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Article

Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions

1
i2t Research Group, Department of Communication and Information Technologies, Universidad Icesi, Calle 18 No. 122-135, Pance, Cali 760031, Colombia
2
Arts and Sciences, New York University Shanghai, 1555 Century Avenue, Pudong New District, Shanghai 200122, China
3
Geospatial Lab, Indian Institute for Human Settlements, IIHS Bengaluru City Campus, 197/36, 2nd Main Road, Sadashivanagar, Bengaluru 560080, India
4
Research in Spatial Economics (RiSE) Group, Department of Mathematical Sciences, EAFIT University, Carrera 48 A 10 Sur 107, Casa 4, oficina RiSE, Medellín 050022, Colombia
5
COMPAS, School of Anthropology, University of Oxford, 58 Banbury Road, Oxford OX2 6QS, UK
6
Key Laboratory of National Forestry and Grassland Administration on Ecological Landscaping of Challenging Urban Sites, Shanghai Engineering Research Center of Landscaping on Challenging Urban Sites, Shanghai Academy of Landscape Architecture Science and Planning, 899 Longwu Road, Xuhui District, Shanghai 200232, China
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editor: Soe Myint
Remote Sens. 2021, 13(3), 512; https://doi.org/10.3390/rs13030512
Received: 28 December 2020 / Revised: 23 January 2021 / Accepted: 27 January 2021 / Published: 1 February 2021
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. View Full-Text
Keywords: urban growth; urbanization processes; spatiotemporal uncertainty; urban planning tools; urban science; SLEUTH CA; machine learning urban growth; urbanization processes; spatiotemporal uncertainty; urban planning tools; urban science; SLEUTH CA; machine learning
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MDPI and ACS Style

Gómez, J.A.; Guan, C.; Tripathy, P.; Duque, J.C.; Passos, S.; Keith, M.; Liu, J. Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions. Remote Sens. 2021, 13, 512. https://doi.org/10.3390/rs13030512

AMA Style

Gómez JA, Guan C, Tripathy P, Duque JC, Passos S, Keith M, Liu J. Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions. Remote Sensing. 2021; 13(3):512. https://doi.org/10.3390/rs13030512

Chicago/Turabian Style

Gómez, Jairo A.; Guan, ChengHe; Tripathy, Pratyush; Duque, Juan C.; Passos, Santiago; Keith, Michael; Liu, Jialin. 2021. "Analyzing the Spatiotemporal Uncertainty in Urbanization Predictions" Remote Sens. 13, no. 3: 512. https://doi.org/10.3390/rs13030512

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