Urban Insights Data Lab Overview
- Headquarters
- København S, Dk
- Website
- oicc.dk
- Industry
- Community Development And Urban Planning
- Employees
- 1
- NAICS
-
Administration of Urban Planning and Community and Rural Development
Keywords
About Urban Insights Data Lab
Why Urban Insights Data Lab? Cities are developing at great speed, often resulting in fast and less appropriate solutions. One of the reasons is a lack of understanding of how the many users and citizens of the city act and use the urban space. Access to shared data is essential for stakeholders across sectors to create vibrant, attractive, healthy and sustainable cities, evaluate actions and adjust direction. However, the existing data landscape is difficult to navigate. As a result, municipalities, developers, architects, tourism organisations, retailers, and other stakeholders do not make sufficient use of the existing public and private sector data sources available. Urban Insights Data Lab will be the platform where both public and private stakeholders can get easy access to relevant data sources and together create data-driven solutions for sustainable urban development. The target groups are: * Municipalities in Denmark * Actors in the urban space (culture, tourism, retail, transport, etc.) * Organisations working in urban development (developers, architects, mobility, energy, etc.) Urban Insights Data Lab's new data model is being developed and tested in the neighborhood of Ørestad in Copenhagen, as the district is similar in size to a major provincial city and houses many of the features that characterise a city. However, the data models that are being developed will be applicable throughout Denmark and internationally. Urban Insights Data Lab is supported by Realdania and run by a consortium of organisations consisting of Ørestad Innovation City Copenhagen (ØICC), Smart City Insights (project leader), IT University, Patrade and VOLCANO. In addition, we have an international and a national monitoring group, which receives regular information about the project and provides input for the development of the model and the data collaboration.
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