European Commission Joint Research Centre

1st Urbanization Workshop

Day 2, Session 1:

Tools and Methods for Global Urban Analysis

Ellen Hamilton Lead Urban Specialist

World Bank

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Outline

• Global Agendas: SDG indicators

• Perspectives from World Bank as a data user

– Quick history

– What are we doing now?

• What next?

2 Image source: DGREGIO fine scale analysis of the whole European settlements using 2.5-m-res input image data (GMES/Copernicus CORE003 2012) Credits: European Commission, DG Regional Development /Joint Research Centre

SDGs, Habitat III: The New Urban

Agenda Goal 11 - Make cities and human settlements safe,

inclusive, resilient and sustainable.

• 11.1 By 2030, ensure access for all to adequate, safe and affordable housing and basic

services and upgrade slums

• 11.2 By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all, 8improving road safety, notably by expanding public transport, with special attention to the needs of those in vulnerable situations, women, children, persons with disabilities and older persons

• 11.3 By 2030, enhance inclusive and sustainable urbanization and capacity for participatory, integrated and sustainable human settlement planning and management in all countries

• 11.4 Strengthen efforts to protect and safeguard the world’s cultural and natural heritage

• 11.5 By 2030, significantly reduce the number of deaths and the number of people affected and decrease by [x] per cent the economic losses relative to gross domestic product caused by disasters, including water-related disasters, with a focus on protecting the poor and people in vulnerable situations

• 11.6 By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management

• 11.7 By 2030, provide universal access to safe, inclusive and accessible, green and public spaces, in particular for women and children, older persons and persons with disabilities

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World Bank as user: What’s in

it for us?

• Helps understand the evolution, drivers and impacts of urban

form, and make better decisions about location of infrastructure

projects which ‘lock in’ urban form.

• Allows easier, cheaper data collection in typically data-scarce

environments.

• Allows comparability of trends in urbanization between cities/

countries.

• Allows better spatial targeting of the poor.

• Crowd-sourcing and open data makes beneficiaries part of data

generation and application development, and creates public

dialogue around development issues.

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5

Past Work on Mapping

Urbanization:

Some Examples

Measuring Global Urban Expansion

c1990-2000

• 120-city

sample

• MODIS 500m

satellite

imagery

• Calculated

several metrics

to describe the

built form

Source: Angel et al (2005), The Dynamics of Global Urban Expansion, World Bank

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Measuring Urban Growth across

East Asia c2000-2010

• Measured expansion

of built-up area

between 2000 and

2010 across East

Asia and the Pacific

using MODIS 250m

satellite imagery

• Used WorldPop

population

distribution mapping

• Overlaid

administrative

boundaries (GADM)

• Competition for data

analysis and

visualization

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Measuring Urban Growth across

South Asia c2000-2010

Night Time Lights

(DMSP-OLS) data:

• A cost-effective

option for analyzing

broad spatial

patterns of urban

expansion and

economic growth in

a data scarce

environment

• Intensity of lights is

strongly correlated

with economic

activity

• Helped to identify

dozens of cities

merging into urban

corridors across

borders

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Ongoing Work

• Simplified version of the OECD ‘core and hinterland’ approach

• Uses population size and density thresholds

• Currently being tested on Argentina data

Global Urban Definition (in progress)

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Inputs:

Global Human Settlement Layer (EC-Joint

Research Centre) • Automatic image information retrieval

• Possibility to process consistently global fine-scale information

• Multi-sensor, multi-scale

• Sustainable information production

• Information democratization

• Open, public and reproducible information

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• Binary built and non-built

layer using fully automated

classification (to extract human

settlement data)

• Very high resolution radar

missions: TerraSAR-

X/TanDEM-X: About 50-70m

resolution output

• Global

Inputs:

Global Urban Footprint (DLR)

Source: High-Resolution Global Monitoring of Urban Settlements, DLR 2013

Rom

e,

IT

Inputs: WorldPop

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• Spatial resolution: 100m

• Year(s): 2000-2020

• Cost: free to download existing layers

• Regularity of update: Ongoing

• Availability/documentation of input data: Yes

• Reproducible methods: Yes (with code)

Example of use: Sri Lanka (in progress)

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• GHSL ‘alpha’ version used to

understand broad trends in

urban growth as input into a

Systematic Country Diagnostic

• WorldPop mapping to begin

shortly, using GHSL as an

input: allows population

distribution in conflict-affected

areas that have no recent

census

Example of use: Argentina (in progress)

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• GHSL ‘alpha’ version used to

understand broad trends in

urban growth, as input into an

analysis of demographic

trends and urbanization

• WorldPop mapping using

GHSL as an input recently

completed

– Provides quantitative evidence of

misalignment between official

definitions of urban and where

population densities really are

African Cities: Measuring Urban Change at

the Metropolitan Scale

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Mapping city form and

its evolution over time

in 10 African cities:

• Nairobi, Dakar, Addis,

Kigali, Lagos,

Maputo, Accra,

Kinshasa, Dar es

Salam, and Durban

(TBC)

• VHR imagery; c2000–

2010

• Will combine earth

observation with other

layers from the city,

geo-referenced

household surveys,

etc.

Kigali

Other examples (in progress)

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• Nepal

• India

• Kazakhstan

• Kyrgyz Republic

• ECA Shrinking Cities Project

• Guatemala/Central America

• Argentina

• Sri Lanka

• Africa (huge demand)

PUMA – Platform for Urban

Management and Analysis An online geospatial tool which allows users with no prior GIS experience to

access, analyze and share urban spatial data in an interactive and customizable

way.

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Can we commit to next steps?

• Short-term:

• Global Population Grid (1 km, by fall?)

• Consensus on universe of cities (can we agree on a shared definition?)

• Medium-term:

• Time series for population and built up area

• Using GHSL for SDGs

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