With the rapid increase of the number and influence of floating population in China,it is urgently needed to understand the regional types of China＇s floating population and their spatial characteristics. After revie...With the rapid increase of the number and influence of floating population in China,it is urgently needed to understand the regional types of China＇s floating population and their spatial characteristics. After reviewing the current methods for identifying regional types of floating population,this paper puts forward a new composite-index identification method and its modification version which is consisted of two indexes of the net migration rate and gross migration rate. Then,the traditional single-index and the new composite-index identification methods are empirically tested to explore their spatial patterns and characteristics by using China＇s 2000 census data at county level. The results show：（1） The composite-index identification method is much better than traditional single-index method because it can measure the migration direction and scale of floating simultaneously,and in particular it can identify the unique regional types of floating population with large scale of immigration and emigration. （2） The modified composite-index identification method,by using the share of a region＇s certain type of floating population to the total in China as weights,can effectively correct the over-or under-estimated errors due to the rather large or small total population of a region. （3） The spatial patterns of different regional types of China＇s floating population are closely related to the regional differentiation of their natural environment,population density and socio-economic development level. The three active regional types of floating population are mainly located in the eastern part of China with lower elevation,more than 800 mm precipitation,rather higher population densities and economic development levels.展开更多
Based on the population census data,this paper analyzed the influencing factors and urbanization effects of the floating population in Anhui Province using ArcGIS spatial analysis,factor analysis,multiple liner regres...Based on the population census data,this paper analyzed the influencing factors and urbanization effects of the floating population in Anhui Province using ArcGIS spatial analysis,factor analysis,multiple liner regression,and spatial autocorrelation,and reached the following conclusions:① From 2000 to 2010,the floating population in Anhui Province was concentrated in cities dotted the Huai River and the Yangtze River,and Hefei City absorbed the most inter-provincial floating population and intra-provincial floating population.② The overall economic strength had the greatest impact on attracting floating population,while the income level factor has less impact on attracting floating population.The overall economic strength and the strength of science,education,culture,and health of prefecture-level cities in Anhui Province were more attractive to the intra-provincial floating population and less attractive to the inter-provincial floating population.③ Population mobility promoted urbanization.Large cities could attract more migrants from counties.The urban population system in Anhui Province was generally developing towards the concentration of large cities,while the proportion of the county population in the total population was decreased.展开更多
Detecting and describing movement of vehicles in established transportation infrastructures is an important task.It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the fun...Detecting and describing movement of vehicles in established transportation infrastructures is an important task.It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures.The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories,but also of the inspection of the embedded geographical context.In this paper,we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments.Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters,which are then represented as polygons.For representing temporal variations of the created polygons,we enrich these with vehicle trajectories of other times of the day and additional road network information.In a case study,we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project.The first test results show strong correlations with periodical traffic events in Shanghai.Based on these results,we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.展开更多
基金Foundation： Knowledge Innovation Program of the Chinese Academy of Sciences, No.KZCX2-YW-322, National Natural Science Foundation of China, No.40971102 The National Science and Technology Support Plan, No.2006BAJ11B02-04
文摘With the rapid increase of the number and influence of floating population in China,it is urgently needed to understand the regional types of China＇s floating population and their spatial characteristics. After reviewing the current methods for identifying regional types of floating population,this paper puts forward a new composite-index identification method and its modification version which is consisted of two indexes of the net migration rate and gross migration rate. Then,the traditional single-index and the new composite-index identification methods are empirically tested to explore their spatial patterns and characteristics by using China＇s 2000 census data at county level. The results show：（1） The composite-index identification method is much better than traditional single-index method because it can measure the migration direction and scale of floating simultaneously,and in particular it can identify the unique regional types of floating population with large scale of immigration and emigration. （2） The modified composite-index identification method,by using the share of a region＇s certain type of floating population to the total in China as weights,can effectively correct the over-or under-estimated errors due to the rather large or small total population of a region. （3） The spatial patterns of different regional types of China＇s floating population are closely related to the regional differentiation of their natural environment,population density and socio-economic development level. The three active regional types of floating population are mainly located in the eastern part of China with lower elevation,more than 800 mm precipitation,rather higher population densities and economic development levels.
文摘Based on the population census data,this paper analyzed the influencing factors and urbanization effects of the floating population in Anhui Province using ArcGIS spatial analysis,factor analysis,multiple liner regression,and spatial autocorrelation,and reached the following conclusions:① From 2000 to 2010,the floating population in Anhui Province was concentrated in cities dotted the Huai River and the Yangtze River,and Hefei City absorbed the most inter-provincial floating population and intra-provincial floating population.② The overall economic strength had the greatest impact on attracting floating population,while the income level factor has less impact on attracting floating population.The overall economic strength and the strength of science,education,culture,and health of prefecture-level cities in Anhui Province were more attractive to the intra-provincial floating population and less attractive to the inter-provincial floating population.③ Population mobility promoted urbanization.Large cities could attract more migrants from counties.The urban population system in Anhui Province was generally developing towards the concentration of large cities,while the proportion of the county population in the total population was decreased.
文摘Detecting and describing movement of vehicles in established transportation infrastructures is an important task.It helps to predict periodical traffic patterns for optimizing traffic regulations and extending the functions of established transportation infrastructures.The detection of traffic patterns consists not only of analyses of arrangement patterns of multiple vehicle trajectories,but also of the inspection of the embedded geographical context.In this paper,we introduce a method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments.Those vehicle trajectory intersection points (TIP) are frequently visited locations within urban road networks and are subsequently formed into density-connected clusters,which are then represented as polygons.For representing temporal variations of the created polygons,we enrich these with vehicle trajectories of other times of the day and additional road network information.In a case study,we test our approach on massive taxi Floating Car Data (FCD) from Shanghai and road network data from the OpenStreetMap (OSM) project.The first test results show strong correlations with periodical traffic events in Shanghai.Based on these results,we reason out the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering.
