The Bloustein School is pursuing a range of ongoing initiatives related to the adoption of smart civic technology and the use of data for public good. The 21st century has presented new challenges along with new opportunities to use data to solve grand public challenges. Leveraging data presents new opportunities to encourage civic engagement through cross-disciplinary collaboration and the implementation of cutting-edge technologies.
Minds and Machines
Intelligent tools are changing the world, our lives, and our work in expected and unexpected ways. They have led to advancements in our health, livelihood, and overall quality of life. At the same time, their misuse can have harmful individual and societal outcomes, such as loss of privacy, algorithmic bias, group marginalization, and the propagation of misinformation. Today’s interconnected world requires intelligence in science and technology to simultaneously support growing needs and safeguard us from unintended consequences.
Minds and Machines is a visionary interdisciplinary effort that will push the frontiers of science and responsible innovation through intelligence to achieve the transformational advancements necessary for economic change and public good in a world with intelligent machines. It will build on existing strengths at Rutgers to position the university as a hub for emerging areas of digital technology done in partnership with industry and government agencies and through engagement with the public. The core of its work will be the education of the next generation of scientists, businesspeople, and civic leaders, who will play a crucial role in mitigating threats, meeting the challenges, and exploiting the social opportunities of a new era.
Smart Civic Technology and Social Justice Cluster
The Smart Civic Technology and Social Justice Cluster is an interdisciplinary team of faculty and staff researchers who are committed to deploying civic technology with a focus on responsibly using technology to improve social justice and equity in communities.
Tracking COVID-19’s Impact on New Jersey With Anonymized Cell Phone Location Data
Using data from anonymized mobile devices and building footprints, we examined how mobility patterns changed in NJ for the period of March 1, 2020, to May 17, 2020. This two and half month span includes the period with the maximum restrictions on individuals and businesses.
An interactive dashboard was built based upon this work allowing comparisons between 2019 and 2020 movement patterns and visits to points of interest in all 21 counties of New Jersey.
The Rutgers Urban & Civic Informatics Laboratory is taking this work further with additional ongoing projects and initiatives to leverage new and emerging technologies for the public good.
Machine learning neighborhood-level preferences for automated vehicles
Wenwen Zhang, Assistant Professor, Informatics
Automated Vehicles (AVs) have gained substantial attention in recent years as the technology has matured. Researchers and policymakers envision that AV deployment will change transportation, development patterns, and other urban systems. Researchers have examined AVs and their potential impacts with two methods: (1) survey-based studies of AV preferences and (2) simulation-based estimation of secondary impacts of varied AV deployment strategies, such as Shared AVs (SAVs) and Privately-owned AVs (PAVs).
While the preference survey literature can inform AV simulation studies, preference study results have so far not been integrated into simulation-based research. This lack of integration stems from the absence of data that measure preferences towards PAVs and SAVs at the neighborhood level. Existing preference studies usually investigate adoption likelihood without collecting appropriate information to link preferences to precise locations or neighborhoods. This study develops a microsimulation approach, incorporating machine learning and population synthesizing, to fill this data gap, leveraging a national AV perception survey (NAVPS) and the latest National Household Travel Survey (NHTS) data.
The model is applied to San Francisco, CA, and Austin, TX, to test the concept. We validate the proposed model by comparing the spatial distributions of synthesized ride-hailing users and observed ride-hailing trips. High correlations between our synthesized user density and empirical trip distributions in two study areas, to some extent, verify our proposed modeling approach.
Future of Work in the Age of Intelligent Machines
Evaluating Workforce and Education Outcomes in New Jersey With State Agency Data
The Bloustein School currently serves as the host and manager for the State of New Jersey’s multi-agency integrated longitudinal data system. The New Jersey Education to Earnings Data System (NJEEDS), which resides at the John J. Heldrich Center for Workforce Development, is a centralized longitudinal data system for education and workforce data. Its mission is to create a single place for evidenced-based policymaking by combining state education, post-secondary education, employment, and workforce longitudinal data to improve governance efforts, policymaking, and the performance of educational, and workforce initiatives.
It helps:
- State agencies be more transparent and accountable by providing accurate and important insights about New Jersey’s overall performance.
- Policymakers in the executive and legislative branches in evaluating existing programs to know whether a new reform initiative is working.
- Students and families make better-informed choices about educational and training options by publishing information on the effectiveness of educational and training programs.
Applied Data Analytics Training Program
In partnership with the State, the Bloustein School is using this data system to train state policymakers, students, and researchers in how to use technology and data science to obtain useful information for public policy and evaluate employment and higher education using the state’s own administrative data.
Intensive and Extensive Approaches to Crowdsourcing Spatial Data
Will Payne, Assistant Professor, Geographic Information Science
Much has been made of how “platform urbanism” firms are altering the rhythms of work through combining novel (and often insecure) labor models with technical affordances based on the recent abundance of high-quality spatial data on cities around the world. How do technology companies monetize information about urban amenities without paying contributors for the millions of local reviews, photos, and tips that draw in audiences attractive to advertisers?
In this project (currently in press; citation to come), I use a combination of key informant interviews and analysis of financial statements, company job posts, and marketing materials to explore how two major location-based services (LBS) in the United States, Yelp and Google Maps, leverage volunteer top contributor programs to ensure access to reliable spatial data. Through the Elite Squad program, paid Yelp staff take an active curatorial role growing the company’s volunteer reviewer base in select urban regions in North America, with a focus on urban regions with high median incomes and education levels. Google’s Local Guides program, on the other hand, uses an extensive, self-service model to collect data on a global scale. Both companies enroll and motivate users in ways that present unpaid review labor as affirming, with emphases that reflect their scalar strategies: Yelp stressing tight-knit sociality and Google global altruism.
Remote Workers Don’t Escape the Power Structure
Even before the COVID pandemic, remote workers were learning that physical untethering does not mean freedom from corporate power relationships. Remote workers often use smart work spaces that are created through access to wireless networks and mobile cloud computing collaboration software. Yet the power relations embedded in these overlapping physical and cyberspaces function to control the spatial and temporal fragmentation of related work activities.
