Conference Agenda

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Session Overview
08-03: Urban Expansion: Measurement Challenges and Implications for Policy
Wednesday, 22/Mar/2017:
4:00pm - 5:30pm

Session Chair: Anjali Mahendra, World Resources Institute, WRI, United States of America
Location: MC 2-800


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Sumila Gulyani

World Bank, United States of America

To be completed

The Atlas of Urban Expansion--2016 Edition: Methodology and Key Results

Shlomo Angel, Alejandro Blei, Patrick Lamson-Hall, Nicolas Galarza Sanchez

New York University, United States of America

The Atlas of Urban Expansion--2016 Edition provides maps and metrics on the urban extent of 200 cities, a stratified sample of cities from the universe of 4,231 cities that had 100,000 -people or more in 2010. The paper will present the methodology we developed for defining and measuring the urban extents of cities and their key attributes: the definition of what constitutes a 'city', the spatial extent of cities, the average population density of built-up areas and the urban extent as a who,e the fragmentation of the built-up areas of cities by open space, and the compactness of the urban extent in geographic space. Using this methodology, we present metrics for the 200 cities in three time periods, circa 1990, circa 2000 and circa 2014 for cities, for the world at large, for less developed countries and for more developed countries. We discuss the statistical difference of means between metrics. We also discuss the changes in three metrics over time and the share of new built-up areas that constitute infill, extension, leapfrog and inclusion. Finally, we also discuss the advantages and limitations of the methods used to define and measure the urban extent of cities.


Characterizing Urban Change Using New Global Data: An Exploratory Analysis

Chandan Deuskar, Eugenie Birch

University of Pennsylvania

The understanding of global trends in urbanization has long been constrained by inconsistencies in the definitions of urban areas and a reliance on administrative boundaries as the units of analysis. However, advances in technology are now beginning to enable a more consistent approach to understanding the spatial form, scale, and pace of global urban development. In particular, the new Global Human Settlement data set allows an evidence-based approach to global urbanization. It combines a global map of built-up areas produced using earth observation data with a global population distribution layer, to which a standard definition of urban areas based on size, density, and contiguity of populations and built-up areas is applied. The result is a global map and catalogue of the changing populations and extents of all such urban clusters around the world (several thousand in total). This paper explores changes in all ‘urban centers’ (or ‘high density clusters’) from this dataset across the world during the 1990-2015 period. We calculate a range of metrics for all such clusters, and summarize the results by country, region, size category, and income group. We also discuss limitations of the data set.


The Policy Implications Of Varying Techniques To Identify Urban Growth Patterns From Satellite Imagery: The Example Of Informal Construction In Ho Chi Minh City, 1994-2001

Arthur Acolin, Annette Kim

University of Southern California, United States of America

This paper demonstrates the potential for using remote sensing imagery to monitor the development of informal settlements in order to inform policy and urban planning in a rapidly changing context. This study applies thresholding and texture analysis of remote sensing imagery developed by Kim et al (2004) to not only identify newly urbanized land patterns but to distinguish between formal and informal urbanization. Using data for Ho Chi Minh City, it finds that 12 percent of the urban expansion area identified for 1994-2001 is informal. In addition, these informal settlements are concentrated in the urban periphery, close to roads and waterways, potentially placing these residents at higher risk of displacement and flooding. Furthermore, this study also compares our identification of urbanized areas with those identified by Angel et al. (2005) and the World Bank (2015) for 2000 in Ho Chi Minh City. We find that while all three converge on classifying urban areas in the city’s core, in the periphery each study’s methods systematically identify different kinds of urban spatial patterns. These differences suggest the importance of customizing algorithms to account for the local context and testing through ground-truthing to establish their accuracy.


Urbanization and Development: Is Latin America and the Caribbean Different from the Rest of the World?

Mark Roberts1, Brian Blankespoor1, Chandan Deuskar2, Benjamin Stewart1

1World Bank, United States of America; 2University of Pennsylvania

Two long-established stylized facts in the urban and development economics literatures are: (a) a country’s level of economic development is strongly positively correlated with its level of urbanization; and (b) a country’s level of urbanization is strongly negatively correlated with the size of its agricultural sector. However, countries in LAC appear to depart significantly from the rest of the world with regards to these two basic relationships. In particular, while Latin American countries appear to be significantly more urbanized than predicted based on these global relationships, Caribbean countries appear significantly less urbanized. Analysis involving cross-country comparisons of urbanization levels are, however, undermined by systematic measurement errors arising from differences in how countries define their urban areas. In this paper, we re-examine whether LAC countries differ from the rest of the world when it comes to the basic stylized facts of urbanization, development and structural transformation. To do this, we make use of alternative methodologies for the consistent definition of urban areas across countries. These methodologies rely on globally gridded population data sets as input. There exist several such data sets and so the paper also assesses the robustness of its findings to the choice of input population layer.


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