Data Visualization with 911 Phone Records

Author: Swaminathan Ramesh Sashi

1. Introduction

Data visualization is a useful tool to analyze exploratory data and display the results. This article attempts to explain a few data visualization exercises which aim to analyze a composite dataset consisting of 911-phone records as well as the subsequent police report filings.

2. Analysis

2.1 Software used

Tableau 9.2 and Excel.

2.2 Dataset

The dataset consists of the following variables derived from the website of UW Madison Police Department:
Incident number (a unique id), incident date, incident time, location, zip codes, significance, incident description, and incident type. The dataset includes data from the years 2012-2015.
The original dataset is well ordered and nominal in most attributes. Instances are extensive but well maintained with no missing values.
One challenge was converting zip code data into latitude and longitude for plotting, which was mitigated using Tableau 9.2.

2.3 Approaches

Tableau has built-in graphing and histogram development features. Graph 1 shows the number of records for each quarter where the records are divided into important records and not important records. It can be seen that the number of records is relatively high in summer and autumn.

1Graph 1: Crime Trends

The proportion of different crime types of Madison is displayed in Graph 2. Petty theft happened to be the major crime and none of the top 10 crimes were of violent nature, which reaffirms Madison as one of the safest cities in USA.

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Graph 2: Crime Types

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Graph 3: Calls Classification

People called 911 for different reasons which are summarized in Graph 3. The most pressing reasons included fire alarms and information in the important and non-important category respectively.

With Madison being famous for its drinking culture, I wanted to see when people got into trouble for drunk driving. It is not surprising that the majority of cases happened around midnight but I was surprised to see that people were drunk driving between 5-7 PM as shown in Graph 4.

Based on zip codes, the crime density for different areas of Madison is displayed in Graph 5. The darkest shade of green is the most crime prone area while the lighter shades denote low crime density. Historically, the area around the east towne mall happens to be the most crime prone. The university area is relatively safe and crime dovetails as we move out of Madison perhaps due to the low population.

4Graph 4: Drunk Driving Hours

 

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Graph 5: Most Crime Prone Areas

 

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Graph 6: Highest Prevalent Crimes

The major crime in each zone is shown in Graph 6. Weapons violations come out on top and not surprisingly because the university area is a no carry zone. The Beltline area has high amount of traffic violations as one of the traffic heavy areas of Madison.

3. Summary

In this article, data visualization techniques are employed to analyze 911-phone records. Further work can be performed like checking the correlation between cases in the police report and 911 calls made and deriving insights. Statistically analyzing (clustering) the data can also narrow down the crimes to street levels and perhaps help better policing.
This is an attempt to build on work done by Sachin Bhat, Girish Krishnan and Abisheik Mani as part of their course in the spring of 2015. I would like to thank Prof. Kaibo Liu for his support.

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6 thoughts on “Data Visualization with 911 Phone Records

  1. Michael Baer

    This was a very interesting study and I really enjoyed reading it. However, I noticed that it was conducted using data from the UW Madison Police Records. I’m curious as to if the proportion of call types (i.e. type of offense, important/non-important call) would be different for the Madison Police department compared to UW PD. Additionally, I think it could also be interesting to include the data of offenses caught by police on duty without any calls being made. Perhaps a Chi-Squared test could be used.

  2. Thomas Magnuson

    Thank you for sharing your findings! I found it interesting that the 3rd Quarter of recent years were the most popular quarter for both important and unimportant calls. I am interested in seeing how Madison’s call statistics compare to other large college towns in the United States, as well as similar-sized cities and how they compare to the top categories of important calls. For example, is theft a common issue nation-wide, or more of a localized problem in Madison, WI.

  3. Erik Pechnick

    This is very interesting, especially living in Madison for the passed few years. The most interesting would have to be that drunk driving occurs so frequently at 5-7 PM. It would be interesting if there was a visualization on where these drunk driving incidents were and if that correlates at all with where there is most crime.

  4. Eric Fleming

    I know it is a trend in many cities that crime spikes in the summer. However, I would not have expected that to happen in the data from UWPD because of the nature of the student population. The campus is usually pretty quiet in the summer because there are far fewer students taking classes in the summer session compared to Fall and Spring. I would have expected crime to peak over fall and winter; this finding is interesting!

  5. Henry Rose

    Thank you for sharing your findings with us. I thought that many of the graphs did a nice job of conveying information in an easily understandable manner. But, I do have one suggestion. In your highest prevalent crimes visualization, I believe it would be easier to understand with proportional labels. I don’t really understand what the colors mean. Overall this is a very interesting project.

  6. Ben Pollak

    Great analysis! It was super interesting to see from the second analysis that petty theft is the major crime in Madison and that none of the top 10 crimes were violent in nature, reaffirming how safe of a city Madison is. It was also comforting to see that campus is a very safe area compared to other Madison neighborhoods. I wonder what the records from UW PD look like. I’m sure almost all of these calls happen on campus and perhaps that would affect how safe Madison would look on the graph with other neighborhoods. But great analysis! Very interesting post.

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