Distance Guide

Understanding Distance

The --distance (or -d) flag controls the map radius in meters from the center point. This determines how much of the city is visible on your poster.

python create_map_poster.py -c "Paris" -C "France" -d 10000

Default: 18,000 meters (18km radius)

The distance represents the radius from the center point, not the diameter. The actual coverage area is a circle with this radius.

Quick Reference

Distance Range	Best For	Example Cities
4,000-6,000m	Small cities, dense centers, high detail	Venice, Marrakech medina, Amsterdam center
8,000-12,000m	Medium cities, downtown focus, iconic districts	Paris, Barcelona, Manhattan
15,000-20,000m	Large metros, full city view, sprawling layout	Tokyo, Mumbai, Los Angeles

Distance by City Type

Small / Dense Cities (4,000-6,000m)

Perfect for capturing intricate street patterns in compact areas.

python create_map_poster.py \
  -c "Venice" -C "Italy" \
  -t blueprint -d 4000
# Captures the entire historic center with canal detail

python create_map_poster.py \
  -c "Marrakech" -C "Morocco" \
  -t terracotta -d 5000
# Shows the dense, winding medina streets

python create_map_poster.py \
  -c "Amsterdam" -C "Netherlands" \
  -t ocean -d 6000
# Displays the iconic concentric canal pattern

Why small distances?

Captures fine-grained street patterns
Shows architectural details
Perfect for walkable historic centers
Emphasizes unique urban layouts

Medium Cities (8,000-12,000m)

Ideal for focused downtown areas and iconic districts.

python create_map_poster.py \
  -c "Paris" -C "France" \
  -t pastel_dream -d 10000
# Shows the radial Haussmann street pattern

python create_map_poster.py \
  -c "Barcelona" -C "Spain" \
  -t warm_beige -d 8000
# Captures the famous chamfered grid

python create_map_poster.py \
  -c "New York" -C "USA" \
  -t noir -d 12000
# Shows the iconic Manhattan street grid

python create_map_poster.py \
  -c "San Francisco" -C "USA" \
  -t sunset -d 10000
# Captures the peninsula with hills and grid

Why medium distances?

Balances coverage with detail
Shows recognizable landmarks and districts
Captures the “heart” of major cities
Good for cities with distinct downtown cores

Large Metropolitan Areas (15,000-20,000m)

For sprawling cities and comprehensive coverage.

python create_map_poster.py \
  -c "Tokyo" -C "Japan" \
  -t japanese_ink -d 15000
# Shows the vast, organic street network

python create_map_poster.py \
  -c "Mumbai" -C "India" \
  -t contrast_zones -d 18000
# Captures the full peninsula with coastline

python create_map_poster.py \
  -c "Dubai" -C "UAE" \
  -t midnight_blue -d 15000
# Shows Palm Jumeirah and coastline

python create_map_poster.py \
  -c "Los Angeles" -C "USA" \
  -t sunset -d 20000
# Captures the sprawling LA basin

Why large distances?

Shows the full scale of megacities
Captures multiple districts at once
Reveals overall urban morphology
Good for cities without a single center

Distance by Urban Pattern

Grid Pattern Cities

Cities with regular street grids look great at medium distances.

# Manhattan, NYC - Iconic grid
python create_map_poster.py -c "New York" -C "USA" -t noir -d 12000

# Barcelona - Eixample district
python create_map_poster.py -c "Barcelona" -C "Spain" -t warm_beige -d 8000

# Chicago - Urban grid
python create_map_poster.py -c "Chicago" -C "USA" -t contrast_zones -d 12000

Recommended: 8,000-12,000m

Radial Pattern Cities

Cities with star/radial patterns shine at medium distances.

# Paris - Haussmann boulevards radiating from Arc de Triomphe
python create_map_poster.py -c "Paris" -C "France" -t pastel_dream -d 10000

# Moscow - Ring road system
python create_map_poster.py -c "Moscow" -C "Russia" -t noir -d 12000

# Washington DC - L'Enfant Plan
python create_map_poster.py -c "Washington" -C "USA" -t blueprint -d 10000

Recommended: 8,000-12,000m

Organic / Historic Cities

Cities with medieval or organic layouts need smaller distances for detail.

# Rome - Ancient street layout
python create_map_poster.py -c "Rome" -C "Italy" -t warm_beige -d 8000

# Marrakech - Medina maze
python create_map_poster.py -c "Marrakech" -C "Morocco" -t terracotta -d 5000

# Jerusalem - Old City
python create_map_poster.py -c "Jerusalem" -C "Israel" -t blueprint -d 6000

Recommended: 4,000-8,000m

Waterfront Cities

Coastal and river cities benefit from distances that capture the water interface.

# Venice - Canal network
python create_map_poster.py -c "Venice" -C "Italy" -t blueprint -d 4000

# Amsterdam - Concentric canals
python create_map_poster.py -c "Amsterdam" -C "Netherlands" -t ocean -d 6000

# Sydney - Harbour city
python create_map_poster.py -c "Sydney" -C "Australia" -t ocean -d 12000

# San Francisco - Bay and peninsula
python create_map_poster.py -c "San Francisco" -C "USA" -t sunset -d 10000

Recommended: 4,000-12,000m (depends on city size)

River Cities

Cities bisected by rivers need enough distance to show both banks.

# London - Thames curves
python create_map_poster.py -c "London" -C "UK" -t noir -d 15000

# Budapest - Danube split
python create_map_poster.py -c "Budapest" -C "Hungary" -t copper_patina -d 8000

# Paris - Seine bends
python create_map_poster.py -c "Paris" -C "France" -t pastel_dream -d 10000

Recommended: 8,000-15,000m

Fine-Tuning Distance

Start with Defaults

If unsure, start with the city-type default:

# Small/dense city - try 5000m
python create_map_poster.py -c "Venice" -C "Italy" -d 5000

# Medium city - try 10000m
python create_map_poster.py -c "Paris" -C "France" -d 10000

# Large city - try 18000m (default)
python create_map_poster.py -c "Tokyo" -C "Japan"

Iterative Adjustment

Generate a test poster
Evaluate coverage:
- Too zoomed out? Reduce distance by 2,000-4,000m
- Too zoomed in? Increase distance by 2,000-4,000m
Regenerate and compare

Generate multiple posters with different distances using --all-themes to compare quickly:

# Generate at 8km
python create_map_poster.py -c "Paris" -C "France" -d 8000 -t noir

# Generate at 12km
python create_map_poster.py -c "Paris" -C "France" -d 12000 -t noir

Override Center Point

For precise control, manually set the center coordinates:

# Focus on Central Park, NYC
python create_map_poster.py \
  -c "New York" -C "USA" \
  -lat 40.776676 -long -73.971321 \
  -d 8000

# Focus on Eiffel Tower, Paris
python create_map_poster.py \
  -c "Paris" -C "France" \
  -lat 48.8584 -long 2.2945 \
  -d 6000

Geocoding services may not always return the ideal center point. Use coordinate overrides for precision.

Distance Comparison Examples

Same city, different distances to show the effect:

Paris

python create_map_poster.py \
  -c "Paris" -C "France" \
  -d 6000
# Shows central arrondissements only

python create_map_poster.py \
  -c "Paris" -C "France" \
  -d 10000
# Shows classic Paris within périphérique

python create_map_poster.py \
  -c "Paris" -C "France" \
  -d 15000
# Includes inner suburbs

New York

python create_map_poster.py \
  -c "New York" -C "USA" \
  -d 8000
# Shows Manhattan core

python create_map_poster.py \
  -c "New York" -C "USA" \
  -d 12000
# Captures most of Manhattan

python create_map_poster.py \
  -c "New York" -C "USA" \
  -d 20000
# Shows parts of all boroughs

Common Distance Values

Frequently used distance presets:

Distance	Coverage	Typical Use
3,000m	3km radius	Neighborhoods, small districts
5,000m	5km radius	Historic centers, old towns
8,000m	8km radius	Downtown cores, central districts
10,000m	10km radius	Full city centers
12,000m	12km radius	Extended metropolitan core
15,000m	15km radius	Major cities, full coverage
18,000m	18km radius	Default - Large city overview
20,000m	20km radius	Megacity sprawl, maximum coverage

Technical Notes

Distance Compensation

The tool automatically compensates for aspect ratio by fetching slightly more data than requested:

compensated_dist = dist * (max(height, width) / min(height, width)) / 4

This ensures the specified distance is fully covered regardless of poster dimensions.

Data Fetching

Larger distances mean:

Longer download times (more OSM data)
Higher memory usage (more features to process)
Larger cache files (data is cached locally)

Very large distances (>25km) may cause:

Slow performance
Memory issues
Overpass API timeouts

Stay within the recommended 4,000-20,000m range for best results.

Basic Usage

Common commands and usage patterns

Command Reference

Complete flag documentation

Resolution Guide

Configure output dimensions

Examples

City-specific examples and recommendations

Get Started

Usage

Guides

Examples

Understanding Distance

Quick Reference

Distance by City Type

Small / Dense Cities (4,000-6,000m)

Medium Cities (8,000-12,000m)

Large Metropolitan Areas (15,000-20,000m)

Distance by Urban Pattern

Grid Pattern Cities

Radial Pattern Cities

Organic / Historic Cities

Waterfront Cities

River Cities

Fine-Tuning Distance

Start with Defaults

Iterative Adjustment

Override Center Point

Distance Comparison Examples

Paris

New York

Common Distance Values

Technical Notes

Distance Compensation

Data Fetching

See Also

Basic Usage

Command Reference

Resolution Guide

Examples

​Understanding Distance

​Quick Reference

​Distance by City Type

​Small / Dense Cities (4,000-6,000m)

​Medium Cities (8,000-12,000m)

​Large Metropolitan Areas (15,000-20,000m)

​Distance by Urban Pattern

​Grid Pattern Cities

​Radial Pattern Cities

​Organic / Historic Cities

​Waterfront Cities

​River Cities

​Fine-Tuning Distance

​Start with Defaults

​Iterative Adjustment

​Override Center Point

​Distance Comparison Examples

​Paris

​New York

​Common Distance Values

​Technical Notes

​Distance Compensation

​Data Fetching

​See Also

Basic Usage

Command Reference

Resolution Guide

Examples

Understanding Distance

Quick Reference

Distance by City Type

Small / Dense Cities (4,000-6,000m)

Medium Cities (8,000-12,000m)

Large Metropolitan Areas (15,000-20,000m)

Distance by Urban Pattern

Grid Pattern Cities

Radial Pattern Cities

Organic / Historic Cities

Waterfront Cities

River Cities

Fine-Tuning Distance

Start with Defaults

Iterative Adjustment

Override Center Point

Distance Comparison Examples

Paris

New York

Common Distance Values

Technical Notes

Distance Compensation

Data Fetching

See Also