Urban Emotions and Realtime Planning Methods

The Urban Emotions approach combines methods and technologies from Volunteered Geographic Information (VGI), Social Media, sensors and bio-statistical sensors to detect people’s perception for a new perspective about urban environment. In short, it is a methodology for gaining and extracting contextual information of emotion by using technologies from real-time human sensing systems and crowdsourcing methods. “Real-time planning” describes a system in which planning disciplines get a toolset for a fast and simple creation of visualization or simulation from municipal geodata in a consistent workflow. This includes applications from Virtual Reality, Augmented Reality as well as the above mentioned combination of real-time humane sensors and urban sensing systems. Due to the fact, that a real existing city never corresponds with a laboratory situation, Virtual Reality can be one of the solutions to fill the gap for detecting people’s perceptions concerning design, while filtering other unintended side effects. Insights and results from Urban Emotions project, granted by German Research Foundation and Austrian Science Fond, will be presented in this contribution. It is based on a German contribution, published earlier this year (Zeile 2017)

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects