Walkability Score Calculator

Introduction

Note: Due to Render's Free Tier memory cap the full functionality is limited on dense urban areas

The Walkability Score Calculator is a tool designed to evaluate how pedestrian-friendly a location is by analyzing key features within a 1,200-meter radius — approximately the distance an average person can walk in 15 minutes.

By entering an address, the calculator assesses the surrounding area, identifying:

• Nearby educational institutions, shops, offices, and healthcare services.
• The percentage of streets reserved for pedestrians.
• The total area of parks and green spaces.
• The number of public transport stops available.

These factors are combined into an overall walkability score, weighted as follows:

• 40%: Proximity to amenities (schools, shops, offices, healthcare).
• 30%: Percentage of streets that are walkable.
• 15%: Area of green spaces.
• 15%: Public transport accessibility.

The result is a clear, data-driven measure of how walkable an area is, helping users evaluate and compare locations easily.

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Features

• Dynamic Input: Enter any address, and the tool will fetch the relevant data.
• Detailed Metrics: Breaks down the walkability score into individual components for better insights.
• Simple UI: Designed for ease of use, whether you're a casual user or a professional urban planner.

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How It Works

1. Enter an Address: Input an address into the search bar on the website.
2. Data Fetching: The tool uses OpenStreetMap (OSM) data to analyze the area.
3. Score Calculation:
   • It identifies and quantifies the presence of amenities, parks, walkable streets, and transport stops.
   • A weighted formula is applied to produce the final walkability score.
4. Results:
   • See the overall score as well as a breakdown of contributing factors.

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Technologies Used

• Frontend:

  • HTML, CSS, and JavaScript for a responsive and user-friendly interface.
  • Deployed on Netlify for fast and reliable hosting.

• Backend: The backend is built using Python (Flask) and relies on the following libraries:

• Mapping and Spatial Analysis: osmnx, geopandas, shapely

• Data Handling: pandas, json

• Location Services: geopy

• Data Source:

  • The app relies on OpenStreetMap (OSM) for mapping and location data.

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Rationale Behind the Weighting (40/30/15/15)

• Proximity to Amenities (40%): This is the most important factor, reflecting the 15-minute city principle, where essential services and amenities should be accessible within a short walk.
• Walkable Streets (30%): For the 15-minute city principle to work, streets must be safe, pedestrian-friendly, and designed to be inclusive for all (including the elderly and disabled). This factor should have a high weight, given its direct impact on the comfort and safety of pedestrians.
• Amount of Green Space (15%): While green spaces are not central to the core concept of the 15-minute city, they enhance walkability by providing a more comfortable and enjoyable walking environment. Green spaces contribute significantly to well-being and quality of life.
• Public Transportation Accessibility (15%): Although outside the immediate scope of walking, public transport supports mobility for longer distances, contributing to overall accessibility.

Explanation for Limiting Features to Those with Names

Limiting features to those with a name attribute improves data quality, avoids duplicates, and ensures relevance to the walkability analysis. Here's a concise breakdown:

Advantages

Avoiding Duplicates: Named features are less likely to overlap or represent the same entity multiple times, reducing overestimation of nearby amenities (e.g., distinguishing two "ABC High School" entries). Higher Data Quality: Features with names are usually more complete and reliable in OpenStreetMap (OSM), while unnamed entries often lack context or specificity. Relevance to Users: Named entities are more likely to represent functional, publicly accessible amenities, ensuring results align better with the tool’s purpose. Improved Clarity: If displayed in results or maps, named features are easier for users to interpret and understand.

Trade-offs

Exclusion of Valid Features: Important but unnamed amenities (e.g., local schools) might be missed, particularly in areas with incomplete OSM data. Urban Bias: Rural or less-mapped areas might have fewer named entries, introducing potential bias toward urbanized locations. Dependence on OSM Quality: The approach relies on consistent tagging by mappers, which isn’t always guaranteed, potentially excluding relevant features.