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![Fig. 1 Integrated simulation framework (Source: [50])](profile/Andrew-Crooks/publication/318793819/figure/fig1/AS:524280255713280@1502009561192/ntegrated-simulation-framework-Source-50_Q320.jpg)
![Fig. 2 Geodatabase with data layers and outputs (Source: [50])](profile/Andrew-Crooks/publication/318793819/figure/fig2/AS:524280255311872@1502009561271/Geodatabase-with-data-layers-and-outputs-Source-50_Q320.jpg)
![Fig. 3 Slum locations and slum sizes in Ahmedabad, 2001 (Source: [50])](profile/Andrew-Crooks/publication/318793819/figure/fig3/AS:524280253632512@1502009561345/Slum-locations-and-slum-sizes-in-Ahmedabad-2001-Source-50_Q320.jpg)
More than 900 million people or one third of the world’s urban population lives in either slum or squatter settlements. Urbanization rates in developing countries are often so rapid that formal housing development cannot meet the demand. In the past decades, international, national and local development communities have taken several policy actions...
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... In the first hypothetical scenario, the model was initiated with populating the "Walled City" without any pre-existing slums. As the simulation progressed, the emergence of slums was observed, purely as a result of human-environment interaction. This experiment partly explains how slums came into existence in a city over time. As seen in Fig. 6, the simulation first showed formation of new slums within Walled City in the starting few years and eventually slums were dispersed to peripheries. Such a pattern is similar to the empirically observed pattern in the city of Ahmedabad as shown in previous section. In order to verify the model behavior, we conducted an urban growth ...
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Postwar migration to “western” countries has gone hand in hand with the development of ethnically segmented labor markets, particularly in low-skill roles where entry requirements are minimal. While numerous theories have been forwarded as to why such situations occur, it has remained difficult to empirically test the relative impact of the many in...
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... The SIMPLAN (SIMplified PLANning) modelling suite has been developed (Adhvaryu and Echenique, 2012) for the case study city of Ahmedabad, India to test alternative urban planning policies (combinations for land use and transport) for the year 2021 Adhvaryu (2010). In another study, Patel et al. (2018) developed a geosimulation model integrating agent-based modeling (ABM) and geographic information system (GIS) to explore slums and they calibrate and validate the model using data from Ahmedabad, where 41% of its population lives in slums. But this ABM suffers from validation issues that CA-ANN does not. ...
... Classification of biological, sociological, and ecological models [31] includes minimal and synthetic models of systems. Synthetic models match the macroscopic results of the model to empirical data or desired results [6,16,28,37], provide explanatory rules [8,17] and, when used with genetic programming, can discover novel rules [15,17,18,36]. In contrast, minimal models do not attempt to calibrate to an empirical objective function. ...
A spatial-temporal agent based model with linear, genetically programmed agents competing and reproducing within the model results in implicit, endogenous objective functions and selection algorithms based on "natural selection". This implicit optimization of genetic programs is explored by application to an artificial foraging ecosystem. Limited computational resources of program memory and execution time emulate real-time and concurrent properties of physical and biological systems. Relative fitness of the agents' programs and efficiency of the resultant populations as functions of these computational resources are measured and compared. Surprising solutions for some configurations provide an unique opportunity to experimentally support neutral code bloating hypotheses. This implicit, endogenous, evolutionary optimization of genetically programmed agents is consistent with biological systems and is shown to be effective in both exploring the solution space and discovering fit, efficient, and novel solutions.
... AI-based tools move from static to dynamic flows to forecast urban growth and enable spatiotemporal modelling [41] with the typical example being the agent-based modelling (ABM) method for the simulation of bottom-up processes to predict future city development. Patel et al. [42] recommended ...
... AI-based tools move from static to dynamic flows to forecast urban growth and enable spatiotemporal modelling [41] with the typical example being the agent-based modelling (ABM) method for the simulation of bottom-up processes to predict future city development. Patel et al. [42] recommended the GIS integration agent-based modelling for testing informal settings' policies in real-life urban environments, while Patt [43] investigated their applications with a focus on the public space networks. ...
In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals with smart designs, data mining and management for complex problem-solving based on experimental data on urban applications (land use and cover, configurations of the built environment and architectural design, etc.), but with few explorations and relevant studies. In this work, a comprehensive and in-depth review is presented to discuss the future opportunities and constraints in meeting the next planning portfolio against the multiple challenges in urban environments in line with Machine Learning progress. Bringing together the theoretical views with practical analyses of cases and examples, the work unveils the huge potential, but also the potential barriers of the complexity of Machine Learning to urban planning strategies.
... Recent work on ABMs focuses on modelling specific unique characteristics/aspects of cities in greater detail. Works such as [Fosset et al.(2016), Patel et al.(2018), Huynh et al.(2015), Hager et al.(2015), Kagho et al.(2020), Waddell et al.(2003)] have focused on modeling different aspects of urban life, including urban population growth, pollution and vulnerability to climate change, mobility and traffic management, resource planning and so on. Several ABM-based software packages like MATSIM [Horni et al.(2016)] and UrbanSim [Waddell(2002)] have also been developed. ...
Agent-Based Models are very useful for simulation of physical or social processes, such as the spreading of a pandemic in a city. Such models proceed by specifying the behavior of individuals (agents) and their interactions, and parameterizing the process of infection based on such interactions based on the geography and demography of the city. However, such models are computationally very expensive, and the complexity is often linear in the total number of agents. This seriously limits the usage of such models for simulations, which often have to be run hundreds of times for policy planning and even model parameter estimation. An alternative is to develop an emulator, a surrogate model that can predict the Agent-Based Simulator's output based on its initial conditions and parameters. In this paper, we discuss a Deep Learning model based on Dilated Convolutional Neural Network that can emulate such an agent based model with high accuracy. We show that use of this model instead of the original Agent-Based Model provides us major gains in the speed of simulations, allowing much quicker calibration to observations, and more extensive scenario analysis. The models we consider are spatially explicit, as the locations of the infected individuals are simulated instead of the gross counts. Another aspect of our emulation framework is its divide-and-conquer approach that divides the city into several small overlapping blocks and carries out the emulation in them parallelly, after which these results are merged together. This ensures that the same emulator can work for a city of any size, and also provides significant improvement of time complexity of the emulator, compared to the original simulator.
... Often regarded as chaotic and non-functional, these settlements present a unique challenge to urban design practitioners and policymakers (Schaur, 1991). Adequate solutions to the substandard living conditions in non-planned settlements require a deep understanding of their initial formation and expansion processes and appropriate tools to evaluate potential policies or interventions (Patel et al., 2018). ...
... In these simulations, decision-makers are represented as agents capable of responding to their environment and taking autonomous action. For instance, Patel et al. (2018) suggested a model that integrates agent-based modelling with Geographical Information System (GIS) to provide a platform for studying the emergence of slums. The authors argued that this model is helpful for testing slum policies before applying them in real-life settings. ...
This study presents generative adversarial networks (GANs), a machine-learning technique that can be used as an urban design tool capable of learning and reproducing complex patterns that express the unique spatial qualities of non-planned settlements. We report preliminary experimental results of training and testing GAN models on different datasets of urban patterns. The results reveal that machine learning models can generate development alternatives with high morphological resemblance to the original urban fabric based on the suggested training process. This study contributes a methodological framework that has the potential to generate development alternatives sensitive to the local practices, thereby promoting preservation of traditional knowledge and cultural sustainability.
... The SIMPLAN (SIMplified PLANning) modelling suite has been developed (Adhvaryu and Echenique, 2012) for the case study city of Ahmedabad, India to test alternative urban planning policies (combinations for land use and transport) for the year 2021 Adhvaryu (2010). In another study, Patel et al. (2018) developed a geosimulation model integrating agent-based modeling (ABM) and geographic information system (GIS) to explore slums and they calibrate and validate the model using data from Ahmedabad, where 41% of its population lives in slums. But this ABM suffers from validation issues that CA-ANN does not. ...
The urban sprawl is one of the potential threat for sustainable development. The present study has been carried out on Ahmedabad Municipal Corporation (AMC) area including Ahmedabad city to monitor the urban sprawl from 1976 to 2017. Normalized Difference Vegetation Index, Normalized Difference Built-Up Index, and Built-Up Index are applied to monitor the urban land use change by using multi-temporal and multi-date remote sensing satellite data from Landsat. The future scenario of urban land use prediction of AMC has been modelled by the use of cellular automata (CA) and artificial neural network (ANN) with GIS techniques. The results of land use monitoring indicate that the built-up area has increased by 156.93 km2 whereas, agricultural land, open spaces, urban vegetation, and water bodies have decreased during the last 4-decades from 1976 to 2017. Moreover, the results of CA simulation reveals that the built-up will continue to increase in the future at the cost of other classes. The decadal built-up growth was 2.58 km2 during the earlier period and it was the highest (5.98 km2) during the decade of 2007–2017. The validation of results related to CA–ANN has been measured by the Kappa coefficient in which the obtained values of Kappa local, Kappa histogram and Kappa overall were 0.92, 0.75, and 0.69 respectively and the measured percentage of correctness was 78.63%. The predicted urban land use growth reveals that the built-up would cover the maximum part of the AMC area by 2027 provided that the present land use trend, demographic growth, and commercial development does not show any major change. Therefore, the study pertaining to LULC change and its future prediction will be highly helpful for urban planners and administrators to prepare a sustainable city planning for the Ahmedabad city and its surroundings.
... Agents were used to represent individual residents in cities and rule-based modeling of their interactions was developed [13,21]. More recent urban ABMs focus on specific aspects of cities in greater details, such as residential areas [23], and transport [17,12,18]. MAT-SIM [16] and UrbanSim [30] are ABM-based software packages to simulate long-term trends in a city related to traffic management, electricity/water usage and gas emissions etc, for use in multi-dimensional policy planning. ...
During the Covid-19 pandemic, most governments across the world imposed policies like lock-down of public spaces and restrictions on people's movements to minimize the spread of the virus through physical contact. However, such policies have grave social and economic costs, and so it is important to pre-assess their impacts. In this work we aim to visualize the dynamics of the pandemic in a city under different intervention policies, by simulating the behavior of the residents. We develop a very detailed agent-based model for a city, including its residents, physical and social spaces like homes, marketplaces, workplaces, schools/colleges etc. We parameterize our model for Kolkata city in India using ward-level demographic and civic data. We demonstrate that under appropriate choice of parameters, our model is able to reproduce the observed dynamics of the Covid-19 pandemic in Kolkata, and also indicate the counter-factual outcomes of alternative intervention policies.
... The flexibility that the agent-based modeling approach provides has allowed such models to be used in a diverse set of applications. These range from archeology (Axtell et al. 2002), agriculture (Hailegiorgis et al. 2018), basketball (Oldham and Crooks 2019), crime (Malleson et al. 2013), diseases (Perez and Dragicevic 2009), disasters (Jumadi et al. 2018), invasive species (Anderson and Dragićević 2018), to urban growth (Xie and Yang 2011), housing markets (Geanakoplos et al. 2012), gentrification (Jackson et al. 2008), slum formation (Patel et al. 2018), and traffic (Manley and Cheng 2018). So, while agent-based modelers have been utilizing geographical data in their models, what has changed is the growth of data and ways of integrating such data within models (which will be discussed more in Sect. ...
Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.
... informal-, spontaneous-and squatter settlements, slums (UN-Habitat, 2015a), ethnically segregated enclaves (Wacquant & Howe, 2008), social succession processes (Hoffmeyer-Zlotnik, 1976), ghettos (Stehle, 2006), and other manifestations of urban poverty. Especially these urban areas related to poverty are presumed to be highly dynamic in relation to other parts of the urban society Patel et al., 2018). ...
... However, we are able to proof some general propositions: As mentioned in Section 1, there is a serious lack of affordable housing in cities due to the massive urban transformation (De Soto, 2000;Woetzel et al., 2014). Thus, urban poor areas are subject to high dynamics (Kuffer et al., 2016, b;Patel et al., 2018). On a global perspective the 'World Atlas of Slum Evolution' (UN-Habitat, 2015b) basically illustrates a substantial transformation of urban slum population of two decades. ...
The urban environment is in constant motion, mostly through construction but also through destruction of urban elements. While formal development is a process with long planning periods and thus the built landscape appears static, informal or spontaneous settlements seem to be subject to high dynamics in their ever unfinished urban form. However, the dynamics and morphological characteristics of physical transformation in such settlements of urban poverty have been hardly empirically studied on a global scale or temporal consistent foundation. This paper aims at filling this gap by using Earth observation data to provide a temporal analysis of built-up transformation over a period of ~7 years in 16 documented manifestations of urban poverty. This work applies visual image interpretation using very high resolution optical satellite data in combination with in-situ- and Google Street View images to derive 3D city models. We measure physical spatial structures through six spatial morphologic variables - number of buildings, size, height, orientation, heterogeneity and density. Our temporal assessment reveals inter- as well intra-urban differences and we find different, yet generally high morphologic dynamic across study sites. This is expressed in manifold ways: from demolished and reconstructed areas to such where changes appeared within the given structures. Geographically, we find advanced dynamics among our sample specifically in areas of the global south. At the same time, we observe a high spatial variability of morphological transformations within the studied areas. Despite partly high morphologic dynamics, spatial patterns of building alignments, streets and open spaces remain predominantly constant.
... Figure 4 depicts the bottom up approach forcz slum formation modeling in Ahemedabath India which are highly dynamic in space and time with Net Logo. (Patel et al., 2018). The ABM application is example of where ABM can be utilized in wider contexts, for a long time. ...
After 30 years long ethnic crisis, Sri Lanka was recognized as a place for development due to its inherent potentials as an island nation. Currently, Colombo and main city centers are experiencing the development boom while the world has recognized countries' potentials of development in different ways. At the same time, the country is investigating the plausibility of adopting smart urban planning solutions. Since Urban Planning is a broader discipline, narrowing down to physical urban planning factors and process of the same is complex in its nature. It could be dealt with only rationalize thinking and some general knowledge and scientific aspects of planning. World is yearning for fast and smart urbanization. Thus urban planning paradigm is trying to link with other supportive paradigms such as technology, software, social science, etc. With that, new planning techniques and technologies are being invented and currently used to an affective urban planning process. As such Space Syntax, Neural network, Cellular Automata (CA) model and Agent Based Model (ABM) play a major role in that aspect. Since 1970 ABM has been used for urban planning process in multifarious stages and for multitude of purposes. This paper mainly concentrates on the ABM as an urban planning tool and its role in the urban planning process in the Sri Lanka. Particularly to uplift the planning process and as tool of fulfilling pitfall of the process.
