Hi everyone,

Over the past few months, I’ve been working on an application that indexes walkability across Seattle. My goal was to create a less biased walkscore alternative that I, and now you, could use to comparatively evaluate how walkable an area is within the city.

Existing measures like Walkscore often rely too heavily on proximity to businesses, rather than by using pedestrian amenities directly, which can misrepresent an area’s walkability. My project differs in that I use Seattle’s publicly available GIS data to generate a walkability index for every point in the city. By using geospatial data for assets like sidewalks, parks, multi-use trails, I’ve created a comprehensive index that evaluates walkability on a finer scale. The result is a score that shows how walkable a single point in Seattle is relative to every other area in Seattle

For instance, if you live in Fremont and are considering a move to Capitol Hill, this app lets you compare how walkable your current block is to the block you might want to move to in Cap Hill.

Here’s a link to the app, if you just want to jump in. Otherwise, continue below.

How It Works

• Maps & Views: Start with a base map showing a gradient of walkability scores (green = good, red = bad). You can toggle between this neighborhood view and a more granular view using hexagonal grids using Uber’s H3 Spatial Index system.
• Interpreting Scores: Both views provide scores on a normalized scale from 0 to 100 (0 = least walkable, 100 = most walkable). These scores are relative to Seattle and don’t compare walkability to other cities worldwide.
• Customization: Adjust the sliders to weigh factors like green space, business density, or slopes according to your preferences. Click “Recalculate” to apply these adjustments and see a personalized walkability index in real time.

If you’re interested in the methodology, there’s more detail below. If you just want to jump in and explore the app, here it is.

Thanks for checking it out! Feel free to share feedback or let me know if you manage to break anything.

(There are some (hopefully small) known issues with the app, but I just started grad school and I can’t commit as much time to fixing them all)

Methodology

The animation in the post header illustrates the process I used to evaluate walkability. Here’s a brief breakdown of the workflow:

1. Take the map of Seattle and divide it into equal hexagons using Uber’s H3 system.
2. Sum the assets (sidewalks, parks, trees, businesses) in each hexagon.
3. Normalize these sums to a scale of 0-1.
4. Generate a score for each hexagon, using the equations outlined below.
5. Aggregate the hexagons within each neighborhood to create a neighborhood score.
6. Normalize the neighborhood score to a scale of 0-100, where 0 is least walkable and 100 is most walkable.

While this gives a general idea, the actual process is more nuanced.

Grid-Level Walkability Score Calculation

The score for each hexagon is calculated using a weighted sum of the normalized areas for different asset types, as follows:

W_unadjusted = (0.5 * A_sidewalk) + (0.4 * A_park) + (0.05 * A_trail) + (0.05 * A_bike)

Where:

• A_sidewalk = total sidewalk area.
• A_park = total park area.
• A_trail = total trail area.
• A_bike = total bike lane area.

Applying Scalers for Adjustments

The final Walkability Score for each hexagon $W_{{walkscore}}$ is calculated by multiplying the unadjusted score $W_{text{unadjusted}}$ by scalers that account for external factors like slope, business density, and calm traffic:

Walkability = W_unadjusted * S_slope * S_business * S_crime * (S_traffic * S_crash)

Where:

• S_slope = slope scaler.
• S_business = business density scaler.
• S_crime = crime density scaler.
• S_traffic = calm traffic scaler (based on speed limits).
• S_crash = crash density scaler (based on historical crash data).

These scalers refine the Walkability Score by accounting for real-world nuances. For example:

• A sidewalk on a steep incline is less walkable than one on flat terrain.
• A sidewalk near a park feels more walkable than one adjacent to a busy interstate.

User Adjustments in the Interface

In the app, users can adjust how these scalers are applied using sliders. If you value proximity to businesses or prefer areas with calm traffic, you can adjust the sliders accordingly.

• At the lowest slider setting, the scaler defaults to 1 (neutral effect).
• At the highest slope slider setting, the scaler is set to 0 for hexagons where the median effective slope exceeds 4%.

Whatever combination you choose, the output represents how walkable each hexagon or neighborhood is, relative to all other points in Seattle.

Final Notes

The scores are rank normalized across Seattle, so each walkability score will always reflect a local comparison. For more technical details, look to the GitHub repo.

This tool is likely imperfect and very much biased to my own interpretation. I’ve done my best to create a tool that reflects reality, but my reality is different than yours. Additionally, There are other aspects that make an area walkable, like transit access or signal density, that I would’ve liked to include, but couldn’t due to performance constraints.