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Dis Prev Res 2023;2:3. 10.20517/dpr.2022.08 © The Author(s) 2023.
Open Access Research Article


NON-EMERGENCY RESPONSES IN THE 311 SYSTEM DURING THE EARLY STAGE OF THE COVID-19
PANDEMIC: A CASE STUDY OF KANSAS CITY

Views: 473 | Downloads: 105 | Cited:  0
Thao Tran1, Majid Bani-Yaghoub2, James R. DeLisle3

1Chapin Hall at the University of Chicago, Chicago, IL 60637, USA.

2School of Science and Engineering, University of Missouri-Kansas City, Kansas
City, MO 64110, USA.

3Henry W. Bloch School of Management, University of Missouri-Kansas City, Kansas
City, MO 64110, USA.

Correspondence to: Thao Tran, Chapin Hall at the University of Chicago, Chicago,
IL 60637, USA. E-mail: ttran@chapinhall.org


This article belongs to the Special Issue Recent Progress on Integrated
Resilience Testbed Studies
Views:473 | Downloads:105 | Cited:0 | Comments:0 | :24
Received: 11 Dec 2022 | First Decision: 20 Feb 2023 | Revised: 6 Mar 2023 |
Accepted: 16 Mar 2023 | Published: 23 Mar 2023
Academic Editor: Naiyu Wang | Copy Editor: Fangling Lan | Production Editor:
Fangling Lan

© The Author(s) 2023. Open Access This article is licensed under a Creative
Commons Attribution 4.0 International License
(https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
sharing, adaptation, distribution and reproduction in any medium or format, for
any purpose, even commercially, as long as you give appropriate credit to the
original author(s) and the source, provide a link to the Creative Commons
license, and indicate if changes were made.


ABSTRACT

In response to the COVID-19 pandemic, many U.S. citizens sought information and
support from their city governance through the 311 non-emergency service request
system. The main purpose of this study was to analyze the temporal trends in the
311 data before and during the first few months of the COVID-19 pandemic
(3/1/2019 to 9/1/2020). Like other major U.S. cities such as Dallas and New York
City, analysis of Kansas City 311 data showed that the COVID-19 pandemic has led
to a considerable decline in the aggregate number of calls. However, five
service categories (“Public Safety”, “Public Health”, “Trash/Recycling”, “Parks
& Recreation”, and “Property/Buildings/Construction”) experienced a substantial
increase in call volume. To explore whether these changes are driven by
COVID-related service requests, we used the description text data and identified
2,379 requests related to the pandemic, accounting for 4.3% of all non-emergency
requests in Kansas City between March and August 2020. More than half of the
COVID-related requests reported mask violations, where people failed to wear
masks or did not wear masks properly. Compared to the non-COVID-related
requests, citizens were more likely to seek non-emergency services through phone
and email and less likely to use the web as means of communication. In addition,
most changes in “Public Safety” and “Public Health” request volumes were driven
by these COVID-related requests. These results can help city officials and
decision makers improve the city’s resilience by allocating resources for the
abovementioned five service categories during a pandemic. In conclusion,
early-stage analysis of open-access 311 data can be a catalyst for local
governments to quickly and properly respond and build long-term resilience
against future pandemics and other health threats.




KEYWORDS

Crisis response, urban resilience, pandemic, 311 data, non-emergency service,
COVID-19


INTRODUCTION

The 311 system is a non-emergency response system where people can make a
request to find information about services, make complaints, or report
non-emergency problems, such as potholes and trash collection. The system was
initially designed to reduce call volume on the overloaded 911 system, allowing
it to be reserved for official police business[1]. In recent years, 311 request
systems have become an integral part of the e-government movement in which
technological innovations are deployed to help local governments deliver more
efficient and effective services to residents[2-4]. The system is not only
useful in reactive perspectives but can also be used in a proactive manner, such
as being merged with other disparate data sources to develop predictive models
of service needs[5].

While the 311 systems are not designed for crisis response, they have shown the
potential to efficiently help local governments serve residents in the face of
unexpected events or dramatic shocks to the system. Some jurisdictions have used
311 systems to communicate with residents, as well as anticipate the need for
resource allocations based on initial reports and predictive models[6]. Cities
have also monitored calls to flag such events and support the deployment of
messaging out to residents about emergencies and responses[7]. The 311 systems
have been used to develop responses to hurricanes, tornadoes, and other natural
disasters, including warning residents and deploying rapid response systems[8].
With respect to unexpected man-made events, 311 systems have been used to
support citizen needs and monitor activity levels. For example, during the
transit strike in New York City, the number of calls and response times varied
significantly from pre and post-strike levels[9].

During the COVID-19 pandemic, the 311 systems were put to the test of resilience
and efficiency, where the pandemic caused several disruptions. Four months after
the first case of COVID-19 in Wuhan, China, the World Health Organization (WHO)
declared COVID-19 a pandemic with more than 2 million confirmed cases and
thousands of deaths around the globe[10]. In the same month, the U.S. declared a
national emergency and many states issued mandatory stay-at-home orders, imposed
social distancing regulations, and banned large gatherings, resulting in
nationwide elementary and high school closures[11-13]. In addition, the pandemic
caused major disruptions across industries and their supply chains, particularly
in influenza, healthcare, and food, which are critical products that require a
resilient and sustainable supply during times of crisis[14-16]. At this early
stage of the COVID-19 pandemic, citizens were faced with a pressing health
crisis, strict mandates, economic impact, and shortages of personal protective
equipment and diagnostic testing. It is in situations like this when public
guidance plays an important role as it significantly affects individual risk
perception and the effective implementation of public health strategies and
measures to control COVID-19[17-19]. The 311 systems can become a resilient
component of providing public guidance, accurate local information, and
essential non-emergency service to its citizens.

The main objective of this study is to determine if the statistical and text
analysis of 311 data can be used as a tool for identifying changes in demand for
city services in the case of a global pandemic. If successful, it can be used as
a framework to improve resilience and crisis response strategies. There are a
few studies on the patterns of 311 systems during COVID-19, focusing mainly on
New York City, Dallas, and Orange County[20,21]. In New York City, the 311
systems added specific categories and descriptors for calls related to the
pandemic, which greatly helped capture the reaction of its citizen to the crisis
and associated responses taken by the municipality agencies. Lieberman-Cribbin
et al., documented the increase in a spike in cumulative calls and covid-related
calls in New York City, most of which were placed to get more information on the
coronavirus, its symptoms, prevention measures, and testing locations[20]. In
addition, Eugene et al., also reported a 71% increase in school-related calls
during 2020, reflecting the concerns of its residents on the updated schooling
plans for the Fall[21]. Furthermore, Pamukcu et al., found declines in requests
for street condition, blocked driveway, and illegal parking and spikes in noise
complaints and non-emergency police matters[22]. These studies showed that the
311 system can be utilized to understand the impact of COVID-19 on the community
as well as its additional covid-related categories providing new insights into
the existing crisis. Florida added new 311 categories related to the pandemic
(PPE for Small Biz, PPE for Houses of Worship, Orange CARES, COVID-19 Rental
Assistance) under the Public Safety category, where Pamukcu et al., found 92% of
the public safety calls between January and October 2020 were related to
COVID-19 information requests[23]. They also identified pandemic-specific calls
using the detailed description of each service request and confirmed that most
of these requests were information requests assigned to the Public Safety
category. Unlike findings for New York City, the Dallas 311 data showed reduced
noise complaints during the COVID-19 timeframe by about 14% compared to the
pre-COVID-19 period, especially in and around the city center[24].

In this paper, we first identify the existing patterns in the Kansas City,
Missouri 311 data during and before the COVID-19 pandemic. Then we explore how
the crisis response during the early stage of the pandemic was reflected in the
311 non-emergency service requests. The Kansas City 311 data showed that the
COVID-19 pandemic has led to a considerable decline in the aggregate number of
calls. However, five service categories, “Public Safety”, “Public Health”,
“Trash/Recycling”, “Parks & Recreation”, and “Property/Buildings/Construction”,
experienced a substantial increase in call volume. Since there is no indicator
or category for pandemic-specific requests, we designed a framework to identify
COVID-related calls based on the description of each request. This
identification allowed us to distinguish changes driven by covid-related service
requests. There were 2,379 requests related to the pandemic, accounting for 4.3%
of all non-emergency requests in Kansas City between March and August 2020. More
than half of COVID-related requests reported mask violations where people failed
to wear or did not wear masks properly. Compared to non-COVID-related requests,
citizens were more likely to seek non-emergency services through phone and email
and less likely to use the web as means of communication. Since there were no
changes in access to the web, this may suggest an expansion of citizens drawn to
the 311 system and an increase in community engagement. In addition, most
changes in “Public Safety” and “Public Health” request volumes were driven by
these covid-related requests. The outcomes of this study can identify the course
of actions that must be taken by the local governments to better help the
citizens at the early stage of a natural disaster or a pandemic.

The present work increases our understanding of the crisis response patterns in
the 311 systems during COVID-19 in two regards. First, the different trends
between New York City and Dallas show that citizen responses during the pandemic
captured in the 311 systems are not spatially homogenous. Thus, our analysis of
Kansas City 311 data brings insights to build a more comprehensive model of
pandemic response in the 311 systems across U.S. cities. Second, the New York
City and Florida literature showed that the proactive decision to categorize
COVID-related requests in the 311 systems during the pandemic can bring
immediate and valuable information to both municipality and its residents.
However, many cities did not identify whether a request was related to COVID-19,
including Kansas City. Here, we showed that text mining tools can be applied to
the text descriptions of the 311 requests to identify patterns in COVID-related
requests. This can assist researchers in exploring crisis response patterns in
the 311 systems during a pandemic.

The rest of this paper is structured as follows. Section "METHODS" describes our
311 data and how we identified covid-related requests. Section "RESULTS"
provides the main findings of the paper and Section "DISCUSSION" discusses the
strength and limitations of our methods and findings.


METHODS


DATA

The 311 service request data came from the Kansas City Open Data website
(3/1/2019 to 9/1/2020) recording non-emergency calls across Jackson, Clay,
Platte, and Cass counties in the Kansas City, Missouri area (KCMO). The data
contained the date the request was made, the date it was completed, the category
it belonged to, whether it was made via phone or email or other methods, and a
brief description of each request.


IDENTIFICATION OF COVID-RELATED REQUESTS

Requests related to COVID-19 were identified by searching for keywords in the
description of the request. There were 20 keywords related to the pandemic and
the policy response to fight the pandemic, including the stay at home orders,
mask mandates, and social distancing measures: “covid”, “corona”, “pandemic”,
“virus”, “positive”, “mask”, “face cover”, “coverings”, “ppe”, “social”,
“distanc”, “6 feet”, “quarantine”, “stay at home”, “gathering”, “essential”,
“still open”, “open for business”, “open and operating”, and “still operating”.
We examined the number of requests containing each keyword over time, before and
during COVID-19, to ascertain whether the identified requests are
pandemic-related as shown in Figure 1. If a keyword is associated with the
pandemic and is only mentioned in the request during the pandemic with no
mentions in the request pre-pandemic, this keyword is likely to accurately
identify covid-related requests in the 311 system. Thus, we looked for keywords
whose number of requests was few to none before the pandemic and spiked during
COVID-19. There are many keywords that did not pass this check despite being
related to the pandemic. This resulted in 2,379 calls identified to be
associated with COVID-19.

Figure 1. Monthly number of requests containing COVID-19-related keywords in
2019-2020.

Figure 1 also shows that the keywords follow three main pattern groups in the
early stage of the pandemic. The first group contains “covid”, “pandemic”,
“ppe”, “social”, “distance”, and “6 feet” with two peaks in April and July. The
second group comprises “stay at home”, “essential”, “still open”, “open for
business”, “open and operating”, “still operating”, “corona”, “virus”,
“quarantine”, and “gathering” with only one main peak in April. This is in line
with the policy implemented to control COVID-19, which imposed a strict
stay-at-home order to stop the spread of the novel coronavirus that prohibited
large gatherings and only allowed essential businesses to operate. The last
group includes “positive”, “mask”, “face cover”, and “coverings” with only one
main peak in July, which is likely in association with mask mandates put in
place to help curb the spread of the coronavirus as recommended by both the
Centers for Disease Control and Prevention and World Health Organization. In
addition, the volume of the requests by each keyword also showed that “mask” is
of the most concern among COVID-related requests, followed by “social” and
“distance”.


RESULTS


TRENDS IN 311 NON-EMERGENCY RESPONSES

We compared the cumulative request volume of the March-August period in 2020 to
its previous year and found a drop of 13%, falling from 64,351 service requests
to 56,014 requests during the early stage of the pandemic. Most of the decline
in the number of requests occurred in the first three months of the season as
shown in Figure 2. In March, the number of calls fell from 11,204 requests
pre-pandemic to 9,273 requests during the pandemic, a decline of 17%. The
largest drop took place in April, going from 11,762 calls in 2019 to 8,122 calls
in 2020, equating to a 31% drop in the total number of calls. In May, the number
of calls continued to fall by 21% compared to the pre-pandemic period. This gap
quickly narrowed in the latter three months of the season, where the number of
calls was within 10% of observations from the previous year.

Figure 2. The number of requests during March-August in 2019 vs. January-August
2020.

However, the large decline in call volume at the early stage of the pandemic was
not universal across request types. We then compared the difference in
cumulative request volume of the March-August period between 2019 and 2020 for
each type of service requested. Table 1 provides all 15 types of services ranked
from the largest (most positive) percentage change to the smallest (most
negative) percentage change with their cumulative number of requests before and
during the pandemic. We excluded requests whose type of service is “Data Not
Available”.

Table 1

The number of requests during March-August 2019 vs. March-August 2020 by type of
services

Type of serviceNumber of requests pre-pandemicNumber of requests during the
pandemicPercentage change (%)Public safety186649248.9Public
health1,3724,077197.2Trash/Recycling16,17019,98823.6Parks &
recreation80388610.3Property/Buildings/Construction7,2447,8598.5City
facilities29290.0Lights/Signals2,5542,372-7.1Sidewalks/Curbs/Ditch1,3081,156-11.6Signs1,6111,326-17.7Government442352-20.4Capital
projects757595-21.4Storm
water/sewer4,0313,088-23.4Mowing/Weeds4,2623,007-29.4Animals/Pets6,9284,357-37.1Streets/Roadways/Alleys16,6486,255-62.4



Changes in call volume vastly differ from one category to another. Table 1 shows
that five service categories received more requests during the pandemic (first
five rows), one category, “City Facilities” had no changes, and the remaining
nine categories received fewer calls when the pandemic hit (the bottom nine
rows). Categories with declines in call volume account for 60% of total call
volume pre-pandemic but only 40% of total call volume during the pandemic. It is
the largest category, “Streets/Roadways/Alleys”, that also experienced the
largest drop from 16,648 requests in the March-August period in 2019 to 6,255
requests in the March-August period in 2020, corresponding to a decline of
62.4%. Other than “Government” and “Capital Projects”, all the remaining
categories are related to street conditions, such as “Lights/Signals”,
“Sidewalks/Curbs/Ditch”, and “Mowing/Weeds”. The decline in these categories
parallels the decrease in mobility and traffic as the stay-at-home orders took
place and people were moved to work from home. This suggests that from an
administrative perspective, it is important to train the city employees in a way
that they can switch from one task (e.g., maintenance and repair of all public
streets) to another (e.g., protecting against environmental health hazards).

Of the top five categories, “Public Safety” and “Public Health” experienced the
largest increases in call volume during the early stage of the pandemic as the
number of requests for “Public Safety” increased by 2.5 times and the number of
requests for “Public Health” also doubled. This is an important result for
measuring the number of resources needed to respond properly to a pandemic. As
these categories did not have any large call volume initially, their surge in
requests was not enough to offset the declines in the aggregate trends. Though
the call volume for “Trash/Recycling” did not double like “Public Safety'' and
“Public health”, it had a significant increase of almost four thousand requests,
corresponding to 23.6%. It is followed by “Parks & Recreation” and
“Property/Buildings/Construction” with increases of 10.3% and 8.5%,
respectively.

While trends of 311 request volume in the early stage of the pandemic are useful
and informative, there are significant events other than COVID-19 that took
place in 2020, confounding whether the trends reflect non-emergency responses
due to the COVID-19 health crisis or other events at the time. For example, we
have learned that the call volume for “Public Safety” substantially increased by
many folds, but we have yet to investigate how much of the increase is driven by
covid-related requests, such as calls about lack of social distancing or
violations of masks mandate, or by non-covid related requests, such as the civil
unrest against systemic racism and the increase in time spent at home.


COVID-RELATED REQUESTS IN THE 311 SYSTEM

Since there was no indicator or category for pandemic-specific requests in
Kansas City, we used the description for request for identification and found
2,379 requests related to the pandemic, accounting for 4.3% of all non-emergency
requests in Kansas City between March and August 2020. The 311 system started
receiving COVID-related requests in March 2020 and experienced a peak of more
than 500 calls in April and another peak of more than 750 calls in July of the
same year. There is also an increasing trend in the volume of pandemic-specific
calls. We explored whether this trend is related to the severity of the
pandemic, for which we used the 7-day moving average of the number of COVID-19
new cases and the number of COVID-19 total cases in Kansas City, Missouri at the
time as a proxy[25]. Figure 3 shows that these trends for covid-related requests
aligned with the trends in the average number of new cases at the time. In
addition, the trend is also reflected in the proportions of requests related to
the pandemic, suggesting that it is not driven by the volume of all
non-emergency calls. In particular, the share of pandemic-related calls was 2.1%
in March, then peaked in April at 6.4%, came down to 1.8% and 2.5% in May and
June, but peaked again in July at 8.3%.

Figure 3. The number of covid-related requests (blue, left axis) vs. COVID-19
new cases (green line, right axis) and COVID-19 total cases (dashed red line,
right axis) in 2020.

Covid-related calls reflect citizens’ responses to the crisis of the pandemic
and pandemic-related policies, including but not limited to the stay-at-home
order, the social distancing rules, and the mask mandates. More than half of
covid-related requests contained text about mask violations where people fail to
wear masks or do not wear masks properly. Some examples of these requests are
“No Employees were wearing masks while helping customers” or “Staff not
enforcing the use of masks”. About a third of covid-related requests reported
instances of social distancing violations where people do not maintain six feet
distance from one another or hold large gatherings in enclosed spaces. For
example, one request reported “Citizen is reporting there was no social
distancing taking place in the outside eating area”. There are about four
hundred COVID-related requests reporting positive cases and failure to
quarantine, such as “They have had two positive cases and have not
sanitized/disinfected areas. They did not have staff members quarantined”.
Another major text mentioned is essential business, where almost 10% of
COVID-related calls reported the violation of opening a non-essential business
during the stay-at-home order. An instance reported “[Business entity] has
continued to operate a non-essential business during the stay-at-home order”. It
is important to note that a covid-related request can reflect multiple
violations at the same time. For example, a request reported that people failed
to wear masks properly and socially distanced themselves, “No social distancing,
very crowded. All staff wearing masks under nose”.

We further investigated how these different topics are interconnected and
highlighted using word association graphs based on the description text of
covid-related quests [Figure 4]. The texts were cleaned, tokenized, stemmed with
stop words removed, and limited to bigrams that were mentioned more than 20
times. Each word is a node and the relationship between two words are
represented with an edge. The transparency and width of the edge are accentuated
based on the relationship between words. The more time a combination of two
words is mentioned in the description of a covid-related request, the bolder and
wider the edge between them is. Figure 4 shows that wearing masks (wear, mask)
is of the most concern among covid-related requests, which also has many ties to
mandate-related words such as policy, mandate, rule, ordinance, enforce, and
require. Mask was mentioned in association with the large gatherings (people,
10, 20, inside) and positive test (employee, test, positive). Another major
concern among covid-related requests highlighted in the word association graph
is social distancing (social, distance, guideline, practice), which stands
independent from the word cluster surrounding mask.

Figure 4. Words association graph for the description text of all COVID-related
requests. The transparency and width of the edge vary based on the strength of
the relationship between words. The more time a combination of two words is
mentioned in the description of a covid-related request, the bolder and wider
the edge between them is.

In addition, it is also imperative to ascertain that citizens have access to the
311 systems during crises caused by natural disasters or pandemics. While there
are numerous methods to submit a request, phone, web, and email remain the most
common methods for outreach, accounting for almost 90% of all non-emergency
requests between March and August of 2020. Thus, we focused on the usage of
these methods among covid-related calls compared to non-covid-related calls to
identify important means of communication in crisis response during COVID-19.
Table 2 shows that people relied more on phone and email for covid-related
requests compared to non-covid-related requests. As noted, 63.1% of all
COVID-related requests were made via phone, 5.3% higher than that of
non-COVID-related requests. More dramatically, the share of covid-related
requests made through email was twice the share of non-COVID-related requests
made through email. On the other hand, the web was a less common method used
among covid-related requests at 19% compared to non-covid-related requests at
22.7%.

Table 2

Comparing covid-related requests to non-covid related requests by sources during
March-August in 2020

Covid-related requests (%)Non-covid-related requests (%)All requests
(%)Email16.39.09.3Web19.022.722.6Phone63.157.858.0




THE ROLE OF COVID-RELATED REQUESTS IN AGGREGATE TRENDS

Since COVID-related requests only accounted for less than 5% of all
non-emergency services, their influx had minimal impact on the declining trend
of the aggregate volume in the 311 systems during the early stage of the
pandemic. However, there were five categories, “Public Safety'', “Public
health”, “Parks & Recreation”, “Property/Buildings/Construction”, and
“Trash/Recycling”, that experienced a surge in volume. We investigated whether
COVID-related requests can be the driver behind these volume increases by
looking at the monthly year-over-year change in call volume from March through
August of 2020 for a category and the monthly volume of covid-related requests
in that category during the same time period. [Figure 5] We found that
covid-related requests played an important role in the volume spikes for “Public
Safety”, “Public Health”, and “Trash/Recycling” categories but not for others.
In “Property/Buildings/Construction” and “Trash/Recycling” categories, there are
few to no covid-related requests in this category; thus, the fluctuations in
these two categories are driven by other factors than the pandemic-specific
calls. Meanwhile, the figure showed the sizable volume of covid-related requests
in “Public Safety'', “Public Health”, and “Parks & Recreation” categories.

Figure 5. Monthly year-over-year change in call volume from March through August
of 2020 for a category (lighter blue) and the monthly volume of covid-related
requests in that category during the same time period (darker blue).

In the “Public Safety” category, covid-related requests not only accounted for
almost half of the increase in call volume during the early stage of the
pandemic but also traced the trend of the changes itself, especially with the
peaks in April 2020. COVID-related requests played a larger role in the changes
in the “Public Health” category. The panel for “Public Health” category in
Figure 5 shows that they explained most of the increase in volume for this
category and thus drove its trend. On the other hand, while there are some
COVID-related requests in the “Parks & Recreation” category, their volume
accounted for a small portion of the monthly increases observed in this
category. In addition, the monthly trends of COVID-related requests were
misaligned with the monthly trends of changes in the 311 requests volume of this
category, suggesting other driving factors at play in this scenario.


DISCUSSION


RELATION TO EXISTING LITERATURE

Compared to existing literature, the share of covid-related requests that we
found for Kansas City of 4.3% is similar to the share of covid-specific calls in
New York City (4.14%) and the share of pandemic-specific requests in Orange
County Florida (5.15%)[20,23]. Like Pamukcu et al., 311 data in Kansas City also
showed declines in requests in comparable categories to street conditions and
traffic signal conditions[22]. While Pamukcu et al., and our paper both
identified pandemic-specific requests via text analysis of the description
attribute of the requests, we employed a larger set of keywords with some
differences from their framework. We also proceeded to conduct further checks to
make sure these keywords were not identifying requests pre-pandemic. However,
both sets of results found a significant volume of “Public Safety” calls during
the early stage of the pandemic[23]. Different from the existing literature, we
extended our analysis to explore the difference between the number of requests
made using traditional phone calls vs. web-based services, and how the pandemic
affected the tendency in using alternative modes for accessing public services.
We found residents relied more on phone and email for covid-related requests
compared to non-covid-related requests during the pandemic.


CHARACTERIZE CRISIS RESPONSE

Our results showed that the data from Kansas City 311 system can be used to
characterize the impacts of the pandemic on its residents. These patterns and
characterization are the first essential step toward using 311 non-emergency
service data to make data-driven decisions in terms of resource allocation or
identify changes that should be made to the 311 system. Further studies of the
311 systems would be pertinent next steps, including but not limited to system
performance through metrics such as response and completion rate, the capacity
of the systems by categories and department, and the utilization rate of the
systems among its residents. However, the present works lay the grounds for the
311 systems to become the resilient and efficient centralized system to capture
and characterize citizens’ crisis response and ultimately provide data-driven
timely improvements and reactions during difficult times.


COLLECT CRISIS-SPECIFIC INFORMATION

Literature on New York City and Orange County, Florida showed that the cities’
proactive adjustments to 311 systems were able to provide valuable new
information about the pandemic. These decisions demonstrate the commitment of
municipalities to utilizing the system to capture how residents are responding
to the ongoing crisis. In particular, New York City’s use of the new “Social
Distancing” and “Face Mask Violation” descriptors provides an example of similar
changes to existing systems that can offer vital information during times of
crisis[22]. Similarly, Orange County’s categorization of different CARES funding
programs in their 311 system showed great potential for providing relevant
information to the community and the city itself[23].

The methodology proposed in our study of Kansas City 311 data can be applied to
311 data of any city without pandemic-specific descriptors. Hence a framework
has been developed to identify pandemic-related requests based on the text
description of each request. Note that implementing this framework requires
careful data analysis and critical thinking. For instance, in the text mining of
Kansas City 311 data, there were cases containing pandemic keywords unrelated to
COVID-19 or public health in general. An example is a request under property
violations, “Citizen is calling to report 6 feet-tall weeds in the front of this
property during the pandemic”. There were requests that did not include
COVID-related keywords but were actually related to the pandemic. For example,
there were requests that reported a gathering, but they were actually related to
the COVID-19 pandemic. This includes “Crowd of people, in violation of mayors’
order. with 15-20 cars and lots of people”.

In the Kansas City 311 system (and possibly other cities’ 311 systems), the
existing subcategory “Public Health-Disease Control-All” may be considered as a
catch-all category for pandemic-specific requests; However, our research
revealed that it is inadequate in encapsulating all COVID-related requests in
the system. Only 76.4% of all COVID-related requests were identified through
this method. Other significant categories that COVID-related requests fell under
are “Public Safety-Police-Other” (4.5%), “Public Health-Food
Establishment-Restaurant” (4.4%), “Public Safety-Regulated Industries-Alcohol
Business” (3%), “Property Violations” (1.2%), and “Parks &
Recreation-Services-Service Issue/Problem” (0.7%). On the other hand, while the
“Public Health-Disease Control-All” category started along with the pandemic,
there are requests in this category that are not covid-related. For example,
requests like “Complainant noticed no soap in the bathroom” or “Smell of odor
and staff not wearing gloves while handling food” may not necessarily be related
to COVID-19.

While our methods have the abovementioned limitations, our identification and
analysis of COVID-related calls revealed new valuable information on how
residents of Kansas City were responding to the ongoing crisis. In particular,
the biggest concern among COVID-related requests was mask violations, where
people fail to wear masks or do not wear masks properly, especially in large
gatherings indoors, followed by social distancing. Both tie strongly with mask
mandates and social distancing measures to reduce the spread of the COVID-19
pandemic. In addition, citizens were more likely to seek COVID-related
non-emergency services through phone and email and less likely to use the web
compared to non-COVID-related requests.

There is significant value in collecting crisis-specific information, such as an
indicator for pandemic-related calls, in the 311 system to adequately and
correctly provide valuable information associated with the ongoing crisis. These
adjustments will allow stakeholders to (i) monitor citizens’ crisis-related
responses in real time; (ii) provide better support; and (iii) allocate
appropriate resources to necessary service areas. While our analysis is specific
to Kansas City and its 311 system, there is great potential to generalize this
framework to study the non-emergency responses of other 311 systems during a
crisis.


CONCLUSION

Our analysis showed that the pandemic led to a considerable decline in the
aggregate number of 311 calls in Kansas City. However, the decline was not
ubiquitous across different types of service requests where “Public Safety”,
“Public Health”, “Trash/Recycling”, “Parks & Recreation”, and
“Property/Buildings/Construction” experienced a substantial increase in call
volume. We further explored whether these changes were driven by covid-related
service requests. Using descriptive text data analysis, we identified 2,379
requests related to the pandemic, accounting for 4.3% of all non-emergency
requests in Kansas City between March and August of 2020. More than half of
covid-related requests reported mask violations where people fail to wear masks
or do not wear masks properly. Compared to non-covid-related requests, citizens
were more likely to seek non-emergency services through phone and email and less
likely to use the web as means of communication. The COVID-related requests
accounted for the majority of changes in “Public Safety” and “Public Health”
request volumes as well as the trends in these categories during the early stage
of the pandemic.

The present work showed that the data from Kansas City 311 system can be used to
characterize the impacts of the pandemic on its residents and provided insights
to build a more complete picture of crisis response in the 311 systems across
the U.S during the early stage of the pandemic. We utilized the text description
of the request to build a framework to identify covid-related requests and
conduct further analysis. The designed framework can be utilized and modified to
identify pandemic-related non-emergency requests to assist researchers in
exploring crisis response patterns in the 311 systems during the pandemic.
Hence, local governments can leverage application 311 data to build resilience
in the services they provide to their communities.

There are several limitations of the current research. First, as our study
relies on 311 data for Kansas City, Missouri, our findings and interpretations
are limited to Kansas City. Second, our identification of covid-related requests
may have misclassified some cases where requests contained covid-related
keywords but were not related to COVID-19, or where requests did not include
covid-related keywords but were related to the pandemic. Third, 311 systems are
continuously evolving in terms of data collection, request classification, and
request tracking, creating challenges for longitudinal studies.

There are many possible extensions from the current work for future research
potential, including:

(i) Better data mining and text mining techniques to identify pandemic-related
calls and analyze the relationship between different categories to better
understand the implications of changes in population behavior more accurately.

(ii) Comparison of different 311 systems given that many municipalities have 311
data publicly available such as New Orleans, Chicago, San Diego, and Houston.

(iii) Design and implement a real-time surveillance system to extract vital
information from all 311 systems (or other centralized systems such as 211
systems and 911 systems) during a pandemic.

In summary, the COVID-19 pandemic created a significant, unknown, and
unprecedented shock that completely disrupted the lives of citizens and the
operation of cities, businesses, and organizations. Given its unexpected nature,
311 systems were not pre-programmed to help jurisdictions respond to the crisis.
However, flexibility and resilience that city managers and staff have developed
in dealing with natural and man-made disasters provide a framework that can be
modified and adopted to improve resilience and response systems. Researchers
have coined the term “agile or adoptive governance” to describe the relative
ability of various government agencies to respond to the COVID-19 pandemic[26].
As might be expected, the process of adoption was not smooth and left all levels
of government and residents struggling to figure out what to do. However,
integration of data mining and analytics as presented in this study can be used
to develop early warning systems for the emergence of new and unexpected
disruptors. This will enable managers and 311 system operators to modify data
collection and classification systems to better track how such shocks are
playing out, thus providing a basis for developing proactive responses rather
than being forced to be reactive. Such integration can help develop more
resilient systems that support municipalities and efforts to serve their
communities.


DECLARATIONS

Authors’ contribution

Conceptualization, data, methodology, software, writing - original draft &
review: Tran T

Conceptualization, research design, data, supervision, review: Bani-Yaghoub M

Conceptualization, literature: DeLisle J

Availability of data and materials

To ensure reproducibility, the data and code used in this study have been made
publicly available in our Git repository. Researchers interested in replicating
or building upon our study can access the data and code from this repository.

Financial Support and sponsorship

None.

Conflicts of interest

All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Copyright

© The Author(s) 2023.


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CITE THIS ARTICLE

OAE Style

Tran T, Bani-Yaghoub M, DeLisle JR. Non-emergency responses in the 311 system
during the early stage of the COVID-19 pandemic: a case study of Kansas city.
Dis Prev Res 2023;2:3. http://dx.doi.org/10.20517/dpr.2022.08

AMA Style

Tran T, Bani-Yaghoub M, DeLisle JR. Non-emergency responses in the 311 system
during the early stage of the COVID-19 pandemic: a case study of Kansas city.
Disaster Prevention and Resilience. 2023; 2(1):3.
http://dx.doi.org/10.20517/dpr.2022.08

Chicago/Turabian Style

Tran, Thao, Majid Bani-Yaghoub, James R. DeLisle. 2023. "Non-emergency responses
in the 311 system during the early stage of the COVID-19 pandemic: a case study
of Kansas city" Disaster Prevention and Resilience. 2, no.1: 3.
http://dx.doi.org/10.20517/dpr.2022.08

ACS Style

Tran, T.; Bani-Yaghoub M.; DeLisle JR. Non-emergency responses in the 311 system
during the early stage of the COVID-19 pandemic: a case study of Kansas city.
Dis. Prev. Res. 2023, 2, 3. http://dx.doi.org/10.20517/dpr.2022.08

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CITATION

OAE Style

Tran T, Bani-Yaghoub M, DeLisle JR. Non-emergency responses in the 311 system
during the early stage of the COVID-19 pandemic: a case study of Kansas city.
Dis Prev Res 2023;2:3. http://dx.doi.org/10.20517/dpr.2022.08

AMA Style

Tran T, Bani-Yaghoub M, DeLisle JR. Non-emergency responses in the 311 system
during the early stage of the COVID-19 pandemic: a case study of Kansas city.
Disaster Prevention and Resilience. 2023; 2(1):3.
http://dx.doi.org/10.20517/dpr.2022.08

Chicago/Turabian Style

Tran, Thao, Majid Bani-Yaghoub, James R. DeLisle. 2023. "Non-emergency responses
in the 311 system during the early stage of the COVID-19 pandemic: a case study
of Kansas city" Disaster Prevention and Resilience. 2, no.1: 3.
http://dx.doi.org/10.20517/dpr.2022.08

ACS Style

Tran, T.; Bani-Yaghoub M.; DeLisle JR. Non-emergency responses in the 311 system
during the early stage of the COVID-19 pandemic: a case study of Kansas city.
Dis. Prev. Res. 2023, 2, 3. http://dx.doi.org/10.20517/dpr.2022.08

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