About this Research Topic
Please note the Abstract submission is not compulsory to submit a manuscript but it is recommended.
Cities are complex organisms that host the life of thousands or millions of people with their needs, interactions, and footprints. The many dimensions that such large-scale systems involved must be taken into consideration in order to make cities more livable and sustainable, from the usage of space to energy demand, water supply, urban mobility, environmental impact, economy, public safety, health and cultural development, just to mention a few. Failing to do so means leaving room for massive issues such as overcrowding, traffic congestion, air pollution, crime, and uneven development. To prevent and fight them, modern cities are increasingly adopting a technology-based approach to “smarten” their urban management and operation, with the aim to keep sustainability and quality of life at high standards.
The goal of this Research Topic is to collect state-of-the-art research in Data Science for the multi-dimensional context of modern cities. We seek contributions in the areas of data science, computer science, machine learning, geospatial analysis, data mining, transportation science, social sciences and many others, aiming to analyze and learn from urban big data to address the challenges posed by modern cities.
This Research Topic invites papers in the form of original research, case studies, review papers, data papers and vision papers (perspective, opinion articles) in all areas related to the research and application of data science for the city domain, including (but not limited to) the following:
• Big data collection for data science of the city;
• Machine learning and deep learning for the city domain;
• Data mining for the city domain;
• Human Mobility Analysis;
• Urban human behavior and social pattern analysis;
• Urban and transportation planning empowered by big data;
• Data-driven solutions for sustainable cities;
• Data science for urban quality of life;
• Data science for carbon neutral cities;
• Data-driven approaches for social good;
• Ethical aspects of data science in the city context (data privacy, fairness in decision-making, urban segregation, etc.).
Cities are complex organisms that host the life of thousands or millions of people with their needs, interactions, and footprints. The many dimensions that such large-scale systems involved must be taken into consideration in order to make cities more livable and sustainable, from the usage of space to energy demand, water supply, urban mobility, environmental impact, economy, public safety, health and cultural development, just to mention a few. Failing to do so means leaving room for massive issues such as overcrowding, traffic congestion, air pollution, crime, and uneven development. To prevent and fight them, modern cities are increasingly adopting a technology-based approach to “smarten” their urban management and operation, with the aim to keep sustainability and quality of life at high standards.
The goal of this Research Topic is to collect state-of-the-art research in Data Science for the multi-dimensional context of modern cities. We seek contributions in the areas of data science, computer science, machine learning, geospatial analysis, data mining, transportation science, social sciences and many others, aiming to analyze and learn from urban big data to address the challenges posed by modern cities.
This Research Topic invites papers in the form of original research, case studies, review papers, data papers and vision papers (perspective, opinion articles) in all areas related to the research and application of data science for the city domain, including (but not limited to) the following:
• Big data collection for data science of the city;
• Machine learning and deep learning for the city domain;
• Data mining for the city domain;
• Human Mobility Analysis;
• Urban human behavior and social pattern analysis;
• Urban and transportation planning empowered by big data;
• Data-driven solutions for sustainable cities;
• Data science for urban quality of life;
• Data science for carbon neutral cities;
• Data-driven approaches for social good;
• Ethical aspects of data science in the city context (data privacy, fairness in decision-making, urban segregation, etc.).
Keywords: Urban mobility, mobility data mining, geospatial analytics, big data analytics, geospatial AI
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.
