“A Smart City is one where data and technology improve people’s lives.¹”
Governments, NGOs and academic researchers are looking to data and analytics to create more livable cities. Ideas and innovation are flowering.
The US Department of Transportation recently sponsored a competition among American cities to design the “city of the future,” with the winner taking home a $40 million prize. Not surprisingly, data and analytics were a big part of many of the proposed solutions, including City of Portland’s proposal – Ubiquitous Mobility for Portland – selected as one of seven finalists in the competition.
The list of data sources available to Smart Cities for use in urban analytics is almost endless: data from connected and automated vehicles, data from connected intersections, probe data from cell phones, crowd-sourced data, fixed-sensor data, air quality readings, and more. A critical question is: How can we use this vast store of data to improve people’s lives?
One might create an interactive map combining a variety of data related to pedestrian-involved crashes, to identify potentially unsafe pedestrian intersections so safety modifications, such as flashing yellow beacons, can be proactively installed. One might combine real-time bus arrival information with real-time traffic information so that we can more reliably predict bus arrivals — to give peace of mind to workers wondering if they are going to make it to their jobs on time. Finally, one might improve routing information for transit and bicycle users to allow routes that optimize for factors other than speed of route such as air quality for bikers, availability of seating for transit riders or perceived comfort (i.e., lack of vehicle traffic) for bikers.
Barriers to using data to improve people’s lives are technical, institutional and social. Technical barriers include integrating diverse data sources, particularly integrating and conflating geo-spatial data. Institutional barriers include breaking down silos to share data within and between institutions. Last, but certainly not least, are social barriers — particularly technology adoption.
Research centers such as the ISTC for Big Data and projects such as the BigDAWG polystore and the S-Store system can help overcome the technical barriers. However, the social and institutional barriers are significant and will require a different type of work.
Community engagement and an understanding of user needs are key to overcoming institutional and social barriers. Focusing on users and usability and using processes such as User-Centered Design will be critical to adoption of future technologies produced by research centers — and thus to the ultimate success of urban analytics.
 Smart City Challenge: Ubiquitous Mobility for Portland. Proposal: https://www.portlandoregon.gov/transportation/69999. Video: https://youtu.be/JyiuPiLrM48.
Kristin Tufte is Assistant Professor, Research and Project Manager & Technical Lead of the Portal Data Archive in the Departments of Computer Science and Civil and Environmental Engineering at Portland State University. Her research areas include the use of open data and urban analytics for creating Smart Cities.