UrbanVCA: a vector-based cellular automata framework to simulate the urban land-use change at the land-parcel level

Yao, Yao; Li, Linlong; Liang, Zhaotang; Cheng, Tao; Sun, Zuoliang; Luo, Peng; Guan, Qingfeng; Zhai, Yuling; Kou, Shihao; Cai, Yuyang; Li, Lefei; Ye, Xinyue

doi:10.48550/arxiv.2103.08538

Cited by 3 publications

(5 citation statements)

References 34 publications

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“…Ultimately, the probability of the segmented parcel transitioning into the specific land-use type in the initial year, denoted as Y i , served as the comprehensive suitability measure for land-use transformation when utilizing a VCA model (Figure 4). The use of raw land parcels as the primary simulation units poses a challenge due to their coarse granularity, which ultimately results in a significant reduction in simulation precision [39]. Thus, an appropriate land subdivision tool must be employed.…”

Section: Urban Land-use Change Simulation By Urbanvca Modelmentioning

confidence: 99%

“…Thus, an appropriate land subdivision tool must be employed. The DLPS The use of raw land parcels as the primary simulation units poses a challenge due to their coarse granularity, which ultimately results in a significant reduction in simulation precision [39]. Thus, an appropriate land subdivision tool must be employed.…”

Section: Urban Land-use Change Simulation By Urbanvca Modelmentioning

confidence: 99%

“…First, an area partition strategy with DSC algorithm was employed to generate multiple relatively homogeneous sub-regions, which could effectively capture the geographic heterogeneity in the distribution of land-use change factors. Second, UrbanVCA, a brand-new vector CA-based framework to simulate the urban land-use change at the land parcel level, was adopted for the study [8,39]. By employing a set of pre-defined rules driving urban land-use changes, the UrbanVCA model can not only simulate the process of land fragmentation but also support a variety of machine learning algorithms to mine the probability of urban land-use changes.…”

Section: Introductionmentioning

confidence: 99%

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Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes

Zhu,

et al. 2023

Land

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In cellular automata (CA) modeling, spatial heterogeneity can be delineated by geographical area partitioning. The dual constrained space clustering method is a prevalent approach for providing an objective and effective representation of differences within urban regions. However, previous studies faced issues by ignoring spatial heterogeneity, which could lead to an over- or under-estimation of the simulation results. Accordingly, this study attempts to incorporate spatially heterogeneous area partitioning into vector-based cellular automata (VCA), producing more accurate and reliable simulations of urban land-use change. First, an area partition strategy with DSC algorithm was employed to generate multiple relatively homogeneous sub-regions, which can effectively capture the spatial heterogeneity in the distribution of land-use change factors. Second, UrbanVCA, a brand-new VCA-based framework, was utilized for simulating land-use changes in distinct urban partitions. Finally, the constructed partitioned VCA model was applied to simulate rapid urban development in Jiangyin city from 2012 to 2017. The results indicated that the combination of DSC clustering and UrbanVCA model could obtain satisfying results as the average FoM values for the partitions and the entire study area exceeded 0.22. Furthermore, a comparative analysis of results from traditional area-partitioned CA models revealed that the proposed area partitioning approach had the potential to yield more accurate simulation outcomes as the FoM values were higher and SHDI and LSI metrics were closer to real-world observations, indicating its good performance in simulating fragmented urban landscapes.

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Section: Urban Land-use Change Simulation By Urbanvca Modelmentioning

confidence: 99%

Section: Urban Land-use Change Simulation By Urbanvca Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes

Zhu,

et al. 2023

Land

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“…Most studies of spatial metrics employ raster data because raster has traditionally been the most commonly used output data format in urban simulation models (Aguilera et al, 2011;Barredo et al, 2004;Herold et al, 2005) and it is easier and faster to compute than vector data (Wade et al, 2003;Yao et al, 2022). However, the recent proliferation of significant vector-based cellular automata (CA) models (Abolhasani et al, 2016;Barreira-González et al, 2019;Molinero-Parejo et al, 2023;Moreno et al, 2008;Yao et al, 2017Yao et al, , 2021 has increased the need for new tools for calculating vector spatial metrics.…”

Section: Introductionmentioning

confidence: 99%

Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Molinero‐Parejo,

Aguilera‐Benavente,

Gómez‐Delgado

2024

Transactions in GIS

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Descriptive scenarios about the possible evolution of land use in our cities are essential instruments in urban planning. Although the simulation of these scenarios has enormous potential, further characterization is needed in order to be able to evaluate and compare them so as to provide more effective support for public policy. One of the most commonly used tools for assessing these scenarios is spatial moving‐window metrics, a useful mechanism for extracting accurate information from simulated land‐use maps on urban diversity and urban growth patterns. This article seeks to explore this question further and has two main aims. First, to develop and implement vSHEI and vLEI, two multiscale composition and configuration vector moving‐window metrics for calculating urban diversity and urban growth patterns. Second, to test these metrics using the spatially explicit simulation of three prospective scenarios in the Henares Corridor (Spain), comparing the results and analyzing how well the scenario narratives match their spatial configuration, as measured using vSHEI and vLEI. Via the implementation of vSHEI and vLEI, we obtained urban diversity and urban expansion values at a local level, offering more precise and more realistic, mappable information on the composition and configuration of urban land use than that provided by raster metrics or by vector Patch‐Matrix model metrics. We also used these metrics to test whether the simulated scenarios matched their description in the narrative storylines. Our results demonstrate the potential of vector moving‐window metrics for characterizing the urban patterns that might develop under different scenarios at the parcel level.

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“…To overcome technical challenges in implementing VCA models in practical applications, Yao et al (2021) proposed the UrbanVCA framework.…”

Section: Introductionmentioning

confidence: 99%

Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation

Zhuang

Yan

et al. 2022

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With the capacity to represent irregular geographical entities precisely, vector‐based cellular automata (VCA) have been extensively employed in urban land change simulation at the land parcel level. However, while more driving factors are considered, modeling the complicated nonlinear relationship between land parcel attributes and multiple land‐use changes is increasingly difficult. Moreover, in VCA, the driving factors are unstructured and cannot be directly modeled by traditional deep learning methods, which can only be applied to structured data processing. In order to address these problems, a new VCA model DF‐VCA that adopts the deep forest (DF) algorithm for mining CA transition rules was proposed to simulate urban land‐use change at the parcel level. The DF algorithm is a new deep learning method that can directly mine high‐level features from unstructured data and obtain accurate land‐use transition rules. The proposed DF‐VCA model was applied to simulate the urban land change in Shenzhen, China. Compared with several traditional VCA models, the DF‐VCA model achieved the best simulation performance at parcel‐level (Figure of Merit = 39.88%), pattern‐level (similarity = 96.47%), and community‐level (correlation coefficient = 0.9269). The results show that the DF‐VCA model with strong representative learning ability could simulate urban land change at fine scales precisely. Furthermore, the proposed DF‐VCA model was applied in Shenzhen's future land change simulations to guide sustainable urban development.

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UrbanVCA: a vector-based cellular automata framework to simulate the urban land-use change at the land-parcel level

Cited by 3 publications

References 34 publications

Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes

Incorporation of Spatially Heterogeneous Area Partitioning into Vector-Based Cellular Automata for Simulating Urban Land-Use Changes

Adapting moving‐window metrics to vector datasets for the characterization and comparison of simulated urban scenarios

Integrating a deep forest algorithm with vector‐based cellular automata for urban land change simulation

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