A framework to evaluate the impact of a hazard chain and geographical covariates on spatial extreme water levels: A case study in the Pearl River Delta

The interactions and collective impacts of different types of hazards within a compound hazard system, along with the influence of geographical covariates on flooding are presently unclear. Understanding these relationships is crucial for comprehending the formation and dynamic processes of the hazard chain and improving the ability to identify flood warning signals in complex hazard scenarios. In this study, we presented a multivariate spatial extreme value hierarchical (MSEVH) framework to assess the spatial extreme water levels (EWL) at different return levels under the influence of a hazard chain and geographical covariates. The Pearl River Delta (PRD) was selected as a research example to assess the effectiveness of the MSEVH framework. Firstly, we identified a hazard chain (extreme streamflow from the Xijiang River (XR) - extreme streamflow from the Beijiang River (BR) - extreme sea level) and three geographical covariates influencing EWL in the PRD. Then, we compared four hazard scenarios in the MSEVH framework to evaluate the spatial EWL at different return levels under the influence of the hazard chain in the PRD. The final step involves assessing spatial EWL with the effect of the hazard chain and geographical covariates. The results indicate that when extreme streamflow from XR and BR occurs concurrently, the extreme streamflow from BR weakens the influence of extreme streamflow from XR on EWL in the PRD. However, it cannot fully offset the overall impact of extreme streamflow from XR on EWL. In addition, when extreme streamflow from XR, extreme streamflow from BR, and extreme sea level occur simultaneously, the extreme sea level enhances the influence of concurrent extreme streamflow from XR and BR on EWL in the PRD. The proposed MSEVH is not only applicable to the PRD but also shows promising potential for evaluating extreme hydrometeorological variables under the influence of other hazard chains.

Spatial dependence in physicians' prices and additional fees: Evidence from France

Concerns about healthcare affordability have grown in France as physician additional fees have increased threefold in the last 20 years. In this paper, we develop an innovative structural spatial framework to provide new insights into free-billing physician pricing behavior. We empirically test a closed-form solution of a circular city model with heterogeneous physicians by using a unique geolocalized database that covers more than 4000 private practitioners in three specializations (ophthalmology, gynecology and pediatrics). We highlight a positive spatial dependence in prices for all specialties that increases with physician density. This result reflects markets in which both prices are strategic complements and incentives for quality competition are low. We also find evidence of potential noncompetitive behavior for two specialties for which price and competition measures are positively related. These findings in the context of a growing spatial concentration of free-billing physicians emphasize key mechanisms explaining the increasing of additional fees.

Dynamics of green spaces- Land surface temperature intensity nexus in cities of Ethiopia

In this study, the dynamics of green spaces and land surface temperature patterns in four cities in Ethiopia were investigated using Landsat imagery. The typical characteristics of LST over the past three decades (1990-2020) in relation to green space dynamics were first investigated; subsequently, the spatial distribution of LST was characterized based on hybrid geospatial techniques and mono-window algorithm analysis, in which the contributions of green spaces to LST were studied. In addition, the multiple linear regression method and spatial regression models (SRMs) were employed to investigate and predict the spatial dependence of LST and urbanization-induced green space dynamics. Results show that cities horizontally expanded unceasingly from 1990 to 2020, with a substantial discrepancy in expansion rates and the spatial patterns of UHI intensities among the cities (p < 0.05). Moreover, the area proportion of the UHI is significantly larger than that of the UGS, and the differences in the UGS cooling contribution were found in different land uses and zones of the cities. In the study periods, the spatial pattern of LST was significantly controlled by NDBI, and its coefficient in the OLS followed the pattern NDVI > MNDWI > latitudes > longitudes > population density > DEM. Due to the large proportions of buildings While green land and water bodies show significant capability to mitigate UHI effects, cooling effects are not apparent when their sizes are small. Besides, the SRMs show that UHI intensities were significantly influenced by MNDWI in Bahir Dar and Hawassa (p < 0.01).Cities' LAMBDA coefficients have a positive relationship with UHII (p < 0.01). Our study could help city planners and the government understand the current cooling potential of existing UGS to mitigate the dynamics of UHI and sustain the sustainability of green space management in cities.

The underlying drivers of energy efficiency: a spatial econometric analysis

It is theoretical and practical to investigate the causes and effects of energy efficiency. However, few empirical studies have been conducted to examine the potential underlying drivers of energy efficiency from a spatial perspective. In light of this, we combined the data envelopment analysis and spatial econometric analysis to explore the driving factors of energy efficiency. The results show that China's energy efficiency shows significant characteristics of regional disparity and spatial agglomeration; that is, high energy efficiency has presented a benefit agglomeration, while low energy efficiency has presented a disadvantage agglomeration. The empirical results indicate that technological progress, trade openness, and foreign direct investment have effectively improved energy efficiency, while energy structure and industrial structure adversely affect energy efficiency. Furthermore, technological progress, trade openness, energy structure, foreign direct investment, and industrial structure exert different influences on energy efficiency, but their potential underlying mechanisms vary essentially across regions. Thus, using a spatial econometric model allowing for spatial dependence in analyzing drivers of energy efficiency is urgent and necessary for promulgating energy policies.

Cortical thickness analysis in temporal lobe epilepsy using fully Bayesian spectral method in magnetic resonance imaging

BACKGROUND

Temporal lobe epilepsy (TLE) is the most common type of epilepsy associated with changes in the cerebral cortex throughout the brain. Magnetic resonance imaging (MRI) is widely used for detecting such anomalies; nevertheless, it produces spatially correlated data that cannot be considered by the usual statistical models. This study aimed to compare cortical thicknesses between patients with TLE and healthy controls by considering the spatial dependencies across different regions of the cerebral cortex in MRI.

METHODS

In this study, T1-weighted MRI was performed on 20 healthy controls and 33 TLE patients. Nineteen patients had a left TLE and 14 had a right TLE. Cortical thickness was measured for all individuals in 68 regions of the cerebral cortex based on images. Fully Bayesian spectral method was utilized to compare the cortical thickness of different brain regions between groups. Neural networks model was used to classify the patients using the identified regions.

RESULTS

For the left TLE patients, cortical thinning was observed in bilateral caudal anterior cingulate, lateral orbitofrontal (ipsilateral), the bilateral rostral anterior cingulate, frontal pole and temporal pole (ipsilateral), caudal middle frontal and rostral middle frontal (contralateral side). For the right TLE patients, cortical thinning was only observed in the entorhinal area (ipsilateral). The AUCs of the neural networks for classification of left and right TLE patients versus healthy controls were 0.939 and 1.000, respectively.

CONCLUSION

Alteration of cortical gray matter thickness was evidenced as common effect of epileptogenicity, as manifested by the patients in this study using the fully Bayesian spectral method by taking into account the complex structure of the data.

Impact of the COVID-19 pandemic on air pollution from jet engines at airports in central eastern China

Aircraft engine emissions (AEEs) generated during landing and takeoff (LTO) cycles are important air pollutant sources that directly impact the air quality at airports. Although the COVID-19 pandemic triggered an unprecedented collapse in the civil aviation industry, it also relieved some environmental pressure on airports. To quantify the impact of COVID-19 on AEEs, the amounts of three typical air pollutants (i.e., HC, CO, and NO_x) from LTO cycles at airports in central eastern China were estimated before and after the pandemic. The study also explored the temporal variation and the spatial autocorrelation of both the emission quantity and the emission intensity, as well as their spatial associations with other socioeconomic factors. The results illustrated that the spatiotemporal distribution pattern of AEEs was significantly influenced by the policies implemented and the severity of COVID-19. The variations of AEEs at airports with similar characteristics and functional positions generally followed similar patterns. The results also showed that the studied air pollutants present positive spatial autocorrelation, and a positive spatial dependence was found between the AEEs and other external socioeconomic factors. Based on the findings, some possible policy directions for building a more sustainable and environment-friendly airport group in the post-pandemic era were proposed. This study provides practical guidance on continuous monitoring of the AEEs from LTO cycles and studying the impact of COVID-19 on the airport environment for other regions or countries.

The analysis of spatial-temporal effects of relevant factors on carbon intensity in China

The increasing carbon emissions have been a major concern for most countries around the world. And as a result, every country is concerned about developing appropriate strategies to curtail it. As a major economy and the largest carbon emitter in the world, China has pledged to reduce the carbon intensity by 60-65% by 2030, compared with 2005 levels, and achieve carbon neutrality before 2060. Therefore, the analysis of the impact of China's carbon intensity is becoming an increasing important topic. Due to the spatial heterogeneity of carbon intensity, various spatial econometric models have been applied in this field. However, the existing literatures failed to consider the cross-products of relevant factors. This paper constructs our dynamic general nesting spatial panel model (GNS) with common factors to deal with the dilemma, and examines the direct and spatial-temporal spillover effects of industrial structure, GDP per capita, investment in anti-pollution projects as percentage of GDP and energy price on carbon intensity in China over the period 2003-2017. Our analysis shows that: (1) China's carbon intensity showed the spatial agglomeration and temporal "inertia" from 2003 to 2017. (2) From the time dimension, the long-term effect of industrial structure first increased and then gradually decreased. (3) From the spatial dimension, industrial structure and investment in anti-pollution projects as percentage of GDP accounted for the main spatial heterogeneity. Furthermore, this paper attempts to provide policy implications to help reduce carbon intensity and achieve carbon neutrality in China.

A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy

The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.

Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method

The novel and unprecedented Coronavirus disease (COVID-19) pandemic has negatively impacted most nations of the world within a short period. While its disproportionate social and spatial variability has been established, the reality in Nigeria is yet to be studied. In this paper, advanced spatial statistical techniques were engaged to study the burden of COVID-19 and its risk factors within the first quarter (March-May) of its incidence in Nigeria. The spatial autocorrelation (Moran's I) test reveals a significant but marginal cluster of COVID-19 occurrence in Nigeria (I = 0.11, p < 0.05). A model comparison between ordinary least square (OLS) and spatial error model (SER) was explored having checked for multicollinearity in the dataset. The OLS model explained about 64% (adjusted R² = 0.64) of variation in COVID-19 cases, however with significantly clustered residuals. The SER model performed better with randomly distributed residuals. The significant predictors were population density, international airport, and literacy ratio. Furthermore, this study addressed the spatial planning implications of the ongoing disease outbreak while it advocates transdisciplinary approach to urban planning practices in Nigeria.

Spatial spillover effect of carbon emission efficiency in the construction industry of China

The construction industry plays an important role in energy saving and carbon emissions mitigation of China. Promoting carbon emission efficiency is seen as an efficient way to abate carbon emissions. Using 2005-2016 data, the carbon emission efficiency of the construction sector in 30 provinces is estimated, and the spatial distribution characteristics of the carbon emission efficiency of the construction industry is explored. The spatial Markov transition probability matrix is employed to investigate the influence of the spatial spillover effect on the regional distribution pattern of carbon emission efficiency. The results demonstrate that the carbon emission efficiency of the construction industry exhibits an unbalanced regional distribution, which is high in the east and low in the west. The spatial autocorrelation indicates that the carbon emission efficiency has a spatial dependence and is characterized by spatial agglomeration. Markov Chain results show a significant spatial spillover effect in carbon emission efficiency. The provinces with higher carbon emission efficiency have a positive effect on their neighbors, while the provinces with lower efficiency have a negative effect on neighbors. The findings are of great importance to understand the differences in and interactions of carbon emission efficiency between regions.

The impacts of pollution and its associated spatial spillover effects on ill-health in China

While the adverse health effects of air pollution and its associated spatial spillovers have been extensively explored, there are a paucity of studies examining and comparing the effects of air pollution, water pollution, and their associated spatial spillover consequences for health. This study aims to evaluate and compare the impacts of water pollution, air pollution, and their associated spillover effects on ill-health. This study combined individual-level health data acquired from three waves of the China Health and Retirement Longitudinal Study (CHARLS) for 25,504 residents from 28 Chinese provinces with provincial-level pollution data for 2011, 2013 and 2015. We used Moran's I statistic to examine the existence and direction of the spatial spillover effects of pollution. The Spatial Durbin Model was then employed to assess the impacts of pollution and its associated spatial spillover effects on ill-health. A province's ill-health score increased by 6.649 for every 1 ton per capita per annum increase in the average amount of soot/dust discharged by its adjacent provinces. For every 1 ton per capita per annum increase in wastewater discharged, a province's ill-health score increased by 0.004. Targeted actions through the construction of cooperative action with adjacent provinces are suggested by our study to improve the efficiency of policy interventions.

Differential Impact of Social Distancing on COVID-19 Spread in the U.S.: By Rurality and Social Vulnerability

Background

To quantify lessons learned to better prepare for similar pandemic crisis in the future, we assess the overall impact of social distancing on the daily growth rate of COVID-19 infections in the U.S. during the initial phase of the pandemic and the impacts' heterogeneity by urbanity and social vulnerability of the counties. The initial phase is chosen to purposely identify the essential and largest impact of the first-line of defense measure for similar pandemic: social distancing.

Methods

Spatial Durbin models with county fixed effects were used to account for spatial dependencies and identify spatial spillover effects and spatial heterogeneity.

Results

Besides the substantial curve flattening effects of social distancing, our results show significant spillover effects induced by neighboring counties' social distancing levels even in the absence of significant within-county effects. Urban and areas with high social vulnerability are the ones benefit the most from social distancing and high level of compliance is needed. Moderate level is enough in reaching the peak marginal impact in rural and areas with low social vulnerability.

A comparison of multiple neighborhood matrix specifications for spatio-temporal model fitting: a case study on COVID-19 data

Establishing proper neighbor relations between a set of spatial units under analysis is essential when carrying out a spatial or spatio-temporal analysis. However, it is usual that researchers choose some of the most typical (and simple) neighborhood structures, such as the first-order contiguity matrix, without exploring other options. In this paper, we compare the performance of different neighborhood matrices in the context of modeling the weekly relative risk of COVID-19 over small areas located in or near Valencia, Spain. Specifically, we construct contiguity-based, distance-based, covariate-based (considering mobility flows and sociodemographic characteristics), and hybrid neighborhood matrices. We evaluate the goodness of fit, the overall predictive quality, the ability to detect high-risk spatio-temporal units, the capability to capture the spatio-temporal autocorrelation in the data, and the goodness of smoothing for a set of spatio-temporal models based on each of the neighborhood matrices. The results show that contiguity-based matrices, some of the distance-based matrices, and those based on sociodemographic characteristics perform better than the matrices based on k-nearest neighbors and those involving mobility flows. In addition, we test the linear combination of some of the constructed neighborhood matrices and the reweighting of these matrices after eliminating weak neighbor relations, without any model improvement.

Spatial distribution and ecological assessment of nickel in sediments of a typical small plateau lake from Yunnan Province, China

Nickel (Ni) in small plateau lake sediments plays an important role in influencing the quality of lake ecosystems with a high degree of endemism and toxicity. This paper focuses on the spatial distribution and ecological risks of nickel in the sediments of Jianhu Lake, a small plateau lake in China, and the influence of pH and total organic carbon (TOC) on nickel concentrations. The results showed that average total nickel concentrations were 138.99 ± 57.57 mg/kg (n = 38) and 184.31 ± 92.12 mg/kg (n = 60) in surface sediments (0-10 cm top layer) and sediment cores (0-75 cm depth), respectively, and that the residual fraction was the main form of nickel. Simultaneously, through a semivariogram model, strong spatial dependence among pH, TOC, and the oxidizable fraction was revealed, whereas total nickel, exchangeable and the weak acid soluble fraction, reducible fraction, and residual fraction showed moderate spatial dependence. The vertical distribution revealed that nickel accumulated mainly in the bottom 5 cm (70-75 cm) of the sediment layer and that the pH was higher there, whereas TOC was concentrated mainly in the top 5 cm of sediment. Using geoaccumulation and a potential ecological risk index, moderate nickel pollution and moderate risk levels were found in most surface sediments, but moderate nickel pollution and high risk levels were observed in most sediment cores. In addition, pH and TOC were found to have a strong effect on the distribution and concentration of nickel and its fractions in the small plateau lake. In summary, nickel posed a certain degree of pollution and ecological risk, which deserves attention in the sediments of small plateau lakes.

Do spatial spillovers matter? Estimating the impact of tourism development on CO2 emissions

Most of the extant literature on the environmental impact of tourism has ignored the possible spatial interaction effects across countries. This study thus aims to re-investigate the impact of tourism development on CO2 emissions by taking spatial dependence into account. To that end, the spatial econometric techniques, which can address the issue of potential spatial dependence among countries, are adopted. Using a panel data of 95 countries over 2000-2014, the results confirm that there exists a significant spatial dependence among national CO2 emissions. Besides, the results provide confirmation that tourism development exerts a significant enhancing influence on CO2 emissions. Interestingly, we find that the promoting effect of tourism development on CO2 emissions primarily comes from the spillover effect rather than the direct effect, after considering spatial dependence. Finally, in light of the research findings, some policy implications are put forward to improve environmental quality.

Geospatial analysis of individual-based Parkinson's disease data supports a link with air pollution: A case-control study

BACKGROUND

The etiology of Parkinson's disease (PD) remains unknown. To approach the issue of PD's risk factors from a new perspective, we hypothesized that coupling the geographic distribution of PD with spatial statistics may provide new insights into environmental epidemiology research. The aim of this case-control study was to examine the spatial dependence of PD prevalence in the Canton of Geneva, Switzerland (population = 474,211).

METHODS

PD cases were identified through Geneva University Hospitals, private neurologists and nursing homes medical records (n = 1115). Controls derived from a population-based study (n = 12,614) and a comprehensive population census dataset (n = 237,771). All individuals were geographically localized based on their place of residence. Spatial Getis-Ord Gi* statistics were used to identify clusters of high versus low disease prevalence. Confounder-adjustment was performed for age, sex, nationality and income. Tukey's honestly significant difference was used to determine whether nitrogen dioxide and particulate matters PM₁₀ concentrations were different within PD hotspots, coldspots or neutral areas.

RESULTS

Confounder-adjustment greatly reduced greatly the spatial association. Characteristics of the geographic space influenced PD prevalence in 6% of patients. PD hotspots were concentrated in the urban centre. There was a significant difference in mean annual nitrogen dioxide and PM₁₀ levels (+3.6 μg/m3 [p < 0.001] and +0.63 μg/m3 [p < 0.001] respectively) between PD hotspots and coldspots.

CONCLUSION

PD prevalence exhibited a spatial dependence for a small but significant proportion of patients. A positive association was detected between PD clusters and air pollution. Our data emphasize the multifactorial nature of PD and support a link between PD and air pollution.

COVID-19 does not stop at open borders: Spatial contagion among local authority districts during England's first wave

Infectious diseases generate spatial dependence or contagion not only between individuals but also between geographical units. New infections in one local district do not just depend on properties of the district, but also on the strength of social ties of its population with populations in other districts and their own degree of infectiousness. We show that SARS-CoV-2 infections during the first wave of the pandemic spread across district borders in England as a function of pre-crisis commute to work streams between districts. Crucially, the strength of this spatial contagion depends on the phase of the epidemic. In the first pre-lockdown phase, the spread of the virus across district borders is high. During the lockdown period, the cross-border spread of new infections slows down significantly. Spatial contagion increases again after the lockdown is eased but not statistically significantly so.

Spatial and temporal differentiation of COVID-19 epidemic spread in mainland China and its influencing factors

This paper uses the exploratory spatial data analysis and the geodetector method to analyze the spatial and temporal differentiation characteristics and the influencing factors of the COVID-19 (corona virus disease 2019) epidemic spread in mainland China based on the cumulative confirmed cases, average temperature, and socio-economic data. The results show that: (1) the epidemic spread rapidly from January 24 to February 20, 2020, and the distribution of the epidemic areas tended to be stable over time. The epidemic spread rate in Hubei province, in its surrounding, and in some economically developed cities was higher, while that in western part of China and in remote areas of central and eastern China was lower. (2) The global and local spatial correlation characteristics of the epidemic distribution present a positive correlation. Specifically, the global spatial correlation characteristics experienced a change process from agglomeration to decentralization. The local spatial correlation characteristics were mainly composed of the'high-high' and 'low-low' clustering types, and the situation of the contiguous layout was very significant. (3) The population inflow from Wuhan and the strength of economic connection were the main factors affecting the epidemic spread, together with the population distribution, transport accessibility, average temperature, and medical facilities, which affected the epidemic spread to varying degrees. (4) The detection factors interacted mainly through the mutual enhancement and nonlinear enhancement, and their influence on the epidemic spread rate exceeded that of single factors. Besides, each detection factor has an interval range that is conducive to the epidemic spread.

Modeling the spatio-temporal dynamics of air pollution index based on spatial Markov chain model

An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.

Regional unemployment and cyclical sensitivity in Spain

Unemployment has been routinely used as a measure of the economic cycle. In addition, regional unemployment rates are characterized by, among other factors, their relation to the national unemployment rate. In this regard, the literature on regional sensitivity to the economic cycle has analyzed how fluctuations in the national unemployment rate affect the regions. In recent years, due to the great impact of past crises, the development of new econometric techniques and the possible arrival of new crises, the debate on how sensitive regions are to the economic cycle has reopened. In Spain, this debate is necessary since unemployment rates are very high and display a great deal of heterogeneity. We analyzed regional unemployment rates in Spain between 1978 and 2018 through a recently developed dynamic spatial econometric model with common factors and found that some regions are more sensitive than others to the economic cycle. The results seem to show that in Spain, the sensitivity to the economic cycle displays a geographical pattern where the most sensitive regions are those located on the Mediterranean coast. Specifically, we find that the sensitivity to the economic cycle of unemployment is not determined by the fact that regions have high or low unemployment; it seems that geographical location plays an important role. These results can be useful for the national and regional governments when they implement countercyclical policies.

Assessing environmental interference in northern China using a spatial distance model: From the perspective of geographic detection

The rapid development of society and the expansion of human activities have resulted in interference with the natural environment. Assessing the environmental interference (EI) caused by human activities is highly important for socio-economic sustainable development. In this study, the spatial distance model (SDM) and resource endowment index (REI)-human activity index (HAI) ratio model were developed to calculate the environmental interference index (EII) in northern China (NC). The current spatial distribution and patterns of EII in NC were analyzed based on geographic information system (GIS) technology. In addition, the factors that influence the level of EI were examined through a geographical detector method. The results showed that the EII value in the eastern region was significantly higher than that in the western region and that differences in EI were spatial heterogeneity. The spatial distribution of EI was analyzed at the provincial, municipal and county scales, respectively. It was found that its distribution was closely related to urban development. The spatial distribution of EI displayed longitudinal zonality. East of 104.987°E, there were many large cities, such as Beijing, Tianjin, Qingdao and Zhengzhou, with high population densities and developed economies. Thus, these areas had high EI values. To the west of 104.987°E, such as in the Qinghai, Gansu, Xinjiang and Inner Mongolia regions, the EI values were generally low, with low environmental quality and fewer human activities. The level of EI in the Huang-Huai-Hai Plain region was higher than that in other areas, displaying obvious spatial dependence. Moreover, the distribution of EI exhibited high-high and low-low aggregation patterns, which accounted for 24.06% and 27.35% of the total study area, respectively. Specifically, in NC, the EI caused by human activities displayed obvious regional characteristics. In addition, the factors that influence EI were determined through a geographical detector model. The land use intensity was the direct factor related to changes in and the levels of EI, and the cover and growth of vegetation were the most important factors associated with mitigating human interference. The assessment results can provide a reference for the formulation of environmental governance and related policies.

Assessing the effect of containment measures on the spatio-temporal dynamic of COVID-19 in Italy

This paper aims at investigating empirically whether and to what extent the containment measures adopted in Italy had an impact in reducing the diffusion of the COVID-19 disease across provinces. For this purpose, we extend the multivariate time-series model for infection counts proposed in Paul and Held (Stat Med 30(10):118-1136, 2011) by augmenting the model specification with B-spline regressors in order to account for complex nonlinear spatio-temporal dynamics in the propagation of the disease. The results of the model estimated on the time series of the number of infections for the Italian provinces show that the containment measures, despite being globally effective in reducing both the spread of contagion and its self-sustaining dynamics, have had nonlinear impacts across provinces. The impact has been relatively stronger in the northern local areas, where the disease occurred earlier and with a greater incidence. This evidence may be explained by the shared popular belief that the contagion was not a close-to-home problem but rather restricted to a few distant northern areas, which, in turn, might have led individuals to adhere less strictly to containment measures and lockdown rules.

Does municipal solid waste generation in China support the Environmental Kuznets Curve? New evidence from spatial linkage analysis

Ever increasing municipal solid waste (MSW) is becoming a serious city pollution trouble, extensive studies explored this question from micro demographic factors before. But testing the impact of spatial dependence on MSW from macroeconomic factors with city-level panel data has not received adequate attention it deserves in prior literature, and routinely ignoring spatial dependence is prone to estimation biases. This research utilizes panel data of 285 Chinese cities for the period of 2006-2015 to explore the spatial dependence of the MSW. Employing two elaborate spatial panel models, the empirical result indicates that while road length, tertiary proportion, urbanization rate have significantly accelerated MSW generation, sanitation investment and education level are slightly negatively correlated with MSW generation, and GDP per capita has a mutual improvement relationship with MSW generation instead of inverted U-shape as predicted by Environmental Kuznets curve, which provides a new insight into city planning and environment protection when facing the fierce socio-environmental conflicts. Besides, there obviously exists spatial dependence and convergence phenomenon of MSW generation among China's cities.

The role of socioeconomic and climatic factors in the spatio-temporal variation of human rabies in China

BACKGROUND

Rabies is a significant public health problem in China. Previous spatial epidemiological studies have helped understand the epidemiology of animal and human rabies in China. However, quantification of effects derived from relevant factors was insufficient and complex spatial interactions were not well articulated, which may lead to non-negligible bias. In this study, we aimed to quantify the role of socio-economic and climate factors in the spatial distribution of human rabies to support decision making pertaining to rabies control in China.

METHODS

We conducted a multivariate analysis of human rabies in China with explicit consideration for spatial heterogeneity and spatial dependence effects. The panel of 20,368 cases reported between 2005 and 2013 and their socio-economic and climate factors was implemented in regression models. Several significant covariates were extracted, including the longitude, the average temperature, the distance to county center, the distance to the road network and the distance to the nearest rabies case. The GMM was adopted to provide unbiased estimation with respect to heterogeneity and spatial autocorrelation.

RESULTS

The analysis explained the inferred relationships between the counts of cases aggregated to 271 spatially-defined cells and the explanatory variables. The results suggested that temperature, longitude, the distance to county centers and the distance to the road network are positively associated with the local incidence of human rabies while the distance to newly occurred rabies cases has a negative correlation. With heterogeneity and spatial autocorrelation taken into consideration, the estimation of regression models performed better.

CONCLUSIONS

It was found that climatic and socioeconomic factors have significant influence on the spread of human rabies in China as they continuously affect the living environments of humans and animals, which critically impacts on how timely local citizens can gain access to post-exposure prophylactic services. Moreover, through comparisons between traditional regression models and the aggregation model that allows for heterogeneity and spatial effects, we demonstrated the validity and advantage of the aggregation model. It outperformed the existing models and decreased the estimation bias brought by omission of the spatial heterogeneity and spatial dependence effects. Statistical results are readily translated into public health policy takeaways.

The association between exposure to environmental factors and the occurrence of attention-deficit/hyperactivity disorder (ADHD). A population-based retrospective cohort study

BACKGROUND

A number of factors contribute to attention deficit hyperactivity disorder (ADHD) and although they are not fully known, the occurrence of ADHD seems to be a consequence of an interaction between multiple genetic and environmental factors. However, apart from pesticides, the evidence is inadequate and inconsistent as it differs not only in the population and time period analysed, but also in the type of study, the control of the confounding variables and the statistical methods used. In the latter case, the studies also differ in the adjustment of spatial and temporal variability. Our objective here, is to provide evidence on an association between environmental factors and ADHD.

METHODS

In our study, we used a population-based retrospective cohort in which we matched cases and controls (children free of the disease) by sex and year of birth (n = 5193, 78.9% boys). The cases were children born between 1998 and 2012 and diagnosed with ADHD (n = 116). To evaluate whether there was a geographical pattern in the incidence of ADHD, we first represented the smoothed standardized incidence rates on a map of the region being studied. We then estimated the probability of being a case by using a generalized liner mixed model with a binomial link. As explanatory variables of interest, we included the following environmental variables: distance to agricultural areas, distance to roads (stratified into three categories according to traffic density and intensity), distance to petrol stations, distance to industrial estates, and land use. We control for both observed (individual and family specific variables and deprivation index) and unobserved confounders (in particular, individual and familial heterogeneity). In addition, we adjusted for spatial extra variability.

RESULTS

We found a north-south pattern containing two clusters (one in the centre of the study region and another in the south) in relation to the risk of developing ADHD. The results from the multivariate model suggest that these clusters could be related to some of the environmental variables. Specifically, living within 100 m from an agricultural area or a residential street and/or living fewer than 300 m from a motorway, dual carriageway or one of the industrial estates analysed was associated (statistically significant) with an increased risk of ADHD.

CONCLUSION

Our results indicate that some environmental factors could be associated with ADHD occurring, particularly those associated with exposure to pesticides, organochlorine compounds and air pollutants because of traffic.

On the spatial relationship between ecosystem services and urbanization: A case study in Wuhan, China

A clear understanding of the relationship between ecosystem services (ESs) and urbanization provides new insight into urban landscape planning and decision making. Although a considerable amount of literature has focused on this topic, few studies address the spatial interactions between ESs and urbanization, especially at the local scale. Various models and multisource data were integrated to estimate ESs and urbanization in Wuhan City, China. The bivariate Moran's I methods were employed to test and visualize the spatial correlations between ESs and urbanization. Spatial regression models were used to describe the spatial dependence of ESs on urbanization. Our results showed that all ESs have globally negative spatial correlations with urbanization, but focusing on local scale allowed spatial correlations to be categorized into four types: high ESs and high urbanization, high ESs and low urbanization, low ESs and high urbanization, and low ESs and low urbanization. Spatial regression models were identified as more suitable to measure the spatial dependence of ESs on urbanization, as they account for the effects of spatial autocorrelation. Among ESs, biodiversity conservation was the one most sensitive to increased urbanization, followed by outdoor recreation, water yield, grain productivity, carbon storage, and erosion prevention. The spatial exploration of the relationship between ESs and urbanization provides practical guidance for urban development planning and environmental protection.

Spatial Assessment of the Association between Long-Term Exposure to Environmental Factors and the Occurrence of Amyotrophic Lateral Sclerosis in Catalonia, Spain: A Population-Based Nested Case-Control Study

BACKGROUND

It is believed that an interaction between genetic and non-genetic factors may be involved in the development of amyotrophic lateral sclerosis (ALS). With the exception of exposure to agricultural chemicals like pesticides, evidence of an association between environmental risk factors and ALS is inconsistent. Our objective here was to investigate the association between long-term exposure to environmental factors and the occurrence of ALS in Catalonia, Spain, and to provide evidence that spatial clusters of ALS related to these environmental factors exist.

METHODS

We carried out a nested case-control study constructed from a retrospective population-based cohort, covering the entire region. Environmental variables were the explanatory variables of interest. We controlled for both observed and unobserved confounders.

RESULTS

We have found some spatial clusters of ALS. The results from the multivariate model suggest that these clusters could be related to some of the environmental variables, in particular agricultural chemicals. In addition, in high-risk clusters, besides corresponding to agricultural areas, key road infrastructures with a high density of traffic are also located.

CONCLUSION

Our results indicate that some environmental factors, in particular those associated with exposure to pesticides and air pollutants as a result of urban traffic, could be associated with the occurrence of ALS.

Spatial Dependence and Determinants of Dairy Farmers' Adoption of Best Management Practices for Water Protection in New Zealand

This paper analyses spatial dependence and determinants of the New Zealand dairy farmers' adoption of best management practices to protect water quality. A Bayesian spatial durbin probit model is used to survey data collected from farmers in the Waikato region of New Zealand. The results show that farmers located near each other exhibit similar choice behaviour, indicating the importance of farmer interactions in adoption decisions. The results also address that information acquisition is the most important determinant of farmers' adoption of best management practices. Financial problems are considered a significant barrier to adopting best management practices. Overall, the existence of distance decay effect and spatial dependence in farmers' adoption decisions highlights the importance of accounting for spatial effects in farmers' decision-making, which emerges as crucial to the formulation of sustainable agriculture policy.

Evaluation of spatial dependence of point spread function-based PET reconstruction using a traceable point-like 22Na source

Background

The point spread function (PSF) of positron emission tomography (PET) depends on the position across the field of view (FOV). Reconstruction based on PSF improves spatial resolution and quantitative accuracy. The present study aimed to quantify the effects of PSF correction as a function of the position of a traceable point-like ²²Na source over the FOV on two PET scanners with a different detector design.

Methods

We used Discovery 600 and Discovery 710 (GE Healthcare) PET scanners and traceable point-like ²²Na sources (<1 MBq) with a spherical absorber design that assures uniform angular distribution of the emitted annihilation photons. The source was moved in three directions at intervals of 1 cm from the center towards the peripheral FOV using a three-dimensional (3D)-positioning robot, and data were acquired over a period of 2 min per point. The PET data were reconstructed by filtered back projection (FBP), the ordered subset expectation maximization (OSEM), OSEM + PSF, and OSEM + PSF + time-of-flight (TOF). Full width at half maximum (FWHM) was determined according to the NEMA method, and total counts in regions of interest (ROI) for each reconstruction were quantified.

Results

The radial FWHM of FBP and OSEM increased towards the peripheral FOV, whereas PSF-based reconstruction recovered the FWHM at all points in the FOV of both scanners. The radial FWHM for PSF was 30–50 % lower than that of OSEM at the center of the FOV. The accuracy of PSF correction was independent of detector design. Quantitative values were stable across the FOV in all reconstruction methods. The effect of TOF on spatial resolution and quantitation accuracy was less noticeable.

Conclusions

The traceable ²²Na point-like source allowed the evaluation of spatial resolution and quantitative accuracy across the FOV using different reconstruction methods and scanners. PSF-based reconstruction reduces dependence of the spatial resolution on the position. The quantitative accuracy over the entire FOV of the PET system is good, regardless of the reconstruction methods, although it depends slightly on the position.

Exploring spatiotemporal patterns of PM2.5 in China based on ground-level observations for 190 cities

Whereas air pollution in many Chinese cities has reached epidemic levels in recent years, limited research has explored the spatial and temporal patterns of fine air particles such as PM2.5, or particulate matter with diameter smaller than 2.5 μm, using nationally representative data. This article applied spatial statistical approaches including spatial interpolation and spatial regression to the analysis of ground-level PM2.5 observations for 190 Chinese cities in 2014 obtained from the Chinese Air Quality Online Monitoring Platform. Results of this article suggest that most Chinese cities included in the dataset recorded severe levels of PM2.5 in excess of the WHO's interim target and cities in the North China Plain had the highest levels of PM2.5 regardless of city size. Spatially interpolated maps of PM2.5 and population-weighted PM2.5 indicate vast majority of China's land and population was exposed to disastrous levels of PM2.5 concentrations. The regression results suggest that PM2.5 in a city was positively related to its population size, amount of atmospheric pollutants, and emissions from nearby cities, but inversely related to precipitation and wind speed. Findings from this research can shed new light on the complex spatiotemporal patterns of PM2.5 throughout China and provide insights into policies aiming to mitigate air pollution in China.

Non-stationary spatio-temporal modeling of traffic-related pollutants in near-road environments

A problem often encountered in environmental epidemiological studies assessing the health effects associated with ambient exposure to air pollution is the spatial misalignment between monitors' locations and subjects' actual residential locations. Several strategies have been adopted to circumvent this problem and estimate pollutants concentrations at unsampled sites, including spatial statistical or geostatistical models that rely on the assumption of stationarity to model the spatial dependence in pollution levels. Although computationally convenient, the assumption of stationarity is often untenable for pollutants concentration, particularly in the near-road environment. Building upon the work of Fuentes (2001) and Schmidt et al. (2011), in this paper we present a non-stationary spatio-temporal model for three traffic-related pollutants in a localized near-road environment. Modeling each pollutant separately and independently, we express each pollutant's concentration as a mixture of two independent spatial processes, each equipped with a non-stationary covariance function with covariates driving the non-stationarity and the mixture weights.

Spatial analysis of environment and population at risk of natural gas fracking in the state of Pennsylvania, USA

Hydraulic fracturing, also known as fracking, has been increasing exponentially across the United States, which holds the largest known shale gas reserves in the world. Studies have found that the high-volume horizontal hydraulic fracturing process (HVHFP) threatens water resources, harms air quality, changes landscapes, and damages ecosystems. However, there is minimal research focusing on the spatial study of environmental and human risks of HVHFP, which is necessary for state and federal governments to administer, regulate, and assess fracking. Integrating GIS and spatial kernel functions, we study the presently operating fracking wells across the state of Pennsylvania (PA), which is the main part of the current hottest Marcellus Shale in US. We geographically process the location data of hydraulic fracturing wells, 2010 census block data, urbanized region data, railway data, local road data, open water data, river data, and wetland data for the state of PA. From this we develop a distance based risk assessment in order to understand the environmental and urban risks. We generate the surface data of fracking well intensity and population intensity by integrating spatial dependence, semivariogram modeling, and a quadratic kernel function. The surface data of population risk generated by the division of fracking well intensity and population intensity provide a novel insight into the local and regional regulation of hydraulic fracturing activities in terms of environmental and health related risks due to the proximity of fracking wells.

Spatial analysis of highway incident durations in the context of Hurricane Sandy

The objectives of this study are (1) to develop an incident duration model which can account for the spatial dependence of duration observations, and (2) to investigate the impacts of a hurricane on incident duration. Highway incident data from New York City and its surrounding regions before and after Hurricane Sandy was used for the study. Moran's I statistics confirmed that durations of the neighboring incidents were spatially correlated. Moreover, Lagrange Multiplier tests suggested that the spatial dependence should be captured in a spatial lag specification. A spatial error model, a spatial lag model and a standard model without consideration of spatial effects were developed. The spatial lag model is found to outperform the others by capturing the spatial dependence of incident durations via a spatially lagged dependent variable. It was further used to assess the effects of hurricane-related variables on incident duration. The results show that the incidents during and post the hurricane are expected to have 116.3% and 79.8% longer durations than those that occurred in the regular time. However, no significant increase in incident duration is observed in the evacuation period before Sandy's landfall. Results of temporal stability tests further confirm the existence of the significant changes in incident duration patterns during and post the hurricane. Those findings can provide insights to aid in the development of hurricane evacuation plans and emergency management strategies.

A composite likelihood approach for spatially correlated survival data

The aim of this paper is to provide a composite likelihood approach to handle spatially correlated survival data using pairwise joint distributions. With e-commerce data, a recent question of interest in marketing research has been to describe spatially clustered purchasing behavior and to assess whether geographic distance is the appropriate metric to describe purchasing dependence. We present a model for the dependence structure of time-to-event data subject to spatial dependence to characterize purchasing behavior from the motivating example from e-commerce data. We assume the Farlie-Gumbel-Morgenstern (FGM) distribution and then model the dependence parameter as a function of geographic and demographic pairwise distances. For estimation of the dependence parameters, we present pairwise composite likelihood equations. We prove that the resulting estimators exhibit key properties of consistency and asymptotic normality under certain regularity conditions in the increasing-domain framework of spatial asymptotic theory.

Making a Place for Space: Spatial Thinking in Social Science

New technologies and multilevel data sets that include geographic identifiers have heightened sociologists' interest in spatial analysis. I review several of the key concepts, measures, and methods that are brought into play in this work, and offer examples of their application in a variety of substantive fields. I argue that the most effective use of the new tools requires greater emphasis on spatial thinking. A device as simple as an illustrative map requires some understanding of how people respond to visual cues; models as complex as HLM with spatial lags require thoughtful measurement decisions and raise questions about what a spatial effect represents.

The effect of grazing on the spatial heterogeneity of vegetation

Grazing can alter the spatial heterogeneity of vegetation, influencing ecosystem processes and biodiversity. Our objective was to identify why grazing causes increases in the spatial heterogeneity of vegetation in some cases, but decreases in others. The immediate effect of grazing on heterogeneity depends on the interaction between the spatial pattern of grazing and the pre-existing spatial pattern of vegetation. Depending on the scale of observation and on the factors that determine animal distribution, grazing patterns may be stronger or weaker than vegetation patterns, or may mirror the spatial structure of vegetation. For each possible interaction between these patterns, we make a prediction about resulting changes in the spatial heterogeneity of vegetation. Case studies from the literature support our predictions, although ecosystems characterized by strong plant-soil interactions present important exceptions. While the processes by which grazing causes increases in heterogeneity are clear, how grazing leads to decreases in heterogeneity is less so. To explore how grazing can consistently dampen the fine-scale spatial patterns of competing plant species, we built a cell-based simulation model that features two competing plant species, different grazing patterns, and different sources of vegetation pattern. Only the simulations that included neighborhood interactions as a source of vegetation pattern produced results consistent with the predictions we derived from the literature review.