Lessons From the Trenches: using Mobile Phone Data for Official Statistics

Transcription

1 Lessons From the Trenches: using Mobile Phone Data for Official Statistics Maarten Vanhoof Orange Labs/Newcastle Hoof MaartenVanhoof.com

2 Mobile Phone Data (Call Detail Records)

3 Mobile Phone Data (Signaling)

4 Mobile Phone Data (Call Detail Records) Metadata Caller (phone) Called phone Timestamp Type of event Duration of call/length of text Location of celltower

5 Mobile Phone Data (Call Detail Records) Toole et al. (2015) Coupling Human Mobilities and Social Ties.

6 Individual indicators: Bandicoot active days number of contacts number of interactions call duration percent nocturnal percent initiated interactions response delay text entropy of contacts balance of contacts interactions per contact inter-event time percent pareto interactions percent pareto durations number of antennas entropy of antennas percent at home radius of gyration frequent antennas

7 Individual indicators for official statistics Behavioural Contextual Individual mobility e.g. diversity of mobility Car ownership Access to public transport Income Marital status Membership Home location Etc.

8 Individual indicators for official statistics Pappalardo,Vanhoof, et al. (2016) An Analytical Framework to Nowcast Well-Being using Mobile Phone Data.

9 Individual indicators for official statistics Pappalardo,Vanhoof, et al. (2016) An Analytical Framework to Nowcast Well-Being using Mobile Phone Data.

10 Individual indicators for official statistics

11 (Geographical) Veracity Spatial allocation Spatial delineation Spatial aggregation

12 (Geographical) Veracity Spatial allocation Spatial delineation Spatial aggregation

13 Spatial allocation: Home detection Pappalardo,Vanhoof, et al. (2016) An Analytical Framework to Nowcast Well-Being using Mobile Phone Data.

14 Spatial allocation: Home detection Uncertainty of home allocation algorithms No knowledge on how certain we can geographically pinpoint users Because no ground truth is available

15 Spatial allocation: Home detection

16 Spatial allocation: Home detection Uncertainty Performance Vanhoof et al. (Submitted) Investigating Performance and Spatial Uncertainty of Home Detection Criteria for CDR data

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18 Spatial allocation: Solution? In short term, we need to: Create a better understanding on the uncertainty that comes with home detection Test heuristics for home detection on different databases and for different countries Design surveys to gather ground truth at the individual level In long term, we need to: Understand how change in mobile phone use/available datasets influence allocation Decide on standardizing home detection and error assessment Design a platform where all operators, researchers, policy makers can easily do this and compare results between different datasets

19 (Geographical) Veracity Spatial allocation Spatial delineation Spatial aggregation

20 Spatial delineation Uneven delineations of space Between antennas (high-density vs. low-density, operator 1 vs. operator 2,..) Between antennas and administrative regions (cell-tower coverage vs. municipalities) Between different definitions of urban areas (Urban Units vs. Urban Areas) Create errors that are poorly understood and challenging to address Is relevant for Population Density Estimations Mobility Derivation Parameter estimation (e.g. for urban scaling laws) in statistical analysis Error/uncertainty assessment

21 Spatial delineation: Mobility Entropy Vanhoof, et al. (Submitted) Correcting Mobility Entropy from CDR data for large-scale comparison of individual movement patterns

22 Spatial delineation: Mobility Entropy Vanhoof, et al. (Submitted) Correcting Mobility Entropy from CDR data for large-scale comparison of individual movement patterns

23 Spatial delineation: Urban scaling laws Cottineau et al. (2016) Paradoxal Interpretations of Urban Scaling Laws

24 Spatial delineation: Solution? In short term, we need to work on : Minimizing the influence of spatial delineations on our measurements Techniques that allow translation between different spatial delineations Assessments of the influence of spatial delineation (geo-computation) In long term, we need to: Overthink possibilities to standardize spatial delineations Develop practices in Official Statistics that express the effect of spatial delineation

25 Spatial delineation: Urban scaling laws Cottineau et al. (2016) Paradoxal Interpretations of Urban Scaling Laws

26 (Geographical) Veracity Spatial allocation Spatial delineation Spatial aggregation

27 Spatial aggregation Scale does matter for: Unintended selective filtering (e.g. highly active persons, communities) Objective construction of indicators (e.g. 5 km in Paris or in the Pyrenees) Representativeness of single operators (e.g. distorted market shares) Personal behaviour (e.g. long-distance vs. Short-distance trips) Geographical, economical, sociological, ecological,etc. context (e.g. transport infrastructure) Still, there is no single evidence that current (spatial) aggregation practices take into account any of these when studying mobile phone data. In addition, given the highly changing nature of mobile phone use, it is my hypothesis that behavioral data is even more prone to this fallacy.

28 Spatial aggregation Relations between indicators Population Density Estimation vs. Official Statistics Cell-tower level IRIS level

29 Spatial aggregation: Solution? In short term, we need to work on : Techniques that define the best spatial scale for studying certain processes Both empirical, quantitative (e.g. optimal raster sizes for population density estimations) As theoretical, qualitative (e.g. expert judgment) Techniques that express changing nature of observations when (spatially) aggregating E.g. Representativeness in population terms of single operator data at different scales Techniques that investigate, or even incorporate sensitivity of definitions to spatial scale E.g. Fragmented definitions of distance according to scale Techniques that investigate sensitivity of data to spatial aggregation E.g. Spatial autocorrelations In long term, we need to: See how all of this evolves over time as human behaviour & mobile phone use will change

30 Thoughts Why starting from individual indicators? Privacy issues (newer datasets don t allow this) Computationally expensive treatment Temporal resolution is far from optimal Difficult to communicate/visualise Why not using the big aspect of the data and use patterns? Activity patterns of cell-towers High-level communication/commuting patterns Population presence registration

31 High-level analysis: Learning Urban Areas Combes, de Bellefon and Vanhoof (Submitted) Understanding urban centers organization and influence with mobile phone data

32 High-level analysis: Learning Urban Areas Combes, de Bellefon and Vanhoof (Submitted) Understanding urban centers organization and influence with mobile phone data

33 Don t be Batman. The same problems and scientific questions will persist. Only now less visible, and as such, less provoked.

34 Conclusion Work from the trenches on individual data identifies problems but Is done by a limited amount of researchers Not a priority for operators (never was, never will be) Lack of data and knowledge at the institutions (but they are catching up) Limited rewards in academics, limited scientific community Is threatened by protective measurements on data Impossibility to continue pursuing in-depth research Fled to African data, but limited quality of official statistics there Development of shared platforms for analysis, but simplifies workflows Is mostly limited to one-dataset, one-operator Comparison of findings is absolutely necessary for better insights and methods Dream to have full coverage of population is feasible but needs strong policy

35 Thank you, The Hoof MaartenVanhoof.com