Characterizing mobility patterns of people in developing countries using their mobile phone data

Human mobility patterns give insights into how people travel in their day-to-day lives. With availability of cellular data, either at large-scale but with low location accuracy or at small-scale but with high location accuracy, studying mobility patterns is now possible. An example of former dataset is CDRs (Call Detail Records) and that of latter is GSM/WiFi/GPS traces collected from mobile phones. So far the studies have been focussed on data collected in developed countries. In this paper, we make an attempt in finding and analyzing mobility patterns of people in developing countries using both the categories of data. We use publicly available CDRs data and we collect our own data for capturing fine-grained location. Ours is the first dataset of its kind that is publicly available. We analyze this data to find movement as well as place visiting patterns, compare our findings with existing studies, and discuss their implications. For example, urban people in developing countries travel farther distances in their day to day life as compared to people living in non-urban areas. Also, distance travelled by urban people in developing countries is as much as six times lower compared to developed countries.

[1]  Margaret Martonosi,et al.  Identifying Important Places in People's Lives from Cellular Network Data , 2011, Pervasive.

[2]  Klara Nahrstedt,et al.  Jyotish: A novel framework for constructing predictive model of people movement from joint Wifi/Bluetooth trace , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[3]  Cecilia Mascolo,et al.  A Tale of Many Cities: Universal Patterns in Human Urban Mobility , 2011, PloS one.

[4]  Marcos R. Vieira,et al.  Characterizing Dense Urban Areas from Mobile Phone-Call Data: Discovery and Social Dynamics , 2010, 2010 IEEE Second International Conference on Social Computing.

[5]  Hojung Cha,et al.  LifeMap: A Smartphone-Based Context Provider for Location-Based Services , 2011, IEEE Pervasive Computing.

[6]  Vanessa Frías-Martínez,et al.  Forecasting socioeconomic trends with cell phone records , 2013, ACM DEV '13.

[7]  Margaret Martonosi,et al.  Ranges of human mobility in Los Angeles and New York , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[8]  Murat Ali Bayir,et al.  Discovering spatiotemporal mobility profiles of cellphone users , 2009, 2009 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks & Workshops.

[9]  Amarjeet Singh,et al.  MobiShare : cloud-enabled opportunistic content sharing among mobile peers , 2012 .

[10]  Klara Nahrstedt,et al.  Jyotish: Constructive approach for context predictions of people movement from joint Wifi/Bluetooth trace , 2011, Pervasive Mob. Comput..

[11]  Vinayak S. Naik,et al.  Challenges and novelties while using mobile phones as ICT devices for Indian masses: short paper , 2010, NSDR '10.

[12]  Imad Aad,et al.  The Mobile Data Challenge: Big Data for Mobile Computing Research , 2012 .

[13]  Margaret Martonosi,et al.  ON CELLULAR , 2022 .

[14]  Albert-László Barabási,et al.  Understanding individual human mobility patterns , 2008, Nature.

[15]  Gernot Heiser,et al.  An Analysis of Power Consumption in a Smartphone , 2010, USENIX Annual Technical Conference.

[16]  Daniel Gatica-Perez,et al.  The Places of Our Lives: Visiting Patterns and Automatic Labeling from Longitudinal Smartphone Data , 2014, IEEE Transactions on Mobile Computing.

[17]  David L. Smith,et al.  Quantifying the Impact of Human Mobility on Malaria , 2012, Science.

[18]  Vinayak S. Naik,et al.  Low Energy and Sufficiently Accurate Localization for Non-smartphones , 2012, 2012 IEEE 13th International Conference on Mobile Data Management.

[19]  R. Walgate Tale of two cities , 1984, Nature.

[20]  Romit Roy Choudhury,et al.  Micro-Blog: sharing and querying content through mobile phones and social participation , 2008, MobiSys '08.