IDEAMAPS Network

Launched in 2020 with funding from a UK Research and Innovation grant, we grew out of workshops hosted by the African Population and Health Research Center, Slum Dwellers International - Kenya, and UN-Habitat in Nairobi in 2019.

Projects in the IDEAMAPS network engage and link stakeholders, develop datasets of urban deprivation and foster capacity among stakeholders to use data for decision making.

Data Production.

City-wide Degree of Deprivation
Domains (Types) of Deprivation
Insights and data visualisations

Living Data Ecosystem.

Facilitate Fair Data Exchanges
Continuously Updated Inputs & Outputs
Plug Into Existing Data Ecosystems

Learning Materials.

Direct Training and Mentorship in Pilot Cities
Tools to Launch Sub-projects in New Cities
PhD and Researcher Opportunities

Engagement & Co-design.

Communities: Data Collection & Use
Local Gvmts: Secondments & Data Usage
Modellers: Community of Practice

Innovative Modeling.

A “Just” Modelling Framework
Community of Practise for Modellers
Iterative Open Modelling

Platform Design.

Data Lake, Web Application and Mobile Application
Privacy by Design
Centring User Experience

PROJECTS WE’RE CURRENTLY WORKING ON

2022 - 2025
Currently Active
IDEAMAPS Data Ecosystem

LAGOS, Nigeria - NAIROBI, Kenya - KANO, Nigeria

To co-create an integrated data ecosystem that enables routine, accurate mapping of slums, informal settlements, and other deprived areas across LMIC cities 

Currently Active
IDEAtlas

Mexico City, Mexico - Medellín, Colombia - Salvador, Brazil - Buenos Aires, Argentina - Lagos, Nigeria - Nairobi, Kenya - Mumbai, India - Jakarta, Indonesia

The primary objective of IDEAtlas is to develop, implement, validate and showcase advanced AI-based methods to automatically map and characterize the spatial extent of slums from Earth Observation (EO) data.

More Active Projects

Silo-ed Approaches to Slum Mapping

Current methods of mapping “slum” areas take place in isolation.
IDEAMAPS aims towards integration - using strengths from each approach to build a more detailed system.

01. Field-based Mapping

Field-based mapping is commonly performed by community NGOs such as Slum Dwellers International, and linked to advocacy...

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02. Census & Survey

The widely cited statistic from UN-Habitat – 1 billion slum dwellers globally – is calculated by classifying and counting urban “slum ...

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03. Digitising Imagery

Satellite imagery is sometimes used to manually digitize informal settlements. This approach is typically based on a...

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04. Computer Models

Data scientists use computer models to semi-automatically classify deprived urban areas from satellite imagery and other...

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