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Supplemental material, sj-pdf-1-epb-10.1177_2399808321999309 for Urban form and productivity: What shapes are Latin-American cities? by Juan C Duque, Nancy Lozano-Gracia, Jorge E Patino and Paula Restrepo in EPB: Urban Analytics and City Science

This paper provides empirical evidence on the impact of institutional fragmentation and metropolitan coordination on urban productivity in Latin American cities. The use of night-time lights satellite imagery and high-resolution population data allow the use of a broader definition of metropolitan area. Thus, metropolitan area consists of the urban extent that results from the union between the formally defined metropolitan area and the contiguous patches of urbanized areas with more that 500,000 inhabitants. The initial results suggest that the presence of multiple local governments within metropolitan areas generates opposite effects on urban productivity. On the one hand, smaller governments tend to be more responsive and efficient, which increases productivity. But, on the other hand, multiple local governments face coordination costs that reduce productivity.

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 examines the linkages between urban form and city productivity, using alternative metrics for urban form and applying them to a comprehensive sample of Latin American cities. Although most of the literature has concentrated on the effects of population density (compact versus sprawling urban development), this paper seeks to assess whether different dimensions of a city's urban form, such as shape, structure, and land-use, affect its economic performance. The paper finds that the shape of the urban extent, the inner-city connectedness, and fullness have a statistically significant influence on the productivity level of the city.

The presence of slums in a city is an indicator of poverty and its proper delimitation is a matter of interest for researchers and policy makers. Socio-economic data from surveys and censuses are the primary source of information to identify and quantify slumness within a city or a town. One problem of using survey data for quantifying slumness is that this type of data is usually collected every ten years and is an expensive and time consuming process. Based on the premise that the physical appearance of an urban settlement is a reflection of the society that created it and on the assumption that people living in urban areas with similar physical housing conditions will have similar social and demographic characteristics (Jain, 2008; Taubenb¨ock et al., 2009b); this paper uses data from Medellin City, Colombia, to estimate slum index using solely remote sensing data from an orthorectified, pan-sharpened, natural color Quickbird scene. For Medellin city, the percentage of clay roofs cover and the mean swimming pool density at the analytical region level can explain up to 59% of the variability in the slum index. Structure and texture measures are useful to characterize the differences in the homogeneity of the spatial pattern of the urban layout and they improve the explanatory power of the statistical models when taken into account. When no other information is used, they can explain up to 30% of the variability of the slum index. The results of this research are encouraging and many researchers, urban planners and policy makers could benefit from this rapid and low cost approach to characterize the intra-urban variations of slumness in cities with sparse data or no data at all.