An Application of Cellular Automata (CA) and Markov Chain (MC) Model in Urban Growth Prediction: A case of Surat City, Gujarat, India

The main purpose of this study is to detect land use land cover change for 1990-2000, 2000-2010, and 2010-2020 using multispectral Landsat images as well as to simulate and predict urban growth of Surat city using Cellular Automata-based Markov Chain Model. Maximum likelihood supervise classification was used to generate LULC maps of the years 1990,2000,2010, and 2020 and the overall accuracy of these maps were 90%, 95%, 91.25%, and 96.25%, respectively. Two transition rules were commuted to predict the LULC of 2010 and 2020. For validation of these LULC maps, the Area Under Characteristics curve was used, and these maps' accuracy was 95.30% and 86.90%. This validation predicted LULC maps for the years 2035 and 2050. Transition rules of 2010-2035 showed that there will be a probability that 36.33% of vegetation area and 40.27% of the vacant land area will be transited into built-up by the year 2035, and it will be 49.20 % of the total area. Also, 57.77% of the vegetation area and 60.24% of the built-up area will be transformed into urban areas by the year 2050, almost 62.60 %. Analysis of LULC maps 2035 and 2050 exhibits that there will be abundant growth in all directions except the South Zone and Southwest Zone. Therefore, this study helps urban planners and decision-makers decide what to retain, where to plan for new development and type of development, what to connect, and what to protect in coming years

The Economics of Agricultural Land Use Dynamics in Coconut Plantations of Sri Lanka

In this study a spatially explicit economic analysis was employed to determine the land use change in a traditional coconut growing district of Sri Lanka. From a theoretical model of land use, an econometric framework was developed to incorporate spatial and individual effects that would affect the land use decision. Markovian transition probabilities derived from the econometric analysis and spatial analysis was used to predict the land use change over the next 30 years. The results revealed that the fragmentation and conversion of coconut lands to urban continue in the areas close to the urban centre and also with less productive lands. Spatial analysis provides further evidence of the positive trend of conversion of coconut lands to urban uses close to the urban areas.Resource /Energy Economics and Policy,

Doctor of Philosophy

dissertationThis research focuses on the application of geographic information systems (GIS) and spatial analysis methods to urban and regional development studies. GIS-based spatial modeling approaches have recently been used in examining regional development disparities and urban growth. Through the cases of Guangdong province and the city of Dongguan, the study employs a spatial-temporal, multiscale, and multimethodology approach in analyzing geographically referenced socioeconomic and remote sensing data. A general spatial data analysis framework is set through a study of regional development in China's Guangdong province and urban growth in the city of Dongguan. Three intensive spatial statistical analyses are carried out. First, the dissertation investigates the spatial dynamics of regional inequality through Markov chains and spatial Markov-chain analyses. In so doing, it addresses the effect of self-reinforcing agglomeration on regional disparities. Multilevel modeling is further employed to evaluate the relative importance of regional development mechanisms in Guangdong. Second, a spatial filtering perspective is employed for understanding the spatial effects on multiscalar characteristics of regional inequality in Guangdong. Spatial panel and space-time regression models are integrated to detail the spatial and temporal heterogeneity of underlying mechanisms behind regional inequality. Third, drawing upon a set of high-quality remote sensing data in the city of Dongguan, the dissertation analyzes the spatial-temporal dynamics and spatial determinants of urban growth in a rapid industrializing area. Through the application of landscape metrics, three types of urban growth, including infill, spontaneous, and edge expansion, are distinguished, addressing the diverse spatial patterns at different stages of urban growth. A spatial logistic approach is further developed to model the spatial variations of urban growth determinants within the Dongguan city. In short, the dissertation finds that regional inequality in the Guangdong province is sensitive to spatial scales, dependence, and the core-periphery structure therein. The evolution of inequality can hardly be simplified into either convergence or divergence trajectories. Furthermore, development mechanisms and urban growth determinants are apparently different in space and are sensitive to spatial hierarchies and regimes. Overall, through the application of GIS spatial modeling techniques, the dissertation has provided more valuable information about spatial effects on China's urban and regional development under economic transition and highlights the importance of taking into consideration spatial dimensions in urban and regional development studies

On Cells and Agents : Geosimulation of Urban Sprawl in Western Germany by Integrating Spatial and Non-Spatial Dynamics

Urban sprawl is one of the most challenging land-use and land-cover changes in Germany implicating numerous consequences for the anthropogenic and geobiophysical spheres. While the population and job growth rates of most urban areas stagnate or even decrease, the morphological growth of cities is ubiquitous. Against this backdrop, the quantitative and qualitative modeling of urban dynamics proves to be of central importance. Geosimulation models like cellular automata (CA) and multi-agent systems (MAS) treat cities as complex urban systems. While CA focus on their spatial dynamics, MAS are well-suited for capturing autonomous individual decision making. Yet both models are complementary in terms of their focus, status change, mobility, and representations. Hence, the coupling of CA and MAS is a useful way of integrating spatial pattern and non-spatial processes into one modeling infrastructure. The thesis at hand aims at a holistic geosimulation of the future urban sprawl in the Ruhr. This region is particularly challenging as it is characterized by two seemingly antagonistic processes: urban growth and urban shrinkage. Accordingly, a hybrid modeling approach is to be developed as a means of integrating the simulation power of CA and MAS. A modified version of SLEUTH (short for Slope, Land-use, Exclusion, Urban, Transport, and Hillshade) will function as the CA component. SLEUTH makes use of historic urban land-use data sets and growth coefficients for the purpose of modeling physical urban expansion. In order to enhance the simulation performance of the CA and to incorporate important driving forces of urban sprawl, SLEUTH is for the first time combined with support vector machines (SVM). The supported CA will be coupled with ReHoSh (Residential Mobility and the Housing Market of Shrinking City Systems). This MAS models population patterns, housing prices, and housing demand in shrinking regions. All dynamics are based on multiple interactions between different household groups as well as stakeholders of the housing market. Moreover, this thesis will elaborate on the most important driving factors, rates, and most probable locations of urban sprawl in the Ruhr as well as on the future migration tendencies of different household types and the price development in the housing market of a polycentric shrinking region. The results of SLEUTH and ReHoSh are loosely coupled for a spatial analysis in which the municipal differences that have emerged during the simulations are disaggregated. Subsequently, a concept is developed in order to integrate the CA and the MAS into one geosimulation approach. The thesis introduces semi-explicit urban weights as a possibility of assessing settlement-pattern dynamics and the regional housing market dynamics at the same time. The model combination of SLEUTH-SVM and ReHoSh is finally calibrated, validated, and implemented for simulating three different scenarios of individual housing preferences and their effects on the future urban pattern in the Ruhr. Applied to a digital petri dish, the generic urban growth elements of the Ruhr are being detected

An agent-based approach to model farmers' land use cover change intentions

Land Use and Cover Change (LUCC) occurs as a consequence of both natural and human activities, causing impacts on biophysical and agricultural resources. In enlarged urban regions, the major changes are those that occur from agriculture to urban uses. Urban uses compete with rural ones due among others, to population growth and housing demand. This competition and the rapid nature of change can lead to fragmented and scattered land use development generating new challenges, for example, concerning food security, soil and biodiversity preservation, among others. Landowners play a key role in LUCC. In peri-urban contexts, three interrelated key actors are pre-eminent in LUCC complex process: 1) investors or developers, who are waiting to take advantage of urban development to obtain the highest profit margin. They rely on population growth, housing demand and spatial planning strategies; 2) farmers, who are affected by urban development and intend to capitalise on their investment, or farmers who own property for amenity and lifestyle values; 3) and at a broader scale, land use planners/ decision-makers. Farmers’ participation in the real estate market as buyers, sellers or developers and in the land renting market has major implications for LUCC because they have the capacity for financial investment and to control future agricultural land use. Several studies have analysed farmer decision-making processes in peri-urban regions. These studies identified agricultural areas as the most vulnerable to changes, and where farmers are presented with the choice of maintaining their agricultural activities and maximising the production potential of their crops or selling their farmland to land investors. Also, some evaluate the behavioural response of peri-urban farmers to urban development, and income from agricultural production, agritourism, and off-farm employment. Uncertainty about future land profits is a major motivator for decisions to transform farmland into urban development. Thus, LUCC occurs when the value of expected urban development rents exceeds the value of agricultural ones. Some studies have considered two main approaches in analysing farmer decisions: how drivers influence farmer’s decisions; and how their decisions influence LUCC. To analyse farmers’ decisions is to acknowledge the present and future trends and their potential spatial impacts. Simulation models, using cellular automata (CA), artificial neural networks (ANN) or agent-based systems (ABM) are commonly used. This PhD research aims to propose a model to understand the agricultural land-use change in a peri-urban context. We seek to understand how human drivers (e.g., demographic, economic, planning) and biophysical drivers can affect farmer’s intentions regarding the future agricultural land and model those intentions. This study presents an exploratory analysis aimed at understanding the complex dynamics of LUCC based on farmers’ intentions when they are faced with four scenarios with the time horizon of 2025: the A0 scenario – based on current demographic, social and economic trends and investigating what happens if conditions are maintained (BAU); the A1 scenario – based on a regional food security; the A2 scenario – based on climate change; and the B0 scenario – based on farming under urban pressure, and investigating what happens if people start to move to rural areas. These scenarios were selected because of the early urbanisation of the study area, as a consequence of economic, social and demographic development; and because of the interest in preserving and maintaining agriculture as an essential resource. Also, Torres Vedras represents one of the leading suppliers of agricultural goods (mainly fresh fruits, vegetables, and wine) in Portugal. To model LUCC a CA-Markov, an ANN-multilayer perceptron, and an ABM approach were applied. Our results suggest that significant LUCC will occur depending on farmers’ intentions in different scenarios. The highlights are: (1) the highest growth in permanently irrigated land in the A1 scenario; (2) the most significant drop in non-irrigated arable land, and the highest growth in the forest and semi-natural areas in the A2 scenario; and (3) the greatest urban growth was recognised in the B0 scenario. To verify if the fitting simulations performed well, statistical analysis to measure agreement and quantity-allocation disagreements and a participatory workshop with local stakeholders to validate the achieved results were applied. These outcomes could provide decision-makers with the capacity to observe different possible futures in ‘what if’ scenarios, allowing them to anticipate future uncertainties, and consequently allowing them the possibility to choose the more desirable future

Projecting Future Locations for Commercial Wind Energy Development in the Conterminous United States using a Logistic Regression-Cellular Automata Model

Pressures to decarbonize the United States’ electricity production, reduce dependence on foreign energy imports, and the declining levelized cost of renewable electricity is making wind energy an increasingly appealing means of meeting electricity demand in the United States. However, the installation of new commercial wind farms to meet this demand requires knowledge of the most suitable locations for their installation, which depends on a combination of environmental, technical, economic, political, and social characteristics. Wind Farm Site Suitability (WiFSS) models are frequently enlisted to assist in this decision-making process in countries around the world for both onshore and offshore wind farm siting decisions. However, existing WiFSS models serve to assess present-day wind farm siting potential, rather than project specific locations for future wind energy development. Taking cues from Socio-Environmental Systems (SES) models of urban growth, this dissertation presents a Logistic Regression-Cellular Automata (LRCA) model, henceforth referred to as WiFSS-LRCA, conceived to produce maps that identify scenarios of potential future locations and timing of future commercial wind farms across the Conterminous United States (CONUS) between now and the year 2050. Following a review of existing WiFSS modeling approaches, and of common practices by which WiFSS modeling studies select and represent their predictors, the niche that WiFSS-LRCA serves to fill was consequently identified. The majority of WiFSS studies take a Geographic Information Systems-based Multi-Criteria Decision Analysis (GIS-MCDA) approach that combines spatial data layers corresponding to selected predictors to construct a composite suitability surface. Other common approaches include Non-GIS-MCDA models that rank discrete potential wind farm sites to prioritize their order of development, Bayesian Network (BN) models that construct and convey probabilistic relationships between predictors, and Logistic Regression (LR) models that perform either spatial or non-spatial assessment of a wind farm’s suitability of presence based on the log-odds of a linear combination of predictors. The common limitation of these modeling approaches is their lack of a temporal component, meaning that they can assess WiFSS only at a single point in time. WiFSS-LRCA fills this niche by combining an LR equation with the decision rules of Cellular Automata (CA) to iteratively advance the computed probabilities of each grid cell, based on areas constrained from development and neighboring grid cells that already contain wind farms. WiFSS-LRCA enlists a large set of predictors ranging from wind speed to legislation in effect in order for the model to represent the influence that environmental, technical, economic, political, and social predictors have on wind farm siting decisions. Data were aggregated at 20 different grid cell resolutions, collated in four different predictor configurations, and adjustments to the model’s constraint, neighborhood effect, and equation-based scenario transition rules were incorporated into the model’s construction, facilitating WiFSS-LRCA’s sensitivity and scenario analysis of model outputs by end-users. WiFSS-LRCA incorporates both calibration of its LR equation’s predictors and validation of the model’s performance to determine its ability to correctly identify the observed locations of present-day wind farms. Subsequently, the model constructs a WiFSS map whose interpretation and predictive accuracy are informed by the calibration and validation process. Construction of scenarios that modify WiFSS-LRCA’s predictors allow for the model to consider the impacts of changes in these predictors on the locations of future wind energy development (e.g., new transmission line construction, opinions of wind energy improving with time, increasing temperatures due to climate change). The ability of WiFSS-LRCA to produce suitability surfaces with verifiable accuracy is greatest under the following conditions: when running the model over an individual U.S. state rather than the CONUS, when using a smaller grid cell size, when using a more complete (Full configuration) or more refined (Reduced configuration) set of predictors, and when the selected study area contains a larger number of present-day commercial wind farms. Across most study areas, however, WiFSS-LRCA is typically able to correctly identify 75-85% of grid cells that do and do not contain commercial wind farms, with these classifications most often associated with high wind speed, proximity to transmission lines, legislation that supports wind energy development, and large tracts of undeveloped land. CONUS-level model runs indicate five regions as being the most suitable for present wind energy development: Southern California, the Pacific Northwest, the Central Plains, the Great Lakes, and the Northeastern United States. CONUS-level model runs have a tendency to over(under)-estimate grid cell probabilities within (outside) the Central Plains and Great Lakes, which makes state-level model runs useful for revealing smaller-scale differences in the probabilities computed within these five broad regions. Subsequent iterations of WiFSS-LRCA out to the year 2050 show projected wind energy development to remain concentrated within these same regions. Many of the grid cells initially classified as false positive in the model’s first iteration are those that gain wind farms in subsequent iterations, particularly false positive grid cells that were part of high-probability hotspots identified by Getis-Ord statistics. Running WiFSS-LRCA over states outside of these five regions projects wind energy development potential in low-probability areas (as shown in this dissertation for Florida and Kentucky) with projected wind farms in these states concentrated closer to existing infrastructure and away from protected natural areas. The Odds Ratios (ORs) computed during WiFSS-LRCA’s initial calibration provide geographical insight into its projections, with grid cells characterized by high wind speed, undeveloped land, and ambitious Renewable Portfolio Standards (RPS) being the most likely to gain wind farms in future decades. The model’s projections are, however, shown to be sensitive to end-user definitions of parameters, with neighborhood effect and constraint definitions greatly affecting the location and timing of projected wind farm locations. The scenario setup, by contrast, is shown to mostly influence the timing of these projections, with grid cell size moderately affecting both. Multiple limitations exist in the application and interpretation of WiFSS-LRCA. Firstly, the lack of existing LRCA approaches to assessing wind farm siting potential meant few standards existed to guide this model’s development, such as the setting of default constraints and establishing cutoff statistics for refining the model’s enlisted predictors. Secondly, the use of an LR equation to construct suitability surfaces in the model’s first iteration means that both classes of the dependent variable must be filled, requiring a study area to contain at least two commercial wind farms, compromising the model’s reliability in runs over the Southeastern United States. Finally, the lack of spatial stratification during WiFSS-LRCA’s calibration and validation means that the model is trained to recognize predictors associated with wind energy development in regions where many wind farms exist, namely the Central Plains and Great Lakes, hence the greater number of Type 2 errors in CONUS-level model runs outside of these regions. Selecting stratified samples of grid cells that contain wind farms from different parts of the CONUS could be incorporated into WiFSS-LRCA to address this bias. Other directions for future work with WiFSS-LRCA include the following: optimization to assess offshore wind energy development potential by training the model with proposed offshore wind farm sites surrounding the CONUS; adapting WiFSS-LRCA to run over multiple states simultaneously to identify predictors that influence wind farm siting decisions at regional spatial scales; and performing projections of other types decentralized land-use change, such as solar energy development given similarities in the required model predictors

Modelling spatial and temporal urban growth

Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. Summary In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: ? 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; ? 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; ? 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; ? 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; ? 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery. In an effort to better understand the complexity inherent in the urban growth process, the aim of this research was to develop a theoretical framework and methodology that focused on: 1. Analysing the complexity of the urban growth system and evaluating the current methods available for modelling this complexity; 2. Monitoring the urban growth of a fast growing city (Wuhan) in a rapidly developing country (P.R.China), based on remotely sensed imagery, and evaluating its structural and functional changes by modelling; 3. Developing and demonstrating a quantitative method for the comparative measurement of long-term temporal urban growth; 4. Developing and demonstrating an interpretable method for urban growth pattern modelling; 5. Developing and demonstrating a spatially and temporally explicit method for understanding the urban growth process. First, urban growth is defined as a system resulting from the complex dynamic interactions between the developable, developed and planned systems. Second, with remotely sensed imagery (SPOT and aerial photographs) and secondary sources, this research presents a methodology for monitoring and evaluating structural and functional changes in the last five decades. Third, this research presents an innovative method for the temporal measurement of longterm urban growth for the purpose of comparing urban sprawl. By using the concept of relative space, the temporal complexity can be transformed into spatial complexity, indicated by the complex spatial interactions between urban sprawl and urban social and economic systems. Fourth, this research presents a preliminary multi-scale perspective for understanding spatial patterns based on spatial hierarchical theory. The spatial hierarchies comprise planning, analysis and data, which are interrelated. Multi-scale in analysis hierarchy refers to the probability of change (macro), the density of change (meso) and the intensity of change (micro). Fifth, this research presents an innovative method for understanding spatial processes and their temporal dynamics on two interrelated scales (municipality and project), using a multi-stage framework and dynamic weighting concept. The multi-stage framework aims to model local spatial processes and global temporal dynamics by incorporating explicit decision-making processes. Finally, this research has found that complexity theories such as hierarchy theory and selforganising theory are very helpful in conceptually and methodologically understanding the specific complexity of a complex system. Spatial and temporal modelling based on complexity methods such as cellular automata can improve the analytical functions of GIS with the aid of remotely sensed imagery

Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis

[EN] The spatial pattern of urban growth determines how the physical, socio-economic and environmental characteristics of urban areas change over time. Monitoring urban areas for early identification of spatial patterns facilitates assuring their sustainable growth. In this paper, we assess the use of spatio-temporal metrics from land-use/land-cover (LULC) maps to identify growth patterns. We applied LULC change models to simulate different scenarios of urban growth spatial patterns (i.e., expansion, compact, dispersed, road-based and leapfrog) on various baseline urban forms (i.e., monocentric, polycentric, sprawl and linear). Then, we computed the spatio-temporal metrics for the simulated scenarios, selected the most informative metrics by applying discriminant analysis and classified the growth patterns using clustering methods. Two metrics, Weighted mean expansion and Weighted Euclidean distance, which account for the densification, compactness and concentration of urban growth, were the most efficient for classifying the five growth patterns, despite the influence of the baseline urban form. These metrics have the potential to identify growth patterns for monitoring and evaluating the management of developing urban areas.This work was supported by the the Spanish Ministerio de Economia y Competitividad and FEDER [CGL2016-80705-R].Sapena Moll, M.; Ruiz Fernández, LÁ. (2021). Identifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis. International Journal of Geographical Information Science. 35(2):375-396. https://doi.org/10.1080/13658816.2020.181746337539635