An analytical framework to nowcast well-being using mobile phone data
arXiv:1606.06279 · doi:10.1007/s41060-016-0013-2
Abstract
An intriguing open question is whether measurements made on Big Data recording human activities can yield us high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory.
References in corpus (7)
- Understanding individual human mobility patterns
- Structure and tie strengths in mobile communication networks
- The Dynamics of a Mobile Phone Network
- A planetary nervous system for social mining and collective awareness
- Estimating Food Consumption and Poverty Indices with Mobile Phone Data
- Harnessing Mobile Phone Social Network Topology to Infer Users Demographic Attributes
- A survey of results on mobile phone datasets analysis
Cited by in corpus (28)
- Data-driven generation of spatio-temporal routines in human mobility
- Future Directions in Human Mobility Science
- Computational Socioeconomics
- Uncovering the socioeconomic facets of human mobility
- Gross polluters and vehicles' emissions reduction
- Assessing the quality of home detection from mobile phone data for official statistics
- Inferring Personal Economic Status from Social Network Location
- Differences in the spatial landscape of urban mobility: gender and socioeconomic perspectives
- Mobile Communication Signatures of Unemployment
- Mobile phone indicators and their relation to the socioeconomic organisation of cities
- Entropy as a measure of attractiveness and socioeconomic complexity in Rio de Janeiro metropolitan area
- Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown
- Performance and sensitivities of home detection from mobile phone data
- Regional economic status inference from information flow and talent mobility
- Analyzing the Behavior and Financial Status of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study
- Geographical veracity of indicators derived from mobile phone data
- Uncovering the socioeconomic structure of spatial and social interactions in cities
- Awakening City: Traces of the Circadian Rhythm within the Mobile Phone Network Data
- Assessing Refugees' Integration via Spatio-temporal Similarities of Mobility and Calling Behaviors
- Evaluating the Effect of the Financial Status to the Mobility Customs
- An individual-level ground truth dataset for home location detection
- Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information
- Flow descriptors of human mobility networks
- A Comparison of Spatial-based Targeted Disease Containment Strategies using Mobile Phone Data
- Are machine learning technologies ready to be used for humanitarian work and development?
- Analyzing the Behavior of Soccer Fans from a Mobile Phone Network Perspective: Euro 2016, a Case Study
- Understanding peacefulness through the world news
- On the regularity of human mobility patterns at times of a pandemic