| NSF Org: |
IIS Division of Information & Intelligent Systems |
| Recipient: |
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| Initial Amendment Date: | August 11, 2018 |
| Latest Amendment Date: | January 11, 2021 |
| Award Number: | 1816763 |
| Award Instrument: | Continuing Grant |
| Program Manager: |
William Bainbridge
IIS Division of Information & Intelligent Systems CSE Directorate for Computer and Information Science and Engineering |
| Start Date: | September 15, 2018 |
| End Date: | August 31, 2022 (Estimated) |
| Total Intended Award Amount: | $246,805.00 |
| Total Awarded Amount to Date: | $246,805.00 |
| Funds Obligated to Date: |
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| History of Investigator: |
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| Recipient Sponsored Research Office: |
3112 LEE BUILDING COLLEGE PARK MD US 20742-5100 (301)405-6269 |
| Sponsor Congressional District: |
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| Primary Place of Performance: |
4130 Campus Drive College Park MD US 20742-5103 |
| Primary Place of
Performance Congressional District: |
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| Unique Entity Identifier (UEI): |
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| Parent UEI: |
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| NSF Program(s): | HCC-Human-Centered Computing |
| Primary Program Source: |
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| Program Reference Code(s): |
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| Program Element Code(s): |
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| Award Agency Code: | 4900 |
| Fund Agency Code: | 4900 |
| Assistance Listing Number(s): | 47.070 |
ABSTRACT
This research will develop a foundational tool for understanding how civic technologies are used and how information inequalities manifest in a city. User data from new civic technologies that reveal inequalities in the information environments of citizens has only recently become available. Since a large portion of data is demographically or geospatially biased due to varying human-data relationships, computational social scientists have used data modeling and algorithmic techniques to adjust the data and remove biases during data-processing. However, this approach limits our understanding of how and why biased information is created, and our ability to address urban information inequalities and biased data-creation. Consequently, as cities transition to e-government enabled by information and communication technology, they may project the inequities of the past into the smart cities of the future, so a fresh approach is needed. This innovative research analyzes and visualizes data from Boston's 311 system for reporting non-emergency issues to the city government, using computational and qualitative approaches to identify, categorize, and understand the kinds of information disparities that are becoming institutionalized by crowdsourced municipal systems, inhibiting smart city transitions, and perpetuating information deserts. For Boston and its citizens, this research could improve both the function and the equity of the city's 311 system. The resulting insights and tools could also inform other cities' implementation of smart city technologies, identify potential distortions in existing urban datasets, and surface potential corrections that could improve decision making and equitable delivery of services for all residents.
The research will be performed in three phases. First, six years of civic, census, and geospatial data will be combined with interviews with users, then analyzed to discover the socio-technical dimensions of "information deserts," which are conceptual and physical spaces where local information is poorly embedded in diverse infrastructures and/or less available than in other areas of a city. This research will develop a conceptual model to determine where and how information deserts are located, identify a typology of information deserts based on related community features; and, assess relationships between information deserts and major demographic and geospatial features of data biases. Second, the research team will perform semi-structured interviews with civic stakeholders to gather user requirements for a visual analytics tool as well as to validate the ground truths for the initial models. Based on this, a visual analytics tool will be created to show different types of information deserts, their causes, and anticipated results. Third, through an iterative process the research team will conduct participatory modeling activities with municipality officials and relevant stakeholders to refine the computational models with local contextual information. Also, the usability of the visual analytics tool will be improved with additional user studies. The resulting conceptual and computational models of information deserts will support a refined visual analytics tool that displays information deserts and their characteristics.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
PROJECT OUTCOMES REPORT
Disclaimer
This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.
The 311 systems are a government information system for monitoring non-emergency civic issues such as a pile of trash, fallen trees, and potholes. This project aims to understand how the data is reported disproportionately to the 311 systems across different neighborhoods. To effectively understand potential biases or uneven monitoring of civic issues, the project team partnered with the City of Boston, who provided restricted 311 reports datasets through the Boston Area Research Initiative (BARI).
First, the team developed a theoretical framework for ?information deserts? which conceptualizes how data is provided (or not provided) to information systems. This framework allowed the team to generate key variables that measure how Boston residents report civic issues, how much they reported over time, and how much certain types of reports were or were not reported in particular areas.
Based on the information desert framework and the key variables identified from the framework, the team modeled and analyzed the Boston 311 dataset using data science methods. The analyses leveraged machine learning techniques, computational modeling, statistical models, and geospatial modeling techniques. Because of the data availability at the time of analysis, we used the 311 data generated in 2015. The analysis showed that poor neighborhoods tended to (1) report civic issues less, (2) have a smaller number of 311 users, and (3) have higher levels of people?s mobilities and geographical coverage than affluent neighborhoods. These results indicate that there are systematic disadvantages embedded in the use of 311 systems for managing non-emergency issues in neighborhoods.
In addition to these analyses, the team also conducted interviews with city officials in Boston. The interview was conducted to understand how data dashboards could be designed to support local governments? decision-making process. The interview analysis showed that many city officials were using a data dashboard as a means to understand and focus civic issues in their problem identification process. Also, the interview helped the team understand city officials? values and their perceptions of 311 systems in their daily work.
Informed by both the 311 and interview data analysis, the team created a new visualization system, called ?Boston 311 Information Deserts (https://infodeserts.org).? This system is a map-based visualization system that demonstrates how the volumes of 311 reports and users are related to other community characteristics such as poverty level. Also, the visualization system makes it possible to see the differences between reports submitted by government users and non-government users, based on predictions. This system is currently a proof-of-concept version and will be developed further by designing it carefully to support people?s decision making processes.
Overall, this project contributes to (1) science by developing and examining ?information deserts? embedded in government information systems, (2) management of public services through understanding the role of technology in the provision of government services, and (3) data science by leveraging machine learning and modeling techniques in generating key variables that are related to information deserts.
Last Modified: 12/27/2022
Modified by: Susan J Winter
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