metadata: authors: 0: author: Duque,Juan Carlos
1: author: Lozano Gracia,Nancy
2: author: Patino,Jorge E.
3: author: Restrepo Cadavid,Paula
4: author: Velasquez,Wilson A.
count: Latin America
docna: 0: docna: Spatio-Temporal Dynamics of Urban Growth in
            Latin American Cities : An Analysis Using Nighttime Lights Imagery
docty: Policy Research Working Paper
subtopic: City to City Alliances,Urban Economics,Urban Economic Development,National Urban Development Policies & Strategies,Urban Communities,Regional Urban Development,Transport Services,Labor Markets,Energy and Natural Resources,Coastal and Marine Resources
teratopic: Communities and Human Settlements,Urban Development,Transport,Social Protections and Labor,Energy,Water Resources
abstracts: cdata!: The impact of urban form on economic
            performance and quality of life has been extensively
            recognized. The studies on urban form have focused in
            developed countries; only a few cities in developing
            countries have been studied. This paper utilizes nighttime
            lights imagery and information on street networks,
            automatically retrieved from OpenStreetMap, to calculate a
            series of spatial metrics that capture different aspects of
            the urban form of 919 Latin American and Caribbean cities.
            The paper classifies these cities into clusters according to
            these spatial metrics. It also studies the relationship
            between the urban form metrics and some factors that can
            correlate with urban form (topography, size, colony, and
            economic performance) and performs a spatio-temporal
            analysis of urban growth from 1996 to 2010. Among the
            results, the paper highlights the identification of five
            typologies of cities, the tendency of a group of cities to
            grow at a steeper slope, some worrying cases of urban growth
            over protected areas, and a trend toward increasing sprawl
            in some Latin American and Caribbean cities.
repnme: repnme: Spatio-Temporal Dynamics of Urban Growth in
            Latin American Cities : An Analysis Using Nighttime Lights Imagery
display_title: Spatio-Temporal Dynamics of Urban
            Growth in Latin American Cities : An Analysis Using
            Nighttime Lights Imagery
listing_relative_url: /research/2019/01/30760921/spatio-temporal-dynamics-urban-growth-latin-american-cities-analysis-using-nighttime-lights-imagery
topicv3: Mountains m1326647 1986,Transport m1326661 135,Urban Development m1327371 699,Population, Aging and Health m1327736 640,Jobs m1503931 2670
publishtoextweb_dt: 2019-01-15T00:00:00Z
historic_topic: Communities and Human Settlements,Urban Development,Transport,Social Protections and Labor,Energy,Water Resources
no_of_pages: 47
author_keys: 0: author_key: 876533
1: author_key: 611031
2: author_key: 1175220
model_extractions: WPS8702 Policy Research Working Paper 8702 Spatio-Temporal Dynamics of Urban Growth in Latin American Cities An Analysis Using Nighttime Lights Imagery _Juan C. Duque Nancy Lozano-Gracia Jorge E. Patino Paula Restrepo Wilson A. Velasquez_ Social, Urban, Rural and Resilience Global Practice January 2019
Policy Research Working Paper 8702

# **Abstract**

The impact of urban form on economic performance and quality of life has been extensively recognized. The studies on urban form have focused in developed countries; only a few cities in developing countries have been studied. This paper utilizes nighttime lights imagery and information on street networks, automatically retrieved from OpenStreetMap, to calculate a series of spatial metrics that capture different aspects of the urban form of 919 Latin American and Caribbean cities. The paper classifies these cities into clusters according to these spatial metrics.

It also studies the relationship between the urban form metrics and some factors that can correlate with urban form (topography, size, colony, and economic performance) and performs a spatio-temporal analysis of urban growth from 1996 to 2010. Among the results, the paper highlights the identification of five typologies of cities, the tendency of a group of cities to grow at a steeper slope, some worrying cases of urban growth over protected areas, and a trend toward increasing sprawl in some Latin American and Caribbean cities.

This paper is a product of the Social, Urban, Rural and Resilience Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at nlozano@worldbank.org and prestrepocadavid@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Spatio-Temporal Dynamics of Urban Growth in Latin American Cities: An Analysis Using Nighttime Lights Imagery**

## **Juan C. Duque ( )**

Research in Spatial Economics (RiSE-group), Department of Economics, Universidad EAFIT, Medellin, Colombia.e-mail: jduquec1@eafit.edu.co

**Nancy Lozano-Gracia,** Social, Urban, Rural and Resilience.The World Bank. Washington, USA. e-mail: nlozano@worldbank.org

**Jorge E. Patino,** Research in Spatial Economics (RiSE-group), Department of Economics, Universidad EAFIT, Medellin, Colombia.e-mail: jpatinoq@eafit.edu.co

**Paula Restrepo,** Social, Urban, Rural and Resilience. The World Bank. Washington, USA. e-mail: prestrepocadavid@worldbank.org

**Wilson A. Velasquez,** Research in Spatial Economics (RiSE-group), Department of Economics, Universidad EAFIT, Medellin, Colombia.e-mail: wavelasquezh@unal.edu.co

**Keywords** : Urban form ꞏ Latin American and Caribbean cities ꞏ Remote sensing, Cluster Analysis

JEL: N96, O18, O21, C38, R14
- 2 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

# **1 Introduction**

In 2007 the global urban population exceeded the global rural population, and it is projected that urban dwellers will double the rural dwellers by 2050 (United Nations, 2014). Latin America and the Caribbean (LAC) is the most urbanized region on the planet, with 80% of its population living in cities (UN-Habitat, 2012). This growth has not stopped, according to the World Development Indicators, LAC cities added 206 million people between 1960 and 1990, and 191 million people between 1990 and 2015 (The World Bank, 2017). This rapid level of urbanization imposes challenges for local authorities, as many cannot provide services and infrastructure for the new urban areas at the same speed as the urban growth. Urban form has an important influence on the emergence of agglomeration economies and congestion costs, and on cities’ economic productivity, as well as quality of life and environmental sustainability (Squires, 2002). The economies of agglomeration is strongly associated with intraurban connectivity levels, the level of compactness, and land use patterns (Ciccone and Hall, 1996; Cervero, 2001; Rosenthal and Strange, 2004). LAC cities are still spatially growing despite the deceleration in population growth (UN-Habitat, 2012).

However, there is a lack of empirical studies about the characteristics of LAC cities and their changes over time. The challenges of topography may have prevented dense development in some cases, as many Latin American cities are located in rugged topographies with natural barriers. Some of the urbanization in the region is in fact occurring over steep terrain, with slopes greater than 15%, and we verified the trend of urbanizing even steeper terrains in recent years. According to Gencer (2013), these levels of urban concentration of population increase the potential for disasters and most of the cities exposed to at least one natural hazard are located in Asia and Latin America. In some instances, the urbanization occurs at the cost of occupying adjacent protected areas. More than 2,400 square kilometers of protected areas have been lost to urban growth in the region from 1996 to 2010 according to the findings in this work. Rapid urbanization combined with limited infrastructure investments may have also led to urban forms that pose a barrier to the rise of agglomeration economies, limit firm interaction, and make it hard for workers to reach their jobs. A better understanding about the dynamics of urban form in the region can shed light on whether urban policy has a role to play in supporting city productivity, quality of life, and urban sustainability, while mitigating the increased vulnerability that usually comes with urban expansion.
3 In this paper we use nighttime lights (NTL) radiance-calibrated annual image composites, for 1996, 2000 and 2010, to provide a standardized characterization of the urban form of 919 Latin American cities. We make a descriptive analysis of the urban form variables, and their relationships with the potential factors that affect the urban form. We then look for differences in the data across countries and regions and identify the trends of change over time.

The rest of the paper is organized as follows. Section 2 presents a literature review. Section 3 describes the data used in this analysis, and the definitions of urban form variables and factors. Section 4 shows the descriptive analysis of the set of LAC cities, the differences across countries, and the interplay with the factors that affect urban form. Section 5 shows the space-time analysis of urban growth in the region and Section 6 presents the main conclusions of this work.

# **2 Literature review**

The importance of urban form has been extensively recognized in the urban economics literature. The form of cities affects their economic performance (Parr, 1979), sustainability levels (Breheny, 1992; De Roo and Miller, 2000), quality of life (Squires, 2002), commuting costs (Wheeler, 2001), costs of transporting intermediate goods (Ciccone and Hall, 1996), the level of knowledge spillovers through human interactions (Lynch, 1981; Jaffe et al 1993 and Glaeser 1998), and the level of matching of firms and workers (Ciccone and Hall, 1996; Rosenthal and Strange, 2004; Cervero, 2001). All this together makes urban form a key factor from an urban policy standpoint.

However, the literature on urban form is not extensive, and it is mainly focused in North American cities (Hasse and Lathrop, 2003; Ewing et al., 2002; Filion, 2001; Tsai, 2005); European cities (Schwarz, 2010; Kasanko et al., 2006, Antrop, 2004; Cheshire, 1995; CEC, 1992; Champion, 1992), and Asian cities (Deng and Huang, 2004; Lin, 2002; Sorensen, 2000). In these studies, the main focus has been to measure the spatio-temporal patterns of urban sprawl.
- 4 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

Developing countries have been much less studied. Most of them treat specific cases such as Lagos, Nigeria (Barredo et al., 2004); Morelia city, Mexico (Lopez et al., 2001); or Cairo, Arab Republic of Egypt (Sutton and Fahmi, 2001). From the best of our knowledge there is evidence of three crosscountry studies that include LA cities in their sample: Huang et al. (2007) study 17 Latin American cities that are part of a total sample of 77 metropolitan areas in Asia, the United States, Europe, and Australia. Using a semi-automatic process for delineating the urban extents from Landsat ETM imagery, the authors calculate seven spatial metrics that capture five dimensions of urban form: compactness, centrality, complexity, porosity and density. Angel et al. (2010a) include 17 Latin American cities in a total sample of 120 cities from all around the world. Using also satellite images the authors calculate four urban fragmentation metrics: edge index, openness index, core open space ratio, and city footprint ratio. Finally, Inostroza et al. (2013) use Landsat imagery to study urban sprawl and fragmentation in 10 Latin American cities. The metrics for measuring the sprawl are: built-up area, density, spatial configuration, and speed. The common finding in these three studies is that Latin American cities are more compact and denser than their counterparts in either Europe or North America.

In our paper we contribute to the literature on urban form in three ways: First, we combine the use of NTL imagery, for urban extent delineation, with recently developed software on computational geometry and network topology (Angel et al., 2010b; Boeing, 2017), to generate a highly automated procedure that makes possible to analyze 919 urban extents in Latin America and the Caribbean. This is the most comprehensive study of urban form for this region. Second, the indicators used to describe urban form are tightly connected to the definition of urban form from the urban planning perspective, which describes urban form as a combination of external shape and internal structure (Whyte, 1968). In this paper we implement a definition of urban form that includes three dimensions: Shape of the urban extent, internal urban structure and land use pattern (Prosperi et al., 2009; Batty, 2008). This definition allows us to go beyond the debate sprawl vs. compact and cover other important aspects of urban form. Finally, we explore the relationship between urban form and factors such as topography, size of the city, economic strength (measured with NTL), and heritage of colonial urban planning.

**3 Data**
5

# _3.1 Study region_

This analysis focuses on Latin American and Caribbean (LAC) cities with more than 50,000 people in 2010. According to (UN-Habitat, 2012), Latin America and the Caribbean is the most urbanized region on the planet with more than 80% of the population living in urban areas, and built-up area continues to expand despite deceleration in population growth. The LAC region is characterized by large diversity of climatic conditions, including from tropical to temperate climates; and the terrain also shows high variability in altitudes due to the presence of the Andean Mountain Range. That diversity in climate and topographic conditions translates into a high diversity in the shape that urban areas have taken. On the one hand, there are cities located in the mountains constrained by hilly terrain; on the other hand, there are also some cities located in the lower lands in flat areas, constrained only by the presence of large rivers or lakes, and yet other cities have grown located at the Caribbean and Pacific coasts, whose shapes reflect the coastline. Speaking of urbanized area, in the region it accounts for 0.4% of the land, a much lower value than Europe (2.3%) and the United States (1.2%), somehow closer to Asia (0.7%), but higher than the former Soviet Union region (0.2%) and Oceania (0.1%) (Y. Zhou et al., 2015). When compared to other regions of the world, LAC cities show denser populations than Europe and the United States, greater regularity and lower open space (Angel et al., 2010a; Huang et al., 2007). However, Inostroza et al. (2013) reported a general trend towards low density growing for some of the largest LAC cities.

# _3.2 Use of Night‐time Lights Data for urban extents delineation_

The nighttime lights data (NTL) are based on nighttime imagery recorded by the Defense Meteorological Satellite Programs - Operational Linescan System (DMSP-OLS), which reports the recorded intensity of Earth’s surface lights. Nighttime lights products have high correlation to human activities (Hsu et al, 2015), and have been previously used for regional and global analysis of urbanization (Cheng et al., 2016; Pandey et al., 2013; Sutton et al., 2006; Zhang and Seto, 2011; Zhou et al., 2015a; Zhou et al., 2015), population modeling (Anderson et al., 2010; Lo, 2001), and economic performance (Cao et al.,2016; Forbes, 2013).
- 6 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

There are two different nighttime light products from DMSP-OLS that can be used to delineate urban areas: the stable or ordinary product (NTL), and the radiance-calibrated (RC) product. For this work we use the latter, given that such data include a correction for the saturation issue likely to be an important issue for most large cities in the region. Further, previous work has suggested that the RC information provides a better proxy for socioeconomic variables than the stable products (Hsu et al., 2015; Ma et al., 2014). Radiance-calibrated annual composites for 1996, 2000 and 2010 obtained from NOAA National Centers for Environmental Information<sup>1</sup> were used to delineate urban extents. These composites have a spatial resolution of 30 arc-seconds (about 1 km at the Equator).

Another known issue of the DMSP-OLS products is the “overglow” effect: dim lighting detected from light in surrounding areas of cities because of the scattering of lights in the atmosphere (Wu et al., 2014). A novel deblurring process was applied to address the issue of over glow in the radiancecalibrated products. This process involves the use of two sequential filters, a standard deconvolution and the frequency of illumination maxima, to withdraw the light from the surroundings back and restacking it vertically on its source pixels at city centers (Abrahams et al., 2016).

# _3.2.1 Calibration and interannual correction of deblurred RC data_

Deblurred DSMP-OLS RC annual composites for the years 1996, 2000 and 2010 were previously inter-calibrated and corrected for a multi-temporal analysis of urban form and city productivity in Latin America (Duque et al., 2017a). In that work, the three nighttime images were used to delineate urban extents in each year for most of the Latin American and Caribbean cities that had more than 50,000 people in 2010 using a threshold approach. Figure 1 shows the workflow of urban extent delineation in Duque et al. (2017a). Figure 2 shows three examples of the obtained urban extents from the 2010 NTL image composite. Figure 3 shows the location of the obtained urban extents in Latin America and the Caribbean region.

> 1 Image and Data processing by NOAA's National Geophysical Data Center. DMSP data collected by the US Air Force Weather Agency. https://ngdc.noaa.gov/eog/dmsp/download V4composites.html.
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9 degree in which the shape of a polygon deviates from its equal-area circle. It is calculated as the share of the total area of the urban extent that is inside the equal-area circle about its center of gravity (Angel et al., 2010b). An _Exchange_index_ equal to 1 corresponds to a perfect circle. As the _Exchange_index_ moves towards 0, the shape of the polygon becomes irregular, elongated, and noncompact. The smoothness of the perimeter is measured with the _Perimeter_index_ , which indicates how smooth is the perimeter of the urban extent. It is calculated as the ratio of the perimeter of the equal-area circle and the perimeter of the shape (Angel et al., 2010b). A _Perimeter_index_ equal to 1 indicates a totally smooth perimeter, found in a perfect circle. A _Perimeter_index_ close to 0 indicates a highly irregular perimeter, which is very common in natural/organic cities located in rugged topography. Finally, the level of fullness is quantified with the _Fullness_index_ , which measures the presence of built-up areas within the urban extent as a fraction of the urban extent area. It is calculated as the fraction of the total area of the urban extent that is built-up. This measure can be understood as the complement of measures such as porosity or sprawling (Burchfield et al., 2006). A _Fullness_index_ equal to 1 corresponds to a totally compact/built-up city. A _Fullness_index_ close to 0 represents a sprawling city with lots of empty spaces within its borders. In this study, the _Fullness_index_ was calculated using the built-up presence by epoch layers from Global Human Settlement Layer project (GHSL) at 250 meters of spatial resolution for 1990, 2000 and 2014 (Pesaresi et al., 2015; Pesaresi et al., 2016).

Next, we study two dimensions of a city structure related to two dimensions of connectivity: first, the structure given by the layout of the road network, and second, the connectedness of its segments. We measure urban structure with the _Circuity_avg_ , which takes values close to 1 when the streets in the network are mostly straight lines, and greater than 1 when the streets are curvier/organic. This metric is calculated as the average ratio between an edge length and the straight-line distance between the two nodes it links (Boeing, 2017). Regarding the connectivity we applied the two most popular metrics for assessing connectivity: _Intersection_density_ and the _Street_density_ ; _Intersection_density_ is calculated as the sum of all edge lengths divided by the area (Boeing, 2017), and _Street_density_ is calculated as the sum of all edges in the undirected representation of the graph (in km) divided by the area in Km<sup>2</sup> (Boeing, 2017). Both metrics inform about the ease of movement across the city (Boeing, 2017). High values of these two measures are associated to high rates of walking and use of nonmotorized transport modes (Cervero, 1997).
10 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity Finally, in order to measure the third dimension, the land use pattern, we use the metric proposed by Fallah et al (2011), the _Sprawl_index_ , which approaches the land use pattern by measuring the level of evenness in the distribution of population within the urban extent. When the population is evenly distributed, the _Sprawl_index_ takes values close to 0; when the population is highly concentrated in a portion of the urban extent, the _Sprawl_index_ takes values close to 1. It is calculated as the normalized difference between the share of areas with population density below the regional average density and the share of areas with population density above the regional average density (Fallah et al., 2011). We implemented this metric using population counts at pixel level retrieved from GHS population raster layers for 1990, 2000 and 2015 at 250 meters of spatial resolution (Freire and Pesaresi, 2015; Pesaresi et al., 2016) within the urban extents extracted from DMSP-OLS NTL RC images.

As one of our goals is to generate highly automated methods, we used the “Shape Metrics Toolbox”, which is a Python script that runs in ArcGIS, to calculate two of the three the shape metrics of the urban extents (Exchange index, and Perimeter index). This software is intellectual property of the Center for Land Use Education and Research (CLEAR) at the University of Connecticut (http://clear.uconn.edu/tools/Shape_Metrics/index.htm). We used the OSMnx Python library to compute three network topology variables (Circuity, Intersection_density, and Street_density) (Boeing, 2017). This library uses OpenStreetMap to retrieve, automatically, the street networks within a given polygon, and calculates a series of network-based metrics.
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- 12 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity
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<!-- Start of picture text --> . nExchange — 1996<br>— 2000<br>— 210<br>°<br>02 o4 06 o8 10<br><!-- End of picture text --> <!-- Start of picture text --> Fullness<br>— 1996<br>— 2000<br>20 — 2010<br>15<br>10<br>05<br>00<br>-02 00 02 04 os os 10 12<br><!-- End of picture text --> <!-- Start of picture text --> 5 Perimeter — 1996<br>— 2000<br>— 2010<br>3<br>1<br>°<br>00 02 04 06 08 10<br><!-- End of picture text --> <!-- Start of picture text --> Circuity_avg<br>* — 1996<br>— 2000<br>— 210<br>~»<br>18<br>10<br>5<br>o<br>10 14 12 13 14<br><!-- End of picture text -->
<!-- Start of picture text --> Intersection_density<br>ors —— 10962000<br>ootso — 210<br>012s,<br>0100<br>0075<br>0.0050<br>0.0028,<br>0.0000<br>° 50 100 150 200<br><!-- End of picture text --> <!-- Start of picture text --> Street_density<br>00012 —— 20001996<br>0.00010 — 210<br>0.00008<br>0.00008<br>0.00008<br>00002<br>0.00000<br>° 5000 +0000 15000 20000 25000<br><!-- End of picture text --> <!-- Start of picture text --> ‘Sprawl<br>28 —— 20001096<br>— a0<br>20<br>15<br>10<br>os<br>00<br>oo 02 04 os os 10 12<br><!-- End of picture text -->
17 Urban texture variables indicate that LAC cities have a quite dense texture, with values of intersection density (Figure 5e) and street density (Figure 5f) that are rather high. The _Circuity_avg_ values (Figure 5d) indicate that the street network layout in LAC cities is very regular: they are very close to 1, and 75% are below 1.05, meaning that most of their street networks are about only 5% longer than if they were all composed of straight lines. This regularity is in line with the findings by Huang et al. (2007). However, a general small increasing trend is observed in _Circuity_avg_ values from 1996 to 2010, which might be due to recent occupation of rugged terrain in mountainous areas.

The land use variable, sprawl (Figure 5g), measures how uniform is the spatial distribution of population within the urban extents. In general, this variable shows medium values with a small decrease from 1996 to 2010. This could be a weak trend towards a more uniform distribution of the population within urban extents.

The diversity in climate and topographic conditions across the LAC region translates into a high diversity in the shape that urban areas have taken. We look at the 2010 data to highlight country level differences in urban areas (Figure 6). _nExchange_ boxplots from Figure 6 show that Venezuelan cities tend to be more elongated than most of the other cities in the region, while Bolivian cities tend to be more rounded. Bolivian cities tend to have more smoothed perimeters (high values of _nPerimeter_ ), and the cities with less smoothed perimeters are in México, Brazil, Argentina and the República Bolivariana de Venezuela. _Fullness_ boxplots show that lower values occur more frequently in Colombia, Ecuador and Peru than in other countries of the region, and the highest values are in Bolivia and Guatemala. Urban texture plots show that more regular networks are often denser than the organic ones: in general, the República Bolivariana de Venezuela and Guatemala have the cities with more organic street networks in the region (higher values of _Circuity_avg_ ), and they have also the lowest street and intersection densities. With respect to land use, the República Bolivariana de Venezuela and Peru exhibit higher values of _Sprawl_ , which indicates less uniform distribution of urban population in their urban areas and more intra-urban differences, while Bolivia and Cuba exhibit lower values, which indicate a more uniform distribution of the population within their cities.
<!-- Start of picture text --> ° xchange — 1096<br>— 2000<br>— 2000<br>°<br>02 oa os os 10<br><!-- End of picture text --> <!-- Start of picture text --> 5 nPerimeter — 1996<br>— 2000<br>— 2010<br>3<br>1<br>°<br>00 02 04 os os 10<br><!-- End of picture text --> <!-- Start of picture text --> Fullness<br>—— 10062000<br>20 — 2000<br>18<br>10<br>os.<br>oo<br>02 oo 02 oa os os 10 12<br><!-- End of picture text --> <!-- Start of picture text --> Circuity_avg<br>bas —— 20001996<br>— 2010<br>»<br>8<br>10<br>5<br>°<br>10 14 12 13 14<br><!-- End of picture text --> <!-- Start of picture text --> Intersection_density<br>ors —— 20001906<br>gots — 210<br>ors,<br>0.0100<br>0.0075<br>0.0050<br>0.0028,<br>0.0000<br>° 50 100 180 200<br><!-- End of picture text --> <!-- Start of picture text --> Street_density<br>0.00012 —— 20001996<br>0.00010 — 210<br>0.00008<br>0.00008<br>0.00004<br>00002<br>0.00000<br>° 5000 +0000 18000 20000 25000<br><!-- End of picture text --> <!-- Start of picture text --> ‘Sprawl<br>2s —— 20001996<br>— a0<br>20<br>15<br>10<br>os<br>oo<br>oo 02 04 os os 10 12<br><!-- End of picture text -->
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<!-- Start of picture text --> ee<br><!-- End of picture text --> <!-- Start of picture text --> 4 —— slope<toslope>10 7 — Area_km2:prea_km2:0102 7s —— Portugalspain 7 —— arnt:atarnti: 02<br>eTn3223 es<2a46 — MeaArea_km2:Q4 km2: 03 gss23100450 ———mre!NetherlandEngland esS2346 — ant: 3.arnt:Qs<br>z Bs B15 Be<br>aaaa<br>12 50 2<br>1 25 1<br>° ° oo °<br>02 04 ‘Slope_201006 08 10 02 04 ‘Area_km2_2010«206 Sst 00 02 04 Colony06 08 10 12 02 «0488dr_ntl_2010<br>— slope<t0 — Area_km2: 01 — Portugal — drntrat<br>4 — slope>10 8 = —————— Neea_km2:Area_km2:Q3Area_km2:Q2Area_km2:Q3Area_km2:Q2Area_km2:Q2Q2 5 ——— SpainEnglandEngland 8 —— drat:dr_nt:Q3 a2— drat:dr_nt:Q3 a2 drat:dr_nt:Q3 a2dr_nt:Q3 a2 a2<br>23 e 04 e 4 Nethertand — dent a4 a4<br>Fy \ a® FS — France oa°a°<br>ieii iQ\Q\\ #}hs}hshs é7/7//<br>12”22”2”22<br>°°° ° °<br>00 02 04‘Slope_201008‘Slope_20100808 08 10 00 «02k‘Area_km2_2010‘Area_km2_2010 00 02 «(04 Colony080810080810 00 «602sdr_ntl_2010dr_ntl_2010<br><!-- End of picture text --> <!-- Start of picture text --> — slope<t0 — Area_km2: 01 — Portugal — drntrat<br>4 — slope>10 8 = —————— Neea_km2:Area_km2:Q3Area_km2:Q2Area_km2:Q3Area_km2:Q2Area_km2:Q2Q2 5 ——— SpainEnglandEngland 8 —— drat:dr_nt:Q3 a2— drat:dr_nt:Q3 a2 drat:dr_nt:Q3 a2dr_nt:Q3 a2 a2<br>23 e 04 e 4 Nethertand — dent a4 a4<br>Fy \ a® FS — France oa°a°<br>ieii iQ\Q\\ #}hs}hshs é7/7//<br>12”22”2”22<br>°°° ° °<br>00 02 04‘Slope_201008‘Slope_20100808 08 10 00 «02k‘Area_km2_2010‘Area_km2_2010 00 02 «(04 Colony080810080810 00 «602sdr_ntl_2010dr_ntl_2010<br><!-- End of picture text -->
<!-- Start of picture text --> 20 —— slope<t0slope>to 25°—— Area_km2:Q1prea_km2:Q2Area_km2:03 4 ——— SpainPortugalEngland 25 —ar— drntatnti:arnt: a32<br>15 20 — Area km2: 04 3 ——— NethertandFrance 20 — entas<br>? 3” ? ?<br>gt 2 2? z<br>2 210 & 210<br>05 os 1 05<br>00 00 ° 00<br>00 02 ‘Slope_201004 06 08 10 12 02 09 02 ‘Area_km2_201004 06 08 10 12 000 025 050‘Colony075 100 125 02 00 02 ‘dr_ntl_201004 06 08 10 12<br>26 ——— slope<10slope>t0slope>t0 25 ——— Area_km2:Q1frea_km2:frea_km2: 30 — Portugal 25 — dent: Qt Qt<br>g 2 20 ——— Area_km2:Q3frea_km2:0204frea_km2:0204020404 23 — — EnglandNetheriandSpainNetheriandSpainSpain 20 — — ardearnt:arnt: nt: a2Q3a4Q3a4a4<br>g3Hj3HjHjj 15 gR15HqR15HqHqq FF22Bosqi22BosqiBosqiqi fee gR15gqR15gqgqq<br>6FoFo 3°6r6rr 6HH 3°11<br>655 5 5<br>° ° ° °<br>100 105 140Slope_2010145Slope_2010145145 120 125 100 105 ‘Area_km2_2010410410 115 120° 125 100 105 1404.15ColonyColony 1.20 125 100 105 nt140-1152010140-11520102010 1201.25<br>0.020 ——— ‘slope<10shopertoshoperto 020 ——— ‘Area_km2:Area_km2:prea_km2:Area_km2:prea_km2:prea_km2: Q'Q201Q20101 eee ——— PortugalSpainSpain 0.020 ——— aedr_ntl:dr_nti:at: a2Q1dr_ntl:dr_nti:at: a2Q1dr_nti:at: a2Q1at: a2Q1 a2Q1Q1<br>2 — Area_km2:Q3 0.08 — England — dr_nt: Q3<br>Roots2222 2Roos22Roos22222 — Neakm2:a4 Neakm2:a4 2Roos22Roos2222 ——— NetheriandFranceFrance 280015228001522222 — arnt: 04 arnt: 04<br>Soot Sooo 3 00s 00s Soor<br>Fy Fy Fy a<br>iHaHaa3<br>=0.0050.005 = 0.005 z 001 2 0.005<br>0.000 0.000 0.00 0.000<br>° 50 100 150 ° 50 100 150 ° 50 100 150 ° 50 100 150<br>Slope_2010 ‘Area_km2_2010 Colony _nt_2010<br><!-- End of picture text --> <!-- Start of picture text --> 26 ——— slope<10slope>t0slope>t0 25 ——— Area_km2:Q1frea_km2:frea_km2: 30 — Portugal 25 — dent: Qt Qt<br>g 2 20 ——— Area_km2:Q3frea_km2:0204frea_km2:0204020404 23 — — EnglandNetheriandSpainNetheriandSpainSpain 20 — — dearnt:ardearnt:arnt: nt: a2Q3a4Q3a4a4<br>g3Hj3HjHjj 15 gR15HqR15HqHqq FF22Bosqi22BosqiBosqiqi fee gR15gqR15gqgqq<br>6FoFo 3°6r6rr 6HH 63°11<br>655 5 5<br>° ° ° °<br>100 105 140Slope_2010145Slope_2010145145 120 125 100 105 ‘Area_km2_2010410410 115 120° 125 100 105 1404.15ColonyColony 1.20 125 100 105 nt140-1152010140-11520102010 1201.25<br>0.020 ——— ‘slope<10shopertoshoperto 020 ——— ‘Area_km2:Area_km2:prea_km2:Area_km2:prea_km2:prea_km2: Q'Q201Q20101 eee ——— PortugalSpainSpain 0.020 ——— aedr_ntl:dr_nti:at: a2Q1dr_ntl:dr_nti:at: a2Q1dr_nti:at: a2Q1at: a2Q1 a2Q1Q1<br>2 — Area_km2:Q3 0.08 — England — dr_nt: Q3<br>2 — Neakm2:a4 Neakm2:a4 2 ——— NetheriandFranceFrance — arnt: 04 arnt: 04<br>Roots2222 2Roos22Roos22222 2Roos22Roos2222 280015228001522222<br>Soot Sooo 3 00s 00s Soor<br>Fy Fy Fy a<br>iHaHaa3<br>=0.0050.005 = 0.005 z 001 2 0.005<br>0.000 0.000 0.00 0.000<br>° 50 100 150 ° 50 100 150 ° 50 100 150 ° 50 100 150<br>Slope_2010 ‘Area_km2_2010 Colony _nt_2010<br><!-- End of picture text --> <!-- Start of picture text --> 0.00016 — <1 — 7 a — I:<br>0.00014 — slope>10slope<10 9.09014 —— ‘Area_km2:Q1Area_km2:Q2 0.00025 — PortugalSpain 09014 —— dr_ntl:nti: Q2<br>2 0.00012 0.00012 ——— Nrea_km2:Area_km2:Q403 0.00020 — — EnglandNetherland— go0012 — — a rm: a3rnt:Q4<br>0.00010 8 0.00010 g20 — France q2 0.00010<br>0.00008 0.00008 0.00015 0.00008<br>H 0.000060.00004 re0.00004 } 0.00010 re0.00004<br>0.00002 0.00002 0.00005 0.00002<br>0.00000 0.00000 0.00000 0.00000<br>© 8000 Stope_201010000 15000 20000 © 5000‘Area_km2_201010000 15000 20000 5000 0 8000Colony10000 15000 20000 © 5000_nt_201010000 15000 20000<br>25 ——— slope<10slope>10slope>10 30 ——— Area_km2:Q1Area_km2:Q2Area_km2:Q2 3.0 ————— SpainPortugal"Portugal"" 30 ——— dnt:dint: Q2 Qtdint: Q2 Qt Q2 Qt Qt<br>20 25° — ‘Area_km2:Area_km2:Q3Area_km2:Q3 25 ———————— NetherlandEnglandFranceEnglandFranceFrance 25 ——— dent:dr_ntl:Q4Q3dr_ntl:Q4Q3Q3<br>eatsiG10d220B3iq10atsiG10d220B3iq10iG10d220B3iq10G10d220B3iq10d220B3iq10220B3iq10B3iq103iq10iq10q1010 zise5ée5é5éé 10 2205i"é5i"éi"éé 10<br>as 0s os 08<br>00 09 00 00<br>00 02 04Slope_201006Slope_20100606 08 10 00 02 ‘Area_km2_20100404 06 08 10 05 00 Colony0505 10 00 02 04r_ntl_201006r_ntl_20100606 08 10<br><!-- End of picture text --> <!-- Start of picture text --> 25 ——— slope<10slope>10slope>10 30 ——— Area_km2:Q1Area_km2:Q2Area_km2:Q2 3.0 ————— SpainPortugal"Portugal"" 30 ——— dnt:dint: Q2 Qtdint: Q2 Qt Q2 Qt Qt<br>20 25° — ‘Area_km2:Area_km2:Q3Area_km2:Q3 25 ———————— NetherlandEnglandFranceEnglandFranceFrance 25 ——— dent:dr_ntl:Q4Q3dr_ntl:Q4Q3Q3<br>j<br>zise5ée5é5éé 2205i"é5i"éi"éé<br>eatsiG10d220B3iq10atsiG10d220B3iq10iG10d220B3iq10G10d220B3iq10d220B3iq10220B3iq10B3iq103iq10iq10q1010 10 10<br>as 0s os 08<br>00 09 00 00<br>00 02 04Slope_201006Slope_20100606 08 10 00 02 ‘Area_km2_20100404 06 08 10 05 00 Colony0505 10 00 02 04r_ntl_201006r_ntl_20100606 08 10<br><!-- End of picture text -->
<!-- Start of picture text --> nExchange_2010 -0.045 -0.19 0.11<br>0.50<br>nPerimeter_2010 0.011 0.11<br>0.25<br>Fullnes_2010 -0.035 0.24 0.15<br>Circuity_avg_2010 0.021 -0.14 0.00<br>ection_density_2010 -0.19 0.16 0.3<br>-0.25<br><!-- End of picture text --> <!-- Start of picture text --> Street_density_2010 -0.22 0.2 0.29<br>-0.50<br>Sprawl_2010 0.049 -0.25 -0.17<br>ge oe ge<br>we? a wy<br>er on \e<br>x we<br><!-- End of picture text -->
27 curvy roads. Also, according to Batty and Longley (1994), organic cities, which area associated with high levels of _Circuity__ avg, fit their natural landscape. That explains the high and positive correlation between _Circuity__ avg and Slope (0.64). The relation with _Sprawl_ indicates that cities with steeper slopes have a less uniform distribution of their population within the urban areas than cities located in flat terrain.

# _4.3.2 Size of the city_

We classified the urban extent area in square kilometers into quartiles and built kernel density plots of the distribution of urban form variables for each group (Figure 10 second column). Roundness variables _nExchange_ and _nPerimeter_ are very similar for the first, second and third quartile; but the fourth quartile group shows a wider distribution with lower values in both cases. This and the moderate and negative correlation between _nPerimeter_ and _Area_km_<sup>_2_</sup> (-0.55) indicate that larger urban areas in the LAC region deviate more from the circular shape and have less smoothed perimeters (Figure 11). The trend in _Fullness_ is quite the opposite: larger cities show higher values, indicating a more efficient use of urban space, with a low positive correlation (0.24). Urban texture does not seem to be affected by city size. _Circuity_avg_ distributions of all groups are very similar; while _street density_ and _intersection density_ distributions show small differences indicating that larger cities have higher street and intersection densities, as expected from the inspection of _fullness_ distributions. _Sprawl_ distributions also show an interesting trend: larger cities have lower values than the rest and a low negative correlation (-0.25), which indicates that the population in those cities tends to be more uniformly distributed than in the smaller urban areas.

# _4.3.3 Culture/Colonization history_

The colonization process influences urban design and growth trends. We classified the urban extents in LAC region according to the colony they belonged in 1700 (Gascoigne, 2001), and built the kernel density plots of urban form variables for each class (colony). Portugal, Spain, England, the Netherlands and France were the countries that had colonies in Latin America and the Caribbean by 1700. The third column of Figure 10 shows the distribution plots of urban form variables for the colony groups. English and Iberian (Spanish and Portuguese) colonies show a similar trend in
- 28 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

roundness variables, while French and Dutch colonies show smaller values in both cases, indicating cities more elongated and with less smoothed perimeters than their English and Iberian counterparts. _Fullness_ distributions also show some differences, with French and Dutch colonies showing higher values than the rest. Urban texture distributions show an interesting trend: Portuguese colonies have the lower values of _Circuity_avg_ , followed by Spanish colonies, then English, then Dutch, and then the French ones. This corroborates the idea that LAC cities that were Iberian colonies have more regular urban layouts. The density of streets and intersections indicates that the English colonies have less density than the rest, with the higher densities in the Iberian colonies. _Sprawl_ distributions show that French colonies have the lower values, indicating that the population is more uniformly distributed in those cities than in the rest of the groups.

# _4.3.4 GDP (proxy: density of radiance in NTL imagery, dr_ntl)_

We used the density of radiance from the nighttime imagery as proxy for GDP ( _dr_ntl_ ). We classified the LAC cities in quartiles according their registered radiance density in 2010. _nExchange_ and _nPerimeter_ do not show legible differences across quartiles, but _Fullness_ does: the group of cities with lower density radiance values also show a trend towards lower values of _Fullness_ . This indicates the trend that the cities with more open spaces within the urban extent are less productive, in agreement with the findings of Duque et al. (2017a, 2017b). The distributions of urban texture variables corroborate the same finding: street and intersection densities both increase with radiance density, and the group of higher values of radiance density has the more regular urban layouts ( _Circuity_avg_ values closer to 1). The distributions of _Sprawl_ show that the most productive cities have the most uniform distributions of populations within the urban extent. The variable _dr_ntl_ is positively correlated with variables capturing compactness and connectivity. This is in line with the finding by Duque et al (2017a) in which compact and well-connected cities show higher levels of productivity. Good mobility reduces transportation costs, and compactness reduces the cost-perhousehold of public service infrastructure.
<!-- Start of picture text --> 0.16 — slope_ue_1996<10 0.16 — slope_ue_2000<10<br>0.14 —— slope_ue_1996>10 0.14 —— slope_ue_2000>10<br>0.12 0.12<br>0.10 0.10<br>0.08 0.08<br>0.06 0.06<br>0.04 0.04<br>0.02 0.02<br>0.00 0.00<br>~10 0 10 20 30 40 “10 0 10 20 30 40<br>slope_new_a_2000 slope_new_a_2010<br><!-- End of picture text -->
<!-- Start of picture text --> 50<br>year<br>’ 3 2000<br>2010<br>+<br>40<br>+<br>‘’<br>‘<br>+<br>30 ’<br>++4<br>+<br>an82<br>ai ‘ ’ ++<br>z‘<br>‘<br>‘+<br>20 ‘ ’ ‘<br>‘ ‘ ‘ rs<br>+ ‘<br>‘‘‘<br>4’+‘<br>+ +<br>r ‘s ‘ ‘ ‘’ ’‘‘‘<br>¢¢ ’ ‘<br>10 rs¢ ‘‘ ‘‘ ‘<br>rs4 ¢+ ‘¢ ‘ ‘ ‘<br>0<br>SAM NAM CAM CAR<br>REG<br><!-- End of picture text -->
31

# _5.3 Protected areas_

A protected area is a protected geographical space to achieve long-term conservation of nature with the associated ecosystem services and cultural values (UNEP-WCMC, 2016). Urban growth over protected areas implies the loss of ecosystem services and cultural values and it jeopardizes the urban sustainability in the long term. We quantified urban growth over protected areas using the World Database on Protected Areas (WDPA) produced by the United Nations Environment Programme (UNEP-WCMC, 2016), which is the most comprehensive global database of marine and terrestrial protected areas. We considered all types of protected areas included in the WDPA and calculated the intersection with the new urban areas in 2000 and 2010 using geoprocessing tools in ArcGIS. Figure 14 shows the country average of percent urban growth on protected areas. Most countries in the LAC region show relatively low values (up to 6%). Bolivia has the highest values in both periods, both above 50%, which indicates a negative trend in terms of long-term sustainability. Venezuelan urban extents also show high values as well, up to 18%. According to UN (2014), it is expected that Bolivian cities will increase the urban population by 79% between 2014 and 2050; with the second largest annual rate of change in South America: 0.6%. This makes the detected trend an important issue to address in the near future.
<!-- Start of picture text --> der de<br>7. 7.<br>1996 - 2000 country average 2000 - 2010 country average<br>growing on protected areas growing on protected areas<br>0.0 - 2.3 % 0.0- 2.7%<br>2.3 -6.3% 2.7-7.6%<br>MTM 63-90% TM7.6-147%<br>M 9.0-15 % @ 14.7-211%<br>15 -56 % Wi 21.1-55.4%<br>TM no data 1 no data<br>City Size Vs. Spatial growth<br>25 3.0<br>— Area_km2_1996:Q1 — Area_km2_2000:01<br>—— Area_km2_1996:Q2 25 —— Area_km2_2000:Q2<br>—— Area_km2_1996:Q3 , —— Area_km2_2000:Q3<br>20, —— Area_km2_1996:Q4 —— Area_km2_2000:04<br>2.0<br>1:5<br>1.5<br>1.0<br>1.0<br>0.5 05<br>0.0 0.0<br>0 1 2 3 a 5 6 0 1 2 3 4 5 6<br>p_growth_km2_2000 p_growth_km2_2010<br><!-- End of picture text -->
# Population density Vs. Spatial growth

<!-- Start of picture text --> 2.00 —— pop_density_1996:Q1= —— pop_density_2000:Q1.<br>1.75 —— pop _density_1996:Q2 3.0 —— pop_density_2000:Q2<br>—— pop_density_1996:Q3 —— pop_density_2000:Q3<br>1.50 —— pop_density_1996:Q4 2.5 —— pop_density_2000:Q4<br>1.25 20<br>1.00<br>1.5<br>0.75<br>1.0<br>0.50<br>0.25 0.5<br>0.00 0.0<br>0 1 2 3 4 5 0 1 2 3 4 5 6<br>p_growth_km2_2000 p_growth_km2_2010<br><!-- End of picture text -->
- 34 J.C. Duque, N. Lozano-Gracia, J.E. Patino, P. Restrepo, W. Velazquez / LCR Flagship Report on Cities and Productivity

population density in a city is below its upper limit for its urban carrying capacity.<sup>2</sup> In this scenario, the city can increase its population by infill of available spaces within the urban area, or via an increase in population density, before the occupation of new areas. When the population density has reached its maximum or saturation point, the only option left is growth on new adjacent areas. We analyzed if the urban growth is a consequence of saturation by comparing the highest population densities reached in the new urban areas in 2000 and 2010, with the population density reached in the respective date in the urban extent area for the initial date (1996). We built an indicator of saturation for each period as shown in Table 5, using population figures for the last date in each period (i.e., 2000 for the 1996 to 2000 period, and 2010 for the 2000 to 2010 period):

> 2 For a comprehensive discussion on urban carrying capacity, see Li and Lian (2012).
35

**Table 5.** Growth by saturation metrics and rationale_means.

|**Metric**|**Rationale**|
|---|---|
|Saturation�����<br>Pop����∈ NUE����,����<br>NUE����,����<br>Pop����∈ UE����<br>UE����|If growth by saturation, this metric would take<br>values below 1. Which means that the new area<br>built between 1996 and 2000 has a population<br>density lower that the population density<br>within the polygon corresponding to the urban<br>extent in 1996.|
|Saturation�����<br>Pop����∈NUE����,����<br>NUE����,����<br>Pop����∈ UE����<br>UE����|If growth by saturation, then the population<br>density gap between the urban extent of the<br>city in 1996 and the new area between 1996<br>and 2000 should be closer to 1 by 2010.<br>Otherwise, the city’s new area between 2000<br>and 2010 might be unnecessary or hasty.|

Where:

- UE� � Urban extent at year _t_

- NUE�����,� � New urban extent between years ( _t-k_ ) and _t_ .

- Pop� � Population at year _t_

_Saturation_ values above 1 indicate that the population density in the new areas is higher than the population density in the prior-date urban extent area. This indicates low saturation, as it can be assumed that the prior urban extent area can still increase its density to a similar level to those in the new areas. Values below 1 indicate that the population density in the new areas is lower than the population density in the prior-date urban extent, indicating that the prior urban extent area is denser and fuller than the new areas; and that growth could be the result of saturation. Figure 17 shows the distribution plots of the _saturation index_ for both time periods. Both distributions are very similar and show that most of the cities have values below 1 (804 urban extents for 2000, and 809 for 2010), indicating growing by saturation in most of the LAC cities in both periods. However, about 12% of the urban extents show values above 1 in both periods, indicating not saturated growth. When comparing _Saturation_ 2000 with _Saturation_ 2010, we found a decreasing trend: most urban extents, about
<!-- Start of picture text --> —— Saturation_96_00<br>1.2 —— Saturation_00_10<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2<br>0.0<br>-0.5 0.0 0.5 1.0 15 2.0<br>Saturation<br><!-- End of picture text -->
37 Based on the integral definition of urban form, from the urban planning literature, this paper approaches urban form as the combination of three dimensions: shape of the perimeter, urban structure and land use. Several indicators on each dimension where calculated using free data and highly automatized libraries for geometry and network analysis. Those indicators show that LAC cities are more rounded than elongated, with urban perimeters more smoothed than complex, and little open space. The street networks in LAC cities tend to be regular and dense. Using these indicators, LAC cities can be divided into four distinct clusters that range from compact and wellconnected cities, to highly organic, sprawled and poorly connected cities. Cities in each cluster do not show a clear spatial pattern.

Regarding the factors that can be correlated to urban form, we found that factors such as topography and historical legacies linked to urban design (colony history) are highly correlated to urban form indicators. Organic/natural urban structures are usually located in rugged topography; and LAC cities that were Iberian colonies have more regular urban layouts and denser street networks. As found in previous literature, this study finds a positive correlation between compactness/connectivity and levels of productivity.

Finally, the spatiotemporal analysis detected some signs of urban growth over steeper terrains for the cities initially located in rugged terrain, as well as a weak trend toward less fragmented urban growth between 2000 and 2010. The cases of Bolivia and the República Bolivariana de Venezuela emerged as critical cases of urban growth over protected areas, which can jeopardize sustainable urban growth in those countries.

Future research will explore the possibility of using this input data and indicators to forecast the evolution of the urban extents, which can help urban planners and policy makers to identify in advance situations of potential exposure to natural hazards, invasion of aquifer reservoirs or other non-sustainable forms of urban growth.

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