Delineating functional territories from outer space

Berdegué, Julio A. et al. Article Delineating functional territories from outer space Latin American Economic Review Provided in Cooperation with: Centro de Investigación y Docencia Económica (CIDE), Mexico City Suggested Citation: Berdegué, Julio A. et al. (2019) : Delineating functional territories from outer space, Latin American Economic Review, ISSN 2196-436X, Springer, Heidelberg, Vol. 28, Iss. 1, pp. 1-24, https://doi.org/10.1186/s40503-019-0066-4 This Version is available at: https://hdl.handle.net/10419/259435 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Delineating functional territories fromouter space Julio A. Berdegué1, Tatiana Hiller2, Juan Mauricio Ramírez3, Santiago Satizábal3*, Isidro Soloaga4, Juan Soto5, Miguel Uribe4 and Olga Vargas6 1 Introduction Spatial agglomeration is a central aspect of human life and of the geographic space in which most economic and social exchanges take place (Bairoch 1988). The size and shape of this geographic space have key implications for policy design, as they affect the regular patterns of mobility and interactions of people, goods, and ideas. This functional reality is weakly captured by the usual political–administrative units. Functional territories, as we call them in this study, represent a complex socio-spatial picture of overlapping markets between “areas or locational entities which have more interaction or connection with each other than with outside areas” (Brown and Holmes 1971, p 57), and with high frequency of economic and social interactions between their inhabitants, organizations, and firms (Berdegué etal. 2011).1 Abstract The delimitation of functional spatial units or functional territories is an important topic in regional science and economic geography, since the empirical verification of many causal relationships is affected by the size and shape of these areas. This paper proposes a two-step method for the delimitation of functional territories and presents an application for three developing countries: Mexico, Colombia and Chile. The first step of this method uses nighttime satellite images to identify the boundaries of urban continuums (conurbations). When these continuums extend over more than one municipality, we group and redefine them as a new single spatial unit. The second step calculates a dissimilarity index using bidirectional labor-commuting flows between the resulting areas of the first step and then applies a standard clustering procedure to delineate the definitive functional territories. Our results suggest that, using nighttime satellite images, our method can lead to a more accurate definition of functional territories, especially in developing or underdeveloped countries where the official data on labor-commuting flows are often outdated or unreliable. Keywords: Functional territories, Functional economic areas, Local labor market areas, Night light satellite data, Commuting flows JEL Classification: R1, R12, R23 Open Access © The Author(s) 2019. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. RESEARCH Berdeguéetal. Lat Am Econ Rev (2019) 28:4 https://doi.org/10.1186/s40503-019-0066-4 Latin American Economic Review *Correspondence: [email protected] 3 Latin American Center for Rural Development (RIMISP), Carrera 9 No 72-61 Office 303, Bogotá, Colombia Full list of author information is available at the end of the article 1 We use the terms functional territory and functional area interchangeably hereafter. Page 2 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 The delimitation of these functional territories is a decisive factor in several important issues in urban and regional economics, such as proper identification of places for place-based development approaches (Pike etal. 2011; Berdegué etal. 2014a), estimating agglomeration effects and human capital externalities (Dingel etal. 2018), and measuring spillovers from urban to rural areas (Berdegué etal. 2015). In general, the size and shape of these spatial units may have an important effect on a wide range of economic geographic estimations (Briant etal. 2010). However, despite the importance of delimiting these areas to identify the economic effects and spatial scope of public policies appropriately, these methods have not advanced at the same pace as other sources of information or big data sources. Since the 1950s, the analysis of interactions between spatial units has been measured mainly using labor-commuting flows (Klove 1952), and by a wide variety of methodological procedures, such as cluster analysis (Tolbert and Killian 1987), threshold methods (Coombes et al. 1986), network-based methods (Kropp and Schwengler 2016), and, more recently, an evolutionary approach (Casado-Díaz etal. 2017), among others (Duranton 2015). However, recently, some scholars have proposed delineating functional areas (usually metropolitan areas or cities) by using satellite images of nighttime luminosity (Bosker etal. 2018; CAF 2018; Dingel etal. 2018; Henderson etal. 2003; Vogel etal. 2018). Owing to the increasing availability of information from satellite images, nighttime satellite images have been used for different purposes, including monitoring urban extension and expansion (Cheng etal. 2016; Goldblatt 2016), and estimating local economic activity (Donaldson and Storeygard 2016; Henderson etal. 2012). In this context, we present a simple and easily replicable approach to identify functional territories in Chile, Colombia, and Mexico by applying a combination of the two most common empirical approaches to delineate functional areas: nighttime satellite images and clustering using labor-commuting data. Specifically, we propose a two-step method. The first step of this method uses nighttime satellite images and applies a supervised procedure, common in the literature on remote sensing, to identify the boundaries of urbanized areas or urban continuums (conurbations), which can be contained within one or several different administrative areas. The administrative units that are overlapped by the same urbanized continuum are then aggregated as one forming a new area for the second step. The second step calculates a dissimilarity index using bidirectional labor-commuting flows between the resulting areas of the first step and then applies a standard clustering approach to delineate the definitive functional territories. As a result of these two steps, the functional territories we identify include not only urban or metropolitan areas, but also, in general, administrative areas that share frequent economic and social interactions that are partly captured by the existence of common labor markets. Moreover, these functional territories include rural areas together with small urban areas that share strong connections, subjects that do not usually receive much attention in the literature, but that are important for rural economic development studies (Berdegué etal. 2015; Berdegué and Soloaga 2018; Soto etal. 2018). We present the application of our method in Chile, Colombia, and Mexico, with data on labor commuting from censuses in 2002, 2005, and 2010, respectively. These three Latin American countries have similar economic conditions, but important geographic, demographic, and institutional differences, which influence the number and size of the Page 3 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 functional territories obtained using our method. Our method differs considerably from standard approaches using only labor-commuting flows. The rest of this paper is organized as follows. Section two reviews the literature on the delimitation of functional spatial units. Section three describes the empirical approach. Section four summarizes our main results. Section five summarizes what we consider the gains of our analysis. Finally, section six presents our concluding remarks. 2 Literature review The literature on the definition and identification of functional spatial units in general acknowledges that the objective is to distinguish “…locational entities which have more interaction or connection with each other than with outside areas” (Brown and Holmes 1971, p 57). Based on the nature of the data and the purpose of the analysis, many empirical constructions refer to this type of spatial configuration, although it does not usually match administrative areas. Examples are as follows: commuting zones (Tolbert and Sizer 1996), functional regions (Berry 1968; Brown and Holmes 1971; Florida etal. 2008), functional economic areas (Jones 2016; Fox and Kumar 1965), functional urban areas (OECD 2002), labor market areas (Tolbert and Killian 1987; Tolbert and Sizer 1996), travel-to-work areas (Coombes and Openshaw 1982; Coombes etal. 1986), and functional territories (Berdegué etal. 2011, 2015; Fergusson etal. 2018), among others (Green 2007; Rozenfeld etal. 2011; Banai and Wakolbinger 2011). The objective of maximizing interactions inside the defined area while minimizing interactions outside the areas implies that it is ensured that the resulting spatial units have the property of being self-contained (Coombes etal. 1986). This aim is reinforced when a functional spatial unit fulfills the basic restrictions of coherence, partition, and contiguity (Casado-Díaz and Coombes 2011). On the one hand, coherence states that the boundaries established for a functional spatial unit must be recognizable and correspond to a political or administrative configuration.2 On the other hand, partition indicates that a spatial unit must belong to only one functional area. Contiguity is necessary to avoid fragmented spatial units. These considerations may have important econometric implications when the subject of identification is place specific, such as the identification of agglomeration effects (Henderson etal. 2018), spatial sorting of workers in cities (Dingel etal. 2018), and market access effects (Bosker etal. 2018).3 The distance decay nature of commuting flows has the advantage that greater flows are usually found between neighboring spatial units (Casado-Diaz etal. 2000; Simini etal. 2012), which frequently enables the accomplishment of self-containment without much supervision. This characteristic makes commuting flows the most common source of data for the delimitation of functional areas, together with the fact that they also represent a broad socio-economic scope of individuals, being informative of other social, 2 In other words, most cases require an alignment with administrative boundaries or the use of basic local administrative areas (municipalities or communes in our case) as the initial building blocks of the functional territories. 3 Specifically, when working with administrative units rather than functional spatial units, the “reflection problem” in the form of spatial spillovers is a potential source of bias (Gibbons etal. 2015). Therefore, having proper functional spatial units may avoid the need to use more sophisticated spatial econometric techniques, or may enable the definition of spatial interactions using functional neighborhoods to capture those effects (Corrado and Fingleton 2012). Page 4 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 cultural, and political linkages encompassed within the concept of functional territory (Berdegué etal. 2011). Notwithstanding the predominance of commuting flows to delineate functional spatial units, other types of data are also explored in the literature, such as land prices (Bode 2008), travel time and transportation networks (Weiss etal. 2018), mobile phone data (González etal. 2008), job applications (Manning and Petrongolo 2017), shopping data (Andersen 2002), gridded population census (Rozenfeld etal. 2011), global geographical population distribution estimates4 (Henderson etal. 2018), and satellite images (Bosker etal. 2018; Goldblatt etal. 2016; Imhoff etal. 1997; Small etal. 2005). Disregarding the fact that functional units are usually sensitive to the method and data used (Bosker etal. 2018; Rubiera-Morollón and Viñuela 2012), they have for many decades played an important role for scholars and policymakers in developed countries (Coombes and Openshaw 1982; Coombes etal. 1986; Klove 1952; OECD 2002; Tolbert and Killian 1987; Tolbert and Sizer 1996). By contrast, attempts to incorporate the concept of functional areas into political and research agendas are still incipient in developing countries, with some recent exceptions (Berdegué etal. 2011; Bosker etal. 2018; CAF 2018; Casado-Díaz etal. 2017; Dingel etal. 2018; Henderson etal. 2018). However, particularly in Latin America, most of these studies rely on outdated censuses, and therefore, the lack of data is a limiting factor even for identifying metropolitan areas or conurbations. Besides labor-commuting flows, remote sensing data also allow researchers to define contiguous areas with a high degree of economic interaction or self-containment. The rapid expansion of cities and the increasing quality of images captured by satellites have allowed researchers of urban studies to obtain very precise estimations of: the extent of urban areas (Goldblatt etal. 2016), global high-resolution estimations of population (Dobson etal. 2000), accessibility to cities (Weiss etal. 2018), characterization of land use (Gao etal. 2017), and other uses (Ma etal. 2017). All these tools have been receiving increasing attention in the literature in applied economics (Burgess etal. 2012; CAF 2018; Costinot etal. 2016; Donaldson and Storeygard 2016; Henderson etal. 2017). However, the application of satellite images to construct functional spatial units is still in progress, with a few recent exceptions (Bosker etal. 2018; Dingel etal. 2018; Henderson etal. 2018; Vogel etal. 2018).5 The present work contributes to this knowledge by offering a simple and flexible method applied simultaneously to three countries: Chile, Colombia, and Mexico. 3 Empirical approach 3.1 Data This section describes the sources and data used to delimit functional territories. For each country, we use the latest census available with information on labor-commuting flows at the municipality level (2002 for Chile, 2005 for Colombia, and 2010 for Mexico), 5 To the best of our knowledge, Florida etal. (2008) can be considered as one of the earliest contributions using this source of data in economics. However, the objective was to identify and characterize mega-regions leading to areas that may lack purpose in design of territorial policies. 4 The LandScan project provides this information for specific census years https ://lands can.ornl.gov (Dobson etal. 2000). Page 5 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 together with satellite nighttime luminosity images obtained from the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) of the United States Air Force. 3.1.1 Commuting flows The commuting flows matrix at the municipality level considers 2446 municipalities in Mexico, 1124 in Colombia, and 346 in Chile. Commuting flows vary substantially among countries, as shown in Table1. The average number of commuters in the municipality of origin and destination is larger in Chile (origin: 5285; destination: 5347) than in Mexico (origin: 3270; destination: 2840) and Colombia (origin: 1001; destination: 952). In addition, Chile has more dispersion in the number of commuters (origin: 13,963; destination: 23,190) than Mexico (origin: 14,092, destination: 15,623) and in Colombia (origin: 5300; destination: 7143).6 Meanwhile, the percentage of commuters as a proportion of the workforce in the municipality of origin shows that in Mexico, on average, 16.1% of the municipal workforce is composed of workers who commute to other municipalities, while it represents about 6.7% in Chile and 5.3% in Colombia. These values show an important dispersion, especially in the case of Mexico. At the same time, commuting can be as large as 77.3% of a municipality’s workforce in Mexico, 63.8% in Chile, and 52.2% in Colombia. Table 1 Statistics ofcommuting flows Commuters inthemunicipality oforigin Commuters inthemunicipality ofdestination Commuters asa% ofthepopulation inthemunicipality oforigin Commuters asa% ofthepopulation inthemunicipality ofdestination Mexico Mean 3270 2840 16.1 9.5 Maximum 287,836 323,902 77.3 99.3 Minimum 1 1 0.1 0.2 Median 424 254 10.1 6.4 Std. error 14,092 15,623 15.3 10.7 Colombia Mean 1001 952 5.3 7.1 Maximum 78,715 141,949 52.2 733.8 Minimum 1 1 0.02 0.04 Median 87 122 2.6 3.5 Std. error 5300 7143 7.7 26.0 Chile Mean 5285 5346 6.7 7.2 Maximum 119,107 349,033 63.8 173.8 Minimum 2 8 0.4 0.5 Median 622 645.5 3.7 3.5 Std. error 13,963 23,190 7.5 13.8 6 These differences between countries may be influenced by the size of the shapes of the administrative units, making it difficult to make comparisons based on this spatial unit. In addition, in each country, the number of municipalities that send commuters to other places is different to the number of municipalities that receive them. This implies that the average number of commuters per municipality (and its standard deviation) is different if origin or destination is considered. Page 6 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 On the contrary, on average, 9.5% of the workforce of a municipality in Mexico comprises workers who commute from other municipalities. This amount reaches 7.0% in both Colombia and Chile. 3.1.2 Night light data We use the average visible, stable lights, and cloud-free coverage composite. This information comes from a satellite that follows a sun-synchronous orbit at an altitude of approximately 830km and covers any point on Earth once or twice a day depending on the latitude.7 The composite images measure the light intensity from sites with persistent lighting, such as cities, towns, or flares (these flares are cleaned afterwards), and ephemeral events, such as forest fires, are discarded (Lowe 2014). Although the dataset is available yearly starting from 1992, we use only the 2013 composite image, as it was the latest available year at the time of the undertaking the research. The stable satellite night light images are composed of several 1-km2-sized pixels, each one with a light intensity value (digital number) that varies from 0 (when the pixel is basically unlit) to 63 (when the pixel is saturated by light, usually in dense and rich areas) (Henderson etal. 2012). Note that these night light data may overestimate urban boundaries and may be affected by different stages of economic development, climate, and geological differences when performing cross-country comparisons (Henderson etal. 2003). 3.2 Methodology Our methodological approach consists of two main steps. We start with municipalities as basic units. The first step uses a municipality-polygon map that is overlapped with a geo-referenced nighttime luminosity image. We try different intensity thresholds (cutoffs) that result in light continuums of different size. When the light continuums extend over more than one municipality, we group and redefine them as a new single spatial unit comprised of the sum of these municipalities. Incoming and outgoing commuting flows are then recalculated considering that these municipalities now form a new single spatial unit. The second step calculates a dissimilarity coefficient for each pair of spatial units, defined as the ratio between the bidirectional commuting flow and the minimum labor force in the two areas. For each country, the commuting matrix contains commuting flows for all municipalities whose urban areas are totally comprised within their boundaries, as well as for those new spatial units in which a light continuum overlaps two or more administrative areas following the first step. After computing the matrix, we apply a hierarchical clustering procedure (average linkage) using different thresholds for the dissimilarity coefficient. These two steps are explained in more detail below. 7 Despite the existence of other recent sources of satellite information that capture night lights with higher resolution (e.g., the Moderate Resolution Imaging Spectroradiometer), we choose DMSP OLS data to show the exercise with the most popular night light data source and with more years of coverage. Page 7 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 3.2.1 Identifying functional territories using night light data For this step, we choose to use a supervised method because it is easier to replicate and less computationally demanding.8 The use of supervised algorithms to identify urban areas has been widely applied in remote sensing (Goldblatt etal. 2018; Imhoff etal. 1997; Ma etal. 2017; Small etal. 2005), and within similar recent applications in economics (Ellis and Roberts 2015). The first step identifies the location and boundaries of urban settlements using stable satellite night light images. For all countries, the light threshold is selected according to the correspondence between night light satellite images and the urban areas observed through Google Earth imagery. This is described in Fig.1 with an example of Mexico, showing two different cut-off thresholds of light intensity in the yellow and green lines, and the urban area covered by that lit area in each case. Figure1 describes how municipalities are merged in the first step. For this purpose, we overlap a map of political–administrative boundaries at the level of municipalities (red lines) and test different thresholds to detect, in each case, the remaining lit areas (grouped pixels) or light continuums that extend beyond these boundaries. We then merge the municipalities that contain a part of the same light continuum into a single functional area. In the second step, we consider this group of municipalities as a single spatial unit. These actions have several caveats to be discussed. For example, although light continuums cover the whole areas of many municipalities, especially in the larger metropolitan areas, they overlap only a part of the municipal area in multiple scenarios. In these cases, one could argue that peripheral municipalities have a large rural part (unlit in the satellite images) that we might not want to aggregate, as they might lead to overestimating the size of cities. Nonetheless, note that municipal discretization might not be too harmful in our case, since the essence of our approach is not to measure the extent of urban areas, as is the focus of, for example, Henderson etal. (2003) and Vargas (2017), rather Fig. 1 Different night lights intensity thresholds 8 Three types of classification methods use remote sensing data: supervised and unsupervised land cover classification, and object-based or object-oriented detection. Recent advances in the use of machine-learning techniques allow unsupervised algorithms using a predefined classification of satellite images to train algorithms of object identification (Baragwanath etal. 2018). Page 8 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 than to construct functional territories (i.e., urban and rural areas with higher levels of interaction relative to other areas).9 An additional reason to carry out a municipal discretization lies in the fact that commuting data and many other economic and demographic statistics are collected only at municipal level (and apparently will be so for years to come), in most Latin American countries. Consequently, we must preserve municipalities as the basic units to be clustered into larger analytical regions, if we wish to implement the commuting clustering procedure in the second step, or if we aim for our resulting functional areas to have public policy implications.10 As an alternative midpoint, we could restrict the merging of municipalities to cases in which the light continuum overlaps the centroid of the municipality’s polygon, as many coverage location models do (Alexandris and Giannikos 2010; Wei 2015). However, we choose not to do so, because in peripheral municipalities, the coordinates of the centroid seldom match or correlate precisely with the geometry of an urban continuum or with the most densely populated places in the municipality. Having discussed the implications of municipal discretization, we return to Fig.1, where contiguous lit areas are shown for 35 and 50 light intensities (green and yellow lines, respectively). The figure shows that for a light threshold of 35, the urban area may spread to include those merged polygons of four municipalities: Leon, Silao, Guanajuato, and San Francisco del Rincón. Following a light intensity of 50, only the urban areas Leon and Silao are merged into a functional area, whereas Guanajuato and San Francisco del Rincón remain alone. Once all those metropolitan areas and conurbations have been identified for the whole country, larger areas may be constructed by collapsing all those municipalities that share a common lit area into a single spatial unit. Eventually, a single municipality could contain small portions of two or more lit areas. To deal with this issue, we establish a decision-making criterion based on computing the share of each lit area as a percentage of the total municipality-polygon area and use the one with the larger share to make the allocation. Figure2 describes this using an example for Irapuato, a Mexican municipality with two lit areas. The largest one comprises 15.8% of the total municipal area, whereas the other lit area represents only 1.2%. The same figure shows a case in which the largest lit area of a given municipality (in this example, Irapuato) crosses the boundary of one of its neighbors (in this case, Salamanca), but this overlap is unimportant when compared to that of the other lit area in the neighbor (0.03% versus 12.2%). For cases like these, municipalities were not paired up. 3.2.2 Identifying functional territories using census commuting data After identifying the municipalities with single urban cores and those arranged into a single group of municipalities that share a light continuum, we follow Tolbert and Killian 9 This same argument is relevant to explain why our approach needs only an indication of the urban continuum without having to mitigate the blurring problem of the DMSP imagery suggested by Abrahams etal. (2016) and Small etal. (2005), which is useful to reduce the spatial over extent of a blooming urban area in a more precise way. 10 Nonetheless, we acknowledge that preserving complete municipalities or administrative regions as the basic units to be clustered into larger analytical regions can lead to spatial aggregation bias and to potential scenarios in which results may appear counterintuitive to agglomeration economies (Viñuela etal. 2014). Page 15 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 Table 2 Sensitivity analysis *Resulting functional territories without considering the nighttime-light intensity. Functional territories are definedonly by commuting rates Light intensity Number offunctional territories % ofmunicipalities grouped infunctional territories % ofthepopulation grouped infunctional territories 1% 2% μ + 1.5σ μ + 1.96σ10% 1% 2% μ + 1.5σ μ + 1.96σ10% 1% 2% μ + 1.5σ μ + 1.96σ10% a. Mexico 12 603 878 1284 1389 1507 0.90 0.80 0.60 0.57 0.48 0.97 0.94 0.85 0.84 0.79 22 662 964 1407 1551 1683 0.90 0.79 0.56 0.49 0.41 0.97 0.93 0.83 0.80 0.75 35 687 1001 1475 1626 1767 0.89 0.78 0.54 0.46 0.38 0.97 0.93 0.82 0.78 0.73 50 700 1036 1533 1705 1851 0.89 0.77 0.52 0.43 0.34 0.97 0.92 0.81 0.77 0.71 NO* 738 1090 1660 1869 2042 0.89 0.76 0.49 0.38 0.28 0.97 0.92 0.77 0.77 0.64 Light intensity Number offunctional territories % ofmunicipalities grouped infunctional territories % ofthepopulation grouped infunctional territories 1% 2% μ + 1.5σ μ + 1.96σ10% 1% 2% μ + 1.5σ μ + 1.96σ10% 1% 2% μ + 1.5σ μ + 1.96σ10% b. Colombia 12 545 651 720 748 788 0.65 0.54 0.46 0.44 0.38 0.86 0.81 0.78 0.76 0.73 22 602 729 813 847 910 0.61 0.47 0.37 0.34 0.25 0.85 0.79 0.75 0.67 0.67 35 626 760 848 884 955 0.59 0.44 0.33 0.30 0.20 0.84 0.78 0.73 0.63 0.63 50 645 781 872 915 987 0.57 0.42 0.30 0.26 0.16 0.83 0.77 0.71 0.60 0.60 NO* 669 805 910 950 1041 0.56 0.41 0.28 0.23 0.11 0.83 0.76 0.70 0.56 0.56 Light intensity Number offunctional territories % ofmunicipalities grouped infunctional territories % ofthepopulation grouped infunctional territories 1% 2% μ + 1.5σ μ + 1.96σ10% 1% 2% μ + 1.5σ μ + 1.96σ10% 1% 2% μ + 1.5σ μ + 1.96σ10% c. Chile 12 55 93 117 154 195 0.95 0.88 0.82 0.69 0.53 0.99 0.98 0.95 0.91 0.84 22 58 97 123 160 203 0.95 0.89 0.82 0.69 0.52 0.99 0.98 0.95 0.90 0.83 35 61 101 132 172 216 0.95 0.88 0.80 0.66 0.48 0.99 0.97 0.95 0.89 0.81 50 64 103 135 175 222 0.95 0.88 0.80 0.65 0.65 0.99 0.97 0.95 0.89 0.81 NO* 73 114 153 206 273 0.96 0.87 0.80 0.61 0.36 0.99 0.97 0.95 0.83 0.64 Page 16 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 rate of 1% to 2042 with a 10% commuting rate. This increase in the number of functional territories means that there is less grouping of municipalities with a commuting rate threshold of 10% (28% percent of municipalities are grouped into functional territories) than with a commuting rate of 1% (89% of municipalities are grouped into functional territories). Incorporating night light information into the procedure increases the proportion of municipalities that are grouped into functional territories: with a 10% commuting rate, the proportion of municipalities increases from 28% with no lights to 34% with a light intensity of 50, and to 48% with a light intensity of 12. However, the sensitivity of these results decreases when using a lower commuting rate. The same pattern is observed for the population grouped into functional territories. Figure6 describes the sensitivity analysis of the number of functional territories to different commuting rates and night light intensity thresholds. As stated in the above paragraph, it is clear for the three countries that when the commuting rate threshold decreases, the number of functional territories also decreases. In addition, the lower is the light intensity threshold (i.e., the greater the lit area), the larger is the number of functional territories. However, these differences are reduced significantly when a low commuting rate threshold is selected. The vertical red line in each graph shows the commuting rate threshold chosen for each country for the exercise. At these values, the light intensity thresholds make a difference in the number of conurbated areas identified in the first step. In particular, the size of big agglomerations within countries remains stable. To illustrate this point, Table3 describes the statistics of the commuting rate of conurbated areas to light intensity thresholds by country. On the one hand, the term “conurbated” describes the municipalities that are grouped into a functional territory with Fig. 6 Sensitivity analysis by dissimilarity thresholds and night light intensity Page 17 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 Table 3 Statistics ofcommuting rates bylight intensity thresholds Light intensity threshold 12 22 35 50 Not conurbated (%) Conurbated (%) Not conurbated (%) Conurbated (%) Not conurbated (%) Conurbated (%) Not conurbated (%) Conurbated (%) Mexico Mean 0.004 0.568 0.005 1.288 0.005 1.850 0.006 2.111 Std. error 0.000 1.365 0.000 2.293 0.000 2.958 0.000 3.443 Minimum 0.004 0.145 0.005 0.141 0.005 0.404 0.006 0.427 Maximum 0.004 63.32 0.005 63.327 0.005 63.327 0.006 63.327 Colombia Mean 0.017 2.013 0.019 4.087 0.018 6.769 0.021 9.056 Std. error 0.000 1.498 0.000 2.992 0.000 3.817 0.000 3.952 Minimum 0.017 0.000 0.019 0.000 0.018 0.000 0.021 0.581 Maximum 0.017 12.216 0.019 29.020 0.018 37.170 0.021 28.173 Chile Mean 0.080 2.590 0.082 3.850 0.087 4.204 0.087 4.410 Std. error 0.000 2.624 0.000 3.512 0.000 4.077 0.000 4.400 Minimum 0.080 0.000 0.082 0.000 0.087 0.000 0.087 0.000 Maximum 0.080 49.559 0.082 49.559 0.087 48.885 0.087 48.885 Page 18 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 each one of the light intensity thresholds presented in the table. On the other hand, the term “not conurbated” is used to describe municipalities that are not grouped with any other at that stage of the method. There is an important difference in the commuting rate between conurbated and not conurbated areas for all the different commuting rate thresholds. This difference is greater for Chile, which has a higher commuting rate in conurbated areas (2.6%) in the case of light intensity of 12 than Colombia (2%) and Mexico (0.6%). Nevertheless, Colombia has a higher commuting rate for conurbated areas (4.1%) with a light intensity threshold equal or greater than 22 than Chile (3.9%) and Mexico (1.3%). With the higher light intensity threshold (50), this difference in the commuting rate of conurbated areas increases. Colombia has a 9.1% average “intra-urban commuting rate” versus 4.4% for Chile and 2.1% for Mexico. Despite the fact that Mexico reaches a maximum of 63.3% in the commuting rate inside the territory, which is greater than Colombia and Chile, the average is lower than in the other two countries. In addition, the dispersion or variability in the commuting rate in relation to the mean inside the conurbated area is greater in Mexico and lower in Colombia (Table3). Although our choice of an appropriate light threshold for the delimitation of conurbations seems to be an arbitrary decision, some alternative measures can inform this process. Following Kropp and Schwengler (2016), we add the modularity measure to the statistics discussed above to show whether or not the different thresholds of light lead to clusters whose links offer a better description of the functional relationships of the municipalities than the expected link values if the network were random (Rapoport 1957; Watts 2003). This measure varies between 0 and 1, where Q = 0 indicates that clustering is no better than random division, while Q = 1 indicates that clustering is better than a situation in which all are grouped into a single municipal region. Thus, modularity provides complementary evidence that the conurbations are approximated correctly (see Table4). For example, in the case of Chile, the political–administrative grouping has a structure with Q = 0.59, but the grouping of those municipalities in conurbations increases the modularity to Q = 0.7. In the case of Colombia, the modular structure of administrative units seems to be a better option, and for Mexico the Table 4 Modularity ofmethod’s first step *Resulting functional territories without considering the nighttime-light intensity. Functional territories are definedonly by commuting rates Light intensity Modularity Mexico Colombia Chile 12 0.871 0.870 0.686 22 0.875 0.854 0.708 35 0.867 0.839 0.700 50 0.858 0.850 0.692 NO* 0.807 0.863 0.591 Page 19 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 modularity increases with the different light thresholds, which is in contrast to the political–administrative grouping (Q = 0.807), reaching the highest value with intensity of 12. For this case, intensity of 50 offers a more stable approximation for the size of large agglomerations.15 One of the validation criteria of a functional delimitation is reached in the first stage of the method. Internal perfection is achieved, since night light data capture the extension of large metropolitan areas with a high flow of commuting. The contribution of the second step is oriented to account for interactions with low-density municipalities not captured in the first step. We do so, because these municipalities do not project a quantity of light that is intense enough to consider them as conurbated areas, rather than urban agglomerations with a more local outreach. 5 What next? Functional territories are becoming an increasingly popular resort for public policy design, as they capture interdependencies between municipalities and approximate the location of the resident population’s daily activities. The scope of functional territories addresses important border-transcending issues, such as urban sprawl, uncoordinated land-use planning, environmental sustainability, and the supply of specific public utilities (Foster 2001; Yuill etal. 2008; OECD 2013). Other works highlight the importance of functional-area scope for public policy in developed countries. Defining functional regions as spatial units has been useful for calculating the demand for public utilities, such as road networks and community college infrastructure in Canada (Munro etal. 2011). Moreover, municipally fragmented landuse governance has led to sharper, uncontrolled, and unplanned urban expansion of UK metropolitan areas, suggesting that space for regional concentration would encourage coordinated land-use planning (Carruthers 2003). These regions work as productive and innovative clusters by internalizing economic spillovers. Development policies should be conducted on a regional basis and not be assessed as rural versus urban. Specifically, in the countries studied, our method contributes in various ways to previous definitions of functional territories. A former definition of functional territories in Colombia is provided by Duranton (DNP 2015), and it currently plays a pivotal role in a broad set of policies, including productivity-enhancing, connectivity, and land-use policies. His approach delineates metropolitan areas by iteratively aggregating municipalities into clusters according to their labor-commuting flows. While Duranton’s system of cities groups only 113 (10%) municipalities,16 our method results in 370 (33%) municipalities grouped in functional regions, many of which are rural or mid-size municipalities whose interactions, nonetheless, are intense enough to cluster them by pairs or in small functional regions of up to five elements (21.4%). In this fashion, it would help policymakers to recognize not only the interactions that take place inside metropolitan 16 Of the 151 municipalities he identifies as part of the system of cities, 38 are stand-alone municipalities or singletons. 15 The modularity index is computed following Kropp and Schwengler (2016) as Q =  i  eii −a 2 i  , where a i=  j eij , with eij representing commuting between municipality i and municipality j as a proportion of the sum of all commuting flows of the matrix. Page 20 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 areas or the system of cities, but also those that take place in smaller and more rural functional regions. For the case of Chile, most studies and policies regarding functional areas rely on labor-commuting patterns from the population census of 2002, and a question to identify commuting was not included in the most recent census in 2017. Even recent innovative methods rely on labor-commuting flows from 2002 (Casado-Díaz etal. 2017). Therefore, the present study provides updated functional areas that are not only based on commuting flows, but that are also relevant for scholars and policymakers interested in both rural and urban functional territories. This is an increasing concern in the literature on agricultural economics (Berdegué etal. 2014b, 2015; Soto etal. 2018). In the case of Mexico, we believe that our method brings substantial benefits compared with the method currently used by the national government to delineate functional territories. The former consists of delineating isochrones around main urban areas, the length of the isochrones being artificially drawn (1h around metro areas, 40min around intermediate cities, and 20min around small urban centers) to cover the whole Mexican territory (Amador and Vergara 2016). Thus, being a mechanical exercise, the information of interactions between municipalities, which is captured through our method, is lost. The results of this investigation provide input for a broader research agenda that aims to understand how the evolution of rural–urban linkages in terms of (i) labor market diversification, (ii) agri-food systems, and (iii) urbanization patterns lead to economic growth in the defined functional territories (Berdegué et al. 2014a; Berdegué and Soloaga 2018; Fergusson etal. 2018; Soto etal. 2018). We consider that the definition of functional regions is a first step that allows us to develop a proper understanding of the role of these three dynamics, being well aware that their influence seldom matches the geography of political–administrative units. 6 Concluding remarks Attempts to incorporate the concept of functional areas into political and research agendas are still incipient in developing countries. Most of these studies, particularly in Latin America, rely on outdated censuses, and therefore, the lack of data is a limiting factor even for the identification of metropolitan areas or conurbations. Consequently, this study proposes a novel approach for the delimitation of functional spatial units, or functional territories, using satellite imagery in conjunction with commuting data. The purpose of our method is to use night light data to identify urban agglomerations, which in turn allows us to group municipalities before clustering them by using commuting data. Our approach is not intended to measure the extent of urban areas, but rather to construct “functional territories,” which we define as spatial units with more economic interaction inside than outside the area. We describe our method using the cases of three developing countries, namely Mexico, Colombia, and Chile. The resulting functional territories identified with our method can eventually be a valuable tool for public policies or for further research. As they capture interdependencies between municipalities and approximate the location of the resident population’s daily activities, they can be used as spatial units to calculate the demand for infrastructure for Page 21 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 public utilities (Munro etal. 2011), to design land-use and housing policies with a larger regional scope that considers those interdependencies (Cheshire and Hilber 2008), or to foster productive and innovation clusters (Partridge and Olfert 2010). Functional territories as we define them are highly susceptible to MAUP and other aggregation problems (Duque etal. 2007), since they do not cluster spatial units based on their homogeneity but rather on the intensity of their interactions. To test the robustness of our results and to diminish the risk of any spatial aggregation bias, we use a wide range of threshold values to analyze marginal changes in the composition of the functional areas. Most importantly, we focus on the distribution of the dissimilarity coefficients under different scenarios. For a more robust solution, we would need more disaggregated information, which would be useful for reducing spatial aggregation bias and analyzing interactions over space more meticulously. Overall, regarding Mexico, we believe our method is far superior to that currently used by the Mexican government to delineate functional territories, which basically consists of delineating isochrones around main urban areas. Regarding Colombia, a broad set of policies, including productivity-enhancing, connectivity, and land-use policies, often consider the metropolitan areas defined by Duranton, as described in Sect.5 (DNP 2015). Considering this, our results may help to widen the scope of the research and help these policies to capture not only the interactions that take place inside metropolitan areas, but also those in smaller and more rural functional territories. Finally, for the case of Chile, the lack of updated official census information on commuting flows presents a challenge for the delimitation of functional spatial units. The functional territories presented in this study provide a potential solution to this problem, which may have important implications for scholars and policymakers. Additional file Additional file1 Description of Territories. Abbreviations DMSP-OLS: defense meteorological satellite program’s operational linescan system; FA: functional area (FAs); FER: functional economic region; FEA: functional economic area; CR: city region; FUR: functional urban region; LLMA: local labour market area; TTWA : travel-to-work areas; FT: functional territories. Acknowledgements Not applicable. Authors’ contributions The article was a result of a collaborative effort. Each author contributed in the development of a solid conceptual framework and in the design of the empirical approach, and each one played an active role whether it was directly carrying out the required calculations to define the functional territories or surveilling the process. As a group, all the authors participated writing the final manuscript, as well as discussing the detailed responses to the referees. All authors read and approved the final manuscript. Funding The authors acknowledge the financial support of the International Development Research Center (IDRC)—Canada. Julio Berdegué, Milena Vargas, and Juan Soto acknowledge the financial support from the Chilean Fondecyt Grant 1161424 ‘Cities and Development in Chile’. Ethics approval and consent to participate The authors of the manuscript hereby acknowledge that we all have read and agreed to its content and are accountable for all aspects of the accuracy and integrity of the manuscript in accordance with ICMJE criteria. We also agree to the terms of the SpringerOpen Copyright and License Agreement.We wish to submit an original research article entitled Page 22 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 “Delineating Functional Regions from Outer Space” for consideration by Latin American Economic Review. We confirm that this manuscript has not been published elsewhere and is not under consideration by any other journal.Furthermore, we declare that the research did not involve human participants or animals and therefore, no declaration of informed consent is needed from them. Competing interests The authors declare that they have no competing interests. Author details 1 FAO Regional Representative for Latin America and the Caribbean, Santiago, Chile. 2 Department of Economics, University of the Andes, Bogotá, Colombia. 3 Latin American Center for Rural Development (RIMISP), Carrera 9 No 72-61 Office 303, Bogotá, Colombia. 4 Department of Economics, Universidad Iberoamericana, Ciudad de México, Mexico. 5 Instituto de Economía Aplicada Regional (IDEAR), Universidad Católica del Norte (UCN), Av. Angamos 0610, Antofagasta, Chile. 6 Latin American Center for Rural Development (RIMISP), Associate Researcher Fondecyt, Huelén 10, Providencia, Santiago, Chile. Received: 14 March 2018 Accepted: 26 April 2019 References Abrahams A, Lozano-García N, Oram C (2016) Deblurring DMSP nighttime lights. Working paper. World Bank, Washington DC Acemoglu D, García-Jimeno C, Robinson J (2012) Finding El Dorado: slavery and long-run development in Colombia. J Comparat Econ 40(4):534–564 Acemoglu D, Robinson J, Santos R (2013) The monopoly of violence: evidence from Colombia. J Eur Econ Assoc 11:5–44 Alexandris G, Giannikos I (2010) A new model for maximal coverage exploiting GIS capabilities. Eur J Oper Res 202:328–338 Amador A, Vergara D (2016) Regionalización Funcional de México. Metodología, SEDATU Andersen A (2002) Are commuting areas relevant for the delimitation of administrative regions in Denmark? Reg Stud 36(8):833–844 Bairoch P (1988) Cities and economic development. Chicago University Press, New York Banai R, Wakolbinger T (2011) A measure of regional influence with the analytic network process. Socio-Econ Plan Sci 45:165–173 Baragwanath K, Vogel R, Goldblatt R, Hanson G, Khandelwal T (2018) Detecting urban markets with satellite imagery: an application to India. In: NBER working paper series 24796 Berdegué J, Soloaga I (2018) Small and medium cities and development of Mexican rural areas. In: World development Berdegué J, Jara B, Fuentealba R, Tohá J, Modrego F, Schejtman A, Bro N (2011) Territorios funcionales en Chile. Documento de Trabajo N102 Programa Dinámicas Territoriales Rurales Rimisp Centro Latinoamericano para el Desarrollo Rural, Santiago Berdegué J, Olfert MR, Patridge M, Escobal J, Jara B, Mondrego F (2014a) Places for place based policy. Develop Policy Rev 32:5–32 Berdegué J, Cazzuffi C, Proctor FJ (2014b) Inclusive rural–urban linkages. In: Working group development with territorial cohesion Berdegué JA, Carriazo F, Jara B, Modrego F, Soloaga I (2015) Cities, territories, and inclusive growth: unraveling urban– rural linkages in Chile, Colombia, and Mexico. World Dev 73:56–71 Berry B (1968) A synthesis of formal and functional regions using a general field theory of spatial behaviour. In: Berry B (ed) Marble, spatial analysis. Prentice-Hall, Englewood Cliffs, pp 419–428 Bode E (2008) Delineating metropolitan areas using land prices. J Reg Sci 48(1):131–163 Bosker M, Park J, Roberts M (2018) Definition matters: metropolitan areas and agglomeration economies in a large developing country. In: World Bank Policy research working paper, 8641. pp 1–43 Briant A, Combes PP, Lafourcade M (2010) Dots to boxes: do the size and shape of spatial units jeopardize economic geography estimations? J Urban Econ 67:287 Brown LA, Holmes J (1971) The delimitation of functional regions, nodal regions, and hierarchies by functional distance approaches. J Reg Sci 11:57–72 Burgess R, Hansen M, Olken BA, Potapov P, Sieber S (2012) The political economy of deforestation in the tropics. Q J Econ 127:1707 CAF (2018) Urban growth and access to opportunities: a challenge for Latin America. CAF, Bogota Carruthers JI (2003) Growth at the fringe: the influence of political fragmentation in United States metropolitan areas. In: Papers in regional science. pp 475–499 Casado-Diaz J (2000) Local labour market areas in spain: a case study. Reg Stud 34(9):846–856 Casado-Díaz J, Coombes M (2011) The delineation of 21st century local labour market areas: a critical review and a research agenda. Boletín de la Asociación de Geógrafos Españoles. 7-32 Casado-Díaz J, Martínez-Bernabéu L, Rowe F (2017) An evolutionary approach to the delimitation of labour market areas: an empirical application for Chile. In: Spatial economic analysis. pp 1–25 Cheng Y, Zhao L, Wan W, Li L, Yu T, Gu X (2016) Extracting urban areas in China using DMSP/OLS nighttime light data integrated with biophysical composition information. J Geog Sci 26(3):325–338 Cheshire P, Hilber C (2008) Office space supply restrictions in Britain: the political economy of market revenge. Econ J 118:519 Coombes MG, Openshaw S (1982) The use and definition of travel-to-work areas in Great Britain: some comments Page 23 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 Coombes M, Green E, Openshaw S (1986) An efficient algorithm to generate official statistical reporting areas: the case of the 1984 travel-to-work areas revision in Britain. J Operation Res Soc 37(10):943–953 Corrado L, Fingleton B (2012) Where is the economics in spatial econometrics? J Reg Sci 52:210–239 Costinot A, Donaldson D, Smith C (2016) Evolving comparative advantage and the impact of climate change in agricultural markets: evidence from 1.7 million fields around the world. J Polit Econ 124:205–248 Dingel JI, Miscio A, Davis DR (2018) Cities, lights, and skills in developing economies DNP (2015) Misión Sistema de Ciudades. Bogot’a D.C Dobson JE, Bright EA, Coleman PR, Durfee RC, Worley BA (2000) LandScan: a global population database for estimating population at risk. Photogramm Eng Rem Sens 66(7):849–857 Donaldson D, Storeygard A (2016) The view from above: applications of satellite data in economics. J Econ Perspect 171:198 Duque JC, Ramos R, Suriñach J (2007) Supervised regionalization methods: a survey. Int Reg Sci Rev 30(3):195–220 Duranton G (2015) A proposal to delineate metropolitan areas in Colombia. In: Desarrollo y Sociedad. pp 223–264 Ellis P, Roberts M (2015) Leveraging urbanization in South Asia: managing spatial transformation for prosperity and livability. World Bank, Washington Fergusson L, Molina C, Robinson J, Vargas J (2017) The long shadow of the past: political economy of regional inequality in Colombia. Serie Documentos CEDE 2017–22:1–48 Fergusson L, Hiller T, Ibañez AM (2018) Growth and inclusion trajectories of Colombian functional territories. In: Serie Documentos CEDE. pp 1–49 Florida R, Gulden T, Mellander C (2008) The rise of the mega-region. Cambridge J Reg Econ Soc 2:459 Foster KA (2001) Regionalism on purpose. Lincoln Institute of Land Policy, Cambridge Fox K, Kumar K (1965) The functional economic area: delineation and implication for economic analysis and policy. Papers Reg Sci 15(1):57–85 Gao F, Anderson MC, Zhang X, Yang Z, Alfieri JG, Kustas WP, Prueger JH (2017) Toward mapping crop progress at field scales through fusion of landsat and MODIS imagery. Rem Sens Environ 9:25 Gibbons S, Overman HG, Patacchini E (2015) Spatial methods. Handbook of regional and urban economics. Elsevier, Amsterdam, pp 115–164 Goldblatt R, You W, Gordon H, Khandelwal A (2016) Detecting the Boundaries of Urban Areas in India: a dataset for pixelbased image classification in Google earth engine. Rem Sens 6:634 Goldblatt R, Stuhlmacher MF, Tellman B, Clinton N, Hanson G, Georgescu M, Balling RC Jr (2018) Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sens Environ 205:253–275 González MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 453:779–782 Green N (2007) Functional polycentricity: a formal definition in terms of social network analysis nick. Urban Studies 44(11):2077–2103 Henderson JV, Nigmatulina D, Kriticos S (2018) Measuring urban economic density Henderson M, Yeh ET, Gong P, Elvidge C, Baugh K (2003) Validation of urban boundaries derived from global night-time satellite. Int J Rem Sens 24:595–609 Henderson JV, Storeygard A, Weil DN (2012) Measuring economic growth from outer space. Am Econ Rev 102:994–1028 Henderson JV, Squires T, Storeygard A, Weil D (2017) The global distribution of economic activity: nature, history, and the role of trade. Q J Econ 133:357–406 Imhoff M, Lawrence W, Stutzer D, Elvidge C (1997) A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area. Rem Sens Environ 3:361–370 Jones C (2016) Spatial economy and the geography of functional economic areas. Environ Plan B 1:1–18 Klove RC (1952) The definition of standard metropolitan areas. Econ Geogr 28(2):95–104 Kropp P, Schwengler B (2016) Three-step method for delineating functional labour market regions. Reg Stud 50:429–445 Lowe M (2014) Night lights and ArcGIS: a brief guide Ma L, Li M, Ma X, Cheng L, Du P, Liu Y (2017) A review of supervised object-based land-cover image classification. ISPRS J Photogram Rem Sens 130:227–293 Manning A, Petrongolo B (2017) How local are labor markets? Evidence from a spatial job search model. Am Econ Rev 107(10):2877–2907 Munro A, Alasia A, Bollman R (2011) Self-contained labour areas: a proposed delineation and classification by degree of rurality. Rural Small Town Can Anal Bull 8:2–33 OECD (2002) Redefining territories: the functional regions. OECD Publishing, Paris OECD (2013) Regions at a glance. OECD Publishing, Paris Openshaw S (1977) A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modeling. Trans Instit Br Geogr 2:459–472 Paredes D, Soto J, Fleming DA (2018) Wage compensation for fly-in/fly-out and drive-in/drive-out commuters. Papers Reg Sci 97:1337–1353 Partridge M, Olfert M (2010) The winners’ choice: sustainable economic strategies for successful 21st century regions. Appl Econ Policy Perspect 33:143–178 Pike A, Rodriguez-Pose A, Tomaney J (2011) Desarrollo local y regional. Universitat de València, Valencia Rapoport A (1957) Contribution to the theory of random and biased nets. Bull Math Biophys 19:257–277 Robinson WS (1950) Ecological correlations and the behavior of individuals. Am Sociol Rev 15:351–357 Rozenfeld HD, Rybski D, Gabaix X, Makse HA (2011) The area and population of cities: new insights from a different perspective on cities. Am Econ Rev 101:2205–2225 Rubiera-Morollón F, Viñuela A (2012) From local units to economic regions in Spain: where agglomeration economies are meaningful. In: FernándezVásquez E, RubieraMorollón F (eds) Defining the spatial scale in modern regional analysis: new challenges from data at local level. Springer, Berlin, pp 23–44 Samad TN-G (2012) Colombia urbanization review: amplifying the gains from the urban transition. The World Bank, Washington, D.C. Sarstedt M, Mooi E (2014) A concise guide to market research. Springer Texts in Business and Economics, Heidelberg Page 24 of 24 Berdeguéetal. Lat Am Econ Rev (2019) 28:4 Simini F, González MC, Maritan A, Barabási A-L (2012) A universal model for mobility and migration patters. Nature 484(7392):96–100 Small C, Pozzi F, Elvidge C (2005) Spatial analysis of global urban extent from DMSP-OLS night lights. Rem Sens Environ 96:277–291 Soto J, Paredes D (2016) Cities, wages and the urban hierarchy. J Reg Sci 56:596–614 Soto J, Vargas O, Berdegué JA (2018) How large are the contributions of cities to the development of rural communities? A market access approach for a quarter century of evidence from Chile. In: Working paper series of the latin American center for rural development-Rimisp, vol 239, pp 1–41 Tolbert CM, Killian MS (1987) Labor market areas for the United States Tolbert C, Sizer M (1996) US commuting zones and labor market areas Vargas J (2017) Urbanization without development? In: Vargas J (ed) Urban growth and access to opportunities: a challenge for Latin America. CAF Report of Economic Development, Bogotá DC, pp 17–65 Viñuela A, Rubiera-Morollón F, Fernández-Vásquez E (2014) Applying economic-based analytical regions: a study of the spatial distribution of employment in Spain. Ann Reg Sci 52:87–102 Vogel KB, Goldblatt R, Hanson GH, Khandelwal AK (2018) Detecting urban markets with satellite imagery: an application to India. National Bureau of Economic Research, Cambridge Watts D (2003) Six degrees: the science of a connected age. Norton, New York Wei R (2015) Coverage location models: alternatives, approximation, and uncertainty. Int Reg Sci Rev 39:48–76 Weiss DJ, Nelson A, Gibson HS, Temperley W, Peedell S, Lieber A, Bisanzio (2018) A global map of travel time to cities to assess inequalities in accessibility in 2015. Nature 553:333–336 Yuill D, Ferry M, Vironen H (2008) New policy frameworks, new policy approaches: recent regional policy developments in the EU and Norway. 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