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author:

Zhao, Z. (Zhao, Z..) [1] | Shaw, S.-L. (Shaw, S.-L..) [2] | Yin, L. (Yin, L..) [3] | Fang, Z. (Fang, Z..) [4] | Yang, X. (Yang, X..) [5] | Zhang, F. (Zhang, F..) [6] | Wu, S. (Wu, S..) [7]

Indexed by:

Scopus

Abstract:

Mobile phone location data have been extensively used to understand human mobility patterns through the employment of mobility indicators. The temporal sampling interval (TSI), which is measured by the temporal interval between consecutive records, determines how well such data can describe human activities and influence the values of human mobility indicators. However, systematic investigations of how the TSI affects human mobility indicators remain scarce, and characterizing those relationships is a fundamental research question for many related studies. This study uses a mobile phone location dataset containing 19,370 intensively sampled individual trajectories (TSI < 5 minutes) to systematically assess the impacts of the TSI on four typical mobility indicators that describe human mobility patterns from different aspects, which are movement entropy, radius of gyration, eccentricity, and daily travel frequency. We find that different TSIs have complex impacts on the values of different mobility indicators. Specifically, (1) coarser TSIs tend to underestimate the values of the four selected indicators with different degrees; (2) the degrees of underestimation vary significantly among users for eccentricity and daily travel frequency but exhibit high inter-user consistency for radius of gyration and movement entropy. The above findings can help better understand the variations among human mobility studies. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Keyword:

Human mobility; mobile phone location data; modifiable temporal unit problem(MTUP); temporal sampling intervals

Community:

  • [ 1 ] [Zhao, Z.]State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
  • [ 2 ] [Zhao, Z.]Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou, China
  • [ 3 ] [Shaw, S.-L.]Department of Geography, University of Tennessee, Knoxville, TN, United States
  • [ 4 ] [Yin, L.]Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
  • [ 5 ] [Fang, Z.]State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
  • [ 6 ] [Fang, Z.]Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan, China
  • [ 7 ] [Yang, X.]School of Geography and Tourism, Shaanxi Normal University, Xi’an, China
  • [ 8 ] [Zhang, F.]Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
  • [ 9 ] [Wu, S.]Spatial Information Research Center of Fujian Province, Fuzhou University, Fuzhou, China
  • [ 10 ] [Wu, S.]Fujian Collaborative Innovation Center for Big Data Applications in Governments, Fuzhou, China

Reprint 's Address:

  • [Shaw, S.-L.]Department of Geography, University of TennesseeUnited States

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Source :

International Journal of Geographical Information Science

ISSN: 1365-8816

Year: 2019

Issue: 7

Volume: 33

Page: 1471-1495

3 . 7 3 3

JCR@2019

4 . 3 0 0

JCR@2023

ESI HC Threshold:95

JCR Journal Grade:1

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 35

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 0

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