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Mapping financial inclusion
Kenya Geo-spatial Data Launch
13 March 2014
March 13, 2014 © 2013 Bill & Melinda Gates Foundation | 2
Need to take a diagnostic approach to impact
Example use cases
• Agent network optimization ‘Where should I put my next point?’
• Social payments ‘Who should I choose as a partner?’
• Enabling regulation for access ‘Where are there gaps in access?’
• Standardized metrics for grants ‘Is grantee reaching rural areas?’
Providers
Social protection
Policy makers and
regulators
Donors and
implementing
partners
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Kenya is far ahead of other countries
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Geo-Spatial mapping project components
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Access point data collection
Population and other layers
Mapping engine
Data Sources
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• AfriPop Data 2012 - detailed population and poverty
dataset for Africa, using Kenya National Bureau of
Statistics data
• MODIS Global Urban Areas – 500m resolution showing
urban, built up and settled areas
• Access Points - Financial Service Locations, by type,
mapped very accurately using GPS coordinates by
Brand Fusion
MODIS Urban/Rural Access PointsChance of Poverty
Maps
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Submission Example
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Methodology
• Quality Control Process / Audits
• Real Time System Monitoring
• Field Audits
• Data Validation
• Data Quality checks
AUDIT SHEET
County Name: Nairobi Date: 5 September 2013
Town name: Kayole
Streets Covered: Kayole, Spine, Saba Saba
Name of the Establishment Agent Name Details Confirmed Comments
1 Tripple X communication Mobile Money
2 Post Office Kayole Post Office
3 Post Office Kayole Mobile Money
4 Gateway Building Mobile Money
5 Free Zone Mobile Mobile Money
6 Arata Spine Road Mobile Money
7 Aug Services Mobile Money
8 Cooperative Bank of Kenya Commercial Bank
9 Charcon Propertise Mobile Money
Conducted by: Victor Lazaro
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Challenges & Highlights
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Mapping Results
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Traditional measures of financial access are insufficient
PERU
After 4217 bank agents were
opened, Peru went from
5.8 points per 100,000
to 37 points per 100,000
Increasing access more than six
times
However, from a geospatial
perspective, the population
within 5 km of a financial access
point only increased 1.6%
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Bank Branches
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Bank Branches and Mobile Money Agents
Bank Branches
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Bank Branches and Mobile Money AgentsAll Service Types
Access to Financial Service Locations
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Population within 3kmPopulation within 5km
Population Density vs. Access
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Population Population within 3km
The Rural – Urban Divide
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0
25
50
75
100
Population AccessPoints
Pop within3km
Use FormalServices
Perc
enta
ge
Rural Urban
Population within 3km
Poverty vs Access
Income area Pop % Points %
Poorest 13.2 1.3
Middle 57.2 30.0
Wealthiest 29.6 68.7March 13, 2014 © 2013 Bill & Melinda Gates Foundation | 19
Arid Areas vs. Access
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Population within 3kmAgro-Climatic Zones
Exclusion
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Exclusion vs Distance
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Bank Branches – access vs uptake
Population within 3km of Bank Branches
Currently use Banks
Tana River
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Mobile Money – access vs uptake
Currently uses Mobile Money Population within 3km
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0%
20%
40%
60%
80%
100%
120%
Tan
a R
iver
Turk
ana
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rsab
it
We
st P
okot
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et
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ara
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et
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Ma
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da
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Mig
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ru
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Kakam
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sin
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aB
ay
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om
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Mobile Money Use vs. Population within 3 km
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Mobile Money
WEST POKOT
ISIOLO
NYERI
Use Mobile Money: 21.2%
Mobile ownership: 39.5%
Pop within 3km: 35.5%
Use Mobile Money: 27.5%
Mobile ownership: 28.2%
Pop within 3km: 24.4%
Use Mobile Money: 80.7%
Mobile ownership: 75.9%
Pop within 3km: 86.7%
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3km Access
Supply vs Proximity in Western Kenya
Locations per 100,00 persons
Bomet
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3km AccessService Use
Uptake vs Proximity in Western Kenya
Bomet
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Use of Informal Services vs Distance
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0%
10%
20%
30%
40%
50%
0 10 20 30
Use Info
rmal S
erv
ices O
nly
Mean Distance from Finaincal Service Location
Portfolio of Services vs. Proximity
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Use multiple services Population within 3km
Portfolio of services (informal & formal) vs. access
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0%
20%
40%
60%
0% 20% 40% 60% 80% 100%
Port
folio
Use
Population within 3km
Case Study:Kitui
Population 963,253
Financial Service Locations 776
Population within 5km 52.7%
Population within 3km 33.5%
Bank Usage 19.8%
Mobile Money Usage 56.6%
Use Informal Services Only 15.1%
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33March 13, 2014 © 2013 Bill & Melinda Gates Foundation |
The Question:
Determine the total population within a 5km distance of financial access locations,
with a breakdown by Urban and Rural areas.
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The Analysis Process, using GIS Software
5 kmStep 1. Buffer each
financial access point by
a distance of 5
kilometers
Step 2. Merge the
buffered areas for each
type and combination of
types
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The Analysis Process, using GIS Software
Step 3. Overlay the
combined areas with the
population data
Step 4. Overlay the selected
population data with the
urban / rural layer
14 1 18 62 71
10 1150 678 780 820
350 1230 78 82 44
55 122 52 18 18
11 12 12 1 1
Rural
Urban
Population Dataset
=
=
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FSPMaps.com - Overview
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All functions accessible in one view
Using FSPMaps.com
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Show only MFIs and SACCOs
Using FSPMaps.com
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Calculate 3 km access to MFIs and SACCOs
Using FSPMaps.com
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White areas are within 3 km of the selected access points
Using FSPMaps.com
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Output statistics for MFIs and SACCOs
Using FSPMaps.com
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Output statistics, by county
Using FSPMaps.com
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Access Point Summary Graphic with rural urban split
Using FSPMaps.com
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Access point photos
Using FSPMaps.com
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Drop Pin Tool – determine competition and potential reach
FSPMaps.com – great response so far
7,638 unique visitors
41% return rate
Users from 121 countries
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