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Toward Understanding Civic Data Bias
in 311 Systems: An Information
Deserts Perspective
Myeong Lee
George Mason University
Fairfax, VA 22030, USA
mlee89@gmu.edu
Jieshu Wang
Erik Johnston
Arizona State University
Tempe, AZ 85281, USA
jwang490@asu.edu
erik.johnston@asu.edu
John Harlow
Eric Gordon
Emerson College
Boston, MA 02116, USA
john_harlow@emerson.edu
eric_gordon@emerson.edu
Shawn Janzen
Susan Winter
University of Maryland
College Park, MD 20742, USA
sjanzen@umd.edu
sjwinter@umd.edu
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CSCW’20,, October 17–21, 2020, Miami, FL, USA
ACM 978-1-4503-6819-3/20/04.
https://doi.org/10.1145/3334480.XXXXXXX
Abstract
While civic technologies for public issues and services such
as 311 systems are widely adopted in many U.S. cities, the
impact of the emerging civic technologies and their data-
level dynamics are unclear. Because the provision patterns
of civic issues to technological systems are different across
neighborhoods and populations, it is difficult for city officials
to understand whether the provided data itself reflects civic
issues. Also, the disparities in the information provided to
civic technologies in different neighborhoods may exacer-
bate the existing inequality. To understand how civic data
is created and how people’s use of civic technologies plays
a role as an intermediary process in shaping community
performances, we take an information deserts perspec-
tive in studying 311 systems. The concept of information
deserts is informed by a material understanding of local in-
formation landscapes, making it possible to distinguish local
information’s structural features from its social-construction
process. Based on this theoretical lens, we suggest new
opportunities for civic technology and data research.
Author Keywords
Information deserts; 311 systems; data bias; smart cities
CCS Concepts
•Information systems →Information systems applica-
tions; Data analytics; •Human-centered computing →
Human computer interaction (HCI);
Introduction
Beginning in Baltimore in 1996, cities began using the tele-
phone number “311” to enable residents to report non-
emergency (i.e., not 911) issues, such as potholes, street-
lights, and graffiti, among other urban problems [7]. Subse-
quently, cities have augmented telephone-based 311 ser-
vices with email-, website-, and application-based reporting.
Benefits from these 311 systems have included lower costs
and better service provision [1], as well as more efficient re-
source allocation, such as moving calls from 911 to 311 [5].
Over time, 311 systems have become an important commu-
nication tool between local governments and publics, with
311 mediating public services provision.
Figure 1: The relationship
between data biases,
policy-making, and inequalities.
While 311 systems have greatly expanded the number of
people who contribute to urban data sets, researchers
found that contributors’ motivation is mostly not a sense
of civic duty, but rather territoriality, or a desire to preserve
and protect private space [6]. As a result, socio-economic
divisions tend to be reinforced, not disrupted, through 311
systems [3]. Because a large portion of civic data is his-
torically, demographically, or geospatially biased, compu-
tational social scientists often use algorithmic and data
modeling techniques to understand and adjust for biases
for predicting other community characteristics [8]. However,
these approaches only address biases after the creation
of data, rather than before or during its creation. In addi-
tion, the prediction models that rely on the biased features
in the data could be less generalizable if civic technologies
are revised or enhanced, which may alter the dynamics
of human-system relationships in an ad-hoc manner. If it
were possible to address biases as civic data is created, it
could help researchers and governments improve the eq-
uity of municipal service provision as well as the robustness
of civic data quality. Failing to understand and ameliorate
biases in civic data can exacerbate the inequalities of the
past and institutionalize them in the cities of the future (Fig-
ure 1) [4].
To understand the dynamics of smart cities of the future
and minimize the negative effects of technical interventions
on inequalities, this position paper proposes that it is imper-
ative to:
• Identify the biases of the past in the datasets of the
present
• Understand how those biases proliferate into hybrid
computer-human systems
• Develop computational models that can operational-
ize the information deserts of civic issues to illustrate
those biases
• Improve municipal decision-making and public ser-
vice provision by mapping information deserts as a
map-based visualization tool
• Produce design guidelines for future civic technolo-
gies to identify and address provision-level biases
Local Information Landscapes
To develop new approaches for modeling and visualizing
the inequities in the pipeline of civic data creation, process-
ing, and use, this position paper draws on a theory of local
information landscape (LIL theory) [2]. LIL theory is a meta-
theoretical framework that explains the community-level,
material structures of local information (e.g. flyers, web-
sites, block parties) and their relationships to other com-
munity characteristics. Mapping LILs can help cities better
manage information provision processes for civic repair.
Because 311 data is a result of ad-hoc design of civic tech-
nologies, studying 311 systems and their data from an LIL
perspective may make it possible to examine some struc-
tural features of the civic issue landscape. Some particu-
larly important aspects of that information provision include:
• The types of civic issues reported (e.g. potholes, bro-
ken sidewalks, abandoned vehicles)
• The volume of reporting across issues
• Individual reporting frequency (Boston median one
report per year)
• Territoriality in reporting (range of report locations for
an individual)
• Geographical coverage of individuals in reporting
issues
Information Deserts
By studying the aspects of information provision as vari-
ables, we can uncover the information deserts of civic is-
sues in cities [2]. Information deserts are conceptual and
physical spaces where local information is poorly embed-
ded in diverse infrastructures and/or less available than
other areas of the city: the material pre-conditions of local
information that can give rise to information inequality.
The reasons why people do or do not report issues to the
311 systems can vary significantly, and information about
how and why people report issues to 311 is an important
building block for understanding information deserts in
cities. Only after understanding people’s motivations to re-
port civic issues and their information practices in their daily
lives, does it become possible to understand information
inequality and leverage 311 data in an efficient and equi-
table manner to further refine the civic technology. To thor-
oughly investigate the information deserts in cities requires
(1) partnerships to obtain, interpret, and analyze 311 data,
(2) individual-level data granularity (open data ideally, after
eliminating privacy concerns), (3) a combination of social
scientific and computational methods (to address data qual-
ity and missing variables), and (4) ongoing iterative interac-
tions between city employees, researchers, and publics to
define and visualize insights from this research that can im-
prove municipal decision-making and public service provi-
sion. Our current partnership with the City of Boston makes
the pilot of this approach possible as the individual-level
dataset has been made it available to our research team.
Approach
To determine where and how information deserts are lo-
cated, census and geospatial data complement 311 sys-
tem datasets. By making use of computational models that
describe individuals’ information provision behavior and
mobility, it is possible to identify a typology of Boston’s infor-
mation deserts based on community features that affect or
are affected by information deserts. Then, it will be possible
to assess relationships between information deserts and
major demographic and geospatial features of data biases,
as well as how those biases might proliferate into munici-
pal decision-making. Building on the multi-dimensional LIL
model, the interactions between components of LIL can be
quantified by making use of computational models such as
a flow network model for quantifying the degree of informa-
tion fragmentation, an institutional network for measuring
the embeddedness of information in diverse sources, or
a comparative advantage model for measuring the rela-
tive impact of each information source. From an analytic
perspective, a core part of this approach is that a series of
these studies examines community characteristics, informa-
tion provision behavior of individuals, information deserts
of civic issues, and their outcomes separately as building
blocks of the information inequality embedded in civic tech-
nologies.
Future Work
An early prototype visualization tool for 311 data, produced
through this grant and a partnership with Supernormal, is
available at https://betablocks.city/discover. As the work pro-
gresses, this tool will become the object of iteration in re-
sponse to participatory design sessions with city officials to
link its affordances with the needs of its potential users. Fu-
ture work will include (1) refining and developing computa-
tional models of information provision behavior, (2) access-
ing 311 datasets from other cities through expanding our
partnerships, (3) providing a typology of information deserts
of civic issues through data analytics and interviews and (4)
building data visualizations of information deserts of civic
issues to help reduce bias and inequity in public service
provision. This position paper is a call for civic technology
researchers’ attentions to the material, structural features
of civic data that exists in diverse local infrastructures and
civic technologies.
Acknowledgements
We thank all the municipal employees who participated in
this work. We also gratefully acknowledge the grant from
NSF (#1816763).
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