Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges
Abstract
:1. Introduction
2. Materials and Methods
- The investigation of the spatial and temporal distribution of the selected sources with necessary filtering of the key information of title, authors, years and keywords and focusing mainly on English-language publications;
- The identification of sources by type of data provided (e.g., open or not);
- The chronological constraint from 2000 onwards;
- The identification of specific keywords and research areas (computer science, engineering, environmental and among others) (Figure 2).
3. Taxonomy of ML Methods for Urban Applications
3.1. Supervised Learning
3.2. Unsupervised Learning
3.3. Machine Learning Algorithms: An Overview
3.4. Decision-Making Urban Planning Processes
4. Review on Urban Applications
4.1. Machine Learning and Built Environment
- Forecasting energy consumption to reveal trends and predict future energy uses and assist energy planning, management or conservation to reduce the energy demand and the CO2 emissions [57] and alternative evaluations for an optimized operation to improve demand and supply balances [58]. Nonetheless, the demand for data collection (especially via intelligent sensors/meters) is evident. To that point, Ahmad et al. [59] underline the evolution of energy metering in technological terms, while Chammas et al. [60] analyze the importance of wireless networks and IoT-based methods for energy monitoring and relevant solutions. The literature unveils a significant number of studies related to forecasting activities and prediction performances; for example, Zhaond Liu [61] with respect to a highly-accurate prediction model for the building energy load with dynamic simulations;
- Detection and prediction of faults, in which traditional models do not provide preemptive interventions;
- Seasonality modeling, i.e., correlating themes to seasonal conditions.
4.2. Machine Learning and Land Use
- An overview of the correlations of the existing works and studies of ML and urban fields to identify the authors’ names per year (1.372 documents) (Figure 9).
- An overview of the correlations of the existing works and studies of ML and urban fields to identify the number of citations per country (1.372 documents) (Figure 10).
5. Examples of Case-Studies
5.1. Shanghai Urban Drainage Masterplanning
- Integrated: considering the existing strategies in line with the ‘Sponge City’ and four elements: (a) a critical overarching system of governance mechanisms for collaboration and synergies; (b) green spaces to promote nature-based solutions (NBS); (c) blue equipment for flood defenses and relevant infrastructure; and (d) ‘grey’ equipment for drainage treatment (e.g., pumps, etc.);
- Adaptive: development of flexible approaches for risk management and uncertainties;
- Smart: integration of intelligent and digitalized models for optimization and data treatment of sophisticated scenarios based on planning strategies alongside the stormwater conditions.
5.2. MassMotion Pedestrian Simulation
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Urban Theme | Scope | AI-Based Tools | Reference(s) |
---|---|---|---|
Polycentricity | Flow analysis and linkages, spatial simulations | Artificial neural networks, fuzzy logic, agent-based models | e.g., [31,32] |
Spatial structures and dynamic analyses | Study on the functional structures of the city, mobility configurations, land-use identification | Artificial neural networks, fuzzy logic, agent-based models | e.g., [33,34] |
Flows’ analyses | Analysis of different types of flows in cities (e.g., energy, mobility, etc.) | Stochastic simulation models, artificial neural networks | e.g., [44,45,46,47,48,49,50] |
Typo-morphological analysis | Analysis of urban structure, form and space | Stochastic models, Artificial neural metworks | e.g., [51,52,53] |
Theme | Indicators | Data | Application |
---|---|---|---|
Urban expansion | Density, demographic profile, built/non-built | EO-based data (e.g., classified images, building footprints) | Classification and simulation (CNN, etc.) |
Land restrictions | Land-use/cover, built/non-built-up spaces | A master plan, land-use regulations | Classification, and extraction of EO products (e.g., DEM), |
Land distribution | Policies, demographics | Census, socioeconomic data | spatial logistic regression, Cellular automata |
Zoning | Land-use distribution | Master Plan, classified images | Planned development |
Land-use changes | Settlement patterns, urban growth processes, population growth | Spatiotemporal EO data | Spatial metrics, agent-based modelling |
Machine Learning Use | Scope | Reference(s) |
---|---|---|
Cellular automata model (CA) | Land-use analysis related to transport and mobility systems (e.g., roads, railways, etc.) and population density issues | e.g., [107,108] |
Artificial neural networks (ANN) | Annual population growth and land-use typologies | e.g., [83,84] |
Linear regression models | Population density, land-use typology, economic centers analysis | e.g., [85,86] |
Agent-based models (ABM) | Accessibility to functions and city infrastructure | e.g., [87,88] |
Decision tree model (DT) | Land typologies, proximities to amenities, densities of residential, commercial | e.g., [109,110,111,112,113,114,115] |
Support vector machines (SVM) | Land-uses typologies, built and unbuilt areas | e.g., [116,117,118,119,120] |
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Koutra, S.; Ioakimidis, C.S. Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges. Land 2023, 12, 83. https://doi.org/10.3390/land12010083
Koutra S, Ioakimidis CS. Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges. Land. 2023; 12(1):83. https://doi.org/10.3390/land12010083
Chicago/Turabian StyleKoutra, Sesil, and Christos S. Ioakimidis. 2023. "Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges" Land 12, no. 1: 83. https://doi.org/10.3390/land12010083
APA StyleKoutra, S., & Ioakimidis, C. S. (2023). Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges. Land, 12(1), 83. https://doi.org/10.3390/land12010083