1 Urban Growth Analysis: Calculating Metrics to Quantify Urban Sprawl Jason Parent Academic Assistant – GIS Analyst Daniel Civco Professor of Geomatics Center for Land Use Education And Research (CLEAR) Natural Resources Management and Engineering University of Connecticut Shlomo Angel Adjunct Professor of Urban Planning Robert F. Wagner School of Public Service, New York University Woodrow Wilson School of Public and International Affairs, Princeton University Image courtesy of the Union of Concerned Scientists:

2 Project Overview ► Analyze change in urban spatial structure, over a 10 year period, for a global sample of 120 cities.  Phase I (Angel et al. 2005): Acquire and / or derive necessary data. Develop preliminary set of metrics  Phase II (Angel et al. 2007): Further develop metrics to quantify and characterize the spatial structure of the cities. Create maps to facilitate qualitative assessment of the urban structure

3 Phase I: Land Cover Derivation ► Land cover derived from Landsat satellite imagery. ► Land cover derived for two dates: T 1 (circa 1990) and T 2 (circa 2000). ► Land cover contained 3 categories: urban, water, and other Category Grid cell value 0 No Data 1 Other 2 Water 3 Urban

4 Bacolod, Philippines: Land cover T1T1 T2T2

5 Phase I: Acquisition of City Boundaries ► Administrative districts (i.e. Census tracts) acquired for each city. ► Population data were attributed to the administrative districts.  Interpolated population values at land cover dates. ► Districts define the outer perimeter of the cities and provide population data.

6 Phase I: Slope ► Slope grid derived from Shuttle Radar Topography Mission Digital Elevation Models. ► Slope calculated in percent. ► Maximum slope defined as the slope value below which 99% of the urban area exists.

7 Phase II: The Metrics ► Several sets of metrics were developed to measure specific aspects of the urban spatial structure ► A total of 65 metrics have been developed ► This presentation presents a sample of metrics from each set  Angel et. al. (2007)  The complete set of metrics will be presented in future papers at the conclusion of the study

8 Sprawl Manifestations ► Sprawl manifestations exhibited by the built-up area:  Multiple urban cores  Ribbon or strip developments  Scatter developments  Fragmented and unusable open space ► Manifestations are quantified with the following metrics:  Main core  Secondary core(s)  Urban fringe  Ribbon development  Scatter development

9 Sprawl Manifestation Metrics Derivation ► Manifestation metrics based on the “urbanness” of the neighboring area  Urbanness = % of pixels in neighborhood that are built-up  The neighborhood is a 1 km 2 circle centered on each pixel 252 pixels = 0.2 km 2 Urbanness = 0.2 / 1 = 20%

10 Sprawl Manifestation Metrics Definitions Built-up > 50% urban 30 to 50% urban < 30% urban Largest contiguous group of pixels All other groups Linear semi- contiguous groups approx. 100 meters wide Main core Secondary core FringeRibbonScatter All other groups

11 Bacolod Sprawl Manifestations T1T1 T2T2

12 Attributes of Urban Sprawl ► Sprawl attributes are quantifiable for each city ► A set of metrics was developed to describe each attribute  Metrics in each set tend to be correlated ► Five attributes of urban spatial structure commonly associated with ‘sprawl’…

13 The First Attribute: the extension of the area of cities beyond the walkable range and the emergence of ‘endless’ cities: Landsat image Built-up area (impervious surfaces) Open space (OS) Urbanized OS (> 50 % built-up) Urbanized area Urban footprint Peripheral OS (< 100 m from built-up)

14 Bacolod The First Attribute T1T1 T2T2

15 The Second Attribute: the persistent decline in urban densities and the increasing consumption of land resources by urban dwellers ► Population densities based on the 3 types of urban extent:  Built-up area density  Urbanized area density  Urban footprint density

16 The Third Attribute: ongoing suburbanization and the decreasing share of the population living and working in metropolitan centers ► Minimum Average Distance (MAD) center  The point that has the minimum distance to all other points in the urbanized area ► Central Business District (CBD)  The point defining the original city center  Determined by field surveys ► City center shift: the distance between the MAD center and the CBD

17 The Third Attribute: ongoing suburbanization and the decreasing share of the population living and working in metropolitan centers ► Decentralization:  Based on average distance to the MAD center for the urbanized area  Normalized by the average distance to center of a circle with an area equal to the urbanized area Distance to center

18 The Third Attribute: ongoing suburbanization and the decreasing share of the population living and working in metropolitan centers ► Cohesion:  Based on average interpoint distance of urbanized area  Normalized by average interpoint distance of a circle with an area equal to the urbanized area Interpoint Distance

19 Bacolod The Third Attribute T1T1 T2T2

20 The Fourth Attribute: the diminished contiguity of the built-up areas of cities and the increased fragmentation of open space in and around them ► Openness index: the average percent of open space within a circular 1 km 2 neighborhood for all built-up pixels. ► Open space contiguity: the probability that a built- up pixel will be adjacent to an open space pixel. ► Open space fragmentation: the ratio of the combined urbanized and peripheral open space area to the built-up area.

21 Bacolod The Fourth Attribute T1T1 T2T2

22 The Fourth Attribute Continued New Development ► Development occurring between time periods T 1 and T 2 ► Classification based on location relative to the T 1 urban area  Infill: new development occurring within the T 1 urbanized open space  Extension: new non-infill development intersecting the T 1 urban footprint  Leapfrog: new development not intersecting the T 1 urban footprint

23 Bacolod New Development

24 The Fifth Attribute: the increased compactness of cities as the areas between their fingerlike extensions are filled in. ► These metrics take the circle to be the shape of maximum compactness ► Sprawl circle: the circle with the same average distance to center as the average distance to center of the urbanized area.   Radius = 1.5 * distance to center

25 The Fifth Attribute Metrics ► Single point compactness: area of the urbanized area (km 2 ) area of the sprawl circle (km 2 ) SPC = ► Constrained single point compactness: CSPC = area of the urbanized area (km 2 ) buildable area of the sprawl circle (km 2 ) Bacolod

26 Bacolod, Philippines: The Urban Landscape T1T1 T2T2

27 The Urban Growth Analysis Tool (UGAT) ► Python script that works with ArcGIS 9.2  Executable through python or ArcGIS toolbox ► UGAT performs all analyses needed to derive the metrics and create GIS layers ► Data are input into the tool via a table  Table may contain data for any number of cities  UGAT will run the analysis for each city listed in the table

28 The Urban Growth Analysis Tool ArcToolbox Interface

29 The Urban Growth Analysis Tool Python Interface

30 Conclusions ► Phase II is currently ongoing as is development of the Urban Growth Analysis Tool. Future papers will provide in-depth and comprehensive discussions of the metrics developed in this project ► The metrics, developed so far in this project, allow rigorous quantitative assessment of the change in urban spatial structure over time ► Metrics in a set tend to be highly correlated  provides alternative ways to measure each attribute.  some metrics may be more practical than others ► The Urban Growth Analysis Tool will facilitate the application of this analysis to other cities

31 References ► ► Angel, S, J. R. Parent, and D. L. Civco. May Urban Sprawl Metrics: An Analysis of Global Urban Expansion Using GIS. ASPRS May 2007 Annual Conference. Tampa, FL ► ► Angel, S, S. C. Sheppard, D. L. Civco, R. Buckley, A. Chabaeva, L. Gitlin, A. Kraley, J. Parent, M. Perlin The Dynamics of Global Urban Expansion. Transport and Urban Development Department. The World Bank. Washington D.C., September.

32 Urban Growth Analysis: Calculating Metrics to Quantify Urban Sprawl Jason Parent Academic Assistant – GIS Analyst Daniel Civco Professor of Geomatics Center for Land Use Education And Research (CLEAR) Natural Resources Management and Engineering University of Connecticut Shlomo Angel Adjunct Professor of Urban Planning Robert F. Wagner School of Public Service, New York University Woodrow Wilson School of Public and International Affairs, Princeton University Image courtesy of the Union of Concerned Scientists: QUESTIONS?