A New Perspective for Urban Development Boundary Delineation Based on the MCR Model and CA-Markov Model

Abstract

In order to control the development of urban space, it is important to explore scientific methods to provide a reference for regional territorial space planning. On the basis of the minimum cumulative resistance (MCR) model and the cellular automaton (CA)-Markov model, we constructed a new technical method for delineating urban development boundaries, exploring the temporal and spatial distribution characteristic of land use in Wuhan from 2010 to 2020 through nighttime and remote sensing images, and simulating the urban development boundaries of Wuhan from 2025 to 2035. The results show that: (1) the scales of Wuhan City’s built-up areas in 2010, 2015, and 2020 were 500 km2, 566.13 km2, and 885.11 km2, respectively, and the trends of expansion run to the east and southeast, and (2) on the basis of the MCR model, the urban development boundary scale of Wuhan City in 2025, 2030, and 2035 from the perspective of actual supply will be 903.52 km2, 937.48 km2, and 1021.44 km2, respectively, and based on the CA-Markov model, the urban development boundary scales of Wuhan City in 2025, 2030, and 2035 from the perspective of ideal land demand will be 912.75 km2, 946.40 km2, and 1041.91 km2, respectively. By combining the results of the two methods, we determined areas of 901.62 km2, 944.39 km2, and 1015.36 km2 as the urban development boundaries of Wuhan City in 2025, 2030, and 2035, respectively. According to the principle of supply–demand balance, the urban development boundary delineated by the integration of the MCR model and CA-Markov model, which is in line with the spatial expansion trend of growing cities, could optimize the urban development pattern; solve the contradiction between urban development, farmland protection, and ecological protection; and provide a methodological reference and decision-making basis for planning practice.

Integrating ANNs and Cellular Automata–Markov Chain to Simulate Urban Expansion with Annual Land Use Data

“…They also provided a framework to combine the raw types of ANNs with CA to model population dynamics and economic activities [31,32]. In 2022, Kim et al [33] and Yi et al [34] also pointed out that CA models could combine with machine-learning-based algorithms to enhance simulation accuracy and provide more reliable results. From their work, in line with many other studies in the urban expansion modeling field, all simulation models need to capture the complex nonlinear relationships between various driving factors and urban expansion parcels, via the "bottom-up" model approach and different transition rules.…”

Predicting Land Use/Land Cover Changes in the Lesser Zab River Catchment/Iraq through CA-Markov Synergy Model

“…Markov model and cellular automata have probable pro ts in the investigation of land use variations. The CA-Markov simulation is capable to incorporate the data of geographic information system (GIS) and remote sensing well, therefore; is a strong and appropriate technique for simulating spatiotemporal change of LU/LC (Wang et al 2018;Yi et al 2022).…”

Predicting Land Use/Land Cover Changes in the Lesser Zab River Catchment/Iraq through CA-Markov Synergy Model

“…Furthermore, the CA model has a high spatial resolution; and computational e ciency as the model can be linked with the GIS environs (Wang et al, 2018). The Markov chain model is commonly applied to simulate and predict the LU/LC changes pattern (Yi et al, 2022). Although, the Markov chain model cannot predict changes in spatial trends.…”

“…The CA-Markov simulation can incorporate the data of geographic information systems (GIS) and remote sensing well. Therefore, it is a strong and appropriate technique for simulating spatiotemporal change of LU/LC (Wang et al, 2018;Yi et al, 2022). Recently, the CA Markov model has effectively been applied to simulate the spatiotemporal change patterns of LU/LC by numerous studies (Hailu et al, 2018;Gidado et al, 2019;Msofeet al, 2019;Dibaba et al, 2020;Hishe et al, 2020;Munthali et al, 2020;Cui et al, 2021;Wang & Zheng, 2022;Yi et al, 2022).…”

“…Therefore, it is a strong and appropriate technique for simulating spatiotemporal change of LU/LC (Wang et al, 2018;Yi et al, 2022). Recently, the CA Markov model has effectively been applied to simulate the spatiotemporal change patterns of LU/LC by numerous studies (Hailu et al, 2018;Gidado et al, 2019;Msofeet al, 2019;Dibaba et al, 2020;Hishe et al, 2020;Munthali et al, 2020;Cui et al, 2021;Wang & Zheng, 2022;Yi et al, 2022). Accordingly, considering the ability of the model to expand understanding of the di culty of spatial system components, the current study considered the CA-Markov simulation to examine prospect LU/LC variations within Lesser Zab River Catchment (LZRC), north part of Iraq.…”