Modeling Major Rural Land-Use Changes Using the GIS-Based Cellular Automata Metronamica Model: The Case of Andalusia (Southern Spain)

The effective and efficient planning of rural land-use changes and their impact on the environment is critical for land-use managers. Many land-use growth models have been proposed for forecasting growth patterns in the last few years. In this work; a cellular automata (CA)-based land-use model (Metronamica) was tested to simulate (1999–2007) and predict (2007–2035) land-use dynamics and land-use changes in Andalucía (Spain). The model was calibrated using temporal changes in land-use covers and was evaluated by the Kappa index. GIS-based maps were generated to study major rural land-use changes (agriculture and forests). The change matrix for 1999–2007 showed an overall area change of 674971 ha. The dominant land uses in 2007 were shrubs (30.7%), woody crops on dry land (17.3%), and herbaceous crops on dry land (12.7%). The comparison between the reference and the simulated land-use maps of 2007 showed a Kappa index of 0.91. The land-cover map for the projected PRELUDE scenarios provided the land-cover characteristics of 2035 in Andalusia; developed within the Metronamica model scenarios (Great Escape; Evolved Society; Clustered Network; Lettuce Surprise U; and Big Crisis). The greatest differences were found between Great Escape and Clustered Network and Lettuce Surprise U. The observed trend (1999–2007–2035) showed the greatest similarity with the Big Crisis scenario. Land-use projections facilitate the understanding of the future dynamics of land-use change in rural areas; and hence the development of more appropriate plans and policies

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

A complex network approach to urban growth

The economic geography can be viewed as a large and growing network of interacting activities. This fundamental network structure and the large size of such systems makes complex networks an attractive model for its analysis. In this paper we propose the use of complex networks for geographical modeling and demonstrate how such an application can be combined with a cellular model to produce output that is consistent with large scale regularities such as power laws and fractality. Complex networks can provide a stringent framework for growth dynamic modeling where concepts from e.g. spatial interaction models and multiplicative growth models can be combined with the flexible representation of land and behavior found in cellular automata and agent-based models. In addition, there exists a large body of theory for the analysis of complex networks that have direct applications for urban geographic problems. The intended use of such models is twofold: i) to address the problem of how the empirically observed hierarchical structure of settlements can be explained as a stationary property of a stochastic evolutionary process rather than as equilibrium points in a dynamics, and, ii) to improve the prediction quality of applied urban modeling.evolutionary economics, complex networks, urban growth

BUDEM: An urban growth simulation model using CA for Beijing metropolitan area

金沢大学理工研究域環境デザイン学系It is in great need of identifying the future urban form of Beijing, which faces challenges of rapid growth in urban development projects implemented in Beijing. We develop Beijing Urban Developing Model (BUDEM in short) to support urban planning and corresponding policies evaluation. BUDEM is the spatio-temporal dynamic model for simulating urban growth in Beijing metropolitan area, using cellular automata (CA) and Multi-agent system (MAS) approaches. In this phase, the computer simulation using CA in Beijing metropolitan area is conducted, which attempts to provide a premise of urban activities including different kinds of urban development projects for industrial plants, shopping facilities, houses. In the paper, concept model of BUDEM is introduced, which is established basing on prevalent urban growth theories. The method integrating logistic regression and MonoLoop is used to retrieve weights in the transition rule by MCE. After model sensibility analysis, we apply BUDEM into three aspects of urban planning practices: (1) Identifying urban growth mechanism in various historical phases since 1986; (2) Identifying urban growth policies needed to implement desired urban form (BEIJING2020), namely planned urban form; (3) Simulating urban growth scenarios of 2049 (BEIJING2049) basing on the urban form and parameter set of BEIJING2020. © 2008 SPIE

Application of a Spatially Explicit, Agent-Based Land Use Conversion Model to Assess Water Quality Outcomes under Buffer Policies

Land use changes within watersheds have spatially explicit dynamics and involve decision making by individuals. The role of the spatial dimension of human behavior and its impact on land use change has been analyzed using agent-based modelling approaches. Agent-based land use change has received a significant theoretical attention; however, these models lack empirical implementation and testing due to the lack of spatial modelling tools and data that can capture human land use dynamics.;This research presents a methodology for projecting land use conversions through the implementation of a spatially explicit agent-based simulation model in the Opequon Creek watershed of Berkeley County, West Virginia. Empirical estimates for factors that influence the land use conversion probability are captured using a spatial logistic regression model. Then, agentbased probabilistic land use conversion (APLUC) model is programmed on Python language within a geographic information system (GIS) to explore the impacts of policies on land use conversion decisions using estimates from actual land use change from 2001-2011. A series of model runs are executed under buffer policy scenarios. Three policy scenarios are developed: (1) a scenario where there is no policy implemented, (2) a scenario where 50 ft buffer zones are applied to all streams, and (3) a scenario where 50 ft buffers are applied only on critical source areas (CSAs) watersheds. The land use patterns project in APLUC model are driven by individual land conversion decisions over 50 model runs of 10 iterations each under each policy scenario. The APLUC model is validated at sub-basin level and outcomes are analyzed to identify the influence of various land use policies on land use patterns. The results show that a 50 ft buffer policy everywhere in watershed, greatly reduced the residential land use conversions. Spatial patterns generated under a 50 ft buffer policy in CSAs only showed that future projected land use changes occurred close to major highways. In the baseline policy, most conversions occurred near existing residential land use and urban centers. Results from the APLUC model also suggests that forest is serving as distant amenity for residential land conversion.;Finally, the impacts of these three policies on water quality are estimated using an ArcSWAT model, a graphical user interface for SWAT (Soil and Water Assessment Tool). This model indicates that the 50 ft buffer policy in CSAs is most effective among the three policies in reducing the pollutant loads. This study suggests that carefully designed policies, which discourage residential land use conversion in CSAs, result in less pollutant loads by shifting the location of residential conversion to less critical areas where agricultural land is dominant in the watershed

Understanding spatial and temporal processes of urban growth: cellular automata modelling

An understanding of the dynamic process of urban growth is a prerequisite to the prediction of land-cover change and the support of urban development planning and sustainable growth management. The spatial and temporal complexity inherent in urban growth requires the development of a new simulation approach, which should be process-oriented and have a strong interpretive element. In this paper the authors present an innovative methodology for understanding spatial processes and their temporal dynamics on two interrelated scales -- the municipality and project scale -- by means of a multistage framework and a dynamic weighting concept. The multistage framework is aimed at modelling local spatial processes and global temporal dynamics by the incorporation of explicit decisionmaking processes. It is divided into four stages: project planning, site selection, local growth, and temporal control. These four stages represent the interactions between top-down and bottom-up decisionmaking involved in land development in large-scale projects. Project-based cellular automata modelling is developed for interpreting the spatial and temporal logic between various projects that form the whole of urban growth. Use of dynamic weighting is an attempt to model local temporal dynamics at the project level as an extension of the local growth stage. As nonlinear function of temporal land development, dynamic weighting can link spatial processes and temporal patterns. The methodology is tested with reference to the urban growth of a fast growing city -- Wuhan, in the People's Republic of China -- from 1993 to 2000. The findings from this research suggest that this methodology can be used to interpret and visualise the dynamic process of urban growth temporally and transparently, globally and locally

Projecting land use changes using parcel-level data : model development and application to Hunterdon County, New Jersey

This dissertation is to develop a parcel-based spatial land use change prediction model by coupling various machine learning and interpretation algorithms such as cellular automata (CA) and decision tree (DT). CA is a collection of cells that evolves through a number of discrete time steps according to a set of transition rules based on the state of each cell and the characteristics of its neighboring cells. DT is a data mining and machine learning tool that extracts the patterns of decision process from observed cell behaviors and their affecting factors. In this dissertation, CA is used to predict the future land use status of cadastral parcels based on a set of transition rules derived from a set of identified land use change driving factors using DT. Although CA and DT have been applied separately in various land use change models in the literature, no studies attempted to integrate them. This DT-based CA model developed in this dissertation represents the first kind of such integration in land use change modeling. The coupled model would be able to handle a large set of driving factors and also avoid subjective bias when deriving the transition rules. The coupled model uses the cadastral parcel as a unit of analysis, which has practical policy implications because the responses of land use changes to various policy usually take place at the parcel level. Since parcel varies by their sizes and shapes, its use as a unit of analysis does make it difficult to apply CA, which initially designed to handle regular grid cells. This dissertation improves the treatment of the irregular cell in CA-based land use change models in literature by defining a cell\u27s neighborhood as a fixed distance buffer along the parcel boundary. The DT-based CA model was developed and validated in Hunterdon County, New Jersey. The data on historical land uses and various land use change driving factors for Hunterdon County were collected and processed using a Geographic Information System (GIS). Specifically, the county land uses in 1986, I995 and 2002 were overlaid with a parcel map to create parcel-based land use maps. The single land use in each parcel is based on a classification scheme developed thorough literature review and empirical testing in the study area. The possible land use status considered for each parcel is agriculture, barren land, forest, urban, water or wetlands following the land use/land cover classification by the New Jersey Department of Environment Protection. The identified driving factors for the future status of the parcel includes the present land use type, the number of soil restrictions to urban development, and the size of the parcel, the amount of wetlands within the parcel, the distribution of land uses in the neighborhood of the parcel, the distances to the nearest streams, urban centers and major roads. A set of transition rules illustrating the land use change processes during the period 1986-1995 were developed using a TD software J48 Classifier. The derived transition rules were applied to the 1995 land use data in a CA model Agent Analyst/RePast (Recursive Porous Agent Simulation Toolkit) to predict the spatial land use pattern in 2004, which were then validated by the actual land use map in 2002. The DT-based CA model had an overall accuracy of 84.46 percent in terms of the number of parcels and of 80.92 percent in terms of the total acreage in predicting land use changes. The model shows much higher capacity in predicting the quantitative changes than the locational changes in land use. The validated model was applied to simulate the 2011 land use patterns in Hunterdon County based on its actual land uses in 2002 under both business as usual and policy scenarios. The simulation results shows that successfully implementing current land use policies such as down zoning, open space and farmland preservation would prevent the total of 7,053 acres (741 acres of wetlands, 3,034 acres of agricultural lands, 250 acres of barren land, and 3,028 acres of forest) from future urban development in Hunterdon County during the period 2002-2011. The neighborhood of a parcel was defined by a 475-foot buffer along the parcel boundary in the study. The results of sensitivity analyses using two additional neighborhoods (237- and 712-foot buffers) indicate the insignificant impacts of the neighborhood size on the model outputs in this application