Research Topic

Data Analysis and Data-Driven Modeling for Smart Cities

About this Research Topic

The amount of data generated nowadays by society, city infrastructure, and digital technologies is astonishing and this is only going to exponentially increase. It is estimated that about 2.5 quintillion bytes are created each day and 90% of the world’s data was generated in the last two years. The expanding Internet of Things technologies, sensors and cloud platforms also accelerate the generation process. The analysis, modeling and knowledge extraction of/from such data is a key asset to understand urban environments and improve the efficiency of urban mobility, air quality, heating management, smart buildings and grids, and other forms of sustainability.

This Research Topic aims to provide a platform to share high quality research related to data science methods and technologies for urban environments, a topic of crucial importance for many Sustainable Development Goals (i.e., SDG 7 on Sustainable Energy and SDG 11 on Sustainable Cities and Communities). Another important goal is to establish a meeting point for researchers in academia and industries who develop methodologies and technologies for data science, machine learning and artificial intelligence with specific applications in smart and sustainable cities. The Research Topic is connected to a Special Session on Data Science for Sustainable Cities at the 7th International Conference on Machine Learning, Optimization, and Data Science (LOD 2021, https://lod2021.icas.cc/) which is held from June 29 to July 2, 2021 in Grasmere, Lake District, England - UK. Authors with the best papers accepted at the Special Session will be invited to submit an extended version for publication in this Research Topic.

We welcome contributions in the form of Original Research, Methods, Data Reports, Opinions and Reviews that focus on the following themes:
• Data acquisition in smart and sustainable cities
• Data-driven predictive modeling for complex urban and built environments
• Time series analysis and forecasting for urban environments
• Anomaly detection for multivariate sensor data
• Robust machine learning and model verification
• ICT platforms for collecting, visualizing and analyzing data in urban environments
• Data science for IoT services in smart cities
• Data analysis for mobility and transportation, smart cities and smart lands, air quality and pollution monitoring, heating management, smart buildings and smart grids, health, and education in smart cities
• Model explainability and interpretability in urban applications
• Predictive modeling for energy production and distribution (e.g., district heating networks) in sustainable cities
• Innovative sensing platforms (e.g., mobile sensors) for data gathering: mobile, video, earth observation (UAVs and satellites), nano-sensors
• Data gathering and management for citizen science in urban environments
• Data security and blockchain
• Privacy and ethics in data analysis applications for smart cities
• eGovernance, analytics for municipalities and urban stakeholders
• Cloud and big data platforms for smart cities
• Methods and tools for big data management and analysis
• Smart hospitals and healthcare for sustainable cities
• Data analytics for emergency management
• Analytics for urban environment management
• Information diffusion and social networks for sustainable cities
• Epidemic data analysis in urban environments
• City monitoring and urban planning
• Senseable cities
• Analytics for smart growth and effective infrastructure


Keywords: Data Science Methods, Data Science Methodologies, Machine Learning, Artificial Intelligence, Smart Cities, Data Acquisition, Data-Driven Predictive Modeling, Time Series Analyses, Anomaly Detection, ICT Platforms, Data Analyses, Model Explainability and Interpretability, Predictive Modeling, Innovative Sensing Platforms, Citizen Science, Data Security and Blockchain, eGovernance, Cloud and Big Data Platforms, Epidemic Data Analyses


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

The amount of data generated nowadays by society, city infrastructure, and digital technologies is astonishing and this is only going to exponentially increase. It is estimated that about 2.5 quintillion bytes are created each day and 90% of the world’s data was generated in the last two years. The expanding Internet of Things technologies, sensors and cloud platforms also accelerate the generation process. The analysis, modeling and knowledge extraction of/from such data is a key asset to understand urban environments and improve the efficiency of urban mobility, air quality, heating management, smart buildings and grids, and other forms of sustainability.

This Research Topic aims to provide a platform to share high quality research related to data science methods and technologies for urban environments, a topic of crucial importance for many Sustainable Development Goals (i.e., SDG 7 on Sustainable Energy and SDG 11 on Sustainable Cities and Communities). Another important goal is to establish a meeting point for researchers in academia and industries who develop methodologies and technologies for data science, machine learning and artificial intelligence with specific applications in smart and sustainable cities. The Research Topic is connected to a Special Session on Data Science for Sustainable Cities at the 7th International Conference on Machine Learning, Optimization, and Data Science (LOD 2021, https://lod2021.icas.cc/) which is held from June 29 to July 2, 2021 in Grasmere, Lake District, England - UK. Authors with the best papers accepted at the Special Session will be invited to submit an extended version for publication in this Research Topic.

We welcome contributions in the form of Original Research, Methods, Data Reports, Opinions and Reviews that focus on the following themes:
• Data acquisition in smart and sustainable cities
• Data-driven predictive modeling for complex urban and built environments
• Time series analysis and forecasting for urban environments
• Anomaly detection for multivariate sensor data
• Robust machine learning and model verification
• ICT platforms for collecting, visualizing and analyzing data in urban environments
• Data science for IoT services in smart cities
• Data analysis for mobility and transportation, smart cities and smart lands, air quality and pollution monitoring, heating management, smart buildings and smart grids, health, and education in smart cities
• Model explainability and interpretability in urban applications
• Predictive modeling for energy production and distribution (e.g., district heating networks) in sustainable cities
• Innovative sensing platforms (e.g., mobile sensors) for data gathering: mobile, video, earth observation (UAVs and satellites), nano-sensors
• Data gathering and management for citizen science in urban environments
• Data security and blockchain
• Privacy and ethics in data analysis applications for smart cities
• eGovernance, analytics for municipalities and urban stakeholders
• Cloud and big data platforms for smart cities
• Methods and tools for big data management and analysis
• Smart hospitals and healthcare for sustainable cities
• Data analytics for emergency management
• Analytics for urban environment management
• Information diffusion and social networks for sustainable cities
• Epidemic data analysis in urban environments
• City monitoring and urban planning
• Senseable cities
• Analytics for smart growth and effective infrastructure


Keywords: Data Science Methods, Data Science Methodologies, Machine Learning, Artificial Intelligence, Smart Cities, Data Acquisition, Data-Driven Predictive Modeling, Time Series Analyses, Anomaly Detection, ICT Platforms, Data Analyses, Model Explainability and Interpretability, Predictive Modeling, Innovative Sensing Platforms, Citizen Science, Data Security and Blockchain, eGovernance, Cloud and Big Data Platforms, Epidemic Data Analyses


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

24 July 2021 Abstract
31 July 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

24 July 2021 Abstract
31 July 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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