Linking SLEUTH Urban Growth Modeling to Multi Criteria Evaluation for a Dynamic Allocation of Sites to Landfill

Abstract. Taking timely measures for management of the natural resources requires knowledge of the dynamic environment and land use practices in the rapidly changing post-industrial world. We used the SLUETH urban growth modeling and a multi-criteria evaluation (MCE) technique to predict and allocate land available to landfill as affected by the dynamics of the urban growth. The city is Gorgan, the capital of the Golestan Province of Iran. Landsat TM and ETM+ data were used to derive past changes that had occurred in the city extent. Then we employed slope, exclusion zones, urban areas, transportation network and hillshade layer of the study area in the SLEUTH modeling method to predict town sprawl up to the year 2050. We applied weighted linear combination technique of the MCE to define areas suitable for landfill. Linking the results from the two modeling methods yielded necessary information on the available land and the corresponding location for landfill given two different scenarios of town expansion up to the year 2050. These included two scenarios for city expansion and three scenarios for waste disposal. The study proved the applicability of the modeling methods and the feasibility of linking their results. Also, we showed the usefulness of the approach to decision makers in proactively taking measures in managing the likely environment change and possibly directing it towards more sustainable outcomes. This also provided a basis for dynamic land use allocation with regards to the past, present and likely future changes

Application of SLEUTH Model to Predict Urbanization Along the Emilia-Romagna Coast (Italy): Considerations and Lessons Learned

Coastal zone of Emilia-Romagna region, Italy, has been significantly urbanized during the last decades, as a result of a tourism development. This was the main motivation to estimate future trajectories of urban growth in the area. Cellular automata (CA)-based SLEUTH model was applied for this purpose, by using quality geographical dataset combined with relevant information on environmental management policy. Three different scenarios of urban growth were employed: sprawled growth scenario, compact growth scenario and a scenario with business-as-usual pattern of development. The results showed the maximum increase in urbanization in the area would occur if urban areas continue to grow according to compact growth scenario, while minimum was observed in case of more sprawled-like type of growth. This research goes beyond the domain of the study site, providing future users of SLEUTH detailed discussion on considerations that need to be taken into account in its applicatio

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

Combining evolutionary algorithms and agent-based simulation for the development of urbanisation policies

Urban-planning authorities continually face the problem of optimising the allocation of green space over time in developing urban environments. To help in these decision-making processes, this thesis provides an empirical study of using evolutionary approaches to solve sequential decision making problems under uncertainty in stochastic environments. To achieve this goal, this work is underpinned by developing a theoretical framework based on the economic model of Alonso and the associated methodology for modelling spatial and temporal urban growth, in order to better understand the complexity inherent in this kind of system and to generate and improve relevant knowledge for the urban planning community. The model was hybridised with cellular automata and agent-based model and extended to encompass green space planning based on urban cost and satisfaction. Monte Carlo sampling techniques and the use of the urban model as a surrogate tool were the two main elements investigated and applied to overcome the noise and uncertainty derived from dealing with future trends and expectations. Once the evolutionary algorithms were equipped with these mechanisms, the problem under consideration was defined and characterised as a type of adaptive submodular. Afterwards, the performance of a non-adaptive evolutionary approach with a random search and a very smart greedy algorithm was compared and in which way the complexity that is linked with the configuration of the problem modifies the performance of both algorithms was analysed. Later on, the application of very distinct frameworks incorporating evolutionary algorithm approaches for this problem was explored: (i) an ‘offline’ approach, in which a candidate solution encodes a complete set of decisions, which is then evaluated by full simulation, and (ii) an ‘online’ approach which involves a sequential series of optimizations, each making only a single decision, and starting its simulations from the endpoint of the previous run

Micro-simulation urban land use change modelling : the case of Ladprao, Bankok, Thailand

This thesis focuses on modelling the spatial pattern of urban growth of Ladprao, a district of Bangkok, Thailand. The first part of the thesis reviews the urban growth and land use change problems in Bangkok as well as the current role of urban planning and its limitations, in order to provide the context of this study. A GIS-based cellular automata (CA) model has been developed, where the multinomial logistic regression (MNL) and multicriteria decision analysis (MCDA) methods have been integrated to identify the potential cells for development. Customized tools have been developed using a VBA macro within the ARCGIS environment to facilitate the implementation of urban simulation. The developed model has been applied to replicate the spatial pattern at the detail of the district level, focusing on the change of land from vacant to residential, commercial, and industrial during the period 1993 - 2001. Validation of the model has been undertaken through the comparison between the 2001 simulated and actual land use maps. The simulation was unsuccessful in reproducing the actual growth. In terms of the spatial agreement, the overall accuracy was about 30% (31.59% and 32.01% with MNL and MCDA respectively). In terms of urban morphology, the results showed the emergence of urban development in a space-filling pattern. Urban growth over discrete time-steps acted as a process of building accretion, appearing as a growing cluster around the existing development. In the actual pattern, the emergence of development was dispersed over the study area. The unexpected, but interesting, results of this observation have led to the conclusion of the three possible reasons; the inappropriateness of the CA approach to simulate the pattern of urban district level growth, the inability to include all significant development factors of the study site, and finally the distinctive characteristics of Ladprao and Bangkok area itself. Though the results are unpromising, the developed model can be considered as the first in the Bangkok area that attempts to be used as a spatial micro simulation tool operated at the district level. Future research work, if data permits, also suggests adding more development factors, adapting the agent-based modelling to the application, and extending the simulation to the growth of other areas of Bangkok both in the district and city level in order to help improve the understanding of Bangkok's growth.EThOS - Electronic Theses Online ServiceRoyal Thai GovernmentGBUnited Kingdo

Integrated Environmental Modelling Framework for Cumulative Effects Assessment

Global warming and population growth have resulted in an increase in the intensity of natural and anthropogenic stressors. Investigating the complex nature of environmental problems requires the integration of different environmental processes across major components of the environment, including water, climate, ecology, air, and land. Cumulative effects assessment (CEA) not only includes analyzing and modeling environmental changes, but also supports planning alternatives that promote environmental monitoring and management. Disjointed and narrowly focused environmental management approaches have proved dissatisfactory. The adoption of integrated modelling approaches has sparked interests in the development of frameworks which may be used to investigate the processes of individual environmental component and the ways they interact with each other. Integrated modelling systems and frameworks are often the only way to take into account the important environmental processes and interactions, relevant spatial and temporal scales, and feedback mechanisms of complex systems for CEA. This book examines the ways in which interactions and relationships between environmental components are understood, paying special attention to climate, land, water quantity and quality, and both anthropogenic and natural stressors. It reviews modelling approaches for each component and reviews existing integrated modelling systems for CEA. Finally, it proposes an integrated modelling framework and provides perspectives on future research avenues for cumulative effects assessment

A Multi-Scale Flexible Framework for Urban Modelling

Ph. D. ThesisThe configuration of urban areas, and of infrastructures which serve them is central to managing the urbanisation process. Integrated assessment frameworks aim to inform decisions regarding planning, policy, and design to coordinate projects across sectors. Development of such models poses a number of challenges; (i) scenario generation, (ii) intelligibility to stakeholders, (iii) validity, (iv) control and feedback, (v) execution time, (vi) data requirements, (vii) uncertainties and, (viii) flexibility/reusability. This research has developed a multi-scale flexible framework which disaggregates projected regional employment to ward-level population, and further to rasterised development. This comprises; (i) transport network generalised cost, (ii) cost composition, (iii) spatial interaction incorporating transport accessibility, (iv) development zoning, (v) multi-criteria evaluation of development suitability, and (vi) cellular development. The framework is generically implemented, each model being specified in terms of inputs, outputs, and parameters. Modellinkage is via input/output chaining, providing the opportunity to experiment with alternative solutions. Execution is flexible/configurable to perform multiple model runs whilst varying parameters and propagating metadata through stages. Python controls execution flow, C++ provides performance, PostgreSQL manages data, and QGIS assists input/output. The framework is deployed in baseline scenarios for London and Innsbruck, and in more detailed scenario/uncertainty exploration for London. The framework’s utility is judged by criteria corresponding to the above challenges and is found to be favourable, with performance, flexibility and uncertainty support as key attributes. The framework executes models for London in ~52 seconds on modest hardware (1.6GHz, 8GB). This involves costweighted Dijkstra - 4 transport networks (~42s), cost composition and accessibility conversion (~4s), spatial interaction - 633 wards (~2s), rasterised 4-hectare development zones (~1s), 7 criteria development suitability evaluation (~1s), and cellular development - 100m scale (~2s). Combinatorial uncertainties are accommodated by a flexible, modular structure which promotes reuse, and records run configuration as well as model parameters in chained metadat

Recent Progress in Urbanisation Dynamics Research

This book is dedicated to urbanization, which is observed every day, as well as the methods and techniques of monitoring and analyzing this phenomenon. In the 21st century, urbanization has gained momentum, and the awareness of the significance and influence of this phenomenon on our lives make us take a closer look at it not only with curiosity, but also great attention. There are numerous reasons for this, among which the economy is of special significance, but it also has many results, namely, economic, social, and environmental. First of all, it is a spatial phenomenon, as all of the aspects can be placed in space. We would therefore like to draw special attention to the results of urbanization seen on the Earth's surface and in the surrounding space. The urbanization–land relation seems obvious, but is also interesting and multi-layered. The development of science and technology provides a lot of new tools for observing urbanization, as well as the analyses and inference of the phenomenon in space. This book is devoted to in-depth analysis of past, present and future urbanization processes all over the world. We present the latest trends of research that use experience in the widely understood geography of the area. This book is focused on multidisciplinary phenomenon, i.e., urbanization, with the use of the satellite and photogrammetric observation technologies and GIS analyses