SafeSteps Toronto

Interactive web map Fastest route vs safest route through Toronto

User decision point

SafeSteps makes the tradeoff visible: a route can be slightly longer but meaningfully lower risk.

01 · Question

Fastest is not always safest. Most navigation tools optimize for time, but walking comfort is shaped by safety, lighting, traffic, and local context.

02 · Data

Toronto Police Service crime data is cleaned, spatially matched to walking-route segments, and translated into a risk signal users can actually interpret.

03 · Interface

The map keeps the model legible: users can compare routes and see the reasoning without reading GitHub or trusting a black box.

01 · Brief

A walking route is a decision, not just a line

SafeSteps starts from a common urban problem: the shortest path may not be the path someone feels comfortable taking. The project reframes routing as a tradeoff between efficiency and safety.

The interface is designed for quick comparison. A user enters an origin and destination, then sees routes scored and visualized in a way that makes the difference understandable.

02 · Method

Turning incident geography into route scoring

RoutesGenerate candidate walking paths between two user-entered locations.
SegmentsBreak routes into smaller pieces so risk can be assessed locally rather than as one blunt average.
ScoringCompare route geometry with nearby crime incident patterns from public safety data.
DisplayReturn fastest and safer alternatives with a visible explanation of the tradeoff.

03 · Finding

The safest route needs to explain itself

A safety recommendation is only useful if the user can understand it quickly. SafeSteps avoids hiding the decision inside an algorithmic score and instead treats map design as part of the analysis.

04 · Limitation

Safety data is never the whole story

Crime data is an imperfect proxy for pedestrian safety. A real deployment would also benefit from lighting, street activity, sidewalk quality, traffic exposure, time of day, and community reporting.