Spatialising urban health vulnerability: An analysisof NYC's critical infrastructure during COVID-19.

This paper examines how fragmentation of critical infrastructure impacts the spread of the coronavirus outbreak in New York City at the neighbourhood level. The location of transportation hubs, grocery stores, pharmacies, hospitals and parks plays an important role in shaping spatial disparities in virus spread. Using supervised machine learning and spatial regression modelling we examine how the geography of COVID-19 case rates is influenced by the spatial arrangement of four critical sectors of the built environment during the public health emergency in New York City: health care facilities, mobility networks, food and nutrition and open space. Our models suggest that an analysis of urban health vulnerability is incomplete without the inclusion of critical infrastructure metrics in dense urban geographies. Our findings show that COVID-19 risk at the zip code level is influenced by (1) socio-demographic vulnerability, (2) epidemiological risk, and (3) availability and access to critical infrastructure.

Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants.

Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

Do the determinants of COVID-19 transmission differ by epidemic wave? Evidence from U.S. counties.

This paper uses data from the United States to examine determinants of the spread of COVID-19 during three different epidemic waves. We address how sociodemographic and economic attributes, industry composition, density, crowding in housing, and COVID-19-related variables are associated with the transmission of COVID-19. After controlling for spatial autocorrelation, our findings indicate that the percentage of people in poverty, number of restaurants, and percentage of workers teleworking were associated with the COVID-19 incidence rate during all three waves. Our results also show that dense areas were more vulnerable to the transmission of COVID-19 after the first epidemic wave. Regarding the density of supermarkets, our study elaborates the negative aspects of wholesale retail stores, which likely provide a vulnerable place for virus transmission. Our results suggest that sociodemographic and economic attributes were the determinants of the early phase of the pandemic, while density showed positive association with the transmission during subsequent waves. We provide implications for regions serving as gateway cities with high density and number of population. To add, we further provide evidence that non-pharmaceutical interventions in the early stage may mitigate the virus transmission.

Spatiotemporal differences and influencing factors of high-quality utilization of land resources in the Yellow River Basin of China.

High-quality utilization of land resources (HULR) is critical to the security of land ecosystem and sustainable socioeconomic development. To promote HULR, we explored the spatiotemporal differences and influencing factors of HULR in the Yellow River Basin (an important ecological barrier in China) by using entropy method, spatial panel regression model, and geographically and temporally weighted regression model. We found that the HULR values were 0.22 to 0.28 from 2008 to 2019, showing an increasing trend with obvious spatiotemporal differences. The spatial connectivity, technological innovation, industrialization, industrial upgrading, and marketization are important factors influencing HULR, and different factors have different spatial effects in different regions. Therefore, the important principle of HULR is to pursue the sustainable land utilization within the ecological environment carrying capacity, taking into account the unique ecological and socioeconomic conditions of each region. We hope that our study can provide references for HULR around the world.

A Spatial Analysis of COVID-19 in African Countries: Evaluating the Effects of Socio-Economic Vulnerabilities and Neighbouring.

The ongoing highly contagious coronavirus disease 2019 (COVID-19) pandemic, which started in Wuhan, China, in December 2019, has now become a global public health problem. Using publicly available data from the COVID-19 data repository of Our World in Data, we aimed to investigate the influences of spatial socio-economic vulnerabilities and neighbourliness on the COVID-19 burden in African countries. We analyzed the first wave (January-September 2020) and second wave (October 2020 to May 2021) of the COVID-19 pandemic using spatial statistics regression models. As of 31 May 2021, there was a total of 4,748,948 confirmed COVID-19 cases, with an average, median, and range per country of 101,041, 26,963, and 2191 to 1,665,617, respectively. We found that COVID-19 prevalence in an Africa country was highly dependent on those of neighbouring Africa countries as well as its economic wealth, transparency, and proportion of the population aged 65 or older (p-value < 0.05). Our finding regarding the high COVID-19 burden in countries with better transparency and higher economic wealth is surprising and counterintuitive. We believe this is a reflection on the differences in COVID-19 testing capacity, which is mostly higher in more developed countries, or data modification by less transparent governments. Country-wide integrated COVID suppression strategies such as limiting human mobility from more urbanized to less urbanized countries, as well as an understanding of a county's social-economic characteristics, could prepare a country to promptly and effectively respond to future outbreaks of highly contagious viral infections such as COVID-19.

Spatial Analysis of Ambient Air Pollution and Cardiovascular Disease (CVD) Hospitalization Across Sweden.

The associations of multiple pollutants and cardiovascular disease (CVD) morbidity, and the spatial variations of these associations have not been nationally studied in Sweden. The main aim of this study was, thus, to spatially analyze the associations between ambient air pollution (black carbon, carbon monoxide, particulate matter (both <10 µm and <2.5 µm in diameter) and Sulfur oxides considered) and CVD admissions while controlling for neighborhood deprivation across Sweden from 2005 to 2010. Annual emission estimates across Sweden along with admission records for coronary heart disease, ischemic stroke, atherosclerotic and aortic disease were obtained and aggregated at Small Areas for Market Statistics level. Global associations were analyzed using global Poisson regression and spatially autoregressive Poisson regression models. Spatial non-stationarity of the associations was analyzed using Geographically Weighted Poisson Regression. Generally, weak but significant associations were observed between most of the air pollutants and CVD admissions. These associations were non-homogeneous, with more variability in the southern parts of Sweden. Our study demonstrates significant spatially varying associations between ambient air pollution and CVD admissions across Sweden and provides an empirical basis for developing healthcare policies and intervention strategies with more emphasis on local impacts of ambient air pollution on CVD outcomes in Sweden.

A spatial study on Keshan disease prevalence and selenoprotein P in the Heilongjiang Province, China.

OBJECTIVES: Few spatial studies on Keshan disease (KD) prevalence and serum selenoprotein P (SELENOP) levels have been reported in the Heilongjiang Province, China. This study aimed to investigate the spatial relationships between KD prevalence, SELENOP levels, and the socio-economic status for the precise prevention and control of KD. MATERIAL AND METHODS: The study was carried out in all the 66 KD endemic counties in the Heilongjiang Province using a non-probability sampling method of a key village survey based on county-wide case-searching. The participants completed a questionnaire and had their serum SELENOP levels measured using enzyme-linked immunosorbent assay. Thematic maps were created, and spatial regression analysis was performed by ordinary least squares using ArcGIS 9.0. RESULTS: Overall, 53 676 residents were surveyed based on case-searching, and blood samples were collected from 409 residents. In total, 50 chronic KD cases were identified with a total prevalence of 9.3/10 000 population. The prevalence in the Tangyuan County was the highest (250/10 000 population). The mean serum SELENOP level was 13.96 mg/l. The spatial regression analysis showed that KD prevalence positively correlated with SELENOP levels and negatively with per capita disposable income among rural residents. CONCLUSIONS: The Tangyuan County should be considered for the precise prevention and control of KD. Further research is necessary to verify the reliability of SELENOP for estimating body selenium levels, and to better understand the relationship between selenium intake and KD in the investigated area. Int J Occup Med Environ Health. 2021;34(5):659-66.

Critical Role of the Subways in the Initial Spread of SARS-CoV-2 in New York City.

We studied the possible role of the subways in the spread of SARS-CoV-2 in New York City during late February and March 2020. Data on cases and hospitalizations, along with phylogenetic analyses of viral isolates, demonstrate rapid community transmission throughout all five boroughs within days. The near collapse of subway ridership during the second week of March was followed within 1-2 weeks by the flattening of COVID-19 incidence curve. We observed persistently high entry into stations located along the subway line serving a principal hotspot of infection in Queens. We used smartphone tracking data to estimate the volume of subway visits originating from each zip code tabulation area (ZCTA). Across ZCTAs, the estimated volume of subway visits on March 16 was strongly predictive of subsequent COVID-19 incidence during April 1-8. In a spatial analysis, we distinguished between the conventional notion of geographic contiguity and a novel notion of contiguity along subway lines. We found that the March 16 subway-visit volume in subway-contiguous ZCTAs had an increasing effect on COVID-19 incidence during April 1-8 as we enlarged the radius of influence up to 5 connected subway stops. By contrast, the March 31 cumulative incidence of COVID-19 in geographically-contiguous ZCTAs had an increasing effect on subsequent COVID-19 incidence as we expanded the radius up to three connected ZCTAs. The combined evidence points to the initial citywide dissemination of SARS-CoV-2 via a subway-based network, followed by percolation of new infections within local hotspots.

Identifying the Impact of Landscape Pattern on Ecosystem Services in the Middle Reaches of the Yangtze River Urban Agglomerations, China.

Clarifying the impact mechanisms of landscape patterns on ecosystem services is highly important for effective ecosystem protection, policymaking, and landscape planning. However, previous literature lacks knowledge about the impact mechanisms of landscape patterns on ecosystem services from a spatial perspective. Thus, this study measured landscape patterns and the ecosystem services value (ESV) using a series of landscape pattern metrics and an improved benefit transfer method based on land-use data from 2015. It explores the impact mechanisms of the landscape pattern metrics on the ESV using the ordinary least-squares method and spatial regression models in the middle reaches of the Yangtze River Urban Agglomerations (MRYRUA), China. We found that forestland was the main landscape type in the MRYRUA, followed by cultivated land, and the fragmentation degree of cultivated land was significantly higher than that of forestland. The findings demonstrate that landscape pattern metrics had a significant impact on ecosystem services, but could vary greatly. Moreover, ecosystem services in the MRYRUA exhibited significant spatial spillover effects and cross-regional collaborative governance was an effective means of landscape planning. This paper acts as a scientific reference and effective guidance for landscape planning and regional ecosystem conservation in MRYRUA and other similarly fast-growing urban agglomerations.

Distribution of COVID-19 Morbidity Rate in Association with Social and Economic Factors in Wuhan, China: Implications for Urban Development.

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.

Macro-level accident modeling in Novi Sad: A spatial regression approach.

In this study, a macroscopic analysis was conducted in order to identify the factors which have an effect on traffic accidents in traffic analysis zones. The factors that impact accidents vary according to the characteristics of the observed area, which in turn leads to a discrepancy between research and practice. The total number of accidents was observed in this paper, along with the number of motorized and non-motorized mode accidents within a three-year period in the city of Novi Sad. The models used for this analysis were spatial predictive models comprised of the classical predictive space model, spatial lag model and spatial error model. The spatial lag model showed the best performances concerning the total number of accidents and number of motorized mode accidents, whereas the spatial error model was prominent within the number of non-motorized mode accidents. The results found that increasing Daily Vehicle-Kilometers Traveled, parking spaces, 5-legged intersections and signalized intersections increased all types of accidents. The other demographic, traffic, road and environment characteristics showed that they had a different effect on the observed types of accidents. The results of this research can be benefitial to reserachers who deal with traffic engineering, space planning as well as making decisions with the aim of preparing countermeasures necessary for road safety improvement in the analysed area.

A Spatial Regression Analysis on the Effect of Neighborhood-Level Trust on Cooperative Behaviors: Comparison With a Multilevel Regression Analysis.

There is no reason to suppose that neighborhood effects based on residents' trust vary according to administrative boundaries. We examined the relationship between neighborhood trust and cooperative behaviors using the spatial Durbin model which assumed that people are influenced by closer neighbors regardless of administrative boundaries, comparing the results with those of the multilevel model. We used data from 476 residents in Arakawa Ward, Tokyo, Japan. For each respondent, we assigned a unique 'neighborhood trust' value weighted by the inverse distance between the respondent and all other respondents as an independent variable. The dependent variables were perceived neighbors' cooperative behaviors and respondents' own cooperative behaviors. The spatial Durbin model showed that spatially weighted neighborhood trust was positively associated with cooperative behaviors. Meanwhile, the multilevel models did not show the statistically significant effect of neighborhood trust. We concluded that the spatial model might model the neighborhood effects in society more precisely.

Spatial distribution characteristics analysis of chronic Keshan disease in China / 中华地方病学杂志

Objective To explore the spatial distribution clustering and influencing factors of chronic Keshan disease in China,and to provide evidence for prevention and control of Keshan disease.Methods Using non-probability sampling methods,combined with case search and key surveys,data on national detection rate of chronic Keshan disease,on disease influencing factors in 2013-2014 were collected;a spatial database was established,and ArcGIS 9.0 software was used to perform global Moran'sI,local Moran's I,local Getis-Ord Gi and inverse distance weighted interpolation analysis for the detection rate of national chronic Keshan disease.Spatial regression was used to analyze the influencing factors of chronic Keshan disease.Results Global autocorrelation analysis showed that Moran's I =0.03,Z =2.72,P ＜ 0.01,indicating that there was aggregation in the detection rate of Keshan disease.The results of local Moran's Ii showed that there were local high-detection rate clusters in the wards of Keshan disease,and the high-high aggregation areas were mainly concentrated in the wards of Gansu,Inner Mongolia,and Shanxi;the high-low aggregation areas were mainly located in the wards of Heilongjiang,Jilin,Shandong;the low-high aggregation area were mainly located in the wards of Heilongjiang.Getis-Ord Gi autocorrelation results showed that Keshan disease hotspots were mainly located in the wards of Inner Mongolia,Heilongjiang,Gansu,Shandong,Shanxi and Yunnan;the results of reverse distance weighted interpolation showed that the detection rates of the counties in Gansu and Inner Mongolia were higher than that in Heilongjiang,Jilin,Liaoning,Shanxi,Shandong,Shaanxi and Yunnan,the detection rate of wards in other provinces was at a lower level.Spatial regression analysis showed that the spatial distribution of chronic Keshan disease was negatively related to rural per capita net income and annual average temperature in the ward (Z =-2.808,-2.747,P ＜ 0.05).Conclusions Global chronic Keshan disease exists spatial aggregation,the local gathering area is mainly located in the wards of Gansu,Inner Mongolia.The spatial distribution of chronic Keshan disease may be affected by the level of rural per capita net income and annual average temperature in the ward.

The Association between Environmental Factors and Scarlet Fever Incidence in Beijing Region: Using GIS and Spatial Regression Models.

(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.

The influence of natural factors on the spatio-temporal distribution of Oncomelania hupensis.

BACKGROUND: We analyzed the influence of natural factors, such as temperature, rainfall, vegetation and hydrology, on the spatio-temporal distribution of Oncomelania hupensis and explored the leading factors influencing these parameters. The results will provide reference methods and theoretical a basis for the schistosomiasis control. METHODS: GIS (Geographic Information System) spatial display and analysis were used to describe the spatio-temporal distribution of Oncomelania hupensis in the study area (Dongting Lake in Hunan Province) from 2004 to 2011. Correlation analysis was used to detect the natural factors associated with the spatio-temporal distribution of O. hupensis. Spatial regression analysis was used to quantitatively analyze the effects of related natural factors on the spatio-temporal distribution of snails and explore the dominant factors influencing this parameter. RESULTS: (1) Overall, the spatio-temporal distribution of O. hupensis was governed by the comprehensive effects of natural factors. In the study area, the average density of living snails showed a downward trend, with the exception of a slight rebound in 2009. The density of living snails showed significant spatial clustering, and the degree of aggregation was initially weak but enhanced later. Regions with high snail density and towns with an HH distribution pattern were mostly distributed in the plain areas in the northwestern and inlet and outlet of the lake. (2) There were space-time differences in the influence of natural factors on the spatio-temporal distribution of O. hupensis. Temporally, the comprehensive influence of natural factors on snail distribution increased first and then decreased. Natural factors played an important role in snail distribution in 2005, 2006, 2010 and 2011. Spatially, it decreased from the northeast to the southwest. Snail distributions in more than 20 towns located along the Yuanshui River and on the west side of the Lishui River were less affected by natural factors, whereas relatively larger in areas around the outlet of the lake (Chenglingji) were more affected. (3) The effects of natural factors on the spatio-temporal distribution of O. hupensis were spatio-temporally heterogeneous. Rainfall, land surface temperature, NDVI, and distance from water sources all played an important role in the spatio-temporal distribution of O. hupensis. In addition, due to the effects of the local geographical environment, the direction of the influences the average annual rainfall, land surface temperature, and NDVI had on the spatio-temporal distribution of O. hupensis were all spatio-temporally heterogeneous, and both the distance from water sources and the history of snail distribution always had positive effects on the distribution O. hupensis, but the direction of the influence was spatio-temporally heterogeneous. (4) Of all the natural factors, the leading factors influencing the spatio-temporal distribution of O. hupensis were rainfall and vegetation (NDVI), and the primary factor alternated between these two. The leading role of rainfall decreased year by year, while that of vegetation (NDVI) increased from 2004 to 2011. CONCLUSIONS: The spatio-temporal distribution of O. hupensis was significantly influenced by natural factors, and the influences were heterogeneous across space and time. Additionally, the variation in the spatial-temporal distribution of O. hupensis was mainly affected by rainfall and vegetation.