Next Article in Journal
Measuring Selection Diversity of Emergency Medical Service for Metro Stations: A Case Study in Beijing
Next Article in Special Issue
Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis
Previous Article in Journal
Multi-Objective Optimisation Based Planning of Power-Line Grid Expansions
Previous Article in Special Issue
A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(7), 259; https://doi.org/10.3390/ijgi7070259

Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data Mining

1
Institute of Industrial Science, The University of Tokyo, Tokyo 153-8508, Japan
2
Earth Observation Data Integration and Fusion Research Initiative, The University of Tokyo, Tokyo 153-8505, Japan
3
Center for Spatial Information Science, The University of Tokyo, Chiba 277-8568, Japan
*
Author to whom correspondence should be addressed.
Received: 7 June 2018 / Revised: 24 June 2018 / Accepted: 26 June 2018 / Published: 30 June 2018
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
Full-Text   |   PDF [13300 KB, uploaded 2 July 2018]   |  

Abstract

The mobility patterns and trip behavior of people are usually extracted from data collected by traditional survey methods. However, these methods are generally costly and difficult to implement, especially in developing cities with limited resources. The massive amounts of call detail record (CDR) data passively generated by ubiquitous mobile phone usage provide researchers with the opportunity to innovate alternative methods that are inexpensive and easier and faster to implement than traditional methods. This paper proposes a method based on proven techniques to extract the origin–destination (OD) trips from the raw CDR data of mobile phone users and process the data to capture the mobility of those users. The proposed method was applied to 3.4 million mobile phone users over a 12-day period in Mozambique, and the data processed to capture the mobility of people living in the Greater Maputo metropolitan area in different time frames (weekdays and weekends). Subsequently, trip generation maps, attraction maps, and the OD matrix of the study area, which are all practically usable for urban and transportation planning, were generated. Furthermore, spatiotemporal interpolation was applied to all OD trips to reconstruct the population distribution in the study area on an average weekday and weekend. Comparison of the results obtained with actual survey results from the Japan International Cooperation Agency (JICA) indicate that the proposed method achieves acceptable accuracy. The proposed method and study demonstrate the efficacy of mining big data sources, particularly mobile phone CDR data, to infer the spatiotemporal human mobility of people in a city and understand their flow pattern, which is valuable information for city planning. View Full-Text
Keywords: call detail record (CDR); mobile phone data; origin–destination matrix; spatiotemporal mobility; trip generation and attraction call detail record (CDR); mobile phone data; origin–destination matrix; spatiotemporal mobility; trip generation and attraction
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

MDPI and ACS Style

Batran, M.; Mejia, M.G.; Kanasugi, H.; Sekimoto, Y.; Shibasaki, R. Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data Mining. ISPRS Int. J. Geo-Inf. 2018, 7, 259.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top