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Urban landscape change simulation has received increasing attention in recent times. As a result, many studies have focused on various aspects of land change simulation, ranging from uncertainty of input data to model accuracy. While efforts have been put into improving many of the existing urban land change models and developing new ones, not so much has been done in understanding the significance of methods of classifying the satellite images often used as input maps in many of these models. In addition, few studies have been done to assess the impact of modeled landscapes on surrounding natural ecosystems such as urban wetlands, which have served as sensitive indicator of both human impacts and climate variation. In this study, the aim was to simulate the change in Kansas City landscape, and to assess the impact of the change on wetlands at various spatial scales within the study area. To achieve this, the study was divided into three parts. The first part examined how significant the impacts of classification methods of input land cover maps are on the overall accuracy of the urban land change prediction used in this study. This was done by classifying the historical SPOT satellite images of Kansas City using multi-layer perceptron neural network and maximum likelihood classification techniques. The impact of these two classification methods on the overall accuracy of land change prediction was assessed. The study made use of the classified map of a known year and other historical high-resolution satellite data of the study area from Google Earth to validate results from both predictions. The result from this first part shows that the methods selected in classifying satellite images often used as input in many land change models can significantly affect land change prediction. In the second and third parts, two model methods (Similarity Weighted Instance-based Machine Learning - SimWeight and Multi-layer Perceptron Neural Network - MLP) were compared to determine which is most appropriate for use in this study. To achieve this, the study utilized an integrated approach that combined Similarity Weighted Instance-based Machine Learning and Markov model in one approach and Multi-layer Perceptron Neural Network and Markov chain in a second approach. These two methods were used in simulating the landscape change of three major watersheds in the Kansas City area into a known year. The model that performed best was used in simulating into the future and the impact of change was assessed on wetlands at different scales. In order to achieve this, classified SPOT satellite data covering the three major watersheds were used to generate the historical land cover data series between 1992 and 2010. In addition, the study identified several land change variables associated with the historical change process in the study area. These variables together with the result of the historical land change between 1992 and 2010 were used in modeling urban landscape transformation into an end date of 2014 for both model methods assessed. A Markov model was applied to perform these predictions. The prediction results were verified with a more accurate map that was derived from independently classifying a 2014 SPOT image of the study area. Accuracy assessments for the 2014 predicted maps and the independently classified map of 2014 were compared. Based on a higher accuracy result obtained for the SimWeight approach, prediction into an end date of 2028 was made. The historical impact of human-induced landscape change on wetlands between 1992 & 2010 and the potential impact by 2028 were assessed for the study area. This integrated modeling approach in combination with land change driving variables provided valuable insights about how the landscape of the three major watersheds may develop in the future, and how this development may affect urban wetlands in the study area.
This volume introduces an innovative tool for the development of sustainable cities and the promotion of the quality of life of city inhabitants. It presents a decision-support system to orient public administrations in identifying development scenarios for sustainable urban and territorial transformations. The authors have split the volume into five parts, which respectively describe the theoretical basis of the book, the policies in question and indicators that influence them, the decision-support system that connects indicators to policies, the case study of Ancona, Italy, and potential future directions for this work. This volume is based on transdisciplinary research completed in May 2016 that involved about 40 researchers at The University of Camerino, Italy and other European universities. With purchase of this book, readers will also have access to Electronic Supplementary Material that contains a database with groups of indicators of assessment of urban quality of life and a toolkit containing the data processing system and management information system used in the book’s case study.
This monograph presents urban simulation methods that help in better understanding urban dynamics. Over historical times, cities have progressively absorbed a larger part of human population and will concentrate three quarters of humankind before the end of the century. This “urban transition” that has totally transformed the way we inhabit the planet is globally understood in its socio-economic rationales but is less frequently questioned as a spatio-temporal process. However, the cities, because they are intrinsically linked in a game of competition for resources and development, self organize in “systems of cities” where their future becomes more and more interdependent. The high frequency and intensity of interactions between cities explain that urban systems all over the world exhibit large similarities in their hierarchical and functional structure and rather regular dynamics. They are complex systems whose emergence, structure and further evolution are widely governed by the multiple kinds of interaction that link the various actors and institutions investing in cities their efforts, capital, knowledge and intelligence. Simulation models that reconstruct this dynamics may help in better understanding it and exploring future plausible evolutions of urban systems. This would provide better insight about how societies can manage the ecological transition at local, regional and global scales. The author has developed a series of instruments that greatly improve the techniques of validation for such models of social sciences that can be submitted to many applications in a variety of geographical situations. Examples are given for several BRICS countries, Europe and United States. The target audience primarily comprises research experts in the field of urban dynamics, but the book may also be beneficial for graduate students.
How do cities transform over time? And why do some cities change for the better while others deteriorate? In articulating new ways of viewing urban areas and how they develop over time, Peter Bosselmann offers a stimulating guidebook for students and professionals engaged in urban design, planning, and architecture. By looking through Bosselmann’s eyes (aided by his analysis of numerous color photos and illustrations) readers will learn to “see” cities anew. Bosselmann organizes the book around seven “activities”: comparing, observing, transforming, measuring, defining, modeling, and interpreting. He introduces readers to his way of seeing by comparing satellite-produced “maps” of the world’s twenty largest cities. With Bosselmann’s guidance, we begin to understand the key elements of urban design. Using Copenhagen, Denmark, as an example, he teaches us to observe without prejudice or bias. He demonstrates how cities transform by introducing the idea of “urban morphology” through an examination of more than a century of transformations in downtown Oakland, California. We learn how to measure quality-of-life parameters that are often considered immeasurable, including “vitality,” “livability,” and “belonging.” Utilizing the street grids of San Francisco as examples, Bosselmann explains how to define urban spaces. Modeling, he reveals, is not so much about creating models as it is about bringing others into public, democratic discussions. Finally, we find out how to interpret essential aspects of “life and place” by evaluating aerial images of the San Francisco Bay Area taken in 1962 and those taken forty-three years later. Bosselmann has a unique understanding of cities and how they “work.” His hope is that, with the fresh vision he offers, readers will be empowered to offer inventive new solutions to familiar urban problems.
This book is thematically positioned at the intersections of Urban Design, Architecture, Civil Engineering and Computer Science, and it has the goal to provide specialists coming from respective fields a multi-angle overview of state-of-the-art work currently being carried out. It addresses both newcomers who wish to obtain more knowledge about this growing area of interest, as well as established researchers and practitioners who want to keep up to date. In terms of organization, the volume starts out with chapters looking at the domain at a wide-angle and then moves focus towards technical viewpoints and approaches.
This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity.
Currently, spatial analysis is becoming more important than ever because enormous volumes of spatial data are available from different sources, such as GPS, Remote Sensing, and others. This book deals with spatial analysis and modelling. It provides a comprehensive discussion of spatial analysis, methods, and approaches related to human settlements and associated environment. Key contributions with empirical case studies from Iran, Philippines, Vietnam, Thailand, Nepal, and Japan that apply spatial analysis including autocorrelation, fuzzy, voronoi, cellular automata, analytic hierarchy process, artificial neural network, spatial metrics, spatial statistics, regression, and remote sensing mapping techniques are compiled comprehensively. The core value of this book is a wide variety of results with state of the art discussion including empirical case studies. It provides a milestone reference to students, researchers, planners, and other practitioners dealing the spatial problems on urban and regional issues. We are pleased to announce that this book has been presented with the 2011 publishing award from the GIS Association of Japan. We would like to congratulate the authors!
This book offers a comprehensive exposition of the mathematical methods that can be used to model landscape dynamics. It is systematically shown how mathematical models of progressively higher complexity can be derived from ordinary landscape maps and related data in ways that enable researchers to predict future landscape transformations and to assess landscape stability, sustainability and resilience.These models are deterministic (i.e. linear or non-linear systems of differential equations), stochastic (i.e. Markovian), or combined deterministic-and-stochastic (using stochastic differential equations), whereas topics and challenging problems related to complexity (spatial randomness, chaotic behaviors, riddled systems etc) are also examined in the book.
World-renowned experts in spatial statistics and spatial econometrics present the latest advances in specification and estimation of spatial econometric models. This includes information on the development of tools and software, and various applications. The text introduces new tests and estimators for spatial regression models, including discrete choice and simultaneous equation models. The performance of techniques is demonstrated through simulation results and a wide array of applications related to economic growth, international trade, knowledge externalities, population-employment dynamics, urban crime, land use, and environmental issues. An exciting new text for academics with a theoretical interest in spatial statistics and econometrics, and for practitioners looking for modern and up-to-date techniques.