A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region

Land Use Policy 92 (2020) 104445

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A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region

T

Luis A. Guzmana,*, Francisco Escobarb, Javier Peñaa, Rafael Cardonaa a

Grupo de Sostenibilidad Urbana y Regional, SUR, Departamento de Ingeniería Civil y Ambiental, Universidad de los Andes, Edificio Mario Laserna Cra 1° Este N° 19ª-40, Bogotá, Colombia b Deparment of Geology, Geography and Environmental Sciences, University of Alcala, Madrid, Spain

A R T I C LE I N FO

A B S T R A C T

Keywords: Bogotá Cellular automata Land-use modeling Policy support Urban dynamics Metronamica

Bogotá and the 17 neighboring municipalities make up one of the biggest metropolitan areas in Latin America. However, despite strong functional interactions within the area, there is no official government body at this level in charge of coordinating authorities and providing solutions to the wide variety of issues arising in the regional urban land system. Aiming at providing an insight on future land-use developments linked to new transport infrastructures and at offering a tool to support territorial decision-making, this paper presents a cellular automata-based (CA) model based in Metronamica® software, that allows testing different scenarios based on potential land-use policies, environmental suitability and transport alternatives. There has not been, so far, an urban planning tool that accounts for the complexities of this region. CA-based land-use simulations constitute a useful approach to understanding the impacts of urban planning policies and regulations. This tool can help to improve inter-territorial and inter-institutional coordination, which through planning and management policies seek a spatially integrated development, with a long-term perspective. The CA-based model proposed was calibrated to reproduce land-use changes between 2007 and 2016 using different methods and indicators. The model was used to simulate and analyze eight scenarios with different policy directions of transport infrastructure in the future of the region. The results of the simulations reflect the dynamics of territorial occupation. The calibration indices in the experiment indicate a high degree of suitability for the CA Bogotá model, proving its effectiveness and potential as a useful tool for decision-making. The results show that occupation scenarios with restricted developable zones within the city, tend to have the greater dispersion rate in the study area, compared to scenarios where land development plans are promoted in Bogotá, which representing a more compact development.

1. Introduction While Latin America and the Caribbean (LAC) is the most urbanized region on the planet with 80 % of its population living in cities (Roberts et al., 2017), urbanization in its countries has drawn little attention, mainly due to a lack of detailed and comparable spatial data (Sridhar, 2007; Guzman, 2018). Rapid economic and demographic growth in LAC, along with high migration rates from rural areas, is resulting in a low-quality urban expansion. A quick walk through cities of the region reveals a worrying reality: hours lost in traffic, low quality public services with little coverage, social inequalities, and high levels of pollution. The negative effects

that this has on quality of life and countries’ potential for productivity are aggravated by lack of planning which in turn has slowed down both the social and the economic development of the region. Urban planners must foresee how travel demand is affected by territorial changes and estimate how new developments in transport and land regulations may modify human activities and their associated land-uses. The evolution of cities has attracted interest from decision makers and urban planners as it is believed to affect population wellbeing, and the sustainable development (Meijers and Burger, 2010). Despite this interest, there is a widespread lack of useful planning and management tools in cities of LAC (Steinberg, 2005). Currently, there are no proper tools or processes that appropriately account for important factors

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Corresponding author. E-mail addresses: [email protected] (L.A. Guzman), [email protected] (F. Escobar), [email protected] (J. Peña), [email protected] (R. Cardona). https://doi.org/10.1016/j.landusepol.2019.104445 Received 6 February 2019; Received in revised form 4 December 2019; Accepted 26 December 2019 0264-8377/ © 2019 Elsevier Ltd. All rights reserved.

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Fig. 1. Study area and land-use categories.

produce expected outcomes, similar to those observed for the study area over a period in the past or consistent with a given scenario in real cities (Mas et al., 2018; Wang et al., 2011; Al-Ahmadi et al., 2009; He et al., 2008). The determination of optimal parameters for several factors of a CA land-use model is especially challenging (Stevens et al., 2007). Because the CA models available in the market are generic, welladjusted parameters to a particular geographic area do not necessarily function with the same quality in other locations (Silva and Clarke, 2002). This emphasizes the need for a parameter adjustment process to improve results. This paper presents the development, calibration and application of a spatial decision support tool based on CA modeling for Bogotá and 17 of its neighboring municipalities, implemented with the software Metronamica®, developed by RIKS. The major objective is to help local authorities in decision making related to urban planning, land development and coordinate their Land-Uses Master Plans. For this purpose, a forecasting tool to simulate and assess the integrated effects of planning policies on urban and development based on different scenarios was built. The presented land-use change model possesses a number of qualities worth to be anticipated here: 1) First, it constitutes a unique land-use model in its detail, complexity and quality in a developing country. 2) This study demonstrates that the current level of technological development, and data availability and quality worldwide allows for the implementation of such models in virtually any part of the world, including Global South cities. 3) It shows that interaction between modelers, planners and public authorities results in useful modelling and prospective exercises, and 4) It provides the basis for ulterior analysis on sustainability of future land-use scenarios.

involved in urban development such as transport infrastructure, land regulation, and suitability. The development and implementation of a support tool is crucial, and should be applied in the LAC context to try to understand and assess, through land-use models, the influence of interactions between transport infrastructure location and land-uses. This is the case of the Bogotá metropolitan region, which has experienced rapid urban growth that has caused multifaceted planning challenges for local authorities and complex urban dynamics in the city and neighboring municipalities. In the last 30 years, the population of Bogotá has grown by 61 %, going from 4.6–7.4 million inhabitants. At the same time, population in the city’s surrounding areas has grown around 84 %. This growth has coincided with the use of conventional planning practices, but lack of appropriate and coordinated policies between the local, regional and national authorities has led to an almost accidental metropolitan development. This coordination among the local governments is a challenging task (van Lindert, 2016). This study develops a decision support tool that seeks to help the coordination of urban development policies between authorities through a truly integrated analysis. Quantitative methods have been used for many years to understand the spatial processes of land occupation and urban growth. Land-use models based on cellular automata (CA) are favored due to their simplicity, flexibility, intuition, and their capacity to incorporate the spatial and temporal dimensions of land occupation processes (Santé et al., 2010). These models are commonly used to analyze and simulate urban growth reproducing complex dynamics, similar to those found in real world, using simple rules (Aljoufie et al., 2013; García et al., 2013; van Vliet et al., 2013; Lau and Kam, 2005; Barredo et al., 2004; White and Engelen, 1997). CA models provide a dynamic modeling environment that can simulate complex changes in land-use, transport, and their interactions and have been widely used to study the spatial process of changes in land-uses over time (Aljoufie et al., 2013; van Vliet et al., 2012; Liu and Phinn, 2003). In recent years, CA models have been used to simulate a great variety of urban phenomena - urban growth and urban sprawl for example - as well as to evaluate the distribution of population and services, analyze traffic flow, and model competition for location (Lau and Kam, 2005). The calibration of CA land-use models is a complex task, being most classical methods based on trial and error (García et al., 2013). Nevertheless, the calibration process is a crucial step of the modeling exercise. Good calibration parameters ensure that the model will

2. Methods 2.1. Study area – a highly urbanized Latin American metropole Bogotá, as most Colombian cities, has historically had a high-unsatisfied demand for housing which, in response, has encouraged the low-income population to occupy mostly un-planned and informal urban settlements. Consequently, several informal neighborhoods with poor urban living conditions have emerged on the outskirts of the city and in neighboring municipalities, principally in Soacha (see Fig. 1) (Oviedo and Dávila, 2016). As of 2018, Bogotá is home to approximately 7.4 million people and 2

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occupies an urban area of around 380 km2. Additionally, Bogotá is surrounded by cities that have a close relationship with the capital, making it in practice, a large, although not officially constituted, metropolitan area (Guzman et al., 2017). These municipalities, which include Zipaquirá, Gachancipá, Tabio, Tocancipá, Cajicá, Chía, Sopó, Tenjo, Cota, La Calera, Funza, Madrid, Mosquera, Facatativá. Bojacá, Soacha, and Sibaté (Fig. 1), are home of some 1.4 million inhabitants in their 2272 km2. They make around 2.4 million trips per day. Of those who work in Bogotá, 42.5 % use the Integrated Public Transport System, which is only available in the city. By not having any form of official common government body, the municipalities make decisions autonomously, which has led to a disorganized growth processes. Regarding land occupation, residential areas in the country were classified into socioeconomic strata (SES, henceforth). This socioeconomic stratification system was created in 1994 by National Government, classifying the households in six different categories from one (the poorest) to six (the wealthiest). The lower strata, SES one to three, receive subsidies for essential utilities (water, gas, electricity). To pay for these subsidies, residents in SES five and six pay utility bills higher than their consumption. The classification of housing by strata is done based on characteristics of the dwelling and the urban environment. This unique model was devised in the mid-nineties for the benefit of the lower income population, in a country that, at the time had poverty rates close to 40 %. Fig. 1 also shows the location of the different land-uses over the study area. As these maps show, land occupation trends in Bogotá indicate a concentration of low SES settlements in the south and southwest of the region, with wealthiest areas located in the northeast. As available land within Bogotá’s boundaries runs out and costs increase for the little that remains, urban trends suggest that most of the region’s urban growth will occur outside the city of Bogotá and that the majority of inhabitants in these outer municipalities will be of either low or middle-income. The region has grown not only through economic development and industrialization, but also due to nation-wide violence. Terrorist violence has caused massive waves of migration from rural areas. This has caused serious problems with congestion, exclusion, and uncontrolled territory occupation. The metropolitan region’s population went from 7,910,000 inhabitants in 2005 to 9,350,000 in 2016 and the urban footprint has expanded dramatically, particularly in neighboring municipalities. During a ten-year period 2007–2016, the built-up area in the Bogotá region grew by 89.1 km2 (+17.3 %). High-income residential land-use showed the highest increase over this period, with a growth of 8.3 km2 (+44.5 %), while low-income residential land-use grew just a +3.5 %.

potential for development. The “transition potential” is a measure of the probability of a landuse change, and considers that land-use development is influenced not only by current land-use, but also by other factors, namely: neighborhood rules (van Vliet et al., 2013; Hagoort et al., 2008; Verburg et al., 2004a). The way the vicinity influences the target pixel depends on the rules of repulsion and attraction observed among land-uses between 2007 and 2016. The calibration of these parameters for any pair of land-use categories is based on a detailed analysis and evaluation of current land-use patterns in the study area and their changes over time. At a distance of zero there is an inertia effect from one land-use to another, while values at distance greater than zero indicate the over (positive values) or under (negative values) representation of land-uses in the neighborhood. As distance increases these effects decreases. Suitability is the effect of biophysical characteristics of the land on the generation of a future use and contemplates the presence of flood, landslide or fire risk. The composite map of suitability for each land-use categories include values from 0 (not suitable at all) to 10 (most suitable). Zoning represents external restrictions due to political decisions or human-made elements. Each of these maps includes four values: from 0 (strictly forbidden) to 3 (actively encouraged). Finally, accessibility measures the impact of proximity to transport infrastructure with certain land-uses activities (Escobar and Paez, 2018). This factor was obtained from over or under representation curves that demonstrate the density of a land-use in the area surrounding the transport infrastructure. As seen in Fig. 2, the CA simulation process consists of three stages. First, it is necessary to collect information for the study area at two different points of time, separated by a sufficient number of years. These points are referred to as the beginning and end of the calibration period. Given that the purpose of the model is to allocate changes, the simulated period must be long enough as to produce representative changes. This period would be of different length depending on the dynamics found in the study area land-uses. In Bogotá, being a highly dynamic capital of a developing country, we assume, in line with Barredo et al. (2004), that ten years, as it is the case of the proposed model (see Section 2.5) is right. In the second stage, neighborhood rules must be established in order to calibrate the model and achieve simulation patterns similar to reality. The third stage is the generation of results from different scenarios using the calibrated model. At this stage, the model uses the parameters calibrated in stage two to ensure plausibility of results. In this step, a simulation end year is determined and a set of possible scenarios are designed to introduce new transport infrastructure, demand changes by land-use category, or zoning regulations.

2.2. Model description

2.3. Data sources and model preparation

The model developed and calibrated followed the CA-based model utilized in Metronamica® (Aljoufie, 2014; van Vliet et al., 2012). The model is composed of a grid of cells (pixels), where each pixel represents a land-use category and the state of adjacent pixels, or landuses in the surrounding neighborhood, is an input for the transition rules (van Vliet et al., 2012). The state change potential of each pixel is calculated in discrete steps (one year was set as step length), while following a set of neighborhood (transition) rules that depend on geographic features such as neighborhood potential, suitability, zoning, and accessibility. The model has three categories for land-uses: Active (also named function) uses are those to which location is actively assigned based on the potential derived from neighborhood rules and are modelled. Passive (vacant) use, only change as a result of other land-use dynamics. Finally, feature land-uses are the classes not programmed to change within the simulation period. However, they do influence the dynamics of the active land-uses, and their location. At each moment of time, active land-uses are located in the places with the greatest

Complete land-use information for different years was only available for the city of Bogotá (cadastral data). In municipalities where there was not enough information, Landsat 7 etm + slc-off satellite images (USGS, 2017) and official documents were used to complete the data sets. Due to the asymmetric conditions of information availability and the importance of studying Bogotá and its region as a unique urban area, our proposal uses a mixed approach, which is based on data and local knowledge. We also assumed that the processes are stationary, and therefore we model the study area as a single region with identical calibration parameters. We are aware of the fact that there might be some bias introduced in the model using two different dataset sources. However, the goal of this study is to develop a model as accurate and valid as possible for Bogotá authorities. The satellite images, used only for municipalities, were then classified with the aid of the ArcGIS® tool “maximum likelihood classification” in a “supervised classification” process. This was made to make an initial classification. This receives a signature file with the probabilities of belonging to a given class that are associated to a pixel, 3

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Fig. 2. Application of the CA Bogotá model.

was available and show a ten-year land-use change as to allow for a proper calibration. Year 2050 was used as the final simulation year for the evaluation of different scenarios as this is the evaluation horizon year for the Land-Use Master Plan. To input the accessibility parameters, additional geographic information was collected. Road data, the public transport network, Transmilenio stations (the Bus Rapid Transit -BRT- system), and the bicycle network were gathered from various official sources. Information on landslides, forest fires, and flooding phenomena was used for the environmental suitability layers. Finally, zoning information came from the current Land-Use Master Plans of the municipalities. These maps were used as restrictions on land-uses and land regulations that affect certain zones in the future.

according to its characteristics in the electromagnetic spectrum. The result from the images presented four categories (chosen beforehand): water bodies, available land1, industrial land and populated centers. Then statistics of mean and variance of the values of the pixels that fall inside each class are computed to obtain the signature file, which in turn is used by the computer to evaluate the remaining unknown area. Land identified as occupied by industrial and populated centers was further assigned a land-use category compatible with the model (see active uses see below) and in accordance with the available information from official documents of municipalities. Further refinements were done to this information in subsequent steps of the modeling process. There is a positive correlation between SES and average household income in Bogotá. Therefore, for the purposes of this study, residential areas were divided into 3 categories based on SES: low (SES 1 and 2), medium (3 and 4) and high (5 and 6) as seen in section 2.1. In turn, economic land-uses were divided into 4 categories: industrial, commercial, mixed, and services. Therefore, for the purposes of this study, land-use categories were classified into the following: 1) Available,2 2) Low-SES residential, 3) Medium-SES residential 4) High-SES residential, 5) Industrial, 6) Commercial, 7) Mixed (residential land-use between 40 and 60 %), 8) Services, 9) Green zones, parks and country clubs, 10) Surface water, 11) Institutional, 12) Airport, 13) Main roads, 14) Landfill, and 15) Mining. Of these, uses 2 through 8 were considered active uses (function), use 1 was considered passive (vacant), and uses 9 through 15 were classified as fixed (feature). Fig. 3 shows the resulting base map. The pixel size3 for the study area was 60 × 60 m, and the study considered the years 2007 (143,099 cells in active uses) and 2016 (167,858 cells in active uses) as the base and final years for calibration. In those years is where the information of Bogotá and the municipalities

2.4. Data adjustments and calibration indices In order to obtain better-input information, data quality issues associated to cloudiness in the satellite images (just in the municipalities) were corrected through a visual interpretation stage: some land-uses in 2007 disappeared for 2016 because were covered by clouds, or, some water bodies appeared due to the rainy season. Then and in order to grasp past dynamics among land-use categories, a contingency tables was computed. The contingency tables are considered a useful complement to visual interpretation as they identify errors that escape visual interpretation by highlighting the volume and type of changes that occurred during the simulation period. There are also very useful due to the need to know attraction forces and dynamics among the land-uses categories. To evaluate the result of the calibration, Kappa indices were used to compute a comparison of land-use changes between 2007 and 2016. This is a per pixel based method to describe the differences between 2007 and 2016 conditions. The Kappa index (KI), a pixel to pixel comparison procedure (Congalton, 1991) is more reliable than a simple percent agreement calculation because it considers random agreement (Zheng et al., 2015). If two maps are in total agreement, KI = +1. This indicator has been utilized before to measure agreement between the results of a simulation and a real map for the end of the simulation period (Berberoğlu et al., 2016; He et al., 2008). The down side is that even small displacements in location are classified as incorrect. The Fuzzy Kappa index (FKI) accounts for the uncertainty in special

1 The classification of available land corresponds to pixels identified from the satellite images as agricultural land, forests and protected areas. 2 The land classified as available corresponds to the land into the study area whose classification falls within the zones of expansion of the municipalities or to the rural land that is classified as agricultural use. This was defined as vacant land ready to be occupied by active uses. 3 This pixel size was used due to the resolution of the available satellite images and to the information available to assign the land-use category to each pixel.

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Fig. 3. Land-use categories 2007: active, passive and fixed.

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correction method was visual interpretation, in which changes that occurred during the years of study were visually identified, giving a clear idea of the distribution of land-uses throughout the period (Engelen et al., 1997). Some studies utilize this method as a first step to identify the most significant differences between simulated and actual data (Aljoufie et al., 2013). Visual calibration requires modelers to be familiar with the territory. Subsequently, contingency tables were prepared to compare overall performance of the model. These tables calculate the total number of pixels that changed from a certain category to another in the data set as determined by the simulation. Therefore, Kappa indices were used to evaluate the calibration procedure accuracy. Finally, the calibration process focuses on the adjustment of parameters to obtain a simulated map analogue to the real map of the final date chosen for the calibration period. The calibration procedure was divide into two main steps: 1) The adjusted parameters were the neighborhood relationships between categories, and 2) the accessibility, which consisting of the assignment of values of distance decays and relative weight of the transport infrastructure with respect to the active land-uses. As shown in Fig. 2, this consists of an iterative process in which all Kappa indices are calculated, after each parameter adjustment. However, this process is computationally intensive, often requiring parallel computing resources. This approach also brings lack of interpretability that can result in problems understanding the model instead of producing a model with good performance (Newland et al., 2018). For this study, we prioritized the empirical, trial and error tested against benchmark calibration (Mas et al., 2018).

process and was developed to lessen the impact of KI limitations (Hagen-Zanker, 2009). This technique was developed with a view to the calibration process of high spatial resolution models (Hagen-Zanker, 2009; Hagen, 2003). It introduces proximity of location by a distance decay function that specifies the degree to which a pixel belongs to the categories found in its vicinity (Hagen-Zanker, 2009). Despite criticism found in the scientific literature against Kappa indices (Pontius and Santacruz, 2014; Pontius and Millones, 2011), we consider these indices still valid to characterize changes in line with work developed by Newland et al. (2018); van Vliet et al. (2011), or Barredo et al. (2004). The Kappa and Fuzzy Kappa Simulation Indices (Ksim and FKsim) attempts at correcting the agreement between two maps for the sizes of class transition4 . These indices computes a comparison of exclusively the changes of land-uses. Therefore, an initial map is required (year 2007). By taking the class transition instead of the class itself, the absolute value of the Kappa can be interpreted. When the comparison between a model and the actual data, given the original land-use map, is higher than 0, it indicates that the model does explain some changes in land-use (van Vliet et al., 2011). The Kappa indices were calculated with Map Comparison Kit 3, a software developed by RIKS. Calculation of the FKI requires the configuration of radius of the pixel group to evaluate and slope of the decay function that expresses loss of importance with distance increase. This study used a 4-pixel radius and a linear decay function with a slope of 0.5. The radius in pixels is the range within which the algorithm looks for a land-use that will be added to the neighborhood of each pixel. While the slope of decay function determines the speed with which the influence of the neighborhood falls (or increases).

2.6. Scenarios 2.5. Calibration procedure

Eight (8) development scenarios possessing different levels of complexity were defined. The first is a business as usual scenario (BAU) whereas the last includes the higher degree of changes. Officials from the Bogotá Urban Planning Office expressed their interest in knowing what the impact of new and different transport infrastructures and zoning regulations would have over the land-use pattern within the study area. For this reason, the land demand remains the same for all scenarios, being what the region expects to grow in the next 30 years, according to its demographic estimates. Therefore, the Urban Planning Office of Bogotá projected land-use demands for the year 2050, as is shown in Table 1. These projections considered population growth, residential density, and gross domestic product (GDP) to estimate area demand by land-use category. The purpose of each scenario is to evaluate the impact of combinations of territorial planning policies and transport infrastructure projects on the territorial occupation of the region of Bogotá for the year 2050. The scenarios were developed from formal regional plans and projections discussed in recent years, and the results serve as support to decision-making and policy formulation. A summary of the policies and infrastructure projects included in each scenario is summarized below.

The quality of urban CA models depends on a number of parameters whose values are at the base of the calibration process (Straatman et al., 2004). The model is calibrated using a reliable historic data before it can be used to simulate future land-use dynamics. The model was calibrated to reproduce land-use changes from 2007 to 2016 using different methods and indicators. Previous experiences with this type of model in Bogotá (Escobar and Paez, 2018) have shown some limitations, mainly due to lack of information, zoning and calibration process adjustment. The new model and calibration process considers the complete Bogotá region area and the socioeconomic behaviors unique to it, therefore directly associating the results with the site of application. As demonstrated in previous studies, different land-use categories exert different forces of attraction and repulsion towards each other (Straatman et al., 2004; White and Engelen, 1997). This implies that the development of, for instance, residential land, would attract commercial and mixed land to their surroundings. This attraction may be effective up to a certain distance after which the attraction force fades away and disappears. On the other hand, accessibility constitutes another important factor in the development of land-use changes. This means that, for instance, proximity to main roads would attract residential or commercial land, while this would not be that relevant in natural of agricultural land. By adjusting neighborhood parameters between categories and accessibility parameters on function classes to roads and other transport modes infrastructure, the model attempts at reproducing actual land-use dynamics. The values of Kappa indices can vary enormously depending on the quality of information collected. Bearing this in mind, different methods were utilized in the calibration of the model to improve the quality of both input data and the results of the model. The first

• Scenario 1 – BAU: Bogotá and the municipalities maintain their • •

4

Class transitions can be interpreted as conditional probabilities; the probability of finding a certain class at a location will depend on the class that was originally there. 6

current expansion perimeter and restrictions are applied to prevent the occupation of land with agricultural capacity. There is no introduction of new transport systems, construction of regional roads, or any important infrastructure changes. Scenario 2 – Infrastructure: Bogotá and the municipalities maintain the same land expansion restrictions but the scenario includes new Bus Rapid Transit (BRT) lines (Transmilenio phases 4 and 5), a proposal of a regional road network, a road network in the north of the city, and public transport interchange hubs. Scenario 3 – Development areas: Residential development in the north and west of Bogotá is enabled. Municipalities enable urban and suburban land expansion. There are no new infrastructure projects.

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been ideal to have official spatial information related to future land regulations and restrictions. Nevertheless, they were impossible to take into account because and integrated urban planning in the region was never designed in the long-term.

Table 1 Demand by land-use type (Km2). Source: Urban Planning Office of Bogotá Land Use

Current 2016

%

Expected 2050

%

Relative increase [%]

Low Residential Medium Residential High Residential Industrial Commercial Mixed Services Total

66.8 118.4 26.9 51.9 11.2 21.0 27.2 323.4

20.7 36.6 8.3 16.0 3.5 6.5 8.4 100.0

207.7 235.3 93.4 104.0 22.4 42.1 54.5 759.4

27.4 31.0 12.3 13.7 2.9 5.5 7.2 100.0

+211 +99 +247 +100 +100 +100 +100 +135

3. Results and discussion As a first step and after calibration process, the contingency table details the cross-distribution of land-use categories on the two maps (2007 and 2016). Table 2 shows the last contingency table constructed which was the basis for calculating the Kappa indices. The table is expressed in number of pixels. For example, Table 2 shows that 11,104 pixels of available land (40 km2) were converted into middle-SES residential land and that 2245 pixels (8.1 km2) of low-SES residential land were converted to middleSES residential land. Of course, some minor errors remain, but these tables help to correct the most obvious ones. With this table, it is possible to see where land-use changes occurred and correct any erroneous changes.

• Scenario 4 – Southern transition edge: Bogotá regulates the occu• • • •

pation of the southern transition edge and includes a new transport mode (cable car phases 1 and 2). Additionally, restrictions on the occupation of agricultural land are eliminated. Scenario 5 – Regional rail transport system: A regional train is developed in the municipalities, and Bogotá’s first metro line is included. Land for residential and commercial development is allocated under Transit Oriented Development (TOD) strategies with a 1500 m buffer around the stations. Scenario 6 – El Dorado Airport II: The region enables the second stage of the El Dorado Airport as a new infrastructure facility but does not include a land occupation pattern around the airport, so it is considered an isolated infrastructure area. Scenario 7 – Mosquera development: New residential and economic land-use are enabled for the city of Mosquera (see Fig. 1). Transport infrastructure such as the road network and the expansion of the metro is included. Scenario 8 – All changes: Bogotá enables residential area development in the north of the city and the municipalities enable urban and suburban land expansion. Additionally, restrictions on the occupation of agricultural capacity are eliminated. In terms of infrastructure, this scenario includes public transport systems such as new BRT lines and the first metro as well as a regional train and the new airport. Land for development is allocated under TOD strategies with a buffer around the stations of new modes of transport in the region.

3.1. Neighborhood rules One of the most important factors in calibration is the neighborhood attraction and repulsion effects between land-uses. Fig. 5 shows the neighborhood rules around residential and economic land-uses between 2007 and 2016 as a function of the distance (in pixels) from the initial location. Commercial land-use does not appear, but has an effect similar to that of services. One clear result exhibited in this figure is that high-residential landuses are underrepresented in the neighborhood of low-residential landuses, and low-residential land-uses are underrepresented in the neighborhood of new high-residential areas. This implies that land for the wealthy does not appear near the poor or that the poor cannot live near the rich. It can also be observed that no residential land appears near a main road (as demonstrated by the grey dashed line in the first pixels which has a negative value for all residential uses), although the wealthy have the largest repulsion factor, potentially due to their ability to pay more to be located further away. Some of the curves were modified or not included at all as determined manually during the calibration process. This manual method is perhaps more realistic in developing cities, where urban regulations are not fully met. In addition, the spatial segregation includes informal settlements with a large land property division, a high mix of uses and very high densities (Guzman and Bocarejo, 2017). In these zones, data related with land-uses may not be very reliable. In short, although the neighborhood curves serve as a guide for the calibration process, it is important to test different configurations for the best simulation results, and it should be noted that manual calibration is an iterative time

Fig. 4 summarizes all the transport infrastructure improvements including private and public networks taken into account for this model from the new Land-Use Master Plan as the general infrastructure plan for the study area. To complement the transport infrastructure scenarios in which either improved roads or a new public transport systems were constructed, land regulation options were also modeled. It would have

Fig. 4. New infrastructure project for the Bogotá region. 7

Land Use Policy 92 (2020) 104445 1 615,495 3,852 11,104 2,690 6,673 1,121 864 8 1,526 858 1,071 2 950 13,034 2245 14 460 238 316 20 300 260 84 3 536 16,698 271 937 317 589 33 512 187 386 4 515 12 145 3,978 107 104 124 11 71 43 53 5 1,871 507 828 69 4,958 258 238 36 1,603 379 229 6 189 69 193 53 148 713 44 148 26 38 7 196 195 519 138 259 99 3,227 7 214 181 98 8 9,486 9 259 11 19,321 115 10 552 83 659 143 708 206 160 2,834 314 144 11 482 265 505 104 157 50 279 213 342 4,155 100 12 1,550 13 30,709 14 1,213 15 38 6,657 Note: Number represents: 1. Available, 2. Low Residential, 3. Medium Residential, 4. High Residential, 5. Commercial, 6. Industrial, 7. Mixed, 8. Parks, 9. Water bodies, 10. Services, 11. Institutional, 12. Airport, 13. Highways, 14. Landfill, 15. Mining.

3 2 1 Land-use 2007 (Units: 60 × 60 m pixel)

Table 2 Final contingency table.

Land-use 2016 (Units: 60 × 60 m pixel)

4

5

6

7

8

9

10

11

12

13

14

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consuming process. 3.2. Results of calibrated model The model was calibrated by comparing results from a simulated period with actual changes of land-use: simulated 2016 vs observed 2016 (Verburg et al., 2004b). This assessment of change map’s accuracy was made according in the procedures described in Sections 2.4 and 2.5. Fig. 6 (left) shows the simulated land occupation map of 2016 with the resulting KI for the study area. According to the literature, for KI, values above 0.8 represent strong agreement between two maps while values between 0.6 and 0.8 represent substantial agreement, 0.4 to 0.6 moderate agreement, and values below 0.4 poor agreement (Landis and Koch, 1977). Ksim and FKsim range from −1, meaning total disagreement, and to 1, for total agreement. Values above 0 represents agreement above a special situation where the model is as good as can be expected by chance given a random distribution of the given categories transitions (van Vliet et al., 2011). The overall KI indicates an accuracy of 79.6 % and represents the percent of pixels simulated correctly in the total number of pixels. The overall FKI of 85.3 % represents substantial agreement between simulated and real land-uses and indicates the model’s settings to be effective for future simulations. Although the KI is not greater than 0.80, 0.796 was considered high enough to be adopted. Finally, to evaluate the change map’s accuracy, the KSim and FKSim indicates values of 0.256 and 0.274 respectively, pointing out results above the expected in a model like this (van Vliet et al., 2011). With these results, the model was concluded to be sufficiently calibrated to continue with the next stage in the simulation process. 3.3. Scenario simulation After accepting calibration results as valid, we ran the model 8 times, one per scenario. A set of quantitative indicators was calculated to assess the changes and differences between scenarios. The scenarios simulated for 2050 are shown in Fig. 7. For representation purposes, the fifteen land-use categories have been grouped into six: low, medium, and high residential, economic urban built-up areas and other categories (feature uses). The obtained maps demonstrate patterns of growth consistent with traditional development in the region. Northern municipalities continue to have higher purchasing power and serve as residences to high SES households, while the principal growth of medium and low SES residential uses takes place in the southwest where these land-uses are already consolidated. Industries choose to locate as close as possible to the main road corridors, generating industrial clusters around Bogotá. It is important to note, however, that zoning policies and the implementation of new proposed transport infrastructures in the scenarios did have an impact on the development of the region. In general, all areas enabled for development near Bogotá were occupied during the simulation period regardless of scenario. Areas away from the capital tended to have less economic and residential development. This may imply a more compact development of the Bogotá region. In turn, this may cause negative economic impacts and bring some of the negative effects related with population density and the arrival of industrial uses to the municipalities if not controlled in advance with appropriate policies. A second analysis of the results indicates that with northern expansion enabled, there is a relocation of low and medium SES residential land-uses to the road that connects Bogotá with the municipality of Cota (northwest), which then affects the location of high-SES residential uses. In scenario 1, high SES residential areas are mostly grouped on the western side of the northern corridor, but for scenario 8 they tend to spread to areas outside of Bogotá. Potentially, as amount of available land increases, the upper SES look for quieter and less dense areas in surrounding municipalities, preferring not to compete for 8

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Fig. 5. Neighborhood rules between 2007 and 2016.

Fig. 6. Maps of Kappa indices result.

often rural areas implement less risk prevention measures, whereas in Bogotá, hazards such as floods are controlled by artificial interventions. In scenarios 1 and 2, due to the high restrictions on development, the population’s exposure to areas of high risk is higher than in the other scenarios as shown in the first column of Table 3. Average distance from residential land-use to public transport (ADPT) was calculated as an indicator of accessibility. As seen in Table 3, although there were important changes in the municipalities,

exclusive areas north of the capital. Both groups then, take advantage of the greater accessibility supplied by the introduction of new transport infrastructure in the region and the relocation of industries. The model also permits to evaluate the quantity of potential residential land-use appearing in areas of low environmental suitability (environmentally risky areas). While in general the study area does not contain areas at very high risk, the municipalities may contain more areas at risk than the city of Bogotá. The main reason for this is that 9

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Fig. 7. Results of the land use simulation for the year 2050.

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Table 3 Average distance to public transport (ADPT) network and % residential land uses in high risk areas. Residential land-use in high risk areas

Scenario Scenario Scenario Scenario Scenario Scenario Scenario Scenario

1 2 3 4 5 6 7 8

7.38 7.43 6.79 6.27 6.41 6.31 5.90 5.85

% % % % % % % %

ADPT Bogotá (km)

ADPT Municipalities (km)

Low SES

Medium SES

High SES

Low SES

Medium SES

High SES

2.0 1.8 1.6 1.2 1.2 1.5 1.1 1.1

1.5 1.3 1.1 0.9 0.9 0.9 0.8 0.8

1.4 0.4 0.5 0.5 0.5 0.7 0.5 0.5

0.5 0.4 0.4 0.4 0.4 0.5 0.4 0.4

0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.4

0.5 0.5 0.4 0.4 0.4 0.8 0.4 0.4

an indicator that attempts to measure the degree of dispersion with respect to a center of the metropolitan region. This indicator is calculated by the mean “minimum distance” of new developed areas to a predefined reference point and was constructed based on the change in the average distance from residential and economic land-uses (pixels) to the economic center of Bogotá by each scenario, and then compared to the average distance in the base year. As shown in Fig. 8, scenarios 1 and 2 refer to the greater dispersion rate (+35 % greater distance) due to the land restrictions that these scenarios entail in the developable areas within Bogotá. On the contrary, scenarios 7 and 8, in which greater land development plans are promoted in Bogotá and the entire region, is where this indicator is lower, representing a more compact region. A final analysis compares the scenarios in terms of area developed both inside and outside of Bogotá. This information corroborates assumptions about expansions modeled in scenario 8, indicating that active land-uses will continue to occupy Bogotá as long as there is available space in the city (see Table 4). This can be explained by people’s desire to live where there are the higher opportunities for work, study and leisure activities. It should be acknowledged that the worth and validity of the simulation results are still uncertain, as land-use change is inherently linked to human decisions, which hardly have a deterministic behavior (Brown et al., 2005). Additionally, the changes shown are caused by the combination of mutually influential physical and socioeconomic factors, implying that the probability of a certain event occurrence or landuse development is subject to the past events of multiple actors. In economics this is known as “path dependence” and indicates that this evaluation is not simple or direct (Batty and Torrens, 2005). Said this, the presented modeling exercise serves to provide the city with a tool to help it in its decision-making process and, in addition, to estimate the land occupation impacts due to changes in transport infrastructures and

the main changes to this indicator occur in the Bogotá area. ADPT results demonstrate that all SES benefit when public transport expansion plans are introduced. In scenario 1, the high-SES residential land-uses is at least 1.4 km away from the public transport system in Bogotá and 500 m in municipalities. In fact, the mean distance from low SES residential in Bogotá is 2 km. This shows some inequalities, since the lower SES is the one that most uses public transport. In scenario 8, however, with the expansion of the public transport system, most of the study area is only an average distance of 500 m from the public transport, although ADPT for low SES residential in Bogotá is still twice that of high SES. As the majority of low SES residential areas are located south of Bogotá the introduction of cable car and metro stations significantly influence ADPT for this specific area. The regional train and implementation of TOD strategies influence the development of active uses in the north and west of Bogotá. However, with the growth trends after the introduction of new zoning policies, western municipalities such as Mosquera and Facatativá reflect the greater relationship between infrastructure and land development. In the same way, the El Dorado II airport promotes the development of industrial, service, and mixed uses, in turn prompting industrial expansion of Funza to the north-west of Bogotá. Despite the construction of new BRT corridors, the known strong attraction of the BRT system with active land-uses, and the introduction of modifications to the BRT system in scenario 2, there are no noticeable effects of BRT on the urban form and land-use mix. These results are in accordance with those presented by Bocarejo et al. (2013). This lack of noticeable impact is possibly due to the inertia of resistance to change, but could also be due to the difficulty of a land-use to relocate into an already consolidated area. It remains as future work to develop accessibility parameters that allow for a more detailed identification of the relationship between land-uses and available infrastructure across the territory. One way to summarize the overall result of each of the scenarios is

Fig. 8. Dispersion rate of urban area in Bogotá region (SC = scenario#). 11

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Table 4 Changes in occupation by land-use and scenario 2050 (Km2). Area

Scenario

Available

Low Residential

Medium Residential

High Residential

Industrial

Commercial

Mixed

Services

Bogotá Municipalities Bogotá Municipalities Bogotá Municipalities Bogotá Municipalities Bogotá Municipalities Bogotá Municipalities Bogotá Municipalities Bogotá Municipalities Total

1

392.3 1,405.6 388.4 1,409.5 345.3 1,452.6 352.1 1,445.9 353.2 1,444.8 353.6 1,444.3 354.9 1,443.0 357.0 1,440.9 1,797.9

88.0 119.6 92.4 115.3 97.7 110.0 94.7 112.9 93.1 114.5 93.7 113.9 92.6 115.1 93.1 114.5 207.7

100.6 134.7 101.6 133.7 109.5 125.7 107.3 127.9 106.9 128.3 107.0 128.2 106.5 128.8 107.1 128.2 235.2

18.9 74.5 18.7 74.7 39.7 53.7 38.1 55.3 39.0 54.3 38.4 55.0 37.6 55.8 38.1 55.2 93.4

25.7 78.3 25.4 78.6 27.8 76.2 26.7 77.3 26.3 77.7 26.5 77.5 26.5 77.5 26.3 77.7 104.0

13.8 8.7 13.8 8.7 14.3 8.1 13.6 8.8 13.5 8.9 12.9 9.5 13.0 9.4 13.1 9.3 22.4

24.6 17.5 24.2 17.9 25.7 16.4 27.3 14.9 27.6 14.6 27.7 14.5 28.5 13.7 26.6 15.5 42.1

30.2 24.2 29.6 24.9 34.1 20.4 34.2 20.2 34.4 20.1 34.2 20.3 34.6 19.9 32.7 21.8 54.5

2 3 4 5 6 7 8

territorial and inter-institutional collaboration, which through planning and management policies seeks a spatially integrated development through public, private and social actions, with a long-term perspective. In fact, the Urban Planning Office of Bogotá is currently using this tool for its Master Land-Use Plan and for coordination with local authorities in the region. The Bogotá CA-based model presented here has demonstrated to be a useful tool for urban planners. As shown, it is being used to study different planning policies to measure the spatial consequences of local and regional decisions, and, most importantly, to foresee future landuse patterns in the Bogotá region. The results of this investigation provide several directions for future urban polices in the region of Bogotá. First, given the promising results of calibration, a solid and complete basis is left for the exploration of urban dynamics in the study area. Second, taking into account the rapid growth of Bogotá’s neighboring municipalities and the vehicle fleet (automobiles and motorcycles), this tool is both useful and necessary for urban decision making.

zoning regulations. Finally, this tool can be the starting point to reach agreements regarding the planning of the region. 4. Concluding remarks This research proposed a CA model to explore land-use change in the region of Bogotá. This is noteworthy, as a tool has never before been developed to assist in decision-making in urban planning that takes into account the complexities of Bogotá and its 17 surrounding municipalities. CA urban models have been shown to reasonably represent the future of developing cities (Barredo et al., 2004) and this study has presented a new and dynamic land-use planning approach for the region of Bogotá using the Metronamica tool. With consideration of both temporal and spatial dimensions, a land-use change tool was developed and validated to simulate different land-use scenarios for 2050. The consequences of different land-use and transport policy interventions were depicted by the tool through the development scenarios. The calibration of CA models is a complex process and there is still discussion about which tools and results should be used to estimate the accuracy of the land-use patterns represented in these models. In the model developed here, calibration was performed using an iterative manual method of calibration and model validity was determined by four different indices. According to values reported in literature, all indices suggest a highly accurate model. Simulations of the 8 scenarios are consistent with the expectations of city authorities, in that they reflect the dynamics of territorial occupation, particularly when comparing policies that effect the zoning of northern Bogotá. With the enabling of northern expansion, economic as well as middle and low-SES residential uses start to develop in the north, since it offers proximity to employment centers and connectivity to public transport. To improve sustainability and diminish the negative effects of urban expansion, urban renewal policies should be considered and could improve the effectiveness of the model. Furthermore, due to data limitations and the complexity of land-use patterns in the region, the analysis results may not be enough. More and better local factors (for example, accessibility) and scenarios should be included in the model to facilitate the decision-making process. This is a first step to try to better understand the urban land system as a whole and could serve as an example for other urban systems in the region. The model was successfully applied and calibrated for the Bogotá metropolitan region, contributing to the development of territorial planning tools in the context of cities of the LAC. A comprehensive understanding of land-use change mechanisms is essential for planning decisions and the sustainability of the region and this tool will be especially helpful in bringing local authorities together to evaluate joint projects. The results of this research can help to improve inter-

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