Tools and Methods for Global Urban Analysis

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1 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 1

2 Outline Global Agendas: SDG indicators Perspectives from World Bank as a data user Quick history What are we doing now? What next? Image source: DGREGIO fine scale analysis of the whole European settlements using 2.5-m-res input image data (GMES/Copernicus CORE ) Credits: European Commission, DG Regional Development /Joint Research Centre 2

3 SDGs, Habitat III: The New Urban Agenda Goal 11 - Make cities and human settlements safe, inclusive, resilient and sustainable 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 3

4 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. 4

5 Past Work on Mapping Urbanization: Some Examples 5

6 Measuring Global Urban Expansion c 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 6

7 Measuring Urban Growth across East Asia c 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 7

8 Measuring Urban Growth across South Asia c 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 8

9 Ongoing Work 9

10 Global Urban Definition (in progress) Simplified version of the OECD core and hinterland approach Uses population size and density thresholds Currently being tested on Argentina data 10

11 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 11

12 Rome, IT Inputs: Global Urban Footprint (DLR) 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 Source: High-Resolution Global Monitoring of Urban Settlements, DLR 2013

13 Inputs: WorldPop Spatial resolution: 100m Year(s): Cost: free to download existing layers Regularity of update: Ongoing Availability/documentation of input data: Yes Reproducible methods: Yes (with code) 13

14 Example of use: Sri Lanka (in progress) 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 14

15 Example of use: Argentina (in progress) 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 15

16 African Cities: Measuring Urban Change at Kigali the Metropolitan Scale 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; c Will combine earth observation with other layers from the city, geo-referenced 16 household surveys,

17 Other examples (in progress) Nepal India Kazakhstan Kyrgyz Republic ECA Shrinking Cities Project Guatemala/Central America Argentina Sri Lanka Africa (huge demand) 17

18 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. 18

19 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 19