range of models of social vulnerability using outcomes from Hurricane Sandy. Using spatial regression, relative measures of assistance applicants, affected renters, housing damage, and property loss were regressed on four social vulnerability models and their constituent pillars while controlling for flood exposure. The indexes best explained housing assistance applicants, whereas they poorly explained property loss. At the pillar level, themes related to access and functional needs, age, transportation, and housing were the most explanatory. Overall, social vulnerability models with weighted and profile configurations demonstrated higher construct validity than the prevailing social vulnerability indexes. The findings highlight the need to expand the number and breadth of empirical validation studies to better understand relationships among social vulnerability models and disaster outcomes.

With the deterioration of environmental quality caused by fossil energy use, the research on energy internet and energy misallocation is of critical relevance to achieve low-carbon sustainable development. However, we find that the relevant research that analyzes energy internet and energy misallocation on carbon emissions under the same framework is ignored. For this purpose, the generalized method of moments (GMM), panel threshold model, and spatial analysis (deviation ellipse, hotspot analysis, and geographically and temporally weighted regression (GTWR)) model were applied to investigate the impact of energy internet and energy misallocation on carbon emissions using panel data of 30 provinces in China from 2004 to 2018. The major statistical results include the following: (1) energy misallocation significantly contributes to carbon emissions, while energy internet inhibits carbon emissions. Energy internet can negatively moderate the positive effect of energy misallocation on carbon emissions. (2) The effect of energy misallocation on carbon emissions reveals an inverted “U-shaped” characteristic of first promoting and later inhibiting, but the inhibiting effect is insignificant. Moreover, the marginal effect of energy misallocation on carbon emissions decreases when the energy internet crosses the second thresholds consecutively, while the marginal effect of the energy internet on carbon emissions shows an inverted “N” shape. (3) Compared with the under-allocated regions, the promotion effect of energy misallocation on carbon emissions and the inhibitory effect of energy internet on carbon emissions are stronger in the over-allocated regions, while the energy internet has a more significant negative moderating effect on energy misallocation. (4) The gravity center of China’s carbon emissions gradually shifts to the northwest with time. The longitude of the gravity center (east–west direction) changes greatly, while the latitude of the gravity center (north–south direction) changes less. Besides, the carbon emission hotspot regions centered on Shanxi spread to the neighboring provinces, which form a high-high agglomeration region, and the cold spot region dominated by Qinghai, Guangxi, and Guangdong forms low-low agglomeration characteristics. Finally, the GTWR model shows that the impact of energy internet and energy misallocation on carbon emissions shows significant hierarchical, banded, or block-like characteristics in spatial distribution.