UNHabitat Urban Indicators Database
Practicing data inclusion Cocreation of an urban data dashboard
Smart cities leveraging advanced data analytics predictive models and digital twin techniques offer a transformative model for sustainable urban development Predictive analytics is critical to proactive planning enabling cities to adapt to evolving challenges Concurrently digital twin techniques provide a virtual replica of the urban environment fostering realtime monitoring
Cities are central to achieving the 2030 Agenda for Sustainable Development yet many remain disengaged from the process This paper examines nine city and subnational pilot projects conducted
Smart cities big data and urban policy Towards urban analytics for
Human mobility data and analysis for urban resilience A systematic
Smart cities and urban data platforms Designing interfaces for smart
Big data and more generally digital technologies are regarded as paramount in the governance and planning of smart cities Numerous scholars in urban research argue that a new type of big data analytics promises benefits in terms of realtime prediction adaptation higher energy efficiency higher quality of life and greater ease of movement Batty 2019 Batty et al 2012 Kourtit et al
Urban Data Response
Datadriven smart sustainable cities of the future urban computing and
In their article Urban Planning and Smart City Decision Management Empowered by RealTime Data Processing Using Big Data Analytics Silva et al propose a big data analyticsembedded experimental architecture for smart cities in response to the issue of realtime processing requirements and exponential data growth in relation to their
Big and open data offer considerable potential for analyzing and predicting human mobility during disaster events including the COVID19 pandemic leading to better disaster risk reduction DRR planning However the value of human mobility data and analysis HMDA in urban resilience research is poorly understood
Urbanization Our World in Data
Urban observatories share experiences and innovations UNHabitats Data and Analytics Section the global coordinator of the Global Urban Observatory Network GUONet in coordination with the GUONet Steering Committee organized the second experiences sharing webinar among urban observatories on June5 2023
Deep learning solutions for smart city challenges in urban Nature
Urban Data Response
As Goldsmith and Crawford write in The Responsive City 2014 3 our ability to collect analyse and share information today has great potential to transform and even reinvigorate the governance of cities Smart city investments are now accelerating across the globe resulting in the proliferation of datadriven tools and platforms designed to usher in more responsive urban services
Kitchin and McArdle 2016 define urban data and city dashboards as platforms presenting key information about cities which are updated and often interactive They contend that the widespread adoption of urban dashboards not only addresses practical requirements for managing city services and catering to the needs of datadriven public administration but also responds to broader subjective
Whenever possible data classified according to the concept of urban agglomeration are used However some countries do not produce data according to the concept of urban agglomeration but use instead that of metropolitan area or city proper If possible such data are adjusted to conform to the concept of urban agglomeration
Lessons from nine urban areas using data to drive local Nature
Deep learning will play a crucial part in the development of smart cities and urban planning according to a plethora of studies Anguita et als 3 demonstration of the use of neural networks for
Enhancing Urban Resilience Smart City Data Analyses Forecasts and