A Spatial Epidemiology Case Study of Coronavirus (COVID-19) Disease and Geospatial Technologies

Spatiotemporal pattern analysis provides a new dimension for data interpretation due to new trends in computer vision and big data analysis. The main aim of this study was to explore the recent advances in geospatial technologies to examine the spatiotemporal pattern of COVID-19 at the Public Health Unit (PHU) level in Ontario, Canada. The spatial autocorrelation results showed that the incidence rate (no. of confirmed cases per 100,000 population–IR/100K) was clustered at the PHU level and found a tendency of clustering high values. Some PHUs in Southern Ontario were identified as hot spots, while Northern PHUs were cold spots. The space-time cube showed an overall trend with a 99% confidence level. Considerable spatial variability in incidence intensity at different times suggested that risk factors were unevenly distributed in space and time. The study also created a regression model that explains the correlation between IR/100K values and potential socioeconomic factors.

Hypertension Prevalence,Awareness,Treatment,and Control and Their Associated Socioeconomic Factors in China:A Spatial Analysis of A National Representative Survey

Objective We aimed to investigate and interpret the associations between socioeconomic factors and the prevalence, awareness, treatment, and control of hypertension at the provincial level in China.Methods A nationally and provincially representative sample of 179,059 adults from the China Chronic Disease and Nutrition Surveillance study in 2015–2016 was used to estimate hypertension burden. The spatial Durbin error model was fitted to investigate socioeconomic factors associated with hypertension indicators.Results Overall, it was estimated that 29.20% of the participants were hypertensive nationwide,among whom, 34.32% were aware of their condition, 27.69% had received antihypertensive treatment,and 7.81% had controlled their condition. Per capita gross domestic product(GDP) was associated with hypertension prevalence(coefficient:-2.95, 95% CI:-5.46,-0.45) and control(coefficient: 6.35, 95% CI:1.36, 11.34) among adjacent provinces and was also associated with awareness(coefficient: 2.93, 95%CI: 1.12, 4.74) and treatment(coefficient: 2.67, 95% CI: 1.21, 4.14) in local province. Beds of internal medicine(coefficient: 2.66, 95% CI: 1.08, 4.23) was associated with control in local province. Old dependency ratio(coefficient:-3.58, 95% CI:-5.35,-1.81) was associated with treatment among adjacent provinces and with control(coefficient:-1.69, 95% CI:-2.42,-0.96) in local province.Conclusion Hypertension indicators were not only directly influenced by socioeconomic factors of local area but also indirectly affected by characteristics of geographical neighbors. Population-level strategies should involve optimizing supportive socioeconomic environment by integrating clinical care and public health services to decrease hypertension burden.

Spatial Nonparametric Regression Estimation: Non-isotropic Case

Data collected on the surface of the earth often has spatial interaction. In this paper, a non-isotropic mixing spatial data process is introduced, and under such a spatial structure a nonparametric kernel method is suggested to estimate a spatial conditional regression. Under mild regularities, sufficient conditions are derived to ensure the weak consistency as well as the convergence rates for the kernel estimator. Of interest are the following: (1) All the conditions imposed on the mixing coefficient and the bandwidth are simple; (2) Differently from the time series setting, the bandwidth is found to be dependent on the dimension of the site in space as well; (3) For weak consistency, the mixing coefficient is allowed to be unsummable and the tendency of sample size to infinity may be in different manners along different direction in space; (4) However, to have an optimal convergence rate, faster decreasing rates of mixing coefficient and the tendency of sample size to infinity along each direction are required.

Spatial econometric analysis of carbon emissions from energy consumption in China

Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support, this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy consumption, spatial autocorrelation analysis of carbon emissions, spatial regression analysis between carbon emissions and their influencing factors. The analyzed results are shown as follows. （1） Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly. （2） The global spatial autocorrelation of carbon emissions from energy consumption in- creased from 1997 to 2009, the spatial autocorrelation analysis showed that there exists a ＂polarization＂ phenomenon, the centre of ＂High-High＂ agglomeration did not change greatly but expanded currently, the centre of ＂Low-Low＂ agglomeration also did not change greatly but narrowed currently. （3） The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population, R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population. The contribution of population to carbon emissions increased but the contribution of GDP decreased from 1997 to 2009. The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population, so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.

Understanding the pattern and mechanism of spatial concentration of urban land use, population and economic activities: a case study in Wuhan, China

Quantifying the aggregation patterns of urban population, economic activities, and land use are essential for understanding compact development, but little is known about the difference among the distribution characteristics and how the built environment influences urban aggre-gation. In this study, five elements are collected in Wuhan, China, namely population density, floor area ratio, business POIs, road network and built-up area as the representative of urban population, economic activities and land use. An inverse S-shape function is employed to fit the elements’ macro distribution. An aggregation degree index is proposed to measure the aggregation level of urban elements. The kernel density estimation is used to identify the aggregation patterns. The spatial regression model is used to identify the built environment factors influencing the spatial distribution of urban elements. Results indicates that all urban elements decay outward from the city center in an inverse S-shape manner. The business Pointof- Interest (POI) density and population density are highly aggregated;floor area ratio and road density are moderately aggregated, whereas the built-up density is poorly aggregated. Three types of spatial aggregation patterns are identified: a point-shaped pattern, an axial pattern and a planar pattern. The spatial regression modeling shows that the built environment is associated with the distribution of the urban population, economic activities and land use. Destination accessibility factors, transit accessibility factors and land use diversity factors shape the distribution of the business POI density, floor area ratio and road density. Design factors are positively associated with population density, floor area ratio and built-up density. Future planning should consider the varying spatial concentration of urban population, economic activities and land use as well as their relationships with built environment attributes. Results of this study will provide a systematic understanding of aggregation of urban land use, popula-tion, and economic activities in megacities as well as some suggestions for planning and compact development.

长江中游城市群交通可达性对生态系统服务供需耦合影响研究

The spatial relationships between traffic accessibility and supply and demand(S&D)of ecosystem services(ESs)are essential for the formulation of ecological compensation policies and ESs regulation.In this study,an ESs matrix and coupling analysis method were used to assess ESs S&D based on land-use data for 2000,2010,and 2020,and spatial regression models were used to analyze the correlated impacts of traffic accessibility.The results showed that the ESs supply and balance index in the middle reaches of the Yangtze River urban agglomeration(MRYRUA)continuously decreased,while the demand index increased from 2000 to 2020.The Gini coefficients of these indices continued to increase but did not exceed the warning value(0.4).The coupling degree of ESs S&D continued to increase,and its spatial distribution patterns were similar to that of the ESs demand index,with significantly higher values in the plains than in the montane areas,contrasting with those of the ESs supply index.The results of global bivariate Moran’s I analysis showed a significant spatial dependence between traffic accessibility and the degree of coupling between ESs S&D;the spatial regression results showed that an increase in traffic accessibility promoted the coupling degree.The present results provide a new perspective on the relationship between traffic accessibility and the coupling degree of ESs S&D,representing a case study for similar future research in other regions,and a reference for policy creation based on the matching between ESs S&D in the MRYRUA.

交通可达性对生态系统服务的影响——一个集成空间方法

The continuous degradation of ecosystem services is an important challenge faced by the world.Improvements in transportation infrastructure have had substantial impacts on economic development and ecosystem services.Exploring the influence of traffic accessibility on ecosystem services can delay or stop their deterioration;however,studies on its impact are lacking.This study addresses this gap by analysing the impact of traffic accessibility on ecosystem services using an integrated spatial regression approach based on an evaluation of the ecosystem services value(ESV)and traffic accessibility in the Middle Reaches of the Yangtze River Urban Agglomeration(MRYRUA)in China.The results indicated that the ESV in the MRYRUA continuously decreased during the study period,and the average ESV in plain areas,areas surrounding the core cities,and areas along the main traffic routes was significantly lower than that in areas along the Yangtze River and the surrounding mountainous areas.Traffic accessibility continued to increase during the study period,and the high-value areas centred on Wuhan,Changsha,Nanchang,and Yichang were radially distributed.The global bivariate spatial autocorrelation coefficient between the average ESV and traffic accessibility was negative.The average ESV and traffic accessibility exhibited significant spatial dependence and spatial heterogeneity.Spatial regression also proved that there was a negative association between the average ESV and traffic accessibility,and scale effects were evident.The findings of this study have important policy implications for future ecological protection and transportation planning.

Simulated impacts of 3D urban morphology on urban transportation in megacities: case study in Beijing

Urban morphology and morphology change and their impacts on urban transportation have been studied extensively in planar urban space.The essential feature of urban space,however,is its three-dimensionality(3D),and few studies have been conducted from a 3D perspective,overly limiting the accuracy of studies on the relationships between urban morphology and transportation.The aim of this paper is to simulate the impacts of 3D urban morphologies on urban transportation under the Digital Earth framework.On the basis of the principle that population distribution and movement are largely confined by 3D urban morphologies,which affect transportation,high spatial resolution remote sensing imagery and a thematic vector data-set were used to extract urban morphology and transportation-related variables.With a combination of three research methods-factor analysis,spatial regression analysis and Euclidean allocation-we provide an effective method to construct a simulation model.The paper indicates three general results.First,building capacity in the urban space has the most significant impact on traffic condition.Second,obvious urban space otherness,reflecting both use density characteristics and functional character-istics of urban space,mostly results in heavier traffic flow pressure.Third,no single morphology density indicator or single urban structure indicator can reflect its contribution to the pressure of traffic flow directly,but a combination of these different indicators has the ability to do so.

Visceral leishmaniasis in northwest China from 2004 to 2018:a spatio-temporal analysis

Background: Although visceral leishmaniasis(VL),a disease caused by parasites,is controlled in most provinces in China,it is still a serious public health problem and remains fundamentally uncontrolled in some northwest provinces and autonomous regions.The objective of this study is to explore the spatial and temporal characteristics of VL in Sichuan Province,Gansu Province and Xinjiang Uygur Autonomous Region in China from 2004 to 2018 and to identify the risk areas for VL transmission.Methods:: Spatiotemporal models were applied to explore the spatio-temporal distribution characteristics of VL and the association between VL and meteorological factors in western China from 2004 to 2018.Geographic information of patients from the National Diseases Reporting Information System operated by the Chinese Center for Disease Control and Prevention was defined according to the address code from the surveillance data.Results: During our study period,nearly 90%of cases occurred in some counties in three western regions(Sichuan Province,Gansu Province and Xinjiang Uygur Autonomous Region),and a significant spatial clustering pattern was observed.With our spatiotemporal model,the transmission risk,autoregressive risk and epidemic risk of these counties during our study period were also well predicted.The number of VL cases in three regions of western China concentrated on a few of counties.VL in Kashi Prefecture,Xinjiang Uygur Autonomous Region is still serious prevalent,and integrated control measures must be taken in different endemic areas.Conclusions: The number of VL cases in three regions of western China concentrated on a few of counties.VL in Kashi Prefecture,Xinjiang Uygur Autonomous Region is still serious prevalent,and integrated control measures must be taken in different endemic areas.Our findings will strengthen the VL control programme in China.

MODIS-based estimation of air temperature of the Tibetan Plateau

The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature （LST） data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression （OLS） and geographical weighted regression （GWR） of monthly mean air temperature （Ta） with monthly mean land surface temperature （Ts） were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July （the warmest month） at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.