> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/originalankur/maptoposter/llms.txt
> Use this file to discover all available pages before exploring further.

# Distance Guide

> Choose the optimal map radius distance for different city types and layouts

## 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.

```bash theme={null}
python create_map_poster.py -c "Paris" -C "France" -d 10000
```

**Default**: 18,000 meters (18km radius)

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

## 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.

<CodeGroup>
  ```bash Venice - Canal Network theme={null}
  python create_map_poster.py \
    -c "Venice" -C "Italy" \
    -t blueprint -d 4000
  # Captures the entire historic center with canal detail
  ```

  ```bash Marrakech - Medina Maze theme={null}
  python create_map_poster.py \
    -c "Marrakech" -C "Morocco" \
    -t terracotta -d 5000
  # Shows the dense, winding medina streets
  ```

  ```bash Amsterdam - Canal Rings theme={null}
  python create_map_poster.py \
    -c "Amsterdam" -C "Netherlands" \
    -t ocean -d 6000
  # Displays the iconic concentric canal pattern
  ```
</CodeGroup>

**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.

<CodeGroup>
  ```bash Paris - Haussmann Boulevards theme={null}
  python create_map_poster.py \
    -c "Paris" -C "France" \
    -t pastel_dream -d 10000
  # Shows the radial Haussmann street pattern
  ```

  ```bash Barcelona - Eixample Grid theme={null}
  python create_map_poster.py \
    -c "Barcelona" -C "Spain" \
    -t warm_beige -d 8000
  # Captures the famous chamfered grid
  ```

  ```bash New York - Manhattan Grid theme={null}
  python create_map_poster.py \
    -c "New York" -C "USA" \
    -t noir -d 12000
  # Shows the iconic Manhattan street grid
  ```

  ```bash San Francisco - Peninsula theme={null}
  python create_map_poster.py \
    -c "San Francisco" -C "USA" \
    -t sunset -d 10000
  # Captures the peninsula with hills and grid
  ```
</CodeGroup>

**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.

<CodeGroup>
  ```bash Tokyo - Urban Sprawl theme={null}
  python create_map_poster.py \
    -c "Tokyo" -C "Japan" \
    -t japanese_ink -d 15000
  # Shows the vast, organic street network
  ```

  ```bash Mumbai - Coastal Peninsula theme={null}
  python create_map_poster.py \
    -c "Mumbai" -C "India" \
    -t contrast_zones -d 18000
  # Captures the full peninsula with coastline
  ```

  ```bash Dubai - Coastal Development theme={null}
  python create_map_poster.py \
    -c "Dubai" -C "UAE" \
    -t midnight_blue -d 15000
  # Shows Palm Jumeirah and coastline
  ```

  ```bash Los Angeles - Basin View theme={null}
  python create_map_poster.py \
    -c "Los Angeles" -C "USA" \
    -t sunset -d 20000
  # Captures the sprawling LA basin
  ```
</CodeGroup>

**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.

```bash theme={null}
# 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.

```bash theme={null}
# 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.

```bash theme={null}
# 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.

```bash theme={null}
# 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.

```bash theme={null}
# 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:

```bash theme={null}
# 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

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

<Tip>
  Generate multiple posters with different distances using `--all-themes` to compare quickly:

  ```bash theme={null}
  # 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
  ```
</Tip>

## Override Center Point

For precise control, manually set the center coordinates:

```bash theme={null}
# 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
```

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

## Distance Comparison Examples

Same city, different distances to show the effect:

### Paris

<CodeGroup>
  ```bash Tight Focus (6km) theme={null}
  python create_map_poster.py \
    -c "Paris" -C "France" \
    -d 6000
  # Shows central arrondissements only
  ```

  ```bash Balanced (10km) theme={null}
  python create_map_poster.py \
    -c "Paris" -C "France" \
    -d 10000
  # Shows classic Paris within périphérique
  ```

  ```bash Wide View (15km) theme={null}
  python create_map_poster.py \
    -c "Paris" -C "France" \
    -d 15000
  # Includes inner suburbs
  ```
</CodeGroup>

### New York

<CodeGroup>
  ```bash Midtown Focus (8km) theme={null}
  python create_map_poster.py \
    -c "New York" -C "USA" \
    -d 8000
  # Shows Manhattan core
  ```

  ```bash Manhattan Island (12km) theme={null}
  python create_map_poster.py \
    -c "New York" -C "USA" \
    -d 12000
  # Captures most of Manhattan
  ```

  ```bash Five Boroughs (20km) theme={null}
  python create_map_poster.py \
    -c "New York" -C "USA" \
    -d 20000
  # Shows parts of all boroughs
  ```
</CodeGroup>

## 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:

```python theme={null}
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)

<Warning>
  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.
</Warning>

## See Also

<CardGroup cols={2}>
  <Card title="Basic Usage" icon="rocket" href="/usage/basic-usage">
    Common commands and usage patterns
  </Card>

  <Card title="Command Reference" icon="terminal" href="/usage/command-reference">
    Complete flag documentation
  </Card>

  <Card title="Resolution Guide" icon="image" href="/usage/resolution-guide">
    Configure output dimensions
  </Card>

  <Card title="Examples" icon="map" href="/examples/gallery">
    City-specific examples and recommendations
  </Card>
</CardGroup>