The social functionality of places(e.g.school,restaurant)partly determines human behaviors and reflects a region’s functional configuration.Semantic descriptions of places are thus valuable to a range of studies of h...The social functionality of places(e.g.school,restaurant)partly determines human behaviors and reflects a region’s functional configuration.Semantic descriptions of places are thus valuable to a range of studies of humans and geographic spaces.Assuming their potential impacts on human verbalization behaviors,one possibility is to link the functions of places to verbal representations such as users’postings in location-based social networks(LBSNs).In this study,we examine whether the heterogeneous user-generated text snippets found in LBSNs reliably reflect the semantic concepts attached with check-in places.We investigate Foursquare because its available categorization hierarchy provides rich a-priori semantic knowledge about its check-in places,which enables a reliable verification of the semantic concepts identified fromuser-generated text snippets.A latent semantic analysis is conducted on a large Foursquare check-in dataset.The results confirm that attached text messages can represent semantic concepts by demonstrating their large correspondence to the official Foursquare venue categorization.To further elaborate the representativeness of text messages,this work also performs an investigation on the textual terms to quantify their abilities of representing semantic concepts(i.e.,representativeness),and another investigation on semantic concepts to quantify how well they can be represented by text messages(i.e.,representability).The results shed light on featured terms with strong locational characteristics,as well as on distinctive semantic concepts with potentially strong impacts on human verbalizations.展开更多
近年来,以微博、微信朋友圈、Foursquare、Gowalla、Facebook Place等基于位置的社交网络(Location Based Social Network,LBSN)得到迅速发展,庞大的用户群体每天都会通过这些服务产生大量的签到数据,这些异构的网络数据为研究用户的行...近年来,以微博、微信朋友圈、Foursquare、Gowalla、Facebook Place等基于位置的社交网络(Location Based Social Network,LBSN)得到迅速发展,庞大的用户群体每天都会通过这些服务产生大量的签到数据,这些异构的网络数据为研究用户的行为特征及潜在特征提供巨大的机遇与挑战。然而现有研究少有对LBSN签到数据进行具体描述与分析,以服务于兴趣点推荐为最终目的,利用Foursquare、Gowalla数据集从用户签到轨迹、用户签到频次、用户签到位置3个方面对用户签到数据进行分析、可视化,探索了用户签到数据中存在的空间特征及个性化行为。展开更多
The purpose of this study is to analyze the spatial patterns of location-based social network (LBSN) data in Seoul using the spatial analysis techniques of geographic information system (GIS). The study explores the a...The purpose of this study is to analyze the spatial patterns of location-based social network (LBSN) data in Seoul using the spatial analysis techniques of geographic information system (GIS). The study explores the applications of LBSN data by analyzing the association between Seoul’s Foursquare venues data created based on user participation and the city’s characteristics. The data regarding Foursquare venues were compiled with a program we created based on Foursquare’s Python API. The compiled information was converted into GIS data, which in turn was depicted as a heat map. Cluster analysis was then performed based on hotspots and the correlation with census variables was analyzed for each administrative unit using geographically weighted regression (GWR). Based on analytical results, we were able to identify venue clusters around city centers, as well as differences in hotspots for various venue categories and correlations with census variables.展开更多
Embraced within the framework of crime opportunities integrated with Social Disorganization theory and Broken Windows theory,this paper intends to explore the patterns of four types of acquisitive crimes,using social ...Embraced within the framework of crime opportunities integrated with Social Disorganization theory and Broken Windows theory,this paper intends to explore the patterns of four types of acquisitive crimes,using social media data,i.e.,Twitter,Foursquare and cross-sectional data acquired through text analysis technique.With Greater London as the study area,models like negative binominal regression(NBR)and geographically weighted regression(GWR)are performed to illustrate the aggregated relationships between acquisitive crimes and crime opportunities at London-wide and sub-regional MSOAs levels respectively.The results work towards to hypotheses that:the tweets sentiment could reflect property-related crime rates positively in light of Broken Windows Theory;more tweets with negative sentiment may incur increases in acquisitive crimes.It contributed to existing studies in(1)providing empirical evidence for integrating these three theories;(2)complementing current research on local discrepancies of acquisitive crimes by utilising both GWR and NBR models;(3)challenging the traditional stereotypes about racial disparities with the finding that ethnic heterogeneity and instrumental crimes have counterintuitive association,especially taking education factor into consideration;(4)implicating some localised acquisitive crime prevention strategies to policy makers in light of the reality that the relationship between local variations and different crime types may vary by place.展开更多
基金supported by the German Research Foundation(DFG)through the priority program“Volunteered Geographic Information:Interpretation,Visualisation and Social Computing”(SPP 1894).
文摘The social functionality of places(e.g.school,restaurant)partly determines human behaviors and reflects a region’s functional configuration.Semantic descriptions of places are thus valuable to a range of studies of humans and geographic spaces.Assuming their potential impacts on human verbalization behaviors,one possibility is to link the functions of places to verbal representations such as users’postings in location-based social networks(LBSNs).In this study,we examine whether the heterogeneous user-generated text snippets found in LBSNs reliably reflect the semantic concepts attached with check-in places.We investigate Foursquare because its available categorization hierarchy provides rich a-priori semantic knowledge about its check-in places,which enables a reliable verification of the semantic concepts identified fromuser-generated text snippets.A latent semantic analysis is conducted on a large Foursquare check-in dataset.The results confirm that attached text messages can represent semantic concepts by demonstrating their large correspondence to the official Foursquare venue categorization.To further elaborate the representativeness of text messages,this work also performs an investigation on the textual terms to quantify their abilities of representing semantic concepts(i.e.,representativeness),and another investigation on semantic concepts to quantify how well they can be represented by text messages(i.e.,representability).The results shed light on featured terms with strong locational characteristics,as well as on distinctive semantic concepts with potentially strong impacts on human verbalizations.
文摘近年来,以微博、微信朋友圈、Foursquare、Gowalla、Facebook Place等基于位置的社交网络(Location Based Social Network,LBSN)得到迅速发展,庞大的用户群体每天都会通过这些服务产生大量的签到数据,这些异构的网络数据为研究用户的行为特征及潜在特征提供巨大的机遇与挑战。然而现有研究少有对LBSN签到数据进行具体描述与分析,以服务于兴趣点推荐为最终目的,利用Foursquare、Gowalla数据集从用户签到轨迹、用户签到频次、用户签到位置3个方面对用户签到数据进行分析、可视化,探索了用户签到数据中存在的空间特征及个性化行为。
文摘The purpose of this study is to analyze the spatial patterns of location-based social network (LBSN) data in Seoul using the spatial analysis techniques of geographic information system (GIS). The study explores the applications of LBSN data by analyzing the association between Seoul’s Foursquare venues data created based on user participation and the city’s characteristics. The data regarding Foursquare venues were compiled with a program we created based on Foursquare’s Python API. The compiled information was converted into GIS data, which in turn was depicted as a heat map. Cluster analysis was then performed based on hotspots and the correlation with census variables was analyzed for each administrative unit using geographically weighted regression (GWR). Based on analytical results, we were able to identify venue clusters around city centers, as well as differences in hotspots for various venue categories and correlations with census variables.
文摘Embraced within the framework of crime opportunities integrated with Social Disorganization theory and Broken Windows theory,this paper intends to explore the patterns of four types of acquisitive crimes,using social media data,i.e.,Twitter,Foursquare and cross-sectional data acquired through text analysis technique.With Greater London as the study area,models like negative binominal regression(NBR)and geographically weighted regression(GWR)are performed to illustrate the aggregated relationships between acquisitive crimes and crime opportunities at London-wide and sub-regional MSOAs levels respectively.The results work towards to hypotheses that:the tweets sentiment could reflect property-related crime rates positively in light of Broken Windows Theory;more tweets with negative sentiment may incur increases in acquisitive crimes.It contributed to existing studies in(1)providing empirical evidence for integrating these three theories;(2)complementing current research on local discrepancies of acquisitive crimes by utilising both GWR and NBR models;(3)challenging the traditional stereotypes about racial disparities with the finding that ethnic heterogeneity and instrumental crimes have counterintuitive association,especially taking education factor into consideration;(4)implicating some localised acquisitive crime prevention strategies to policy makers in light of the reality that the relationship between local variations and different crime types may vary by place.
