Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis(CL) in Fars province, Iran.Methods: Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. T...Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis(CL) in Fars province, Iran.Methods: Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. Time-series data were recorded from 29 201 cases in Fars province, Iran from 2010 to 2015, which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters. Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using Sa TScan software.Results: This study resulted in statistically significant spatiotemporal clusters of CL(P < 0.05). The most likely cluster contained 350 cases from 1 July 2010 to 30 November2010. Besides, 5 secondary clusters were detected in different periods of time. Finally,statistically significant sub-clusters were found within the three large clusters(P < 0.05).Conclusions: Transmission of CL followed spatiotemporal pattern in Fars province,Iran. This can have an important effect on future studies on prediction and prevention of CL.展开更多
Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clus...Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.展开更多
Since joining the WTO, China’s automobile market has shown a rapid development trend, and the automobile market is becoming more and more important to China’s economic recovery and high-quality development. The auto...Since joining the WTO, China’s automobile market has shown a rapid development trend, and the automobile market is becoming more and more important to China’s economic recovery and high-quality development. The automobile manufacturing industry is one of the pillar industries of China, but facing downward pressure since 2018. The paper studies spatiotemporal evolution characteristics and influencing factors of automobile market since WTO Accession using methods including ESDA, DTW cluster analysis and Spatial panel Dubin model. The result shows that: 1) China’s automobile sales have grown rapidly and three development stages have occurred since WTO Accession;2) Four types of China’s automobile markets have significant spatial differentiation, while the same pattern present spatial agglomeration characteristics;3) The crucial reasons for spatial separation of production and sales in China’s automobile market include implementation of purchase restrictions in more and more cities, gradual consolidation of spatial pattern of automobile production, and the fact that some automobile production areas are far away from consumer market;4) The provincial spatial weighted average centers of automobile sales are mainly distributed in southeast Henan, and show a trend of moving to the southwest;5) The estimated coefficients of factors such as GDP, financial added value, the proportion of highway, the volume of highway freight, and implementation of automobile consumption incentive policies are all significantly positive, and some factors have positive spatial spillover effects. Existing research on the automobile market lacks analysis based on long-time series data. This study uses long-time series data to provide a certain reference for future research in related directions.展开更多
There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteri...There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.展开更多
基金the PhD dissertation(pro-posal No.12439)written by Marjan Zare and approved by Research Vice-chancellor of Shiraz University of Medical Sci-ences.
文摘Objective: To assess the spatiotemporal trait of cutaneous leishmaniasis(CL) in Fars province, Iran.Methods: Spatiotemporal cluster analysis was conducted retrospectively to find spatiotemporal clusters of CL cases. Time-series data were recorded from 29 201 cases in Fars province, Iran from 2010 to 2015, which were used to verify if the cases were distributed randomly over time and place. Then, subgroup analysis was applied to find significant sub-clusters within large clusters. Spatiotemporal permutation scans statistics in addition to subgroup analysis were implemented using Sa TScan software.Results: This study resulted in statistically significant spatiotemporal clusters of CL(P < 0.05). The most likely cluster contained 350 cases from 1 July 2010 to 30 November2010. Besides, 5 secondary clusters were detected in different periods of time. Finally,statistically significant sub-clusters were found within the three large clusters(P < 0.05).Conclusions: Transmission of CL followed spatiotemporal pattern in Fars province,Iran. This can have an important effect on future studies on prediction and prevention of CL.
基金The study is fully supported by the National Natural Science Foundation of China(61873283)the Changsha Science&Technology Project(KQ1707017)the Innovation Driven Project of the Central South University(2019CX005).
文摘Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.
文摘Since joining the WTO, China’s automobile market has shown a rapid development trend, and the automobile market is becoming more and more important to China’s economic recovery and high-quality development. The automobile manufacturing industry is one of the pillar industries of China, but facing downward pressure since 2018. The paper studies spatiotemporal evolution characteristics and influencing factors of automobile market since WTO Accession using methods including ESDA, DTW cluster analysis and Spatial panel Dubin model. The result shows that: 1) China’s automobile sales have grown rapidly and three development stages have occurred since WTO Accession;2) Four types of China’s automobile markets have significant spatial differentiation, while the same pattern present spatial agglomeration characteristics;3) The crucial reasons for spatial separation of production and sales in China’s automobile market include implementation of purchase restrictions in more and more cities, gradual consolidation of spatial pattern of automobile production, and the fact that some automobile production areas are far away from consumer market;4) The provincial spatial weighted average centers of automobile sales are mainly distributed in southeast Henan, and show a trend of moving to the southwest;5) The estimated coefficients of factors such as GDP, financial added value, the proportion of highway, the volume of highway freight, and implementation of automobile consumption incentive policies are all significantly positive, and some factors have positive spatial spillover effects. Existing research on the automobile market lacks analysis based on long-time series data. This study uses long-time series data to provide a certain reference for future research in related directions.
基金Under the auspices of National Social Science Foundation of China (No.21BJY202)。
文摘There are significant differences between urban and rural bed-and-breakfasts(B&Bs)in terms of customer positioning,economic strength and spatial carrier.Accurately identifying the differences in spatial characteristics and influencing factors of each type,is essential for creating urban and rural B&B agglomeration areas.This study used density-based spatial clustering of applications with noise(DBSCAN)and the multi-scale geographically weighted regression(MGWR)model to explore similarities and differences in the spatial distribution patterns and influencing factors for urban and rural B&Bs on the Jiaodong Peninsula of China from 2010 to 2022.The results showed that:1)both urban and rural B&Bs in Jiaodong Peninsula went through three stages:a slow start from 2010 to 2015,rapid development from 2015 to 2019,and hindered development from 2019 to 2022.However,urban B&Bs demonstrated a higher development speed and agglomeration intensity,leading to an increasingly evident trend of uneven development between the two sectors.2)The clustering scale of both urban and rural B&Bs continued to expand in terms of quantity and volume.Urban B&B clusters characterized by a limited number,but a higher likelihood of transitioning from low-level to high-level clusters.While the number of rural B&B clusters steadily increased over time,their clustering scale was comparatively lower than that of urban B&Bs,and they lacked the presence of high-level clustering.3)In terms of development direction,urban B&B clusters exhibited a relatively stable pattern and evolved into high-level clustering centers within the main urban areas.Conversely,rural B&Bs exhibited a more pronounced spatial diffusion effect,with clusters showing a trend of multi-center development along the coastline.4)Transport emerged as a common influencing factor for both urban and rural B&Bs,with the density of road network having the strongest explanatory power for their spatial distribution.In terms of differences,population agglomeration had a positive impact on the distribution of urban B&Bs and a negative effect on the distribution of rural B&Bs.Rural B&Bs clustering was more influenced by tourism resources compared with urban B&Bs,but increasing tourist stay duration remains an urgent issue to be addressed.The findings of this study could provide a more precise basis for government planning and management of urban and rural B&B agglomeration areas.
