The extent to which cities and urban regions can transform depends on their responses towards contemporary innovation and emerging challenges. Data-driven approaches can provide valuable insights into real-world decision-making when adopting proven computational methods such as machine and deep learning, as well as fulfilling contemporary regulatory requirements such as FAIR and Open Science. Nevertheless, recent trends reflect the lack of digital competence that have created a new group of vulnerable communities and neighborhoods in the absence of bottom-up principles in planning, implementation, and product and service provision.
The key goal of data-driven science is the combination of openness in scientific approaches, utilization of open data or technology, the dissemination of resulting products such as open prototype tools or analytical infrastructure, and alternative business models. Openness gives people an opportunity to reinvestigate and explore solutions and services to foster transformation. This Research Topic aims to explore data-driven and proven technologies that offer innovative solutions for local and emerging urban issues and address environmental and social concerns. This will allow policymakers and the broader community to learn from data analytics and integrate multi-sourced open data and scientific insights within an urban context to advance the knowledge society.
Contributions to this Research Topic should focus on, but are not limited to, the following spatiotemporal and practice-oriented themes in the form of Original Research, Systematic Reviews, Methods, Policy and Practice Reviews, Hypotheses and Theories, and Perspectives:
• The dynamics of the urban form
• Active mobility patterns and behaviors
• Green space creation and use
• Revisitation of parking space
• Public transport infrastructure and services
• Housing provision
• Public health infrastructure services
• Renewable energy production and consumption
The extent to which cities and urban regions can transform depends on their responses towards contemporary innovation and emerging challenges. Data-driven approaches can provide valuable insights into real-world decision-making when adopting proven computational methods such as machine and deep learning, as well as fulfilling contemporary regulatory requirements such as FAIR and Open Science. Nevertheless, recent trends reflect the lack of digital competence that have created a new group of vulnerable communities and neighborhoods in the absence of bottom-up principles in planning, implementation, and product and service provision.
The key goal of data-driven science is the combination of openness in scientific approaches, utilization of open data or technology, the dissemination of resulting products such as open prototype tools or analytical infrastructure, and alternative business models. Openness gives people an opportunity to reinvestigate and explore solutions and services to foster transformation. This Research Topic aims to explore data-driven and proven technologies that offer innovative solutions for local and emerging urban issues and address environmental and social concerns. This will allow policymakers and the broader community to learn from data analytics and integrate multi-sourced open data and scientific insights within an urban context to advance the knowledge society.
Contributions to this Research Topic should focus on, but are not limited to, the following spatiotemporal and practice-oriented themes in the form of Original Research, Systematic Reviews, Methods, Policy and Practice Reviews, Hypotheses and Theories, and Perspectives:
• The dynamics of the urban form
• Active mobility patterns and behaviors
• Green space creation and use
• Revisitation of parking space
• Public transport infrastructure and services
• Housing provision
• Public health infrastructure services
• Renewable energy production and consumption
