Land use change simulation and analysis using a vector cellular automata (CA) model: A case study of Ipswich City, Queensland, Australia

2019 ◽  
Vol 47 (9) ◽  
pp. 1605-1621 ◽  
Author(s):  
Yi Lu ◽  
Shawn Laffan ◽  
Chris Pettit ◽  
Min Cao
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Growth ◽  
Basic Unit ◽  
Change Models ◽  
Processing Times ◽  
Ca Model ◽  
Artificial Neural Network Ann ◽  
Model Transition

The loss of accuracy in vector-raster conversion has always been an issue for land use change models, particularly for raster based Cellular Automata models. Here we describe a vector-based cellular automata (CA) model that uses land parcels as the basic unit of analysis, and compare its results with a raster CA model. Transition rules are calibrated using an artificial neural network (ANN) and historical land use data. Using Ipswich City in Queensland, Australia as the study area, the simulation results show that the vector and raster CA models achieve 96.64% and 93.88% producer’s spatial accuracy, respectively. In addition, the vector CA model achieves a higher kappa coefficient and more consistent frequency of misclassification, while also having faster processing times. Consequently, the vector-based CA model can be applied to explore regulations of land use transformation in urban growth process, and provide a better understanding of likely urban growth to inform city planners.

Cellular automata for simulating land-use change with a constrained irregular space representation: A case study in Nanjing city, China

2020 ◽  
pp. 239980832094988
Author(s):  
Jie Zhu ◽  
Yizhong Sun ◽  
Shuyin Song ◽  
Jing Yang ◽  
Hu Ding
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Space ◽  
Spatial Reasoning ◽  
Land Use Changes ◽  
Space Representation ◽  
Change Models ◽  
Geographic Space ◽  
Ca Model

Traditional cell-based cellular automata (CA) models use a regular cellular grid to represent geographic space, and new approaches to CA models have explored the use of a vector representation of space instead of a regular grid to characterize urban space more realistically. However, less attention has been paid to modeling the interaction between the geospatial information and the irregular cells. To date, the majority of spatial boundaries have been created by individual agencies in an uncoordinated manner. As a consequence, the potential uses of the data collected for land-use change models are limited. In this paper, we propose a new vector-based CA model based on a new constrained irregular space representation using the theory of hierarchical spatial reasoning. For dividing the geographic space considering different items, first land patches are considered as the minimum division unit; then aggregation rules, including attribute, geometric and boundary barrier constraints, are defined; and finally different levels of spatial units are formed through land patches based on aggregation rules. The proposed model is used to simulate the land-use changes in Nanjing, Jiangsu Province, China. The performance validation and comparison illustrate the feasibility of the proposed space representation in a CA model. By using this model, it is expected that the use of the real spatial boundaries that are employed in urban planning could help provide a flexible paradigm to consider various drivers or constraints for realistically simulating land-use changes.

A GIS-Based Irregular Cellular Automata Model of Land-Use Change

2007 ◽  
Vol 34 (4) ◽  
pp. 708-724 ◽  
Author(s):  
Daniel Stevens ◽  
Suzana Dragićević
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Spatial Data ◽  
Cellular Automata Model ◽  
Population Growth Rates ◽  
Asynchronous Development ◽  
Canadian City ◽  
Ca Model ◽  
Developing Area

This study proposes an alternative cellular automata (CA) model, which relaxes the traditional CA regular square grid and synchronous growth, and is designed for representations of land-use change in rural-urban fringe settings. The model uses high-resolution spatial data in the form of irregularly sized and shaped land parcels, and incorporates synchronous and asynchronous development in order to model more realistically land-use change at the land parcel scale. The model allows urban planners and other stakeholders to evaluate how different subdivision designs will influence development under varying population growth rates and buyer preferences. A model prototype has been developed in a common desktop GIS and applied to a rapidly developing area of a midsized Canadian city.

scholarly journals Multi-Temporal Built-Up Grids of Brazilian Cities: How Trends and Dynamic Modelling Could Help on Resilience Challenges?

2021 ◽  
Vol 13 (2) ◽  
pp. 748
Author(s):  
Iana Rufino ◽  
Slobodan Djordjević ◽  
Higor Costa de Brito ◽  
Priscila Barros Ramalho Alves
Keyword(s):  
Land Use ◽  
Urban Planning ◽  
Cellular Automata ◽  
Land Use Change ◽  
Spatial Pattern ◽  
Urban Growth ◽  
Spatial Information ◽  
Dynamic Modelling ◽  
Urban Models ◽  
Extreme Hazards

The northeastern Brazilian region has been vulnerable to hydrometeorological extremes, especially droughts, for centuries. A combination of natural climate variability (most of the area is semi-arid) and water governance problems increases extreme events’ impacts, especially in urban areas. Spatial analysis and visualisation of possible land-use change (LUC) zones and trends (urban growth vectors) can be useful for planning actions or decision-making policies for sustainable development. The Global Human Settlement Layer (GHSL) produces global spatial information, evidence-based analytics, and knowledge describing Earth’s human presence. In this work, the GHSL built-up grids for selected Brazilian cities were used to generate urban models using GIS (geographic information system) technologies and cellular automata for spatial pattern simulations of urban growth. In this work, six Brazilian cities were selected to generate urban models using GIS technologies and cellular automata for spatial pattern simulations of urban sprawl. The main goal was to provide predictive scenarios for water management (including simulations) and urban planning in a region highly susceptible to extreme hazards, such as floods and droughts. The northeastern Brazilian cities’ analysis raises more significant challenges because of the lack of land-use change field data. Findings and conclusions show the potential of dynamic modelling to predict scenarios and support water sensitive urban planning, increasing cities’ coping capacity for extreme hazards.

scholarly journals A Cellular Automata Model for Integrated Simulation of Land Use and Transport Interactions

2021 ◽  
Vol 10 (3) ◽  
pp. 149
Author(s):  
Nuno Pinto ◽  
António P. Antunes ◽  
Josep Roca
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Change ◽  
Urban Studies ◽  
Particle Swarm Algorithm ◽  
Cellular Automata Model ◽  
Integrated Simulation ◽  
The Road ◽  
Ca Model ◽  
The City

Cellular automata (CA) models have been used in urban studies for dealing with land use change. Transport and accessibility are arguably the main drivers of urban change and have a direct influence on land use. Land use and transport interaction models deal with the complexity of this relationship using many different approaches. CA models incorporate these drivers, but usually consider transport (and accessibility) variables as exogenous. Our paper presents a CA model where transport variables are endogenous to the model and are calibrated along with the land use variables to capture the interdependent complexity of these phenomena. The model uses irregular cells and a variable neighborhood to simulate land use change, taking into account the effect of the road network. Calibration is performed through a particle swarm algorithm. We present an application of the model to a comparison of scenarios for the construction of a ring road in the city of Coimbra, Portugal. The results show the ability of the CA model to capture the influence of change of the transport network (and thus in accessibility) in the land use dynamics.

scholarly journals Remote Sensing, Gis and Cellular Automata for Urban Growth Simulation

2017 ◽  
Vol 10 (4) ◽  
pp. 38 ◽  
Author(s):  
Munira Al-Ageili ◽  
Malek Mouhoub ◽  
Joseph Piwowar
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Cellular Automata ◽  
Urban Growth ◽  
Remote Sensing Data ◽  
Gis Tools ◽  
Spatial Systems ◽  
Ca Model ◽  
Computational Simplicity ◽  
Land Cover Maps

Cities are complex spatial systems and modeling their dynamics of growth using traditional modeling techniques is a challenging task. Cellular automata (CA) have been widely used for modeling urban growth because of their computational simplicity, their explicit representation of time and space and their ability to generate complex patterns from the interaction of simple components of the system using simple rules. Integrating GIS tools and remote sensing data with CA has the potential to provide realistic simulation of the future urban growth of cities. The proposed approach is applied to model the growth of the City of Montreal. Land use/land cover maps derived from Landsat data acquired in 1975 and 1990 were used to train a CA model which was then used to project the land use in 2005.  A comparison of the projected and actual land uses for 2005 is presented and discussed.

scholarly journals Urban Sprawl and Growth Prediction for Lagos Using GlobeLand30 Data and Cellular Automata Model

Sci ◽  
2020 ◽  
Vol 2 (4) ◽  
pp. 80
Author(s):  
Olalekan O. Onilude ◽  
Eric Vaz
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Growth ◽  
Metropolitan Region ◽  
Change Analysis ◽  
Cellular Automata Model ◽  
Ogun State ◽  
Modeling And Prediction ◽  
Artificial Surfaces ◽  
Ca Model

Urban growth in various cities across the world, especially in developing countries, leads to land use change. Thus, predicting future urban growth in the most rapidly growing region of Nigeria becomes a significant endeavor. This study analyzes land use and land cover (LULC) change and predicts the future urban growth of the Lagos metropolitan region, using Cellular Automata (CA) model. To achieve this, the GlobeLand30 datasets from years 2000 and 2010 were used to obtain LULC maps, which were utilized for modeling and prediction. Change analysis and prediction for LULC scenario for 2030 were performed using LCM and CA_Markov chain modeling. The results show a substantial growth of artificial surfaces, which will cause further reductions in cultivated land, grassland, shrubland, wetland, and waterbodies. There was no appreciable impact of change for bare land, as its initial extent of cover later disappeared completely. Additionally, artificial surfaces/urban growth in Lagos expanded to the neighboring towns and localities in Ogun State during the study period, and it is expected that such growth will be higher in 2030. Lastly, the study findings will be beneficial to urban planners and land use managers in making key decisions regarding urban growth and improved land use management in Nigeria.

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