OTTAWA DATA STORYTELLING
OTTAWA DATA STORYTELLING
An analytical project addressing urban bike theft. I transformed complex, aggregated police datasets into a human-centered narrative that provided actionable safety insights for local residents in River Ward.
Client
ACADEMIC (CITY CONTEXT)
Year
2024
Category
DATA VISUALIZATION
ROLE
DATA STORYTELLER
CONTEXT
CONTEXT
Public data on bike theft existed but was aggregated and dry, making it difficult for residents to understand personal risk. The goal was to transform cold CSV files into a narrative that urged the community to take informed, preventative action.
Public data on bike theft existed but was aggregated and dry, making it difficult for residents to understand personal risk. The goal was to transform cold CSV files into a narrative that urged the community to take informed, preventative action.
CONTEXT
Public data on bike theft existed but was aggregated and dry, making it difficult for residents to understand personal risk. The goal was to transform cold CSV files into a narrative that urged the community to take informed, preventative action.
STRATEGY
STRATEGY
I adopted a human-centered storytelling approach. By narrowing the focus to the River Ward area and revealing hidden patterns—like specific "Friday Afternoon" risk spikes—I turned abstract statistics into relatable, location-specific warnings for everyday cyclists.
I adopted a human-centered storytelling approach. By narrowing the focus to the River Ward area and revealing hidden patterns—like specific "Friday Afternoon" risk spikes—I turned abstract statistics into relatable, location-specific warnings for everyday cyclists.
STRATEGY
I adopted a human-centered storytelling approach. By narrowing the focus to the River Ward area and revealing hidden patterns—like specific "Friday Afternoon" risk spikes—I turned abstract statistics into relatable, location-specific warnings for everyday cyclists.
EXECUTION
EXECUTION
I analyzed 5 years of police data (2018-2023) to identify trends. The visualization design prioritized pattern recognition over raw volume, comparing local ward data against city-wide trends to give residents a clear sense of relative safety.
I analyzed 5 years of police data (2018-2023) to identify trends. The visualization design prioritized pattern recognition over raw volume, comparing local ward data against city-wide trends to give residents a clear sense of relative safety.
EXECUTION
I analyzed 5 years of police data (2018-2023) to identify trends. The visualization design prioritized pattern recognition over raw volume, comparing local ward data against city-wide trends to give residents a clear sense of relative safety.
RESULT
RESULT
The project was recognized for clarity and strategic insight. It successfully communicated that while overall thefts dropped, risk remained concentrated in specific times. The work earned a full grade and was selected for feature on the City of Ottawa website.
The project was recognized for clarity and strategic insight. It successfully communicated that while overall thefts dropped, risk remained concentrated in specific times. The work earned a full grade and was selected for feature on the City of Ottawa website.
RESULT
The project was recognized for clarity and strategic insight. It successfully communicated that while overall thefts dropped, risk remained concentrated in specific times. The work earned a full grade and was selected for feature on the City of Ottawa website.
OTTAWA DATA STORYTELLING
OTTAWA DATA STORYTELLING
