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#+date: <2024-01-27 Sat 00:00:00>
#+title: Visualizing Omaha Crime Trends with Tableau Dashboards
#+description: A comprehensive guide to creating interactive Tableau dashboards using Omaha crime data from 2015 to 2023, including data preparation and visualization tips.
#+slug: tableau-dashboard
In this project, I am going to show you how to use Tableau Public for free to
create simple dashboards.
I will be creating simple visuals from an Omaha crime data set and combining
them to create the dashboard below. You can view this dashboard interactively
online here: [[https://public.tableau.com/app/profile/c.c7042/viz/OmahaCrimeData2015-2023/OmahaCrimeData2015-2023#1][Omaha Crime Data (2015 - 2023)]].
#+caption: Tableau Dashboard
[[https://img.cleberg.net/blog/20240127-tableau-dashboard/dashboard.png]]
* Gather the Data
You can download incident data from the Omaha Police Department on their
[[https://police.cityofomaha.org/crime-information/incident-data-download][Incident Data Download]] page. They currently have files for the years 2015
through 2023.
Each file will be downloaded as a CSV file, approximately 3 MB - 8 MB.
* Clean and Transform the Data
I have used Python to combine the files into a single CSV file, as well as
adding a custom =datetime= column. You could do this step in any software you
prefer, but I prefer Python as its free, easy to use, and has a plethora of
support resources online.
Start by opening a terminal, navigating to your Downloads directory, and
creating a python script.
#+begin_src sh
cd ~/Downloads
nano data_processing.py
#+end_src
Within the Python script, paste the following:
#+begin_src python
# Import modules
import pandas as pd
import glob
import os
# Import the data
path = r"~/Downloads/*.csv"
files = glob.glob(path)
list = []
for file in files:
df_tmp = pd.read_csv(file)
li.append(df_tmp)
df = pd.concat(list, axis=0, ignore_index=True)
# Create a combined datetime column
df["datetime"] = pd.to_datetime(
df["date"] + " " + df["time"],
format="%m/%d/%Y %H:%M:%S"
)
df.head()
# Export the combined data
df.to_csv(r"~/Downloads/combined_incidents.csv")
#+end_src
Once pasted, save and close the file. You can execute the file like so:
#+begin_src sh
python3 data_processing.py
#+end_src
After this, you should have a combined data file that contains all incidents
between 2015 and 2023. Mine was approximately 55 MB.
* Tableau Public
[[https://public.tableau.com/][Tableau Public]] is a free-to-use web application that allows you to create
visualizations by uploading data sources. Note that there's no way to keep the
data and visualizations private, so don't upload anything private.
After creating an account, you can click the =Create= > =Web Authoring= link to
create your first visualization.
** Upload the Data
Once you've opened your first project, Tableau will ask you to connect to your
data. For this project, click the =Upload from computer= button and select the
CSV file previously combined in the step above.
Once connected, you can refresh the preview of the data with the =Refresh Data
Source= button in the toolbar.
If you need to edit any of the data types, column names, etc., you can do so
now. Once complete, generate an extract so that you can start creating
visualizations.
** Create Visualizations
To start, create a worksheet in the toolbar at the bottom of the screen.
Within this screen, select a column from the =Data= side bar on the left and
drag it into the =Columns= or =Rows= area of the canvas.
See below for the map visualization. You can recreate this by adding the
following fields:
- =Columns=: Lon
- =Rows=: Lat
- =Marks=:
- Description
- Datetime
- =Filters=: Datetime
You can repeat this process for each visualization you want to create. Explore
your options by dragging data fields to different areas and by opening the field
options to explore what operations can be performed on different data types
(e.g., average, count, etc.).
** Create Dashboard
To create a dashboard, click the button on the toolbar at the bottom of the
screen. Within the dashboard, drag each sheet from the left side bar onto the
dashboard canvas.
** Formatting
You can explore a ton of different formatting options throughout the worksheets
and dashboard. Specifically for maps, you can alter the map layers, background,
and visible features through the =Map= menu in the top file menu of the editing
screen.
In the finished dashboard below, I opted for a dark mode with a map that showed
county lines and city names.
There's a ton of other options available to be used in a dashboard like this,
but this project shows a quick preview of what you can do in Tableau Public.
#+caption: Tableau Dashboard
[[https://img.cleberg.net/blog/20240127-tableau-dashboard/dashboard.png]]
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