JDR Vol.13 No.2 pp. 380-386
doi: 10.20965/jdr.2018.p0380


Development of GIS Integrated Big Data Research Toolbox (BigGIS-RTX) for Mobile CDR Data Processing in Disasters Management

Ko Ko Lwin, Yoshihide Sekimoto, and Wataru Takeuchi

Institute of Industrial Science, The University of Tokyo
4-6-1 Komaba, Meguro-ku, Tokyo 153-8505, Japan

Corresponding author

September 1, 2017
March 1, 2018
Online released:
March 19, 2018
March 20, 2018
BigGIS-RTX, CDR, OD trips, OD matrix, OD routes, OD links

This article reports the development of a geographical information system (GIS) embedded text-based geospatial Big Data research toolbox (BigGIS-RTX) designed especially for mobile CDR (Call Details Record) data processing in urban transport planning and disaster management. BigGIS-RTX is a standalone computer program that aims to provide a bridge between geospatial Big Data and end users (i.e. students and researchers) by reducing difficulties in handling geospatial Big Data processing and analysis tasks. This research toolbox makes it possible to handle text-based geospatial Big Data cleaning, formatting, subsetting, and extraction by keywords or structured query language (SQL), CDR data aggregation by base transceiver stations (BTSs), generation of origin–destination (OD) trips, OD matrices, and OD routes, and computation of OD links. Moreover, this research toolbox can be integrated with current commercial GIS software to perform further geospatial analysis functions to improve spatial decision making in urban and transport planning and disaster management. In this report, we discuss two current research outputs using BigGIS-RTX: first, multitemporal grid square population estimation and second, human mobility studies in transportation planning. These research outputs are essential for disaster management and emergency preparedness in terms of providing knowledge and information about population distribution changes over space and time, human mobility flow by a user defined time frame, and travel volume or link count information for individual road segments. This research is part of the core project “Development of a Comprehensive Disaster Resilience System and Collaboration Platform in Myanmar” in a research collaboration between Yangon Technological University, Myanmar, and The University of Tokyo, Japan, sponsored by the Japan Science and Technology Agency (JST) and the Japan International Cooperation Agency (JICA).

Cite this article as:
K. Lwin, Y. Sekimoto, and W. Takeuchi, “Development of GIS Integrated Big Data Research Toolbox (BigGIS-RTX) for Mobile CDR Data Processing in Disasters Management,” J. Disaster Res., Vol.13 No.2, pp. 380-386, 2018.
Data files:
  1. [1] M. Hilbert, “Big Data for development: A review of promises and challenges,” Development Policy Review, Vol.34, No.1, pp. 135-174, 2015.
  2. [2] C. Dobre and F. Xhafa, “Parallel programming paradigms and frameworks in Big Data era,” Int. Journal of Parallel Programming, Vol.42, No.5, pp. 710-738, 2013.
  3. [3] J. Lee and M. Kang, “Geospatial Big Data: Challenges and opportunities,” Big Data Research, Vol.2, No.2, pp. 74-81, 2015.
  4. [4] K. Lwin and Y. Murayama, “Person trip data browser and space-time visualizer,” 2nd IEEE Int. Conf. on Spatial Data Mining and Geographical Knowledge Services, 2015.
  5. [5] K. Lwin, K. Zettsu, and K. Sugiura, “Geovisualization and correlation analysis between geotagged Twitter and JMA rainfall data: Case of heavy rain disaster in Hiroshima,” 2nd IEEE Int. Conf. on Spatial Data Mining and Geographical Knowledge Services, 2015.
  6. [6] K. Lwin, K. Sugiura, and K. Zettsu, “Space–time multiple regression model for grid-based population estimation in urban areas,” Int. Journal of Geographical Information Science, Vol.30, No.8, pp. 1579-1593, 2016.
  7. [7] K. Lwin, Y. Sekimoto, and W. Takeuchi, “Land cover weighted mobile CDR data disaggregation for multitemporal grid based population estimation: A case of Yangon central business area, Myanmar,” Monitoring of Global Environment and Disaster Risk Assessment from Space: the IIS Forum proceedings, Vol.25, pp. 47-50, 2017.
  8. [8] K. Lwin and Y. Sekimoto, “Mapping the spatial distribution patterns of personal time spent based on trip purpose,” Int. Journal of Applied Geospatial Research (IJAGR), Vol.9, No.2, 2018.
  9. [9] D. Kyaing, K. Lwin, and Y. Sekimoto, “Human mobility patterns for different regions in Myanmar based on CDRs data,” 3rd Int. Conf. on Civil Engineering Research (ICCER) and Regional Conf. in Civil Engineering (RCCE), 2017.
  10. [10] D. Kyaing, K. Lwin, and Y. Sekimoto, “Estimation of trip generation in Yangon City by using CDRs data,” Proc. of 12th Int. Conf. of Eastern Asia Society For Transportation Studies, Vol.11, 2017.
  11. [11] T. Zin, D. Kyaing, K. Lwin, and Y. Sekimoto, “Estimation of Origin-Destination Trips by Using Big Data Source in Yangon,” Journal of Disaster Research, Vol.13, No.1, 2018.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on May. 10, 2024