the gambia the hidden potential of cdr data · africell. 60% (1,557,004) • the largest market...
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The Gambia
The hidden potential of CDR data
September 2020
Logo 1 Logo 2 Logo 3
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In The Gambia, CDR data analysis can inform discussion on mobility, human mobility and rapid urban development
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Stylized fact 1: One in three households have an international migrant … yet, it remains unclear how migration and remittances evolve over time and how they differ across the country.
Stylized fact 2: Better economic opportunities and access to services are pull factors for internal migration … but lack of timely and spatially aggregated data complicates policy response.
In The Gambia,
33.5 percent of households have at least one international migrant
and 55.0 percent of migrants send remittances
Source: World Bank Migration Survey, The Gambia, 2019.
0%10%20%30%40%50%60%70%80%90%
100%
Banjul Kanifing Brikama Mansakonko Kerewan Kuntaur Janjanbureh Basse
By LGA, share of population being native or immigrant Native Immigrant
Source: The Gambia Population Census, 2013.
World Bank and University of Tokyo – The Gambia: The hidden potential of CDR data
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Engagement model for the use of big data in The Gambia: ownership, capacity, and sustainability
3World Bank and University of Tokyo – The Gambia: The hidden potential of CDR data
Ownership
• Big Data is not owned by traditional partners of
National Statistical System (private sector)
Capacity
• CDR data is highly confidential
• CDR data is technically complex
Sustainability
• New CDR data becomes available every single
second; hence, importance of country systems
Proof of concept
Workshop on “Big Data for Development” to build consensus with stakeholders and end users
Capacity building
Capacity building to the data controller (the regulator) for establishing a system for handling the CDR data in their premise
Technical support
Support to the mobile network operators (MNOs) for extracting and de-identifying CDR data
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Contributing Team: Big Data for Development in The Gambia
4World Bank and University of Tokyo – The Gambia: The hidden potential of CDR data
The Gambia
Big Data for Development
PURA
GBoS
Ministries and
Agencies
Private Sector, MNOs
World Bank
Development
Partners
University of Tokyo
Contact Erwin Knippenberg [email protected] Meyer [email protected]
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Market share of MNOs and subscribers in The Gambia
• Penetration rate: 143 % (2020)• # of subscribers: 2,584,714 (2020)
Market share (%)(# of subscribers)
Africell 60%(1,557,004)
• The largest market share with the widest network coverage
Qcell 26 %(676,739)
• Wider network coverage in rural areas
Comium 8%(194,909)
• Only voice and SMS (2G)• Popular in rural areas• People in urban areas also use to communicate with families in villages (A
dual/multi SIM device is quite common)
Gamcel 6%(156,062)
• Originally state-owned
5World Bank and University of Tokyo – The Gambia: The hidden potential of CDR data
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CDR statistics: total unique usersMIN MAX AVG Total Unique ID
756,611.00 921,461.00 815,523.79 2,039,375.00
• Constant number of daily users during study period
• Data from 2 MNOs capture 2/3 of the market
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Validation of CDR data against known population density
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Source: World Bank Migration Survey, The Gambia, 2019.
World Bank and University of Tokyo – The Gambia: The hidden potential of CDR data
Source: The Gambia Population Census, 2013.
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Timeline
Mar 1WK1
Mar 8WK2
Mar 15WK3
Mar 22WK4
Mar 29WK5
Apr 5WK6
Apr 12WK7
Apr 19WK8
Apr 26WK9
May 3WK10
May 10WK11
May 17WK12
May 24WK13
● WK3: Mobility restrictions (Mar 18 – Apr 7)
Average of WK1 & WK 2: Used as the baseline to examine % changes
● WK4: Border closed(Mar 23)
● WK4: A state of emergency (Mar 27)
● WK5: A state of emergency extended (Apr 3)
● WK12: A state of emergency extended(May 19)
● WK8-12: Ramadan (Apr 23 – Mar 23)
● WK6: Good Friday (Apr 10)● WK7: Easter Monday (Apr 13)
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WK3: Mar15-21
WK6: Apr5-11
WK9: Apr26-May2
WK12: May17-23
WK4: Mar22-28 WK5: Mar29-Apr4
WK7: Apr12-18 WK8: Apr19-25
WK10: May3-9 WK11: May10-16
WK13: May24-30Changes (%)
-60 -40 -20 -16 -12 -8 -4 0 4 8 12 16 20 40 60+
Case study 1 – Change in inflow due to Covid-19
9World Bank and University of Tokyo – The Gambia: The hidden potential of CDR data
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Case study 2 – population distribution : CDR data to identify hotspots of international migration
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Figure 1: Telecommunication data, incoming calls
Figure 2: Listing Migration Survey, households with emigrants by ward
Key findings
Share of international calls (incoming)
matches concentration of households
with emigrants
Data available on a highly localized
level and with high frequency
World Bank and University of Tokyo – The Gambia: The hidden potential of CDR data
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1. Examples of CDR data in action
2. CDR data at the World Bank
3. Gambia CDR data for analysis
APPENDIX
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Displacement and Disaster Statistics SubgroupUN GWG on Big Data Task Team on Mobile Phone Data
MembersFlowminder, IOM, ITU, Positium, Pulse Lab Jakarta, Telenor Research, University of Tokyo, World BankContents of Displacement and Disaster Statistics Handbook
• Definition and existing displacement and disaster statistics• Methodologies and standards extracted from use cases
- Data and modality of data access- Methodologies to produce statistical products- Potential architecture of analytical pipeline - Validation data
• Guidance to potential readers
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Cases Introduce in the Handbook
Natural disasters• Haiti earthquake 2010 • Japan earthquake and tsunami 2011• Nepal earthquake 2015• Haiti hurricane Matthew 2016• Indonesia earthquake 2018• Vanuatu volcanos
Methodologies and analytical tools for extracting mobility metrics/statistics• FlowKit (Github)• Flowminder COVID-19 Resources (website)• World Bank COVID19 Mobility Task Force (Github) and more…
Epidemics• Haiti cholera epidemic 2010 • West African Ebola virus
epidemic 2013-17 • COVID-19 2020
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CDR data contains important information about human mobility
14World Bank and University of Tokyo – The Gambia: The hidden potential of CDR data
CDR Data
Population distribution
Daytime population
Night-time population
Population seasonality
Mobility pattern
Number of trips
Travel distance
Radius of gyration
Trajectory
Origin-destination matrix
Cell-tower-level migration
Interpolated trajectories
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The visualization of CDR data shows population distribution, mobility pattern and trajectories
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CDR Data
Figure 1: Population distribution Figure 2: Interpolated trajectories Figure 3: Trip distribution
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In the context of COVID19, just in time information on population distribution and mobility patterns save lives
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Figure 1: Decreases of population inflows after the intervention (computed from indicators)
CDR Data
Population distribution
• Headcounts of subscribers during the daytime and night-time
at district level
• Headcounts of subscribers who are away from home during
the daytime
Mobility pattern
• Inter-district ODs of all subscribers
• Median travel distance at the district level
• Average radius of gyration at the district level
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% changes of population outflow to/inflow from other Wardsby poverty quintileThe poorer, the more significant decreases in outflows & inflows.
-35%
-30%
-25%
-20%
-15%
-10%
-5%
0%
5%
15 Mar(WK3)
22 Mar(WK4)
29 Mar(WK5)
05 Apr(WK6)
12 Apr(WK7)
19 Apr(WK8)
26 Apr(WK9)
03 May(WK10)
10 May(WK11)
17 May(WK12)
24 May(WK13)
Outflow to other Wards (based on origin)
Q1 Q2 Q3 Q4 Q5
-35%
-30%
-25%
-20%
-15%
-10%
-5%
0%
5%
15 Mar(WK3)
22 Mar(WK4)
29 Mar(WK5)
05 Apr(WK6)
12 Apr(WK7)
19 Apr(WK8)
26 Apr(WK9)
03 May(WK10)
10 May(WK11)
17 May(WK12)
24 May(WK13)
Inflow from other Wards (based on destination)
Q1 Q2 Q3 Q4 Q5
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Access to CDR data in World Bank Priority Countries CDR Data
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World Bank and University of Tokyo – The Gambia: The hidden potential of CDR data
Country Cell Phone DataAfghanistan HistoricalArgentina Grandata – dashboardDem. Rep. of the Congo WB: DIAL; FlowminderThe Gambia WB and University of TokyoGhana FlowminderHaiti FlowminderIndia WB with AirtelKenya WB with SafaricomMozambique WB and University of TokyoSenegal WB with Orange/OPAL, FlowminderSri Lanka DataPop Alliane; WB and University of Tokyo