Python computational tools for urban design metric calculations including density, FAR, walkability scoring, parking requirements, green space analysis, and block optimization. Use when the user asks to calculate density, compute FAR, score walkability, determine parking requirements, analyze green space provision, optimize block dimensions, run urban metrics, or perform any quantitative urban design calculation. Also use when precise numbers are needed for any urban design metric rather than rules of thumb.
A suite of Python computational tools for precise urban design metric calculations. Each script is self-contained, accepts inputs via command-line arguments, and outputs both human-readable text and structured JSON.
| Script | Purpose | Key Inputs | Key Outputs |
|---|---|---|---|
density_calculator.py | Population and dwelling density | Site area, FAR, unit sizes, efficiency | DU/ha, persons/ha, population |
far_calculator.py | FAR and GFA optimization | Site area, coverage, floors, uses | FAR, GFA per use, total GFA |
walkability_scorer.py | Walk Score estimation | Amenity distances, network connectivity | Score 0-100, category |
parking_calculator.py | Parking requirements | Uses, areas, ratios, reductions | Total spaces, area needed |
green_space_analyzer.py |
| Green space adequacy |
| Population, park areas, distances |
| Per capita m2, service coverage |
block_optimizer.py | Block dimension optimization | Target FAR, height, coverage, daylight | Optimal block dimensions |
All scripts are located in the scripts/ subdirectory relative to this skill. Run them with Python 3 via the command line. Every script supports a --json flag to output structured JSON instead of formatted text.
Calculate population and dwelling density for a development site:
python scripts/density_calculator.py --site-area 20000 --far 2.5 --avg-unit-size 85 --efficiency 0.75 --household-size 2.5 --residential-pct 0.7 --streets-pct 0.30
Minimal example (uses defaults for optional parameters):
python scripts/density_calculator.py --site-area 15000 --far 3.0
JSON output:
python scripts/density_calculator.py --site-area 20000 --far 2.5 --json
Calculate Floor Area Ratio and Gross Floor Area by use:
python scripts/far_calculator.py --site-area 10000 --coverage 0.6 --floors 4,6,8 --use-split "res:60,com:25,civic:10,open:5"
With bonus FAR for affordable housing:
python scripts/far_calculator.py --site-area 10000 --coverage 0.6 --floors 6 --bonus-far 0.5
With explicit floor areas per zone:
python scripts/far_calculator.py --site-area 10000 --floor-areas "2000,3000,1500" --floors "4,6,8"
Estimate a Walk Score based on amenity distances and intersection density:
python scripts/walkability_scorer.py --grocery 200 --restaurants 150 --shopping 400 --coffee 100 --parks 300 --schools 500 --intersection-density 150
Minimal example (unspecified amenities default to 9999m = not present):
python scripts/walkability_scorer.py --grocery 300 --parks 200
Determine parking requirements with transit and TDM reductions:
python scripts/parking_calculator.py --residential-units 200 --office-area 5000 --retail-area 2000 --transit-reduction 0.2 --shared-reduction 0.1 --space-type structured
Surface parking example:
python scripts/parking_calculator.py --residential-units 50 --space-type surface
Analyze green space provision against international standards:
python scripts/green_space_analyzer.py --population 5000 --parks "Central Park:8000:neighborhood,Pocket Plaza:400:pocket,River Walk:2000:linear" --standard who
JSON output with UN-Habitat standard:
python scripts/green_space_analyzer.py --population 12000 --parks "Main Park:15000:district,Local Green:1200:neighborhood" --standard un-habitat --json
Find optimal block dimensions for a target FAR:
python scripts/block_optimizer.py --target-far 2.5 --max-height 6 --building-depth 14 --min-courtyard 21 --daylight-angle 25
Higher density example:
python scripts/block_optimizer.py --target-far 4.0 --max-height 10 --max-coverage 0.7 --building-depth 16
All formulas, worked examples, unit conversions, and standard assumptions are documented in references/formulas.md. Key formulas used across the calculators:
See references/formulas.md for the complete reference with worked examples.
Other skills in the Urban Design Skills can invoke these calculators to get precise numbers. The recommended integration pattern is:
density_calculator.py and far_calculator.py with the computed areas to validate metrics.block_optimizer.py to determine block dimensions before laying out the street grid.walkability_scorer.py and green_space_analyzer.py to evaluate design proposals against standards.--json flag to pipe outputs between scripts programmatically.Example chain (compute density then check green space):
# Step 1: Calculate density to get population
python scripts/density_calculator.py --site-area 50000 --far 2.5 --json > density_output.json
# Step 2: Use the population estimate to check green space adequacy
python scripts/green_space_analyzer.py --population 3500 --parks "Park A:5000:neighborhood,Park B:800:pocket" --json
All scripts support two output modes:
Human-readable text (default): Formatted tables and summaries suitable for reports and presentations. Includes section headers, aligned columns, and contextual notes.
JSON (with --json flag): Structured data suitable for programmatic consumption, piping between scripts, and integration with other tools. All numeric values are provided as numbers (not strings), and all units are documented in the JSON keys.
Example JSON output structure (density calculator):
{
"inputs": {
"site_area_m2": 20000,
"far": 2.5,
"avg_unit_size_m2": 85,
"efficiency": 0.75,
"household_size": 2.5,
"residential_pct": 0.7,
"streets_pct": 0.30
},
"results": {
"total_gfa_m2": 50000,
"residential_gfa_m2": 35000,
"net_internal_area_m2": 26250,
"dwelling_units": 308,
"population": 771,
"net_density_du_per_ha": 154.0,
"gross_density_du_per_ha": 107.8,
"population_density_persons_per_ha": 385.0
}
}