Administrative Regions Discovery Based on Human Mobility Patterns and Spatio-Temporal Clustering

被引:0
|
作者
Nunez-del-Prado-Cortez, Miguel [1 ]
Alatrista-Salas, Hugo [1 ]
机构
[1] Univ Pacifico, Ave Salaverry 2020, Lima, Peru
来源
PROCEEDINGS 2016 IEEE 13TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SENSOR SYSTEMS (MASS 2016) | 2016年
关键词
Mobility model; Mobility Markov chain; administrative region; clustering; region interactions;
D O I
10.1109/MASS.2016.58
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Currently, the understanding of the human mobility is an important challenge that has a large number of applications, especially in the study of a nations ability to thrive economically and socially. Some works have shown that, it is possible to observe developed and developing countries reviewing their administrative regions borders, in order to reduce costs, or to solve ethnic claims and/or independence movements. In this context, the present work leverages mobile phone data to analyze human mobility patterns. Specifically, we propose a new method to detect administrative regions and paths of interaction between regions, both relying on subscribers mobility patterns extracted from Call Detail Records (CDR). Thus, our method offers a different point of view to redefine administrative boundaries.
引用
收藏
页码:65 / 74
页数:10
相关论文
共 50 条
  • [1] Discovery of Patterns in Spatio-Temporal Data Using Clustering Techniques
    Aryal, Amar Mani
    Wang, Sujing
    2017 2ND INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC 2017), 2017, : 990 - 995
  • [2] Discovery of Localized Spatio-Temporal Patterns from Location-based SNS by Clustering Users
    Nishioka, Ken-ichiro
    Matsuda, Yoshitatsu
    Yamaguchi, Kazunori
    2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), 2015,
  • [3] Local Clustering in Spatio-Temporal Point Patterns
    Mateu, Jorge
    Rodriguez-Cortes, Francisco J.
    MATHEMATICS OF PLANET EARTH, 2014, : 171 - 174
  • [4] Spatio-Temporal Sensor Graphs (STSG): A data model for the discovery of spatio-temporal patterns
    George, Betsy
    Kang, James M.
    Shekhar, Shashi
    INTELLIGENT DATA ANALYSIS, 2009, 13 (03) : 457 - 475
  • [5] Discovery of Spatio-Temporal Patterns from Location Based Social Networks
    Bejar, Javier
    Alvarez, Sergio
    Garcia, Dario
    Gomez, Ignasi
    Oliva, Luis
    Tejeda, Arturo
    Vazquez-Salceda, Javier
    ARTIFICIAL INTELLIGENCE RESEARCH AND DEVELOPMENT: RECENT ADVANCES AND APPLICATIONS, 2014, 269 : 126 - 135
  • [6] Mining spatio-temporal patterns in object mobility databases
    Florian Verhein
    Sanjay Chawla
    Data Mining and Knowledge Discovery, 2008, 16 : 5 - 38
  • [7] Mining spatio-temporal patterns in object mobility databases
    Verhein, Florian
    Chawla, Sanjay
    DATA MINING AND KNOWLEDGE DISCOVERY, 2008, 16 (01) : 5 - 38
  • [8] Identifying multiscale spatio-temporal patterns in human mobility using manifold learning
    Watson, James R.
    Gelbaum, Zach
    Titus, Mathew
    Zoch, Grant
    Wrathall, David
    PEERJ COMPUTER SCIENCE, 2020, 6 : 1 - 17
  • [9] Spatio-Temporal Clustering Approach for Detecting Functional Regions in Cities
    Assem, Haytham
    Xu, Lei
    Buda, Teodora Sandra
    O'Sullivan, Declan
    2016 IEEE 28TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2016), 2016, : 370 - 377
  • [10] Discovery of spatio-temporal patterns from location-based social networks
    Bejar, J.
    Alvarez, S.
    Garcia, D.
    Gomez, I.
    Oliva, L.
    Tejeda, A.
    Vazquez-Salceda, J.
    JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2016, 28 (1-2) : 313 - 329