Machine learning application to spatio-temporal modeling of urban growth

Kim, Yuna; Safikhani, Abolfazl; Tepe, Emre

doi:10.1016/j.compenvurbsys.2022.101801

Cited by 10 publications

(7 citation statements)

References 32 publications

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“…The rise of promising sectors in computer science such as Machine learning (ML) and Articial Intelligence (AI) can boost many elds of research from e.g., medical applications and diagnosis (Shehab et al 2022, Ahsan, Siddique 2022, Qezelbash-Chamak et al 2022, to drug discovery (Patel, Shah 2022), and cybersecurity (Berghout et al 2022). This also includes topics in the general framework of regional science, such as construction and infrastructure applications or seismic performance (Mirzaei et al 2022, Mangalathu et al 2022, regional crop yield forecasting (Paudel et al 2022), spatio-temporal modeling of urban growth (Kim et al 2022), and visual analyses of regional economy (Bai et al 2022). The use of ML and the increasing computing power can support regional research to expand beyond the classic math, quantitative methods, and statistical analysis.…”

Section: Machine Learning and Resiliencementioning

confidence: 99%

“…ML tools can expand the capabilities of traditional models e.g., capture nonlinear eects which are not detected by traditional econometric models. This has been demonstrated by detecting important factors and nonlinear relationships between regional GDP per capita and Higher Education Systems indicators that have provided useful insights and suggestions for policymakers (Bertoletti et al 2022) or to incorporate spatial, contemporaneous, and historical dependencies e.g., lead-lag non-linear relationships among past urban changes in each region and its neighbors (Kim et al 2022). As indicated above, the discussion in literature of comparing traditional models (mainly statistical) with ML models is active.…”

Section: Machine Learning and Resiliencementioning

confidence: 99%

“…A recent study of resilience focused on earthquakes using historical data from previous seismic events and long-term historical behavior of regions (Fantechi, Modica 2022) is another example of combining traditional econometric with ML techniques (Bertoletti et al 2022), which can apply ML to land-use change modeling (Kim et al 2022). ML is also expected to play a major role in building better and modern Community Resilience Assessment tools by incorporating the use of big data, machine learning, and articial intelligence to take care of spatio-temporal dynamism (Abdul-Rahman et al 2021).…”

Section: Machine Learning and Resiliencementioning

confidence: 99%

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Prediction models and testing of resilience in regions

Manousiadis

Gaki

2023

REGION

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A significant amount of research has been conducted regarding the resilience of the regions and the factors that contribute to allow them to face challenges, crises, or disasters. The rise of promising sectors like Machine learning (ML) and Artificial Intelligence (AI) can enhance this research using computing power in regional economic, social, and environmental data analysis to find patterns and create prediction models. Through Machine Learning, the following research introduces the use of models that can predict the performance of a region in disasters. A case study of the performance of USA Counties during the Covid19 first wave period of the pandemic and the related restrictions that were applied by the authorities was used in order to reveal the obvious or hidden parameters and factors that affected their resilience, in particular their economic response, and other interesting patterns between all the involved attributes. This paper aims to contribute to a methodology and to offer useful guidelines in how regional factors can be translated and processed by data and ML/AI tools and techniques. The proposed models were evaluated on their ability to predict the economic performance of each county and in particular the difference of its unemployment rate between March and June of 2020. The former is based on several economic, social, and environmental data -up to that point in time- using classifiers like neural networks and decision trees. A comparison of the different models' execution was performed, and the best models were further analyzed and presented. Further execution results that identified patterns and connections between regional data and attributes are also presented. The main results of this research are i) a methodological framework of how regional status can be translated into digital models and ii) related examples of predictive models in a real case. An effort was also made to decode the results in terms of regional science to produce useful and meaningful conclusions, thus a decision tree is also presented to demonstrate how these models can be interpreted. Finally, the connection between this work and the strong current trend of regional and urban digitalization towards sustainability is established.

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Section: Machine Learning and Resiliencementioning

confidence: 99%

Section: Machine Learning and Resiliencementioning

confidence: 99%

Section: Machine Learning and Resiliencementioning

confidence: 99%

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Prediction models and testing of resilience in regions

Manousiadis

Gaki

2023

REGION

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“…In most aspects, ML-based approaches meet similar applicability requirements for urban exposure simulations as the classical CA model approaches. However, a growing number of publications have already used ML techniques to simulate not only urban growth but additionally future changes in urban morphologies and densities [68][69][70]. A second major difference to CA models concerns the transferability, as ML-based models have higher requirements in data quality, which poses a challenge for data-poor areas [51].…”

Section: Application Potentials For Urban Exposure Modelingmentioning

confidence: 99%

Urban growth modeling for the assessment of future climate and disaster risks: approaches, gaps and needs

Reimuth,

Hagenlocher,

Yang

et al. 2023

Environ. Res. Lett.

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Urban climate-related disaster risks are set to rise, driven by the interaction of two global megatrends: urbanization and climate change. A detailed understanding of whether, where and how cities are growing within or into hazard-prone areas is an urgent prerequisite for assessing future risk trajectories, risk-informed planning, and adaptation decisions. However, this analysis has been mostly neglected to date, as most climate change and disaster risk research has focused on the assessment of future hazard trends but less on the assessment of how socio-economic changes affect future hazard exposure. Urban growth and expansion modeling provide a powerful tool, given that urban growth is a major driver of future disaster risk in cities. The paper reviews the achievements lately made in urban growth and exposure modeling and assesses how they can be applied in the context of future-oriented urban risk assessment and the planning of adaptation measures. It also analyses which methodological challenges persist in urban growth and exposure modeling and how they might be overcome. These points pertain particularly to the need to consider and integrate (1) urban morphology patterns and potential linkages to exposure as well as vulnerability, (2) long-term time horizons to consider long-term developments, (3) feedbacks between urbanization trajectories and hazard trends, (4) the integration of future urban growth drivers and adaptation responses, (5) feedbacks between adaptation and urbanization, and (6) scenarios, which are developed within a commonly defined scenario framework.

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“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

2022

Land

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Accurately simulating and predicting the urban expansion process, especially in expeditious urbanization areas, is an important aspect of managing limited land resources and adjusting flawed land use policies. This research was conducted on the basis of a high-temporal-resolution land use dataset to precisely model urban expansion in a rapidly developing zone by integrating the Artificial Neural Network (ANN), cellular automata (CA), and Markov Chain (MC). An urban suitability index (USI) map was created using ANN and fed to CA–MC to identify possible changed-to-urban cells. Two ANNs, multiple-layer perceptron (MLP) and long short-term memory network (LSTM), were implemented as simulation models for comparison. Due to its ability to capture more temporal information, LSTM outperformed MLP in modeling urban expansion dynamics over a short temporal interval. The simulated results were validated by (fuzzy) kappa simulation and the results revealed that the combination of ANN and CA–MC can precisely model the urban development locations due to its strength in revealing the nonlinear relationship between the expansion process and its drivers. The same model was applied to southern Auckland, and the compared results show that the most simulated variance is caused by the land use policies implemented by different types of governments.

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Machine learning application to spatio-temporal modeling of urban growth

Cited by 10 publications

References 32 publications

Prediction models and testing of resilience in regions

Prediction models and testing of resilience in regions

Urban growth modeling for the assessment of future climate and disaster risks: approaches, gaps and needs

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

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