scholarly journals Modelling urban change with cellular automata: Contemporary issues and future research directions

2019 ◽  
pp. 030913251989530 ◽  
Author(s):  
Yan Liu ◽  
Michael Batty ◽  
Siqin Wang ◽  
Jonathan Corcoran
Keyword(s):  
Land Use ◽  
Big Data ◽  
Cellular Automata ◽  
Land Use Changes ◽  
Analytical Framework ◽  
Urban Land Use ◽  
Urban Systems ◽  
Urban Change ◽  
Future Research ◽  
Change Dynamics

The study of land use change in urban and regional systems has been dramatically transformed in the last four decades by the emergence and application of cellular automata (CA) models. CA models simulate urban land use changes which evolve from the bottom-up. Despite notable achievements in this field, there remain significant gaps between urban processes simulated in CA models and the actual dynamics of evolving urban systems. This article identifies contemporary issues faced in developing urban CA models and draws on this evidence to map out four interrelated thematic areas that require concerted attention by the wider CA urban modelling community. These are: (1) to build models that comprehensively capture the multi-dimensional processes of urban change, including urban regeneration, densification and gentrification, in-fill development, as well as urban shrinkage and vertical urban growth; (2) to establish models that incorporate individual human decision behaviours into the CA analytic framework; (3) to draw on emergent sources of ‘big data’ to calibrate and validate urban CA models and to capture the role of human actors and their impact on urban change dynamics; and (4) to strengthen theory-based CA models that comprehensively explain urban change mechanisms and dynamics. We conclude by advocating cellular automata that embed agent-based models and big data input as the most promising analytical framework through which we can enhance our understanding and planning of the contemporary urban change dynamics.

scholarly journals Measuring Functional Urban Shrinkage with Multi-Source Geospatial Big Data: A Case Study of the Beijing-Tianjin-Hebei Megaregion

2020 ◽  
Vol 12 (16) ◽  
pp. 2513 ◽  
Author(s):  
Qiwei Ma ◽  
Zhaoya Gong ◽  
Jing Kang ◽  
Ran Tao ◽  
Anrong Dang
Keyword(s):  
Big Data ◽  
Urban Areas ◽  
Spatial Clustering ◽  
Landscape Fragmentation ◽  
Analytical Framework ◽  
Urban Land Use ◽  
Urban Systems ◽  
Landsat 8 ◽  
Clustering Methods

Most of the shrinking cities experience an unbalanced deurbanization across different urban areas in cities. However, traditional ways of measuring urban shrinkage are focused on tracking population loss at the city level and are unable to capture the spatially heterogeneous shrinking patterns inside a city. Consequently, the spatial mechanism and patterns of urban shrinkage inside a city remain less understood, which is unhelpful for developing accommodation strategies for shrinkage. The smart city initiatives and practices have provided a rich pool of geospatial big data resources and technologies to tackle the complexity of urban systems. Given this context, we propose a new measure for the delineation of shrinking areas within cities by introducing a new concept of functional urban shrinkage, which aims to capture the mismatch between urban built-up areas and the areas where significantly intensive human activities take place. Taking advantage of a data fusion approach to integrating multi-source geospatial big data and survey data, a general analytical framework is developed to construct functional shrinkage measures. Specifically, Landsat-8 remote sensing images were used for extracting urban built-up areas by supervised neural network classifications and Geographic Information System tools, while cellular signaling data from China Unicom Inc. was used to depict human activity areas generated by spatial clustering methods. Combining geospatial big data with urban land-use functions obtained from land surveys and Points-Of-Interests data, the framework further enables the comparison between cities from dimensions characterized by indices of spatial and urban functional characteristics and the landscape fragmentation; thus, it has the capacity to facilitate an in-depth investigation of fundamental causes and internal mechanisms of urban shrinkage. With a case study of the Beijing-Tianjin-Hebei megaregion using data from various sources collected for the year of 2018, we demonstrate the validity of this approach and its potential generalizability for other spatial contexts in facilitating timely and better-informed planning decision support.

scholarly journals The Exploration of Urban Material Anabolism Based on RS and GIS Methods: Case Study in Jinchang, China

2020 ◽  
Vol 12 (3) ◽  
pp. 370
Author(s):  
Shuqi He ◽  
Xingpeng Chen ◽  
Zilong Zhang ◽  
Zhaoyue Wang ◽  
Mengran Hu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Metabolic Response ◽  
Land Use Changes ◽  
Central Business District ◽  
Urban Land ◽  
Urban Land Use ◽  
Urban Systems ◽  
Building Stock ◽  
High Resolution Satellite Images

As an open artificial ecosystem, the development of a city requires the continuous input and output of material and energy, which is called urban metabolism, and includes catabolic (material-flow) and anabolic (material-accumulation) processes. Previous studies have focused on the catabolic and ignored the anabolic process due to data and technology problems. The combination of remote-sensing technology and high-resolution satellite images facilitates the estimation of cumulative material amounts in urban systems. This study focused on persistent accumulation, which is the metabolic response of urban land use/urban land expansion, building stock, and road stock to land-use changes. Building stock is an extremely cost-intensive and long-lived component of cumulative metabolism. The study measured building stocks of Jinchang, China’s nickel capital by using remote-sensing images and field-research data. The development of the built environment could be analyzed by comparing the stock of buildings on maps representing different time periods. The results indicated that material anabolism in Jinchang is a distance-dependent function, where the amounts and rates of material anabolism decrease with changes in distance to the central business district (CBD) and city administration center (CAC). The cumulative metabolic rate and cumulative total metabolism were observed to be increasing, however, the growth rate has decreased.

scholarly journals Land Use Change Dynamics in Southern California: Does Geographic Elasticity Matter?

2017 ◽  
Vol 38 (1) ◽  
pp. 39-53
Author(s):  
Jae Hong Kim ◽  
John R. Hipp ◽  
Victoria Basolo ◽  
Harya S. Dillon
Keyword(s):  
Land Use ◽  
Land Use Change ◽  
Southern California ◽  
Land Use Changes ◽  
Multinomial Logit Model ◽  
Urban Land Use ◽  
Single Family ◽  
Open Spaces ◽  
Municipal Planning ◽  
Change Dynamics

This article examines how municipal planning contexts can shape urban land use dynamics by investigating the parcel-level land use changes in a five-county Southern California metropolitan area between 1990 and 2005. An analysis, based on a multinomial logit model, shows that land use change patterns significantly vary by municipalities that were situated in heterogeneous planning contexts. More specifically, cities with limited ability to expand their jurisdictional boundaries are found to provide more recreational areas and urban open spaces, while restricting nonconventional land uses. However, no evidence of a shift from single-family to multifamily residential development is detected for such cities.

scholarly journals Simulating Intraurban Land Use Dynamics under Multiple Scenarios Based on Fuzzy Cellular Automata: A Case Study of Jinzhou District, Dalian

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Jun Yang ◽  
Weiling Liu ◽  
Yonghua Li ◽  
Xueming Li ◽  
Quansheng Ge
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Land Use Changes ◽  
Urban Land Use ◽  
Multiple Sources ◽  
Development Mode ◽  
Land Use Types ◽  
Fuzzy Cellular Automata ◽  
Traffic Guidance

The spatial evolution of land use in Jinzhou area was simulated using fuzzy cellular automata to determine all factors influencing urban land use change. For this purpose, land use data of multiple sources and remote sensing images from 2003 to 2015 were analyzed. The following results were obtained: (1) real land use data and simulation data for 2015 were tested using four indices. Under the 5 m × 5 m scale, the model shows good performance for simulation of areas with various land use types. (2) Among simulations under four scenarios, the effect of traffic guidance on the development of construction land was more distinct under the economic development mode, which clearly showed the phenomenon of “agglomeration” along major traffic lines. (3) Jinshitan Street is adjacent to the sea, and land use changes are significant under the 4th scenario, and thus related departments should pay more attention. (4) Shortcomings of conventional cellular automata model in processing complex systems could be mitigated through the integration of fuzzy sets.

scholarly journals Simulation and Analysis of Land Use Changes Applying Cellular Automata in the South of Quito and the Machachi Valley, Province of Pichincha, Ecuador

2021 ◽  
Vol 13 (17) ◽  
pp. 9525
Author(s):  
René Ulloa-Espíndola ◽  
Susana Martín-Fernández
Keyword(s):  
Land Use ◽  
Cellular Automata ◽  
Urban Areas ◽  
Land Use Changes ◽  
Urban Land Use ◽  
The South ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Urban Settlements ◽  
The City

Rapid urban growth has historically led to changes in land use patterns and the degradation of natural resources and the urban environment. Uncontrolled growth of urban areas in the city of Quito has continued to the present day since 1960s, aggravated by illegal or irregular new settlements. The main objective of this paper is to generate spatial predictions of these types of urban settlements and land use changes in 2023, 2028 and 2038, applying the Dinamica EGO cellular automata and multivariable software. The study area was the Machachi Valley between the south of the city of Quito and the rural localities of Alóag and Machachi. The results demonstrate the accuracy of the model and its applicability, thanks to the use of 15 social, physical and climate predictors and the validation process. The analysis of the land use changes throughout the study area shows that urban land use will undergo the greatest net increase. Growth in the south of Quito is predicted to increase by as much as 35% between 2018 and 2038 where new highly vulnerable urban settlements can appear. Native forests in the Andes and forest plantations are expected to decline in the study area due to their substitution by shrub vegetation or agriculture and livestock land use. The implementation of policies to control the land market and protect natural areas could help to mitigate the continuous deterioration of urban and forest areas.

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