Greater Seattle Shoplifting Visualization

Foundation of the Case Study

On the second week of May, I reached out to the local “CanYouID” organization for all of Washington State where the community helps catch shoplifters through identifying their pictures taken from CCTV, security camera and phone cameras. The purpose of this connection was to research what is their purpose and to offer them my experiences to narrow down the areas where most of the shoplifting occurs in the hopes of increasing the security and the response time for the police to arrive. By collecting “Shoplifting” data from the 911 Incident Response Dataset it would help them identify certain “hotspots” for these crimes and potentially reduce the amount of shoplifting in those areas by catching person in interest, and potential suspects.

Visualization #1: I incorporated a scoped down (narrowing) illustration of the Shoplifting data thus throughout my visualizations the data will focus more on the in-depth category from the Research Questions. The first visualization is encompassing the Greater Seattle Area divided into the Districts that are assigned to a certain color to show relationship throughout the map. This is focusing on the “What districts and area experiences the most shoplifting?” research question. Each of the circles represent the streets (Hundred Block Locations) and where the circles are larger then that’s a sign that there has been quite a lot of shopliftings have been happening in that area.

I’ve contacted the organization to find out what their cause is and research questions as well. The communication was a 30 minutes’ phone call and the research questions were the following: “What districts and area experiences the most shoplifting? How frequently these shopliftings occur and if so is there a pattern they follow? When does the average shoplifting occurs (hour of day) in the most shoplifting area throughout Seattle and its neighboring villages? Last but not least, they were also curios about how the previous year data (2014) compares to the data we were provided with (2015). This was an extra step that they assigned but it’s a bit outside of the scope for the assignment, nonetheless I still provided the data in Tableau.

Visualization #2: For my Second illustration, I narrowed down to remove the 2014 data in order to transform the data into a heat map for the most recent year available from the 911 Incident Response Dataset (2015). This is focusing on the “How frequently these shopliftings occur and if so is there a pattern they follow?” thus it transforms all the data points into a heat-map which not only shows which months were the most active but it also highlights the district with the most shoplifting.
Visualization #3: The previous visualization was my clue for this visualization where I narrowed the field to the district that unfortunately received the most shoplifting every month of the year of 2015. This district was Pioneer Square which has one of the most tourist and shopping in the Seattle area if not in Washington State so it makes sense why this is the most popular spot to shoplift. For this illustration, I focused on the “When does the average shoplifting occurs (hour of day) in the most shoplifting area throughout Seattle and its neighboring villages?” research question, thus I created an Area/Line map to show the hours that received the most shopliftings. By looking at the data, more enforcement and security needs to be placed around the shops in Pioneer Square to reduce the shoplifting incident.

Visualization Techniques

Throughout the visualization process I put my Tableau skills into use which allowed me to carefully extract data from the data.seattle.gov website specifically from the City of Seattle’s 911 Incident Response database from 2014 and 2015. There were over 450,000 variety of data points where through several filtering I was able to condense it to only the ones that involves shoplifting. This really gave me the ability to build up several visualizations that not only helps the can local CanYouID organization but it also enhanced my data wrangling expertise. Tableau was quite a bit complex but that’s exactly what I liked about this challange, thus the tutorial that was provided with the case study helped me orientate through the obstacles and generate my data into a helpful concept that hopefully saves our community millions of dollars worth of stolen products.


Stepping Stones for Sketching

As with Usability Testing and Prototyping, Visualization is just as important to show an illustrated data to the public that they can conceptualize and understand without ever thinking about the vast amounts of data and numbers. By making data work, in other words, converting data into sensitive visual information it helps with complex situations that could prevent further damage to our surrounding communities. Of course this can be quite tricky because rarely the data could be either biased or compromised which would not be an approrpiate step if an organization or a comapny wants to receive accurate feedback and statistics. One example would be of why I have choosen shoplifting. In the past I worked as a barista and I observed hundreds of shoplifter cases per year which influenced my curiousness to help my community, not only by preventing further loss of profit (U.S retailers lose +$60 billion due to shoplifting), but by also making it more safer and trusted. Thus by using my past experiences and the datapoints that are available to me by the public I could change the future simply by creating my visualization and contribute it back to the public which communicates that certain areas needs more security at specific times of the day. Therefore I will continue to put emphasis on visualization in my future case studies due to the weight and influence it can deliver to the public and possibly even change the way they think about certain events or even change their point of view.


Tableau Public for the Greater Seattle Shoplifting Visualization I’ve created: https://public.tableau.com/shared/CXKWFXFN9?:display_count=yes