Measurement and characteristics of the temporal-spatial evolution of China's healthcare services efficiency.

BACKGROUND: Healthcare services efficiency (HSE) is directly related to the healthcare demands of the general public and also plays an essential role in the country's coordinated economic and social development. METHODS: In this study, the stochastic frontier approach (SFA)-Malmquist model was applied to measure the HSE of 31 Chinese provinces based on panel data from 2010-2020. Then, kernel density estimation, Markov chain, and exploratory spatial data analysis were adopted to study the temporal-spatial dynamic evolution characteristics of the HSE. RESULTS: The study found that China's HSE showed an average value of approximately 0.841, indicating room for improvement. The HSE varied significantly across regions, presenting an "East > Central > West" distribution layout. The TFP of healthcare services in China grew by 1.6% per year, driven mainly by technological progress of 1.8% per year. The trend of the HSE shifting to a high level in China was significant, but its evolution exhibited stability of maintaining the original state, and it was harder to achieve leapfrog transfer. The temporal-spatial evolution of the HSE was also significantly affected by geospatial factors, with a clear spatial spillover effect and spatial agglomeration characteristics. Provinces with high-level HSE exhibited positive spatial spillover effects, while provinces with low-level HSE had negative spatial spillover effects. Thus, the "club convergence" phenomenon of "high efficiency concentration, low efficiency agglomeration, high levels of radiation, and low levels of suppression" was formed in the spatial distribution. CONCLUSIONS: The results indicate that countermeasures should be taken to improve the HSE in China. Theoretical support for the improvement of HSE is provided in this paper.

A study of primary health care service efficiency and its spatial correlation in China.

BACKGROUND: China's primary health care system has undergone major changes since the new round of medical reform in 2009, but the current status of primary health care institution service efficiency is still unsatisfactory. The purpose of this study is to compare and evaluate the China's primary health care institution service efficiency and provide a reference for improving the efficiency and promoting the development of primary health care institution. METHODS: Based on panel data of 31 provinces (municipalities directly under the central government and autonomous regions) in mainland China from 2011 to 2020, using the super efficiency slack-based measure-data envelopment analysis model, to analyze the data from a static perspective, and the changes in the efficiency of primary health care services were analyzed from a dynamic perspective by using the Malmquist index method. Spatial autocorrelation analysis method was used to verify the spatial correlation of primary health care service efficiency among various regions. RESULTS: The number of Primary health care institutions increased from 918,000 in 2011 to 970,000 in 2020. The average primary health care institution service efficiency in the northeastern region including Jilin (0.324), Heilongjiang (0.460), Liaoning (0.453) and northern regions such as Shaanxi (0.344) and Neimenggu (0.403) was at a low level, while the eastern coastal regions such as Guangdong (1.116), Zhejiang (1.211), Shanghai (1.402) have higher average service efficiency levels. The global Moran's I showed the existence of spatial autocorrelation, and the local Moran's I index suggested that the problem of uneven regional development was prominent, showing a contiguous regional distribution pattern. Among them, H-H (high-efficiency regions) were mainly concentrated in Jiangsu, Anhui and Shanghai, and L-L regions (low-efficiency regions) were mostly in northern and northeastern China. CONCLUSION: The service efficiency of primary health care institution in China showed a rising trend in general, but the overall average efficiency was still at a low level, and there were significant geographical differences, which showed a spatial distribution of "high in the east and low in the west, high in the south and low in the north". The northwestern region, after receiving relevant support, has seen a rapid development of primary health care, and its efficiency was steadily improving and gradually reaching a high level. The average primary health care institution service efficiency in the northeastern region including the northern region of China was at a low level, while the average efficiency in the eastern coastal region and some economically developed regions was high, which also verifies the dependence and high symbiosis of primary health care institution service efficiency on regional economy.

What determines how green crop farming can get? Spatial factors or green awareness spillovers.

This study intends to examine if traditional local factors (seasonal weather conditions) and/or green awareness spillovers contribute to the spatial dependency of farmland allocated to organic farming after its uptake in Taiwan. To investigate the push and pull factors to improve the policy targeting on environmentally-friendly farming practices, we assess spatial autocorrelation of the adoption intensity of organic farming with exploratory analysis, and expand that by exploring how explanatory factors affect the adoption intensity using a spatial Tobit regression analysis, taking into consideration that the adoption intensity is a typical example of censored data. Based on township-level data of 323 townships constructed from 213,534 rice farm households drawn from the 2015 Agriculture Census, we find high-high clusters (hot spots) are mostly in the northern and the eastern parts of Taiwan, whereas the majority of low-low clusters (cold spots) locate in central and southern Taiwan. Such spatial aspects of organic adoption intensity suggest that a spatially targeted program in promoting environmental awareness is pertinent to fostering the development of organic agriculture. The results from the spatial lag Tobit regression estimation provide empirical evidence supporting the role of local weather conditions and green awareness spillovers in explaining the spatial patterns of organic agriculture in Taiwan. In light of the stylized fact that the majority of the rice farm households in Taiwan are small with 84% having farmland areas less than 1 ha, the findings provide practical references to policy practitioners in tailoring farm programs or policies in line with the notion of inclusive and sustainable development.

Spatial Differences and Influential Factors of Urban Carbon Emissions in China under the Target of Carbon Neutrality.

Cities are areas featuring a concentrated population and economy and are major sources of carbon emissions (CEs). The spatial differences and influential factors of urban carbon emissions (UCEs) need to be examined to reduce CEs and achieve the target of carbon neutrality. This paper selected 264 cities at the prefecture level in China from 2008 to 2018 as research objects. Their UCEs were calculated by the CE coefficient, and the spatial differences in them were analyzed using exploratory spatial data analysis (ESDA). The influential factors of UCEs were studied with Geodetector. The results are as follows: (1) The UCEs were increasing gradually. Cities with the highest CEs over the study period were located in the urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta, Pearl River Delta, middle reaches of the Yangtze River, and Chengdu-Chongqing. (2) The UCEs exhibited certain global and local spatial autocorrelations. (3) The industrial structure was the dominant factor influencing UCEs.

Spatio-Temporal Differentiation Characteristics and Urbanization Factors of Urban Household Carbon Emissions in China.

This paper explores the spatial relationship between urbanization and urban household carbon emissions at the prefectural level and above cities in China and uses Exploratory Spatial Data Analysis (ESDA) and Geographically Weighted Regression (GWR) to reveal the extent of the impact of urbanization on urban household carbon emissions and the spatial and temporal variation characteristics. The results show that: Overall carbon emissions of urban households in cities of China showed a decreasing trend during the study period, but there were significant differences in the carbon emissions of urban households in the four major regions. In terms of the spatial and temporal characteristics of urban household carbon emissions, the urban "head effect" of urban household carbon emissions is obvious. The high-high clustering of urban household carbon emissions is characterized by a huge triangular spatial distribution of "Beijing-Tianjin-Hebei, Chengdu-Chongqing, and Shanghai". The level of urbanization in Chinese cities at the prefecture level and above shows a spatial pattern of decreasing levels of urbanization in the east, middle, and west. The four subsystems of urbanization are positively correlated with urban household carbon emissions in the same direction. The urbanization factors have a contributory effect on some cities' carbon emissions of urban households, but there are significant regional differences in the impact of urbanization factors on urban household carbon emissions in the eastern, central, and western regions of China, as they are at different stages of rapid urbanization development.

Spatial effects and impact factors of food nitrogen footprint in China based on spatial durbin panel model.

Reactive nitrogen (Nr) has far-reaching advantages and disadvantages on human beings. Nitrogen footprint (NF) is a tool to quantify the use of Nr in the environment. Food nitrogen footprint (FNF) accounts for the largest proportion of the total NF, and the differences between provinces in China exist objectively. In order to explore the spatial correlation and socio-economic driving factors of China's FNF, this paper uses N-calculator tool to calculate the FNF of 30 provinces in China from 2000 to 2018, and uses exploratory spatial data to analyze the spatial correlation and changes of provincial FNF, The driving factors and spatial effects of FNF change in the province were analyzed by using spatial Durbin panel model and spatial regression partial differential method. The results showed that: (1) There is a significant and stable positive spatial dependence and heterogeneity in the FNF among provinces; (2) The direct effect factors of promoting the growth of FNF in the province are urban household Engel coefficient, per capita disposable income of rural residents and rural household Engel coefficient. The main factors of restraining the growth of FNF in the province are wastewater discharge per unit GDP and per capita GDP; (3) the spillover effect is mainly manifested as the negative effect of the increase of urban household Engel coefficient on neighboring provinces, and the spillover effect of per capita disposable income of urban residents and nitrogen fertilizer application rate per unit grain yield on the growth of FNF of neighboring provinces is significant. From the policy level, it is necessary to guide healthy and scientific eating habits, reduce the proportion of meat and fish in the diet structure, reduce the nitrogen fertilizer application per unit grain yield, and improve the efficiency of chemical fertilizer utilization. When formulating relevant policies, government departments should give consideration to the cooperation between provincial and regional governments.

Research on the development trend, evolution, and spatial local characteristics of the intelligent smart medical industry in the Yangtze River Economic Belt.

A comprehensive survey of the development trends, trend evolution, and spatial non-equilibrium characteristics of the intelligent smart medical industry in the Yangtze River Economic Belt could provide significant policy implications for optimizing the spatial layout of the integrated development of the smart medical industry in this region. Using the Criteria Importance Though Intercriteria Correlation objective evaluation method for a study period from 2016 to 2020, 11 provinces and cities along the Yangtze River Economic Belt were quantitatively evaluated in relation to the development of the smart medical industry. Accordingly, the application of exploratory spatial data analysis, the kernel density estimation, and the Dagum Gini coefficient and its decomposition method were used to comprehensively evaluate the trends in the Yangtze River Economic Belt's smart medical industry regarding trend evolution and unbalanced spatial characteristics. The overall level of development of the smart medical industry in the Yangtze River Economic Belt was not good. It showed an increasing spatial pattern from the western inland to eastern coastal regions. The development of the artificial intelligence industry in the Yangtze River Economic Belt showed a positive spatial autocorrelation with significant "spatial spillover effects." The local agglomeration mode was mainly high (a high cluster). In addition, industrial development showed a multi-polarization trend. Although the degree of spatial disequilibrium in the artificial intelligence industry development along the Yangtze River Economic Belt has decreased in recent years, the degree of spatial disequilibrium remains significant.

Research on Urban Medical and Health Services Efficiency and Its Spatial Correlation in China: Based on Panel Data of 13 Cities in Jiangsu Province.

The improvement of the efficiency of medical and health services is of great significance for improving the high-quality and efficient medical and health services system and meeting the increasingly diverse health needs of residents. Based on the panel data of 13 cities in Jiangsu Province, this research analyzed the relative effectiveness of medical and health services from 2015 to 2019 using the super efficiency slack-based measure-data envelopment analysis model, and the Malmquist index method was used to explore the changes in the efficiency of medical and health services from a dynamic perspective. Furthermore, the spatial autocorrelation analysis method was used to verify the spatial correlation of medical and health services efficiency. In general, there is room for improvement in the efficiency of medical and health services in 13 cities in Jiangsu Province. There are obvious differences in regional efficiency, and there is a certain spatial correlation. In the future, the medical and health services efficiency of China's cities should be improved by increasing the investment in high-quality medical and health resources, optimizing their layout and making full use of the spatial spillover effects between neighboring cities to strengthen inter-regional cooperation and exchanges.

Spatiotemporal coupling measurement of industrial wastewater discharge and industrial economy in China.

With the industrial-level panel data on total output and wastewater discharge over the period of 1997 to 2018, this paper employs GIS and ESDA methods to empirically investigate the spatial relationship between industrial total output and wastewater discharge. In this paper, we empirically examine whether and how industrial wastewater discharge in a particular province may affect the wastewater discharge in its neighboring provinces. Results suggest that provinces (municipalities) with large-scale industrial sewage discharge are located along riversides and coastal areas and these discharges then gradually distribute to coastal, central, and western areas. Results also show a strong spatial autocorrelation of industrial wastewater discharge between the observed local province and its neighboring provinces which is increasing over time. In addition, there is also a significant spatial spillover effect of industrial wastewater discharge among neighboring provinces in China's eastern and central regions, indicating a structural convergence of high-pollution industries.

Spatial Disparity and Influencing Factors of Coupling Coordination Development of Economy-Environment-Tourism-Traffic: A Case Study in the Middle Reaches of Yangtze River Urban Agglomerations.

In the process of rapid development of economic globalization and regional integration, the importance of urban agglomeration has become increasingly prominent. It is not only the main carrier for countries and regions to participate in international competition, but also the main place to promote regional coordination and sustainable development. Coordinated economic, environmental, tourism and traffic development is very necessary for sustainable regional development. However, the existing literature lacks research on coupling coordination of the Economy-Environment-Tourism-Traffic (EETT) system in urban agglomeration. In this study, in order to fill this gap, we establish the index system from four dimensions of economy, environment, tourism and traffic, and select the influencing factors from the natural and human perspectives to exam the spatio-temporal changes and influencing factors in the coupling coordination of the EETT system using an integrated method in the Middle Reaches of Yangtze River Urban Agglomerations (MRYRUA), China. The results indicate that the coupling coordination degree of the EETT system transitioned from the uncoordinated period to the coordinated period, while it showed an increasing trend on the whole from 1995 to 2017. The spatial agglomeration effect has been positive since 2010, while "High-High" and "Low-High" agglomeration regions were transferred from the east to the south. Land used for urban construction as a percentage of the urban area and vegetation index has a great impact on the coupling coordination degree. These results provide important guidance for the formulation of integration and coordinated development policy in the MRYRUA, and then increase China's international competitiveness by improving the contribution of urban agglomerations to GDP.

Exploratory study on the spatial relationship between emerging infectious diseases and urban characteristics: Cases from Korea.

In the modern global context of interconnected populations, the recent emergence of infectious diseases involves complex interactions. The purpose of this study is to investigate the spatial correlations between urban characteristics, taking into account the socio-ecological aspects, and the emergence of infectious diseases. Using exploratory spatial data analysis and spatial regression between the infectious disease emergence data and 14 urban characteristics, we analyzed 225 spatial units in South Korea, where there was a re-emergence of measles and a 2015 outbreak of Middle East Respiratory Syndrome. As results of exploratory spatial data analysis, the emerging infectious diseases had spatial dependence and showed spatial clusters. Spatial regression models showed that urban characteristic factors had different effects according to the type of infectious disease. Common factors were characteristics related to low socioeconomic status in water or food-borne diseases and manageable infectious diseases. Intermittent infections disease epidemics are related to high-quality residential environments and the response capacity of the local government. New infectious diseases are different than other infectious diseases, which are related to the ecological environment. This study suggests spatial policies for preventing infectious diseases considering the spatial relationships between urban characteristics and infectious diseases as well as the management of public health.

Spatial pattern matching analysis of grain production and rainfall in the Ziya River Basin.

The exploratory spatial data analysis (ESDA) method, which includes global spatial autocorrelation and local spatial autocorrelation, is used to analyze the spatial pattern of the annual grain production and annual grain production increment in the Ziya River Basin from 2007 to 2017. Then, the spatial mismatch index is used to analyze the spatial mismatch relationship between the annual grain output and annual precipitation in the Ziya River Basin in 2015. The results showed that (1) the spatial pattern of the annual grain production in the Ziya River Basin is stable, with Low-Low clusters and High-High clusters concentrated on the left and right sides of the Ziya River Basin, respectively. The overall difference in the annual grain production of each district and county increased gradually from 2013 to 2017. (2) The local spatial correlation structure of the annual grain production of adjacent districts and counties in the Ziya River Basin had strong stability, and its space-time transition had a certain path dependence or spatial-locking characteristics. The reason why the High-High clusters are concentrated on the right side of the Ziya River is that there are large cultivated areas, such as the Shijin irrigation district, on the right side of the Ziya River Basin. (3) A spatial change rule "the proportion of grain production is low and the proportion of rainfall is high" changed to "the proportion of grain production and rainfall is balanced" and then to "the proportion of grain production is high and the proportion of rainfall is low" in the Ziya River Basin in 2015. The Shijin irrigation district is mainly located in the area where the spatial mismatch between the annual grain output and the average annual rainfall in the Ziya River Basin in 2015 is assessed as grade V, which indicates that the spatial mismatch between the annual grain output and the annual average rainfall is serious. In summary, grain production in the Shijin irrigation district has been increasing annually, while the supply of water for irrigation has not increased as much. There is a serious deficit between the irrigation water supply and the water demand for grain production in the Shijin irrigation district. Therefore, it is necessary to plan for the development and utilization of surface water and groundwater resources and to adjust the planting structure in the Shijin irrigation district for the purpose of saving water.

Spatial effects of carbon emission intensity and regional development in China.

Due to the imbalance of technological level and industrial structure in regional economic development, the same carbon source can bring differentiated carbon emission levels in different regions, thus making the carbon emission show significant regional differences. In order to explore the regional differences in China's provincial carbon emission intensity and the effect of relevant influencing factors, this paper combines EKC model and STIRPAT model to conduct research. Using carbon emission intensity and other influencing factors of China's 30 provinces ranging from 2005 to 2017 to construct a panel data, the authors use exploratory spatial data analysis and Spatial Durbin Model to study the spatial effect of carbon emission intensity in China's provincial regions and the impact of different development factors on carbon emission intensity. The results show that from 2005 to 2017, China's carbon emission intensity gradually declined from east to west and from south to north. The inter-provincial carbon emission intensity of China presents an agglomeration effect in space, and the agglomeration effect gradually weakens with time. In addition, reducing energy intensity can reduce carbon emission intensity to a large extent. By optimizing industrial structure, increasing the degree of foreign trade and promoting financial development, carbon emission intensity can also be inhibited. Therefore, reducing the energy intensity of various industries and establishing inter-regional carbon emission cooperation mechanism will be effective to control the carbon emission intensity.

A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany.

BACKGROUND: As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale. METHODS: By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations. RESULTS: A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables' partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure. CONCLUSIONS: The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.

Spatial Changes in Work Capacity for Occupations Vulnerable to Heat Stress: Potential Regional Impacts From Global Climate Change.

BACKGROUND: As the impact of climate change intensifies, exposure to heat stress will grow, leading to a loss of work capacity for vulnerable occupations and affecting individual labor decisions. This study estimates the future work capacity under the Representative Concentration Pathways 8.5 scenario and discusses its regional impacts on the occupational structure in the Republic of Korea. METHODS: The data utilized for this study constitute the local wet bulb globe temperature from the Korea Meteorological Administration and information from the Korean Working Condition Survey from the Occupational Safety and Health Research Institute of Korea. Using these data, we classify the occupations vulnerable to heat stress and estimate future changes in work capacity at the local scale, considering the occupational structure. We then identify the spatial cluster of diminishing work capacity using exploratory spatial data analysis. RESULTS: Our findings indicate that 52 occupations are at risk of heat stress, including machine operators and elementary laborers working in the construction, welding, metal, and mining industries. Moreover, spatial clusters with diminished work capacity appear in southwest Korea. CONCLUSION: Although previous studies investigated the work capacity associated with heat stress in terms of climatic impact, this study quantifies the local impacts due to the global risk of climate change. The results suggest the need for mainstreaming an adaptation policy related to work capacity in regional development strategies.

A spatio-temporal perspective of China's industrial circular economy development.

The circular economy pattern is the key to solve the contradiction between industrial development and resource protection. Based on the principle of the "3Rs" of the circular economy (reduction, reuse, and resource utilization), and combined with the characteristics of industrial and provincial circular economy development from the perspective of material flow analysis, this study constructs an evaluation index system of industrial circular economy in China's provinces. A combination of the global entropy and the coefficient of variation methods is used to calculate the comprehensive index of industrial circular economy development. Furthermore, this study analyzes the development of China's provincial industrial circular economy from the two dimensions of time and space. The results show that China's industrial circular economy developed rapidly during the Eleventh and Twelfth Five-Year Plan periods. Resource output, resource consumption, and resource reuse and waste disposal have improved over time, but the outlook for emissions of major pollutants is not optimistic. China's industrial circular economy has significant spatial correlation and obvious regional differences, since the development of the circular economy requires a large amount of capital input, and the capacity of capital input fundamentally depends on the regional economic situation. Therefore, the spatial agglomeration pattern of China's industrial circular economy is roughly the same as that of China's economic development. Eastern China has the fastest development, followed by the central and northeast regions, while the western region, especially the northwest, lags the other regions.

Spatial Regression Analysis of Poverty in R.

Poverty has been studied across many social science disciplines, resulting in a large body of literature. Scholars of poverty research have long recognized that the poor are not uniformly distributed across space. Understanding the spatial aspect of poverty is important because it helps us understand place-based structural inequalities. There are many spatial regression models, but there is a learning curve to learn and apply them to poverty research. This manuscript aims to introduce the concepts of spatial regression modeling and walk the reader through the steps of conducting poverty research using R: standard exploratory data analysis, standard linear regression, neighborhood structure and spatial weight matrix, exploratory spatial data analysis, and spatial linear regression. We also discuss the spatial heterogeneity and spatial panel aspects of poverty. We provide code for data analysis in the R environment and readers can modify it for their own data analyses. We also present results in their raw format to help readers become familiar with the R environment.

Spatial Patterns of Urban Wastewater Discharge and Treatment Plants Efficiency in China.

With the rapid economic development, water pollution has become a major concern in China. Understanding the spatial variation of urban wastewater discharge and measuring the efficiency of wastewater treatment plants are prerequisites for rationally designing schemes and infrastructures to control water pollution. Based on the input and output urban wastewater treatment data of the 31 provinces of mainland China for the period 2011⁻2015, the spatial variation of urban water pollution and the efficiency of wastewater treatment plants were measured and mapped. The exploratory spatial data analysis (ESDA) model and super-efficiency data envelopment analysis (DEA) combined Malmquist index were used to achieve this goal. The following insight was obtained from the results. (1) The intensity of urban wastewater discharge increased, and the urban wastewater discharge showed a spatial agglomeration trend for the period 2011 to 2015. (2) The average inefficiency of wastewater treatment plants (WWTPs) for the study period was 39.2%. The plants' efficiencies worsened from the eastern to western parts of the country. (3) The main reasons for the low efficiency were the lack of technological upgrade and scale-up. The technological upgrade rate was -4.8%, while the scale efficiency increases as a result of scaling up was -0.2%. Therefore, to improve the wastewater treatment efficiency of the country, the provinces should work together to increase capital investment and technological advancement.

Spatial Analysis on Measles in Liaoning Province,China,2013-2015 / 中国医科大学学报

Objective To explore the spatial distribution of measles from 2013 through 2015 in Liaoning province,China and to provide references for measles control and prevention. Methods The GeoDa 1.4. 6 program was used to conduct exploratory spatial data analysis to identify the spatial distribution characteristics and pattern of measles in Liaoning province. Results The frequency analysis showed that the measles epidemic situation appeared to have significant positive skewing within 105 counties of Liaoning province in each year from 2013 through 2015. The global trend analysis indicated a balanced trend in 2013 and 2015,and that the high incidence measles areas were located in the eastern and northern provincial regions in 2014. The global Moran'sⅠwas 0.294 5,0.391 9,and 0.147 7,and general G values were 0.015 9,0.012 0,and 0.013 5,revealing a positive spatial autocorrelation and a high-high aggregation model for each year between 2013 and 2015. The local spatial autocorrelation analysis recognized 5 core areas and 25 hot-spot counties with a high incidence of the measles epidemic,mainly distributed in Shenyang,Fuxin,Tieling,Fushun,Benxi,Liaoyang,Panjin,and Huludao. Conclusion Measles cases were clustered geographically in Liaoning province from 2013 through 2015. Spatial epidemiology methods may offer insights for the epidemiologic study of measles.

A 24-year exploratory spatial data analysis of Lyme disease incidence rate in Connecticut, USA.

Despite efforts to control Lyme disease in Connecticut, USA, it remains endemic in many towns, posing a heavy burden. We examined changes in the spatial distribution of significant spatial clusters of Lyme disease incidence rates at the town level from 1991 to 2014 as an approach for targeted interventions. Lyme disease data were grouped into four discrete time periods and incidence rates were smoothed with Empirical Bayes estimation in GeoDa. Local clustering was measured using a local indicator of spatial autocorrelation (LISA). Elliptic spatial scan statistics (SSS) in different shapes and directions were also performed in SaTScan. The accuracy of these two cluster detection methods was assessed and compared for sensitivity, specificity, and overall accuracy. There was significant clustering during each period and significant clusters persisted predominantly in western and eastern parts of the state. Generally, the SSS method was more sensitive, while LISA was more specific with higher overall accuracy in identifying clusters. Even though the location of clusters changed over time, some towns were persistently (across all four periods) identified as clusters in LISA and their neighbouring towns (three of four periods) in SSS suggesting these regions should be prioritized for targeted interventions.