• Showcase
  • Audience
  • Concepts
  • Takeaway

Crime in LA · Data Visualization
Surfacing
the truth

UX Vision
Are our perceptions of crime informed by reality? We want to surface the truth of crime rates in of LA.
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.
The user visits the web app after recalling posters he saw in the break room.
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.

Presentation goals

We had our persona scenarios describe and define our design decisions, rather than the other way around.

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.

\ 3

The Student

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

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

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.

Meeting of the minds

Two distinct visual directions became one, as key components from each concept informed our final visualization.

Concepts

Visually dimensional

Total Entries
736,632
Data Source
Data.gov
7 Data Fields
Incident ID, District,
Area, Date, Time,
Call code, Call text
\ 9

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.

The initial concept
Early Stages
Concept 1

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

Early Stages
Percieved Crime 1

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

Early Stages
Percieved Crime 2

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

Early Stages
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.

Early Stages
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.

Early Stages
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.

Early Stages
Final

I updated the final version to make the first read the type of crime. This contextualized the data as the second read.

Challenge

This was my first school project to be constrained by real world limitations.

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.

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It works!
Prototype

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The deep dive
Process Book

Crime in LA · Data Visualization
Surfacing
the truth

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an interaction designer
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