Modelling urban spatial change: a review of international and South African modelling initiatives

August 2013Urban growth and land use change models have the potential to become important tools for urban spatial planning and management. Before embarking on any modelling, however, GCRO felt it was important to take note of, and critically assess lessons to be learnt from international experience and scholarship on spatial modelling, as well as a number of South African experiments that model future urban development. In 2012, GCRO initiated preliminary research into current international and South African modelling trends through a desktop study and telephone, email and personal interviews. This Occasional paper sets out to investigate what urban spatial change modelling research is currently being undertaken internationally and within South Africa. At the international level, urban modelling research since 2000 is reviewed according to five main categories: land use transportation (LUT), cellular automata, urban system dynamics, agent-based models (ABMs) and spatial economics/econometric models (SE/EMs). Within South Africa, urban modelling initiatives are categorised differently and include a broader range of urban modelling techniques. Typologies used include: provincial government modelling initiatives in Gauteng; municipal government modelling initiatives; other government-funded modelling research; and academic modelling research. The various modelling initiatives described are by no means a comprehensive review of all urban spatial change modelling projects in South Africa, but provide a broad indication of the types of urban spatial change modelling underway. Importantly, the models may form the basis for more accurate and sophisticated urban modelling projects in the future. The paper concludes by identifying key urban modelling opportunities and challenges for short- to long-term planning in the GCR and South Africa.Written by Chris Wray, Josephine Musango and Kavesha Damon (GCRO) Koech Cheruiyot (NRF:SARChI chair in Development Planning and Modelling at Wits

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

Urban Informatics

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

Urban Informatics

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

Integrating remote sensing and a Markov-FLUS model to simulate future land use changes in Hokkaido, Japan

As the second largest island in Japan, Hokkaido provides precious land resources for the Japanese people. Meanwhile, as the food base of Japan, the gradual decrease of the agricultural population and more intensive agricultural practices on Hokkaido have led its arable land use to change year by year, which has also caused changes to the whole land use pattern of the entire island of Hokkaido. To realize the sustainable use of land resources in Hokkaido, past and future changes in land use patterns must be investigated, and target-based land use planning suggestions should be given on this basis. This study uses remote sensing and GIS technology to analyze the temporal and spatial changes of land use in Hokkaido during the past two decades. The types of land use include cultivated land, forest, waterbody, construction, grassland, and others, by using the satellite images of the Landsat images in 2000, 2010, and 2019 to achieve this goal to make classification. In addition, this study used the coupled Markov-FLUS model to simulate and analyze the land use changes in three different scenarios in Hokkaido in the next 20 years. Scenario-based situational analysis shows that the cultivated land in Hokkaido will drop by about 25% in 2040 under the natural development scenario (ND), while the cultivated land area in Hokkaido will remain basically unchanged in cultivated land protection scenario (CP). In forest protection scenario (FP), the area of forest in Hokkaido will increase by 1580.8 km2. It is believed that the findings reveal that the forest land in Hokkaido has been well protected in the past and will be protected well in the next 20 years. However, in land use planning for future, Hokkaido government and enterprises should pay more attention to the protection of cultivated land.</jats:p

Remote Sensing in Mangroves

The book highlights recent advancements in the mapping and monitoring of mangrove forests using earth observation satellite data. New and historical satellite data and aerial photographs have been used to map the extent, change and bio-physical parameters, such as phenology and biomass. Research was conducted in different parts of the world. Knowledge and understanding gained from this book can be used for the sustainable management of mangrove forests of the worl