- UX Vision
- Are our perceptions of crime informed by reality? We want to surface the truth of crime rates in of LA.
Crime in LA · Data Visualization
Surfacing
the truth
- Who
- 3 member team project. Specific responsibilites: research, interface, presentation narrative.
- What
- Data visualization of crime in Los Angeles.
- When
- Fall 2014.
- Where
- Data Visualization Studio, ArtCenter College of Design, Pasadena CA.

- Why?
-
Promote action
We want to spread knowledge of the types of crimes occuring in the city in the hopes of leading to greater action.
- And How?
-
Real data · Multiple metrics · Digestible
- Data is sourced from the LAPD.
- Multiple metrics surface previously unseen correlations.
- Make the clear enough to glance.
Audience
3 key use cases
Contextualizing the design
The student was the primary use case of our 3 personas. The student's goal is to educate herself on if her perceptions matched reality. We expect most users to use our visualization to inform themselves in this manner. The Activist and Coordinator users are both specialized cases with a goal in mind. We designed multiple modals of displaying data to cater to the Activist and Coordinator personas. We presented the project under the lens of our personas in our final presentation to the class.
The Student

I've heard the USC area has a bad reputation for safety
The student's use case encompasses our general user. She wants to see if reality matches perception.
The Activist

I want to push for the truth in Police response
The activist is interested in perceived vs actual crimes in the city.
The Coordinator

Its tough to make sense of the data we have, see!
The police coordinator wants to know if there is a pattern of crime that occurs in specific locations in the city.
Concepts
Visually dimensional
- Total Entries
- 736,632
- Data Source
- Data.gov
- 7 Data Fields
-
Incident ID, District,
Area, Date, Time,
Call code, Call text
Thinking in dimension and time
I drew on my experience playing games to inspire my explorations. Our dataset was full of opportunity - it was geographic, and therefore dimensional. We decided on time as the primary way to navigate the visualization because of how it framed the context of the type of crime committed. Perhaps our perception of certain activity happening at night is incorrect? This carousel explores how the visualizations evolved from a 3D space to a 2D map.


Concept 1
The tiles stack downward rather than upward for visibility. The histogram displays crime by every hour of the day.

Percieved Crime 1
The histogram shows continuity between the hours of the day. The white line is the perceived crime.

Percieved Crime 2
The white dotted area above the gradient in the histogram area is percieved crime.

2D Map
The LA county map in perspective looked cool, but did not make sense in a 2D space. We flattened it in this concept.

Region View
Here you search for crime between the month and day, you can also scrub time by the hour. This is a hybrid between the team concepts.

Class Final
We presented this as the 'final' in class. There were issues in the way the information was structured, which I iterated on in the next version.

Final
I updated the final version to make the first read the type of crime. This contextualized the data as the second read.
Takeaway
- Contraints of Data
- We ideated multiple narrative hooks for the project such as weather affecting crime rates. We had to kill some interesting proposals because we could not find a dataset to back up our claim.
- Data Neutrality
- It was our responsibility to bring the dataset to life in a authentic and neutral fashion without bias.