Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility最新文献

Next Place Prediction: A Systematic Literature Review 下一站预测:系统文献综述

Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility Pub Date : 2018-11-06 DOI: 10.1145/3283590.3283596

Christian Schreckenberger, Simon Beckmann, Christian Bartelt

引用次数: 9

Measuring Inter-city Network Using Digital Footprints from Twitter Users 利用Twitter用户的数字足迹测量城际网络

Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility Pub Date : 2018-11-06 DOI: 10.1145/3283590.3283594

Yuqin Jiang, Zhenlong Li, X. Ye

{"title":"Measuring Inter-city Network Using Digital Footprints from Twitter Users","authors":"Yuqin Jiang, Zhenlong Li, X. Ye","doi":"10.1145/3283590.3283594","DOIUrl":"https://doi.org/10.1145/3283590.3283594","url":null,"abstract":"City connectivity is an important measurement in characterizing human dynamics from regional to international scales. World City Network has been built based on companies' communication. The interactions between spatial and social dimensions of cities have both conceptual and practical significance. To further expand the studies of inter-city network in the big social data context, this research builds a network at the county level using digital footprints from Twitter users. Retrieving geotags from Twitter users, we identify the connection strength of each pair of counties based on the amounts of shared users who leave digital footprints on both counties. Using the shared user amount as the weighted link and each county as the node, we build a county-to-county user flow network. Various network structures have been detected at the state level. In addition, by creating a direct flow chain, we can identify influential counties and its hinterland. This network demonstrates how human mobility operate across various spatial settings and distances. Results of this study can be used in transportation planning, regional planning and metropolitan management.","PeriodicalId":404513,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132289709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

On the Predictability of a User's Next Check-in Using Data from Different Social Networks 基于不同社交网络数据的用户下一次签到的可预测性

Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility Pub Date : 2018-11-06 DOI: 10.1145/3283590.3283592

D. Teixeira, M. Alvim, J. Almeida

引用次数: 4

Spatial-Data-Driven Student Characterization: Trajectory Sequence Alignment based on Student Smart Card Transactions 空间数据驱动的学生特征:基于学生智能卡交易的轨迹序列对齐

Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility Pub Date : 2018-11-06 DOI: 10.1145/3283590.3283591

Sungha Ju, Sangyoon Park, Hyoungjoon Lim, S. Yun, J. Heo

{"title":"Spatial-Data-Driven Student Characterization: Trajectory Sequence Alignment based on Student Smart Card Transactions","authors":"Sungha Ju, Sangyoon Park, Hyoungjoon Lim, S. Yun, J. Heo","doi":"10.1145/3283590.3283591","DOIUrl":"https://doi.org/10.1145/3283590.3283591","url":null,"abstract":"Analyzing students' characteristic can provide much information for campus planning, education design and student management. This study built students' sequential trajectories based on student smart card transactions and calculate similarity scores for finding relationship between students' trajectories and academic performance. The data used in this study are student smart card transaction data and attendance information of Yonsei university Songdo campus students. Based on this, the trajectory of each student is created into daily context sequence and connected in semester unit. In order to calculate the similarity of one semester trajectory between two students, Needleman-Wunsch Algorithm, which is mainly used for comparison of the DNA nucleotide sequences of two different species, was applied. The similarity score of trajectory sequences for student pair were calculated for 685 students in spring semester. For finding relation with academic performance, authors divided students into two groups; one group with high similarity score for both students in the pair and the other with pair of students with low similarity score. 2-sample T-test was conducted afterward in to determine whether the GPA of these groups were different form the overall distribution of student GPA. As a result, the mean value of GPA of the students with low similarity scores were statistically significantly lower than the overall mean value of GPA. This means that the trajectory sequence of students with lower GPA is less similar than the other students. The results of this study indicate that trajectory information based on spatial data is related to characteristics such as student academic achievement, and it is possible to analyze characteristics of students through spatial trajectory sequence information.","PeriodicalId":404513,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126661881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Implementation of Floating Population Analysis for Smart Cities: A case study in Songdo Incheon South Korea 智慧城市流动人口分析的实施——以韩国仁川松岛为例

Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility Pub Date : 2018-11-06 DOI: 10.1145/3283590.3283595

S. Yun, Hieu Minh Nguyen, Sang Yoon Park, Hyoungjoon Lim, J. Heo

{"title":"Implementation of Floating Population Analysis for Smart Cities: A case study in Songdo Incheon South Korea","authors":"S. Yun, Hieu Minh Nguyen, Sang Yoon Park, Hyoungjoon Lim, J. Heo","doi":"10.1145/3283590.3283595","DOIUrl":"https://doi.org/10.1145/3283590.3283595","url":null,"abstract":"Smart city has been a popular research agenda for the past years and have been trying to provide various new services to aid and improve life quality of the public. In this study, the authors utilize floating population analysis to provide 'floating population map', which can better reflect real movement of publics living in Songdo Incheon area. By implementing floating population analysis which contains more information than traditional census population such as hourly based population and weekly based population, the authors used Getis Ord Gi* algorithm and STSS (Space Time Scan Statistics) algorithm to conduct case studies and provided with key scenario which can be implemented into developing smart cities around the world. By using floating population older than 60 years old, new sights for elderly care facilities were derived, also by using floating population data of night time movement, areas which require more security service in the night time were derived. These new insights derived from floating population data could be used as key information for emerging smart cities.","PeriodicalId":404513,"journal":{"name":"Proceedings of the 2nd ACM SIGSPATIAL Workshop on Prediction of Human Mobility","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121609420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1