An analytical framework to nowcast well-being using mobile phone data

Pappalardo, Luca; Vanhoof, Maarten; Gabrielli, Lorenzo; Smoreda, Zbigniew; Pedreschi, Dino; Giannotti, Fosca

doi:10.1007/s41060-016-0013-2

Cited by 117 publications

(123 citation statements)

References 58 publications

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“…Mobility entropy. The mobility entropy S unc of an individual u is defined as the Shannon entropy of her visited locations (Song et al, 2010b;Eagle and Pentland, 2009;Pappalardo et al, 2016b):…”

Section: Gpsmentioning

confidence: 99%

Data-driven generation of spatio-temporal routines in human mobility

Pappalardo

Simini

2017

Data Min Knowl Disc

Self Cite

102

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The generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present Ditras (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. Ditras operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate Ditras with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating Ditras with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.

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Section: Gpsmentioning

confidence: 99%

Data-driven generation of spatio-temporal routines in human mobility

Pappalardo

Simini

2017

Data Min Knowl Disc

Self Cite

102

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“…Our work is related to studies pertaining to accessibility in cities (see Batty (2009) for a review) and its relationship to the structure of the transport network (Strano et al 2015;Piovani et al 2018). A related strand is concerned with social mixing and the fragmentation of social classes in terms of mobility radius and behaviour (Pappalardo et al 2016;Lotero et al 2016).…”

Section: Related Literaturementioning

confidence: 99%

Commuting times and the mobilisation of skills in emergent cities

2019

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Labour mobility within a large city or metropolitan area is a necessary condition for the optimal exploitation of agglomeration economies. We propose a method to establish which municipalities should be considered part of a metropolitan area based on labour market integration. In order to aggregate geographically proximate urban municipalities, we develop a network-based model that makes industry productivity (manifesting in formal employment creation) dependent on firms' ability to find, within city limits, the diversity of skills they need to move to new complex industries. In this way, we uncover the natural city scale at which firms optimally exploit the range of skills available to them within a broad catchment area. Considering Colombian cities, we find that commuting times between 45 and 75 min, corresponding to between 43 and 62 distinct cities or integrated labour markets, allow firms to maximise formal employment creation (between 2008 and 2013). This result supports the development of passenger transport limiting commuting times within cities, connecting small-and mid-size cities to nearby large cities, and coordinating transportation investments across traditional administrative boundaries.

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“…Compared to the gathering of other data, the gathering of CDR data is more cost-effective, less biased, and available on a much larger scale in terms of users, geographical coverage, and time periods compared to traditional data gathering methods (Järv et al, 2014;Liu et al, 2013), while collected at the individual level, CDR data still allows for a reconstruction and quantification of individual movement patterns. As a consequence, indicators for individual mobility, like the number of visited cell-towers, the radius of gyration, or the mobility entropy, have been derived from mobile phone data and are used to inform large-scale studies on, for instance, the mobility footprint of users (Sridharan and Bolot, 2013), mobility differences between population groups (Bajardi et al, 2015;Cranshaw et al, 2010), or the relation between mobility and poverty (Pappalardo et al, 2016).…”

Section: Related Workmentioning

confidence: 99%

Comparing Regional Patterns of Individual Movement Using Corrected Mobility Entropy

Vanhoof

Schoors

Rompaey

et al. 2018

Journal of Urban Technology

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In this paper, we propose a correction of the Mobility Entropy indicator (ME) used to describe the diversity of individual movement patterns as can be captured by data from mobile phones. We argue that a correction is necessary because standard calculations of ME show a structural dependency on the geographical density of observation points, rendering results biased and comparisons between regions incorrect. As a solution, we propose the Corrected Mobility Entropy (CME). We apply our solution to a French mobile phone dataset with ∼18.5 million users. Results show CME to be less correlated to cell-tower density (r =-0.17 instead of-0.59 for ME). As a spatial pattern of mobility diversity, we find CME values to be higher in suburban regions compared to their related urban centers, while both decrease considerably with lowering urban center sizes. Based on regression models, we find mobility diversity to relate to factors like income and employment. Additionally, using CME reveals the role of car use in relation to land use, which was not recognized when using ME values. Our solution enables a better description of individual mobility at a large scale, which has applications in official statistics, urban planning and policy, and mobility research.

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An analytical framework to nowcast well-being using mobile phone data

Cited by 117 publications

References 58 publications

Data-driven generation of spatio-temporal routines in human mobility

Data-driven generation of spatio-temporal routines in human mobility

Commuting times and the mobilisation of skills in emergent cities

Comparing Regional Patterns of Individual Movement Using Corrected Mobility Entropy

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