# Hit by the Data: a visual data analysis regarding the effects of traffic
public policies

###### Abstract

The availability of Open Government Data (OGD) provides means for citizens to
understand and follow governmental policies and decisions, showing evidence of
how the latter have contributed to both the place they live in and their
lives. In such a scenario, one of the proposals is the use of visualizations
to support the process of data analysis and interpretation. Herein, we present
the use of three different visualization tools - a commercial one and two
academic ones – applied to two specific Brazilian cases: the implementation of
the Drink Driving Law and the construction of a new overpass in an important
city avenue. Our focus was on the analysis of how visualization could help in
the identification of the effects of such traffic public policies. As our main
contributions, we present details on the effects of the observed policies, as
well as new cases showing how visualization tools can assist users to
interpret OGD.

_K_ eywords Information Visualization $\cdot$ Open Government Data $\cdot$
Case Study $\cdot$ Traffic

##  1 Introduction

The increasing availability of Open Government Data (OGD), allied to the lack
of standardization in the way they are presented, leads to great difficulty
when it comes to their use by citizens in general. Every citizen who makes
professional use of these kinds of data needs means to analyze and get insight
about them. In this scenario, Graves [1] advocates the use of “visualizations
as a medium to consume, share and interact with data.” According to Sivarajah
et al. [2], “the use of visualization techniques makes the data analysis task
easier for human interpretation and provides support to decision making”.

We can find several investigations exploring the use of visualization to
support OGD analysis [3, 4]. In the Brazilian context, for instance, the work
from De Mendonça, Maciel and Filho [5] presents a case study in which a map is
created to visualize information regarding the infestation of Aedes aegypti
mosquito in the city of Cuiabá. Still in the Brazilian context, Craveiro and
Martano [6] reported that, despite the data availability on government
portals, these data have not always been understood by the broader public.
Thus, the authors presented a tool called Cuidando do Meu Bairro (Looking
After My Neighborhood), used in São Paulo city to promote a better
visualization of government spending, and, therefore, to foster citizen
engagement.

Despite different attempts to create visualization tools that would help in
better understanding OGD, the systematic mapping on OGD visualization
presented by Eberhard and Silveira [7] shows us that one of the major
challenges in this field refers to the skills required to use such tools [8,
3, 9].

In this context, Méndez, Hinrichs and Nacenta [10] performed a study in which
they compared people’s visualization processes using two visualization tools
(Tableau Desktop and iVoLVER) and discussed how different approaches can
influence the visualization process, the decisions on the visualization
design, the sense of control and authorship, and the enthusiasm to explore
alternative designs.

In the research herein presented, we also chose to investigate different
visualization tools: a commercial one and two academic ones. In our case, the
investigation was based on the analysis of OGD visualizations applied to two
specific Brazilian cases: the implementation of the Drink Driving Law and the
construction of a new overpass. We analyzed the data related to the year
before the events had taken place (the law implementation and the overpass
construction) and 2016, the last year we have associated with the available
OGD. Our focus was the analysis of how visualization could assist in the
identification of the effects of such traffic public policies.

The remainder of this paper is organized as follows. Section 2 describes our
research methodology, including the used dataset, the selected case studies,
and the used visualization tools. The results of our analysis with our
findings are presented in Section 3, to be further discussed in Section 4.
Finally, we end this paper with our conclusions and future work directions in
Section 5.

##  2 Research Methodology

The research methodology followed in this work has been split into five steps:
I) literature review; II) selection of a dataset; III) identification of case
studies; IV) selection of three different tools; V) performance of visual
analysis and finding identification. Moreover, the following subsections
present an explanation on the used data, a description of the selected case
studies, and, lastly, a presentation of the visualization tools used in our
visual analysis. Our findings will be discussed in depth in the next section.

###  2.1 Used Dataset

For our visual analysis, we used OGD related to traffic accidents that had
occurred in a city in southern Brazil, called Porto Alegre. An open government
data portal 111http://datapoa.com.br/dataset/acidentes-de-transito, maintained
by the City Hall, records OGD about traffic accidents that occurred between
2000 and 2016, in csv format. These records generate large multivariate
datasets, with several attributes associated with a specific location
(latitude and longitude), such as accident type, time, date, number of
injuries, fatalities, vehicle category, and weather conditions.

However, as these data have different format depending on the year, we chose
the data referring to the year of 2016 to be used as a model for the
standardization of other years. We also removed some attributes that were
unnecessary for our analysis, because there were either data we did not
consider as useful (e.g. consortium), or data with the same information, or
even because some data were blank in most records. Besides that, we had to
deal with missing data and non-standardized data format, which was the case of
the date. For these standardizations, we have used OpenRefine
222openrefine.org.

###  2.2 Case Studies

We chose two case studies to analyze how visualization could help in the
identification of the effects of traffic public policies. Our main goal is to
identify how laws and street works have helped in reducing traffic accidents
in the city.

The first case study is related to the Drink Driving Law 333Law number 11.705
- https://bit.ly/2v2sXBy, which was enacted on June 19, 2018. This law
prohibits the consumption of alcohol by drivers, imposing more severe
penalties for those who drive under the influence of alcohol. Its main purpose
is to avoid accidents that may occur due to the carelessness of drivers with
impaired operating abilities. Thus, we decided to evaluate the total number of
accidents that happen on weekends (from Friday to Sunday), when people in
Brazil usually go to parties, bars and restaurants, that is, places where
there is typically more consumption of alcoholic beverages. It was mainly on
weekends that "Balada Segura" 444https://baladasegura.rs.gov.br/inicial also
began to be held in 2012. The term, freely translated into English as "Safe
Clubbing", consists of a surveillance operation carried out by traffic organs,
Military and Civil Police, which aims at preventing alcohol-related traffic
criminal offences in places and at times with a higher incidence of accidents.
Considering these dates, we opted for analyzing the total number of accidents
in 2007, before the Drink Driving Law, and in 2016, after people had already
been aware of the zero tolerance laws and drunk driving inspections.

The construction of an overpass on an important avenue of Porto Alegre,
Brazil, was the second chosen case study. The idea was to evaluate how much
the construction of the overpass has influenced the reduction of traffic
accidents once it was a very busy crossroad. Since the overpass construction
began in August 2012, being only launched in June 2015, we chose to analyze
the total number of accidents in 2011 and 2016, before and after its
construction.

###  2.3 Visualization Tools

Three tools were chosen to our proposed analysis: a commercial and two
academic ones (developed in our research group). The next subsections describe
them in details.

####  2.3.1 Tableau

It is a commercial software for data analysis and visualization, both for
individual and group analysis 555https://www.tableau.com. It comprises several
tools required to generate visualizations, from receiving data files of
various formats to the generation of different charts and dashboards. The
provided interface allows a quick selection and optimization of data, as well
as suggesting chart models for visualization, such as treemaps, bars, bubble,
pie, line, scatter, among others.

####  2.3.2 GeoCharts

This tool presents an interactive visualization design for visual analysis of
geospatial multivariate data that facilitates data analysis and knowledge
discovery. It allows the representation of several attributes on the map with
dynamically linked charts and an interactive approach based on the brushing
and linking technique integrated with coordinated multiple views that can
support visual analysis. Thus, a set of interactive visualizations are
combined to stimulate an active interaction of the end user. It offers a way
to interactively apply different filters and analyze several attributes,
updating all the related visual representations, including the map with
clustered charts.

####  2.3.3 Traffic Accidents Analyzer (TrAcc)

This tool provides different visualization techniques specifically for the OGD
of traffic accidents. It provides an animated heat map based on a timeline, as
well as three charts that help visualize the information contained in the
data: a Historical Bar Chart, which shows the number of accidents within a
time range (0h to 23h); a Pie Chart, showing the regions of the city (north,
south, east, and center) where most accidents occur; and a Horizontal Bar
Chart, showing the types of accidents and their total number. Several filter
options are also available, such as the type of vehicle (car, motorcycle,
truck, etc.), time range (morning, afternoon, night, dawn), weather condition
(good, rainy, cloudy), and days of the week.

##  3 Findings

As previously mentioned, we chose two scenarios to support our case studies. A
common step to both case studies was loading the data into the tools, which
enabled us, after the import completion, to perform the visualizations.

Regarding the steps described in Subsection 2.1, it was fairly simple to load
the data into GeoCharts, since it allows the use of any data in the csv
format. Tableau also supports loading csv data. However, some problems
happened when the files were opened, not creating the columns correctly, for
instance. Nonetheless, these problems were easily solved by just converting
the files to xls format. One situation identified in both tools, GeoCharts and
Tableau, was related to the latitude and longitude, which, in some cases, were
not readable by them. The solution to this issue was just to change the
decimal separator format. TrAcc did not present any problems, once the data
were already embedded in it.

The subsections below describe the findings grouped by each case study, also
presenting the visualizations which helped us throughout the analysis.

###  3.1 Case Study: Drink Driving Law

In our first case study on the effects of the Drink Driving Law, we started
analyzing the Geocharts tool. To reach our goal, it was necessary to apply
three filters while using GeoCharts: year, day of the week (Friday, Saturday,
and Sunday), and shift. After that, GeoCharts showed a map and different
associated charts presenting the numbers of traffic accidents in 2007 and 2016
(Figure 1 666The used systems were developed in Portuguese. We provide English
subtitles in the figures.). In order to filter data by a specific year, users
must click and select the year they want in the year chart. We highlight that,
regarding these charts, users could pre-select the variables they wanted to
visualize in charts along the map (five charts at most). At any time, in case
users wanted to change the charts, they only needed to select new variables
and generate a new visualization.

In general terms, the results showed that in 2007, before the implementation
of the law, 5076 accidents had occurred during the day and 3180 at night,
whereas in the year of 2016 the number of accidents had reduced to 3083
occurrences during the day and 1345 during the night. In GeoCharts, the number
of occurrences is numerically presented next to the map at the top, in the
same figure area as the charts.

Figure 1: General overview regarding traffic accidents in 2007, by GeoCharts

By using Tableau and TrAcc to achieve the same results, it was necessary to
apply the same three filters. Regarding these filters, TrAcc offers six
filtering options: by vehicle type, day shift, weather condition, day of the
week, address, and year. Tableau, on the other hand, offers the possibility of
choosing any variable from the dataset to be used as a filter.

Considering forms of visualization presentation, such as GeoCharts, Tableau
allows us to jointly visualize the data from different time periods. However,
it only presents one chart at a time, and users must switch among different
types of charts (for instance, in Figure 2 we present the bar chart, chosen to
present a simple overview regarding the general numbers from both years).

Regarding the visualization provided by TrAcc, it shows a heat map in which it
is possible to visualize the data from the selected years (Figure 3). As
compared to GeoCharts, TrAcc also provides different charts that help bring
signification to the data. Differently from the other tools, in which it is
simple to identify the totals, the information in TrAcc is presented in a
partitioned way through the charts. To verify the totals, it is necessary to
manually calculate the information by summing up the data presented in the
chart Type of Accident, in which the numbers are clearly presented.

Figure 2: General overview regarding traffic accidents in 2007 and 2016, by
Tableau Figure 3: General overview regarding traffic accidents in 2007, by
TrAcc

###  3.2 Case Study: Overpass construction

In regard to the second case study, we analyzed the impact of the construction
of an overpass in an important city avenue in southern Brazil.

By using GeoCharts, it was required to filter by year, and to zoom in and find
the desired location on the map. After finding the location, users need to
analyze and check the number presented on the map. The number we found is
related to accidents which had occurred around the crossroad area, not exactly
in the intersection of the two avenues. We identified 53 accidents in 2011 on
the crossroad where the overpass is located nowadays (Figure 4 left).
Following the same steps, we verified that the number of accidents in the same
region was reduced to 14 in 2016.

Figure 4: Visualization of accidents on the crossroad in 2011 and 2016, by
GeoCharts

By using Tableau, we applied a filter regarding the years, and also filters
regarding the required address (in this case, we informed two addresses,
representing the crossroad). Thus, differently from GeoCharts, the tool
provided us with a chart visualization about the accidents that had taken
place exactly on the intersection of the avenues. By that, we found that, in
the year of 2011, 14 accidents had occurred in this crossroad, and, in 2016,
after the construction of the overpass, the number reduced to only one
accident (Figure 5).

Figure 5: Visualization of accidents in 2011 and 2016 on the crossroad, by
Tableau

Similar to Tableau, TrAcc provides a filter in which it is possible to inform
the year (as presented in Figure 3), and filters users can apply to determine
the addresses they want to analyze. The results presented by TrAcc in the heat
map are not influenced by the addresses. However, the associated charts
change, presenting the numbers related only to the informed addresses.

##  4 Discussion

The tools we investigated present different visualizations, helping us analyze
the available data under different perspectives.

In the next subsections, we firstly discuss aspects related to specific
possibilities of use of the analyzed tools, followed by how these
possibilities helped us during our case study analysis.

###  4.1 Visualization strengths

Regarding data import, TrAcc displays the data related to accidents from Porto
Alegre city already loaded in the system, which prevents the user from having
to deal with details regarding pre-processing and data import. However,
Tableau and GeoCharts accept any type of dataset and can be used for different
purposes. Also, after the data import, it is very simple for the user to
change the charts that are being presented (GeoCharts allows the presentation
of five charts at the same time, as well as allowing the user to choose five
visulization variables in these charts), while in TrAcc the three charts and
six filtering variables provided by the tool (besides the heat map) cannot be
changed.

In regard to filtering precision, in the overpass case study, it calls our
attention that GeoCharts, differently from Tableau and TrAcc, did not allow us
to inform the addresses we wanted to visualize and, consequently, did not
allow us to precisely check for accidents in a particular location. On the
other hand, by using the zoom, GeoCharts provides a macro view, as presented
in Figure 4 (right), that allows us to have a broader perspective of the
analysis. By using the other tools, the users would need to know the exact
description of surrounding addresses in order to visualize them, although a
single accident occurred at the exact crossroad address (as presented by the
other tools), there were 14 accidents in the area.

Also, GeoCharts allows us to click in each one of the bullets presented in the
image, providing detailed information about the accident (such as the kind of
vehicle, weather conditions, day of the week, etc.). TrAcc, in turn, also
allows zooming in and out the map to approach a location (Figure 6). However,
it does not allow to get more information regarding the accidents. Despite the
lack of information, the user has a fast overview of the accidents that had
happened in that area through the heat visualization, whereas only numbers are
presented in GeoCharts.

Figure 6: Visualization of accidents in 2016 around the crossroad area, by
TrAcc

Regarding the possibility of comparing different periods in the same chart,
Tableau allows it to be done, while this is not possible by using the other
tools. In both GeoCharts and TrAcc the user needs to check the periods one at
the time. The user can select different years to create the visualization, but
the results are presented based on a concatenation of the data from the
selected years. As presented in Figure 1, there is a chart (located in the
figure’s bottom right corner) with information regarding the years 2007 and
2016, and the user must click on the period he wants to visualize with more
details. The presentation of the results in Tableau makes the visualization
easier, especially comparing different periods, as presented in Figure 5, but
it does not offer more details about those years. If the user wants to explore
such information, he needs to generate a new visualization, choosing the
variables he wants to visualize.

###  4.2 Interpretation support

In regard to how these different visualizations assist the interpretation of
data, we would like to discuss and focus on each case study’s needs.

The analysis of the Drink Driving Law effects requires an overview regarding
the situation in the whole city. To achieve a simple and straightforward
analysis, the visualization from Tableau may be enough, once it shows the
numbers and charts related to it. Since we are dealing with information from
locations all over the city, a visualization with many details could generate
a negative effect on the users, since it would take a lot of effort to
interpret this view. However, in need of a detailed perspective, both
GeoCharts and TrAcc allow the users to identify the areas from the city in
which the new law impacted more, and this insight is even simpler by the
visualization through the heat map offered by TrAcc. Furthermore, the charts
from GeoCharts allow fast filtering and the visualization of numbers,
providing a quick response and helping users while they are interacting and
interpreting these data.

About the second case study, we wanted to visualize a situation encompassing a
very specific part of the city (a crossroad) in order to understand if the
construction of an overpass had effectively helped in traffic. Unlike the
first case study, in this one details can improve interpretation and are easy
to be visualized (since our analysis involved a very small area of the city).
Regarding the precise view required in this case, both Tableau and TrAcc allow
the users to inform the address they want to visualize (in this case, two
addresses corresponding to the intersection of two avenues). However, this
resource can lead users to some false interpretation, once the accidents that
had happened around the crossroad are not presented to them. GeoCharts, on the
other hand, does not allow users to inform the address to be visualized;
therefore, users need to zoom in to find the location they want (a task that
can become costly in cases where the city is large and the user is not
familiar with the city map). Thus, the possibility to inform the address must
be available to users; however, it cannot limit the visualization without
presenting the areas around it. Also, to provide a richer interpretation, once
the scenario is limited to a small part of the city, details regarding the
accidents must support the user, like those presented by GeoCharts and TrAcc.

##  5 Conclusion and Future Work

Nowadays, it is easy to find Open Government Data (OGD) available on
governmental portals, but it is not so easy to employ them without some help.
Similar to other researchers in the field, we advocate that the use of
visualization techniques makes it easier for citizens to analyze and interpret
these kinds of data.

In the research herein presented, we used three different visualization tools
to analyze the OGD referring to two Brazilian cases: the implementation of the
Drink Driving Law in 2007 and the construction of a new overpass in an
important crossroad, which started in 2011 and was concluded in 2016. We
focused on analyzing how visualizations could support the identification of
the effects of the studied traffic public policies, and we presented a twofold
contribution: first, the details on the effects of the observed policies, and,
second, two more cases showing how visualization tools could help users to
interpret OGD.

As for our next research steps, we intend to analyze the same cases with
professionals who closely work with traffic and traffic policies to gather
their opinion about the tools’ usage and their possibilities. We also intend,
as future work, to analyze new case studies, enabling us to explore other
perspectives of those given tools.

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