Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions

Abstract

With the availability of computational resources, geographical information systems, and remote sensing data, urban growth modeling has become a viable tool for predicting urbanization of cities and towns, regions, and nations around the world. This information allows policy makers, urban planners, environmental and civil organizations to make investments, design infrastructure, extend public utility networks, plan housing solutions, and mitigate adverse environmental impacts. Despite its importance, urban growth models often discard the spatiotemporal uncertainties in their prediction estimates. In this paper, we analyzed the uncertainty in the urban land predictions by comparing the outcomes of two different growth models, one based on a widely applied cellular automata model known as the SLEUTH CA and the other one based on a previously published machine learning framework. We selected these two models because they are complementary, the first is based on human knowledge and pre-defined and understandable policies while the second is more data-driven and might be less influenced by any a priori knowledge or bias. To test our methodology, we chose the cities of Jiaxing and Lishui in China because they are representative of new town planning policies and have different characteristics in terms of land extension, geographical conditions, growth rates, and economic drivers. We focused on the spatiotemporal uncertainty, understood as the inherent doubt in the predictions of where and when will a piece of land become urban, using the concepts of certainty area in space and certainty area in time. The proposed analyses in this paper aim to contribute to better urban planning exercises, and they can be extended to other cities worldwide.

Integration of Aerobiological Information for Construction Engineering Based on LiDAR and BIM

“…Using these BIM objects and building CAD information, it was possible to create a detailed 3D model of the EAS in Badajoz (Extremadura). This model shows that BIM can provide a workflow for managing LiDAR information in the analysis and classification of different tree species [94].…”

Evaluating the impact of water protection policy on urban growth: A case study of Jiaxing

“…Through these calibration steps, the SLEUTH model finds the optimum values for all five coefficients. Calibrating the SLEUTH model requires a certain level of human intervention and depending on the number of rounds of Monte Carlo simulation, the prediction process can be computationally intensive (Gomez et al, 2021).…”

“…On the one hand, we have the well-known and widely used SLEUTH model based on cellular automata (CA) (Clarke et al, 1996;Clarke and Gaydos, 1998). On the other hand, we have a recently published model for urban growth prediction based on machine learning, which we will call the ML model (Gómez et al, 2020(Gómez et al, , 2021.…”

Spatio-Temporal Evolution and Driving Mechanism of Urbanization in Small Cities: Case Study from Guangxi

“…Polinesi [95] conducted a comprehensive assessment of the spatio-temporal distribution of population 1961-2011 in 1000 cities based on a geographically weighted regression method to analyze the changing urbanization trends. Gomez [96] insisted that for urbanization prediction, spatio-temporal uncertainty must be taken into account, and after a comparative analysis of meta-automata and machine learning models, it was concluded that the former predefines policies while the latter is driven by data and the two complement each other well. Sumari [97] analyzed the spatio-temporal changes in population and land use 2000-2016 in the Morogoro of Tanzania and analyzed the land urbanization patterns by random forest.…”