An optimised cellular automata model based on adaptive genetic algorithm for urban growth simulation

This paper presents an improved cellular automata (CA) model optimized using an adaptive genetic algorithm (AGA) to simulate the spatiooral process of urban growth. The AGA technique can be used to optimize the transition rules of the CA model defined through conventional methods such as logistic regression approach, resulting in higher simulation efficiency and improved results. Application of the AGA-CA model in Shanghai's Jiading District, Eastern China demonstrates that the model was able to generate reasonable representation of urban growth even with limited input data in defining its transition rules. The research shows that AGA technique can be integrated within a conventional CA based urban simulation model to improve human understanding on urban dynamics

Residential equilibrium in a multifractal metropolitan area

A residential location model derived from urban economics is combined with the geometry of a multifractal Sierpinski carpet to represent and model a metropolitan area. This area is made up of a system of built-up patches hierarchically organised around a city centre, and green areas arranged in an inverse hierarchical order (large open-spaces in the periphery). An analytical solution is obtained by using a specific geographic coding system for computing distances. The values of the parameters used in the model are based on the French medium sized metropolitan areas; a realistic benchmark is proposed and comparative-statics simulations are performed. The results show that the French peri-urbanisation process (which took place from 1970 onward) can be explained by an increase in income and a reduction in transport costs. Nevertheless, changes in household preferences, in particular an increased taste for open spaces, can also contribute to urban sprawl by making the gradient of land rents less steep and by making peripheral household locations more desirableperi-urban, residential localisation, fractal geometry, amenities

Simulating Land Use Land Cover Change Using Data Mining and Machine Learning Algorithms

The objectives of this dissertation are to: (1) review the breadth and depth of land use land cover (LUCC) issues that are being addressed by the land change science community by discussing how an existing model, Purdue\u27s Land Transformation Model (LTM), has been used to better understand these very important issues; (2) summarize the current state-of-the-art in LUCC modeling in an attempt to provide a context for the advances in LUCC modeling presented here; (3) use a variety of statistical, data mining and machine learning algorithms to model single LUCC transitions in diverse regions of the world (e.g. United States and Africa) in order to determine which tools are most effective in modeling common LUCC patterns that are nonlinear; (4) develop new techniques for modeling multiple class (MC) transitions at the same time using existing LUCC models as these models are rare and in great demand; (5) reconfigure the existing LTM for urban growth boundary (UGB) simulation because UGB modeling has been ignored by the LUCC modeling community, and (6) compare two rule based models for urban growth boundary simulation for use in UGB land use planning. The review of LTM applications during the last decade indicates that a model like the LTM has addressed a majority of land change science issues although it has not explicitly been used to study terrestrial biodiversity issues. The review of the existing LUCC models indicates that there is no unique typology to differentiate between LUCC model structures and no models exist for UGB. Simulations designed to compare multiple models show that ANN-based LTM results are similar to Multivariate Adaptive Regression Spline (MARS)-based models and both ANN and MARS-based models outperform Classification and Regression Tree (CART)-based models for modeling single LULC transition; however, for modeling MC, an ANN-based LTM-MC is similar in goodness of fit to CART and both models outperform MARS in different regions of the world. In simulations across three regions (two in United States and one in Africa), the LTM had better goodness of fit measures while the outcome of CART and MARS were more interpretable and understandable than the ANN-based LTM. Modeling MC LUCC require the examination of several class separation rules and is thus more complicated than single LULC transition modeling; more research is clearly needed in this area. One of the greatest challenges identified with MC modeling is evaluating error distributions and map accuracies for multiple classes. A modified ANN-based LTM and a simple rule based UGBM outperformed a null model in all cardinal directions. For UGBM model to be useful for planning, other factors need to be considered including a separate routine that would determine urban quantity over time

Linking Climate Change and Socio-economic Impact for Long-term Urban Growth in Three Mega-cities

Urbanization has become a global trend under the impact of population growth, socio-economic development, and globalization. However, the interactions between climate change and urban growth in the context of economic geography are unclear due to missing links in between the recent planning megacities. This study aims to conduct a multi-temporal change analysis of land use and land cover in New York City, City of London, and Beijing using a cellular automata-based Markov chain model collaborating with fuzzy set theory and multi-criteria evaluation to predict the city\u27s future land use changes for 2030 and 2050 under the background of climate change. To determine future natural forcing impacts on land use in these megacities, the study highlighted the need for integrating spatiotemporal modeling analyses, such as Statistical Downscale Modeling (SDSM) driven by climate change, and geospatial intelligence techniques, such as remote sensing and geographical information system, in support of urban growth assessment. These SDSM findings along with current land use policies and socio-economic impact were included as either factors or constraints in a cellular automata-based Markov Chain model to simulate and predict land use changes in megacities for 2030 and 2050. Urban expansion is expected in these megacities given the assumption of stationarity in urban growth process, although climate change impacts the land use changes and management. More land use protection should be addressed in order to alleviate the impact of climate change

Modeling urban growth and land use/land cover change in the Houston Metropolitan Area from 2002 - 2030

The Houston-Galveston-Brazoria Consolidated Metropolitan Statistical Area (Houston CMSA) has experienced rapid population growth during the past decades and is the only major US metropolitan area with no zoning regulations. We use SLEUTH, a spatially explicit cellular automata model, to simulate future (2002-2030) urban growth in the Houston metropolitan area, one of the fastest growing metropolises in the United States during the past decades. The model is calibrated with historical data for the period 1974-2002 that are extracted from a time series of satellite images. The dataset consists of four historical urban extents (1974, 1984, 1992, 2002), two land use layers (1992, 2002), five transportation layers (1974, 1984, 1990, 2002, 2025), slope layer, hillshade layer, and excluded layer. Future growth patterns are predicted based on growth coefficients derived during the calibration phase. After calibrating the model successfully, the spatial pattern of urban growth of the Houston CMSA for the period from 2002 to 2030 is predicted. Within SLEUTH, growth in the Houston CMSA is predominately "organic" with most growth occurring along the urban/rural fringe. Projected increases in urban area from 2002 to 2030 parallel projected increases in population growth within the Houston CMSA. We design three specific scenarios to simulate the spatial consequences of urban growth under different environmental conditions. The first scenario is to simulate the unmanaged growth with no restrictions. The second scenario is to project the moderate growth trend by taking into consideration environmental protection, specifically for agricultural areas, forests and wetlands. The last scenario is to simulate the managed growth with maximum environmental protection. Adjusting the level of protection for different land cover types was found to markedly affect the land use changes in the Houston CMSA. Without any protection on resource lands, Houston CMSA is estimated to lose 2,000 km2 of forest land by 2030, about 600 km2 of agricultural land, and approximately 400 km2 of wetland. Approximately half of all resource land could be saved by the third scenario, managed growth with maximum protection

Modelling Urban Growth: Towards an Agent Based Microeconomic Approach to Urban Dynamics and Spatial Policy Simulation

Urban growth, urban sprawl if uncoordinated and dispersed, can be considered one of the most important policy agendas in modern urban regions. While no single policy option or remedy exists, understanding the urban growth system is the first step towards sustainable urban growth futures. Spatially explicit and dynamic urban growth models provide valuable simulations that encapsulate essential knowledge in planning and policy making such as how and where urban growth can occur and what the driving forces of such changes are. Over the past two decades, cellular automata (CA) models have proven to be an effective modelling approach to the study of complex urban growth systems. More recently Agent Based Modelling (ABM) has developed to yield a useful framework for understanding complex urban systems and this provides an arena for exploring the possible outcome states of various policy actions. Yet most research efforts of this sort adopt physical and heuristic approaches which tend to neglect socio-economic dynamics which is critical in shaping urban form and its transformation. This thesis aims to develop an agent based urban simulation model which has a more rigid theoretical explanation of agent behaviour than most such models hitherto. However, before developing such an agent based model, this study first conducted a series of experimental simulations with two well-known generic CA based urban models, SLEUTH and Metronamica, in order to better understand the complexity of designing and applying this class of urban models. Although CA and ABM are two distinctive modelling approaches, they share certain fundamentals concerning the complexity of systems and thus the empirical simulations with widely used CA models provide useful insights for the development of a new dedicated agent based urban growth model. For this purpose, each CA model is calibrated to the study area of the Seoul Metropolitan Area, Korea. The research then moves towards developing an agent based model based on microeconomic foundations. Utility maximising residential location choices made by households are modelled as the main impetus for urban growth through agglomeration and sprawl. Furthermore, based on such urban dynamics, alternative planning policy options such as greenbelts and public transportation are simulated so that their impacts can be clarified and assessed. In this way, the model is also able to examine how planning policies alter the economic utility of households and redirect market-led urban development. These results confirm the unique value of such modelling approaches. Yet, new research challenges such as the estimation of model parameters and the use of such models in planning support continue to dominate this field and in conclusion, we identify future research directions which build on these challenge

Understanding Urban Mobility and Pedestrian Movement

Urban environments continue to expand and mutate, both in terms of size of urban area and number of people commuting daily as well as the number of options for personal mobility. City layouts and infrastructure also change constantly, subject to both short-term and long-term imperatives. Transportation networks have attracted particular attention in recent years, due to efforts to incorporate “green” options, enabling positive lifestyle choices such as walking or cycling commutes. In this chapter we explore the pedestrian viewpoint, aids to familiarity with and ease of navigation in the urban environment, and the impact of novel modes of individual transport (as options such as smart urban bicycles and electric scooters increasingly become the norm). We discuss principal factors influencing rapid transit to daily and leisure destinations, such as schools, offices, parks, and entertainment venues, but also those which facilitate rapid evacuation and movement of large crowds from these locations, characterized by high occupation density or throughput. The focus of the chapter is on understanding and representing pedestrian behavior through the agent-based modeling paradigm, allowing both large numbers of individual actions with active awareness of the environment to be simulated and pedestrian group movements to be modeled on real urban networks, together with congestion and evacuation pattern visualization

An agent-based simulation model for business reopenings in New Orleans post Hurricane Katrina

The empirical study of business responses to disasters is relatively scarce, despite that they are a fundamental part of the cities, providing services, jobs and, taxes that are essential for urban sustainability. This dissertation develops an agent-based simulation model to represent and understand the businesses reopening process in a dynamic environment in New Orleans after Hurricane Katrina. The objectives are two-fold: 1) To identify the main reopening predictors involved and estimate their relative importance through time, using an empirical data set collected from another study; 2) To represent the business reopening process through a computer simulation model, using the parameters derived from the first objective. The results show that businesses located in flooded areas had lower reopening probabilities, however the effect was significant only in the first nine months after the disaster. Larger businesses had better reopening probabilities than smaller ones, although this variable stopped being significant after six months. Variables associated with higher social vulnerability, such as percent non-white population and percent population under 18, had a negative effect on the business reopening probabilities at different points of time. The influence of neighboring firms using 1-km buffer was found significantly positive only immediately after the disaster; it became significantly negative one year after the disaster. The simulation model developed proved to mimic the reopening process at a suitable level. The model was used to simulate two scenarios: 1) First, the flood depth was reduced by 1 meter as a way to represent the implementation of measures designed to increase the buildings and infrastructure resistance to floods. The simulation results indicate that there are specific areas that would obtain greater benefit from these measures, however ten months after the disaster the effect of the measures tends to diminish. 2) Second, the spatial effects of aids were simulated by making a limited number of businesses in specific locations totally resilient to the disaster. The results indicate that the beneficial effect is influenced by variables such as business density and socio-economic conditions of the area. The positive effect is perceivable until four months after the disaster, after this point it diminishes

Land Change Science and the STEPLand Framework : An Assessment of Its Progress

This contribution assesses a new term that is proposed to be established within Land Change Science: Spatio-TEmporal Patterns of Land ('STEPLand'). It refers to a specific workflow for analyzing land-use/land cover (LUC) patterns, identifying and modeling driving forces of LUC changes, assessing socio-environmental consequences, and contributing to defining future scenarios of land transformations. In this article, we define this framework based on a comprehensive meta-analysis of 250 selected articles published in international scientific journals from 2000 to 2019. The empirical results demonstrate that STEPLand is a consolidated protocol applied globally, and the large diversity of journals, disciplines, and countries involved shows that it is becoming ubiquitous. In this paper, the main characteristics of STEPLand are provided and discussed, demonstrating that the operational procedure can facilitate the interaction among researchers from different fields, and communication between researchers and policy makers