Abstract
The accurate prediction of urban growth is pivotal for managing urbanization, especially in fast-urbanizing countries. For this purpose, cellular automata-based (CA) simulation tools have been widely developed and applied. Previous studies have extensively discussed various model building and calibration techniques to improve simulation performance. However, it has been a common practice that the simulation is conducted at and only at the spatial extent where the results are needed, while as we know, urban development in one place can also be influenced by the situations in the broader contexts. To tackle this gap, in this paper, the impact of the simulation of spatial extent on simulation performance is tested and discussed. We used five villages at the rural–urban fringe in Chengdu, China as the case study. Urban growth CA models are built and trained at the spatial extent of the village and the whole city. Comparisons between the simulation results and the actual urban growth in the study area from 2005 to 2015 show that the accuracy of the city model was 7.33% higher than the village model and the latter had more errors in simulating the growth of small clusters. Our experiment suggests that, at least in some cases, urban growth modeling at a larger spatial extent can yield better results than merely modeling the area of interest, and the impacts of the spatial extent of simulation should be considered by modelers.
Highlights
It is important to study land use changes in the rural–urban fringe to understand the location of the transition area between urban and non-urban land use types [1,2]
Taabbllee 22 shows that the urban land increased prominently in the study period and the non-urban land was the main source of lland ccoonversion in bboth tthhe cciittyy aanndd tthhee vviillllaaggeess
This study demonstrated that the simulation accuracy of land use changes was higher when conducted at a larger spatial extent, in this case, at the city level compared with the village level
Summary
It is important to study land use changes in the rural–urban fringe to understand the location of the transition area between urban and non-urban land use types [1,2]. This is an area with prominent conflicts between farmland protection and urban expansion [3,4]. Among the many approaches and methods used to study this issue, the simulation method, especially the cellular automata (CA) model has attracted the attention of many scholars [5,6,7]. The CA model has gained widespread application in the simulation of land use dynamics for its ability to integrate the spatial and temporal dimensions [8]. To enhance the performance of CA, researchers have conducted vast research to calibrate and optimize the models [10,11,12]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
