1st Air Quality and Open Data: Challenges for Data Science, HCI and Artificial Intelligence


Urban systems are deeply penetrated by large sensor networks of many kind. But one of the largest sensor networks is still comparably untouched by urban data and data science: the ubiquity of private mobile and wearable technology holds the possibility of unprecedented monitoring systems beyond the scope of institutionalized platforms.

The challenges of such Volunteered Geographic Information (VGI) are mostly connected with the essential necessity to understand, engage and collaborate with citizens. They are the ones carrying the phone, firing up an app, constructing air quality sensors for their balconies or collecting and gathering data for community maps and archives. People organized in communities and platforms, with different agendas, training and motivation without the direct control of work order.

For Data Experts and analysts this creates the need to collaborate in participatory modes beyond the usual practice of participation. Data from participatory sensing platforms and volunteered geographic information systems pose different challenges for the use of that data. The problem is that non-experts are typically not familiarized with the specific data collection task and thus cannot ensure standardized sensing processes. Furthermore the various communities have own agendas and do not necessarily follow a common approach or might be even avoiding this.

For data science, HCI and artificial intelligence unusual questions arise here both in terms of technical solutions as well as for the research and collaboration process. While research in collaborative computing has primarily explored infrastructures that support volunteering on a technology-by-technology basis (e.g., Wikipedia or Twitter), we have yet to examine empirically the activity that constitutes volunteering and the ways in which technology supports that without being the centre structure for volunteering work. This is mainly important for the question if and how you the resulting data can be used.


List of Topics

Focussing on the resulting volunteered geographic information from participatory sensing platforms, we are interested in contributions and positions situated in five areas:

·         Accessibility: How to deal with ad-hoc networks, autonomous sensing communities, unreliability of technical and social activities, need of trust to access data or varying network protocols and what models of accessibility can be applied to ensure the accessibility of data?

·         Plausibility: How to decide or improve the plausibility of environmental data when human error, manipulative behaviour, lack of standards, missing calibration, low-quality equipment in varying states of maintenance and technical model is the source of data?

·         Anomaly Detection: Anomalies are errors or useful new information. How to identify useful anomalies within large heterogenous datasets?

·         Data Quality: How can environmental data as time and location dependant data from multiple sources be aggregated, contextualized and linked to improve data quality in a heterogenous network of things, that measure something?

·         Visualization: How to visualize highly complex datasets for non-IT and non-data experts and activists as explorative tool in highly agile and experimental situations as workshops, makerlabs or private houses?


Organizing Committee

·         Kai v. Luck, Creative Space for Technical Innovation, HAW Hamburg

·         Susanne Draheim, Creative Space for Technical Innovation, HAW Hamburg

·         Jessica Broscheit, Artist, Hamburg

·         Martin Kohler, HafenCity University Hamburg


Programme Committee

·         Ingo Börsch, Technische Hochschule Brandenburg | Brandenburg University of Applied Sciences, Germany

·         Susanne Draheim, Hamburg University of Applied Science, Germany

·         Stefan Wölwer, HAWK University of Applied Sciences and Arts Hildesheim/Holzminden/Goettingen, Germany

·         Kai v. Luck, Creative Space for Technical Innovation, HAW Hamburg, Germany

·         Tim Tiedemann, Hamburg University of Applied Science, Germany

·         Marcelo Tramontano, University of São Paulo, Brazil


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