Urban Growth Modeling and Future Scenario Projection Using Cellular Automata (CA) Models and the R Package Optimx

Abstract

Cellular automata (CA) is a spatially explicit modeling tool that has been shown to be effective in simulating urban growth dynamics and in projecting future scenarios across scales. At the core of urban CA models are transition rules that define land transformation from non-urban to urban. Our objective is to compare the urban growth simulation and prediction abilities of different metaheuristics included in the R package optimx. We applied five metaheuristics in optimx to near-optimally parameterize CA transition rules and construct CA models for urban simulation. One advantage of metaheuristics is their ability to optimize complexly constrained computational problems, yielding objective parameterization with strong predictive power. From these five models, we selected conjugate gradient-based CA (CG-CA) and spectral projected gradient-based CA (SPG-CA) to simulate the 2005–2015 urban growth and to project future scenarios to 2035 with four strategies for Su-Xi-Chang Agglomeration in China. The two CA models produced about 86% overall accuracy with standard Kappa coefficient above 69%, indicating their good ability to capture urban growth dynamics. Four alternative scenarios out to the year 2035 were constructed considering the overall effect of all candidate influencing factors and the enhanced effects of county centers, road networks and population density. These scenarios can provide insight into future urban patterns resulting from today’s urban planning and infrastructure, and can inform future development strategies for sustainable cities. Our proposed metaheuristic CA models are also applicable in modeling land-use and urban growth in other rapidly developing areas.

Performance testing of selected hybrid Markovian models in urban growth simulation of the Kolkata Metropolitan Area, India

“…Perhaps the most widely used, the CA_MC is almost a de-facto choice for urban growth modelling. It employs a bottom-up approach to model ULULC changes (Feng et al, 2018;Xu et al, 2019). It is based on the interaction between the lattice and the cell that forms it, the state in which the cell is, the rules regarding its transition to other cell states, the temporal variability regarding the state of change and the neighborhood which determines the extent of change possible for the cell in its present state (Keshtkar & Voigt, 2015).…”

Spatial Models or Random Forest? Evaluating the Use of Spatially Explicit Machine Learning Methods to Predict Employment Density around New Transit Stations in Los Angeles

“…While image classification is one of the most prominent predictive applications in urban geography, there are of course other important predictive questions that can be answered in the era of "big" data: small area estimation and interpolation for socioeconomic data (Singleton and Arribas-Bel 2019), spatial patterns in large, open, georeferenced municipal data sets such as crimes, "311" calls, and parking violations (Gao et al 2019), spatiotemporal patterns in disease outbreaks using georeferenced sentiment data from social media (e.g., Allen et al 2016), the spatial distribution of pollution (Walsh et al 2017), the prediction of housing prices and rents (Mu, Wu, and Zhang 2014;Fan, Cui, and Zhong 2018;Phan 2018;Truong et al 2020), and gentrification (Alejandro and Palafox 2019; Knorr 2019), among others. In an urban planning context, predicting the future distribution of population and land use with greater precision is an area of significant opportunity for predictive model applications (Feng et al 2018). Indeed, while this article's application is concerned primarily with predicting employment density around transit, the methods delineated here could be used to predict regional (workplace-level) employment and residential population growth more generally.…”

A cellular-automata model for assessing the sensitivity of the street network to natural terrain

“…In SIMLANDER, single land use is modelled at a time (Hewitt, Díaz-Pacheco, and Moya-Gómez 2013;Roodposhti, Hewitt, and Bryan 2020, 2), which suits this work's objective where the focus is on simulating only the 'street' cells. The SIMLANDER, though a simple landuse model like SLEUTH, is helpful for rapid exploration and simulation of urban growth, pattern, and form (Clarke, Hoppen, and Gaydos 1997;Feng et al 2018a;Feng, Liu, and Tong 2018b;Feng and Tong 2019;Xian and Crane 2005).…”