During the QROWD webinar “The Human Factor in Big Data” hosted by BDVA_PPP, QROWD coordinator Elena Simperl gave an overview on how human interaction and crowdsourcing help in the different steps of the data value chain and provided examples on how to add human in the loop in the domains of Smart Cities and Smart Transportation.
In our present-day data economy data has had a transformative impact on the economy which demands new approaches from regulators. Cities have access to more data than ever to inform policy and service design.
Yet there are numerous challenges regarding data and data availability, and here, human involvement is used to help machine learning. The presence of the human factor in big data allows for – among others – better data and validation of algorithms and it also empowers citizens. There are also challenges to it. How do organizations leverage the human factor?
Two examples of the “what, who, how, why” methodology which has been used by QROWD in the transportation sector are for urban auditing (for collection of information about urban spaces in a city) and for prediction of the modal split (the use of the different means of transport for one trip).
To answer the “how” question, two tools developed for urban auditing are: The Virtual City Explorer, by which data can be analyzed and validated; and the i-Log app, which is used for people-centric sensing.
The platform being developed by QROWD features integrated crowdsourcing to deploy hybrid data collection and analysis workflows.
- The full slide presentation can be seen here: https://www.slideshare.net/BDVA/bdve-webinar-series-qrowd-the-human-factor-in-big-data
- The webinar recording can be followed here: https://www.youtube.com/watch?v=gdL4Wg8F3Fo&feature=youtu.be