“calling abidjan” - estimating population distribution through analysis of mobile phone call...

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“Calling Abidjan”Estimating population distribution

through analysis ofmobile phone call data records

Harald Sterly1, Benjamin Hennig2, Kouassi Dongo3

1Institute of Geography, University of Cologne ([email protected])2School of Geography and the Environment, University of Oxford ([email protected])3Université de Cocody-Abidjan / Centre Suisse de Recherches Scientifiques en Côte d'Ivoire, CSRS

([email protected])

Overview

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• Urbanisation in Africa – an information challenge?

• Data• Approach• Results• Discussion – Conclusion

Harald SterlyBenjamin HennigKouassi Dongo

Calling AbidjanEstimating populatiopn distribution through analysis of mobile phone call data recordsDeutscher Kongress für Geogrtaphie, 1 October 2015, Berlin (Germany)

Urbanisation in Africa: a considerable challenge

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(Source: UN 2010)

• Cities in low and middle income countries to accomodate 89% of global population increase (UNDESA 2012)

• High urban growth in Subsahara-Africa: 3,7% (2005-10)

• Côte d‘Ivoire: high urbanpoverty rates, ~2/3 in low-income housing



Urbanisation: also an information challenge?

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• Limited resources for more sustainable and equitable urban development

• Very limited knowledge on population distribution & dynamics

• Côte d‘Ivoire: last census 1998, latest official national estimates 2008

need for actual data on local level



Urbanisation and Visualisation

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Urban Slums

City Living

City Growth



Mobile phone use

Slide: 6Harald SterlyBenjamin HennigKouassi Dongo


Proof of Concept• Deduce information on

population distribution from existing data (mobile phone call data records)

• Use standard hardware and open source software

• approach by non-experts for big data analysis (social geographers, urban planner)



The Dataset„Data for Development“ (D4D) Challenge (Orange Télécom Côte d‘Ivoire)

• Anonymised call data records, Base station, timestamp, caller ID• 500,000 Orange Télecom

users• 1079 base stations• 183 days• 13 GB data



Approach• Analysis of diurnal patterns:

„being home“ = 7pm–5am• Attribution of residence to

base stations (diurnal patterns, highest number of calls in 14 days)

• Extrapolation to 21 Million Ivorians (national level)

• Spatial attribution to base stations (Voronoi polygons)



Results - national

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• Roughly consistent with AfriPoP and Landscan data;

• Reproduces the general patterns

• Urban areas: overestimation of population; rural areas: underestimation(differential usage)

CRD Analysis

Afripop



Results - national

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CRD Analysis

Afripop



Results - population



Results - urban

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CRD AnalysisAfriPop data



Discussion• Potential for improving knowledge on

inner-urban population distribution (and dynamics)

• Non-homogeneity of subscription and usage socio-spatial correction factors (calling patterns, service provider, surveys)

• Data availability / accessibility • Privacy



“calling abidjan” - estimating population distribution through analysis of mobile phone call...

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