🏙️ Development Analysis

About Development Analysis

Overview

This tool analyzes the built environment around transit stations by examining building patterns, density, and development intensity. The analysis provides insights into how land is being used within walking distance of transit and identifies opportunities for housing and transit-oriented development.

What We Measure

The analysis examines building patterns within an 800-meter (½ mile) walking radius and calculates several key metrics:

Building Density

• Total Buildings: Count of all structures within the study area
• Provides a baseline measure of development intensity
• Higher counts typically indicate more urbanized areas

Building Heights

• Tallest Building: Maximum height in stories within the area
• Indicates the vertical development potential and existing urban form

Building Coverage

• Coverage Ratio: Percentage of land area covered by building footprints
• Calculated as: (Total building footprint area ÷ Total land area) × 100%
• Ranges typically from <10% (suburban) to 50%+ (dense urban cores)

Urban Form Typology

The development pattern classifies each station area into one of six recognizable urban form archetypes based on how building coverage varies across three zones (0-200m, 200-400m, 400-800m from the station):

• Suburban Station: Low building coverage throughout (<10% average) - typical of park-and-ride locations and low-density areas
• Transit Village: High coverage immediately at the station (>30%) that drops off significantly farther out (<15%) - classic concentrated transit-oriented development
• Urban Corridor: Consistently high coverage (>25% average) with relatively even distribution - typical of subway lines through established urban neighborhoods
• Edge City Pattern: Higher coverage away from the station than near it - often indicates auto-oriented office parks or retail centers
• Transit Neighborhood: Classic transit-oriented gradient where coverage decreases steadily outward from the station
• Mixed Development: Irregular pattern that doesn't fit other categories - common in transitional areas

Parking Supply

• Surface Lots and Garages: Count and total area of parking facilities
• Surface parking represents land that could potentially be redeveloped for housing or mixed-use

Housing Potential

The analysis provides conservative and aggressive estimates of housing units that could be built on existing surface parking lots and parking garages within ½ mile of each station.

Key assumptions:

• Zoning-agnostic: Estimates do not account for existing regulations on building height, setbacks, or land uses
• No minimum parking requirements: Assumes parking minimums are eliminated within ½ mile of stations
• Lot size threshold: Only parking facilities 2,000 square meters or larger are considered, as building on larger lots may be more straightforward
• Height constraint: New development height is limited to the tallest existing building within ½ mile of the station (e.g., if the tallest building is 2 stories, new development cannot exceed 2 stories; if the tallest building is 44 stories, apartments could rise to that height)

Conservative scenario assumptions:

• Building height at 60% of tallest nearby building
• 75% lot coverage (accounting for setbacks, landscaping, walkways, driveways)
• 20% of building reserved for commercial/public use (ground-floor retail, lobbies, stairwells, elevators, community facilities like daycares, offices, civic space)
• 1,000 square feet per housing unit

Aggressive scenario assumptions:

• Building height at 100% of tallest nearby building
• 85% lot coverage
• 10% of building reserved for public/commercial use
• 800 square feet per housing unit

These estimates can translate into hundreds of thousands of new housing units around stations, especially those surrounded by large amounts of surface parking and existing high-rise buildings. It is unlikely that any station area will be entirely redeveloped to these parameters. Rather, the scenarios illustrate the potential for context-sensitive development near transit and how future development may vary depending on the surrounding built environment.

How the Analysis Works

This analysis uses building footprint and attribute data from OpenStreetMap:

1. Data Retrieval

   The system queries OpenStreetMap for all building footprints within 800 meters of the station, including:

   • Building outlines (traced from satellite imagery)
   • Height information (when available - typically from local knowledge or surveys)
2. Building Coverage Calculation

   For each zone:

   • Total building footprint area is measured from satellite-traced outlines
   • Zone land area is calculated (circular zones, ring-shaped for middle/outer)
   • Coverage ratio = (building footprint ÷ land area) × 100%

   This metric is highly reliable because building footprints are consistently mapped from aerial imagery.

3. Parking and Housing Analysis
   • Number of parking lots, garages, and square footage occupied by parking identified from OpenStreetMap
   • Housing potential estimates apply the scenarios described above to parking lot areas of 2,000 square meters or larger

Why This Matters

Understanding development patterns around transit stations has important implications for TOD.

Transit Ridership and Efficiency

• Higher building coverage and density near stations increase potential ridership
• Transit-oriented development patterns (high density near stations) maximize the efficiency of transit infrastructure investments
• Suburban patterns with low coverage may indicate underutilized transit access

Housing Affordability and Supply

• Areas with low building coverage represent potential for infill development and new housing
• Surface parking lots are often identified as prime redevelopment opportunities
• Housing potential estimates help visualize the scale of opportunity

Urban Form and Livability

• Building coverage affects the character and walkability of neighborhoods
• Transit villages and urban corridors typically support vibrant street life
• Very low coverage may indicate auto-dependent patterns that limit accessibility
• The typology helps identify whether development patterns match transit-oriented best practices

Land Use Efficiency

• Coverage ratios reveal how efficiently land is being used
• Large surface parking areas represent significant opportunity costs
• Comparing inner vs. outer zones shows whether the most accessible land (nearest the station) is being used productively

Important Notes and Caveats

Data Quality - Building Heights

Critical Limitation: Height information in OpenStreetMap is highly incomplete. In many areas, only 6-50% of buildings have height tags, even when footprints are comprehensively mapped. This means:

• The "Tallest Building" metric shows the tallest building WITH height data, which may significantly underestimate actual building heights
• Housing estimates are conservative: Because housing potential is capped at the tallest tagged building, and taller untagged buildings may exist, the actual development potential could be higher than shown
• Building coverage is used instead of Floor Area Ratio for this reason - it relies only on footprints, which are reliably mapped from satellite imagery

Data Quality - Building Footprints

• Building footprints are generally accurate and complete, traced from satellite imagery by OpenStreetMap contributors
• Quality varies by location - areas with active mapping communities have more complete data
• Some areas may show as "undermapped" when building data is known to be incomplete

Parking and Housing Analysis

• Housing potential estimates are theoretical - actual development would face zoning, market, and community constraints
• Not all surface parking can or should be redeveloped
• Estimates help visualize scale of opportunity, not specific development proposals
• Estimates are particularly conservative in undermapped areas where the tallest building height may be underreported

Scope Limitations

This analysis does NOT measure:

• Building quality, age, or condition
• Land use mix (residential vs. commercial vs. institutional)
• Detailed architectural character
• Property values or market dynamics
• Existing zoning regulations or development restrictions
• Environmental constraints (slopes, wetlands, etc.)

Comparative Use

The metrics are most useful for:

• Comparing different stations to identify development patterns
• Tracking changes over time (if analysis is repeated periodically)
• Identifying stations with particularly low density that might benefit from transit-oriented development policies
• Finding stations with significant surface parking that represents redevelopment opportunity

Context matters: A suburban commuter rail station and an urban subway station serve different purposes and should have different development patterns. The analysis helps identify whether patterns align with the station's role in the regional transit network.