How to add Footfall data
You can easily add footfall data to your analysis.
- Go to Settings
- Click on the Custom analysis tab
- Click on Add data
- Select the Footfall dataset you want to use
- Choose the catchment radius
- Select the counting method
- Click "Add dataset"
What does each setting mean?
Catchment (radius)
Defines how far from your location footfall will be considered.
- Smaller radius (e.g., 30m)
Measures footfall very close to your store (doorstep level) - Larger radius (e.g., 50m+)
Includes nearby streets and surrounding area
Counting method
- Weighted average (recommended for most cases)
Considers all nearby cells, giving more importance to closer ones
→ Best for realistic, balanced view of footfall - Maximum
Picks the highest footfall value within the radius
→ Captures the busiest nearby point (e.g., main street)
Best practices (with real examples)
1. High-street retail store
- Catchment: 30m
- Method: Weighted average
Why: You want to measure footfall directly passing your storefront, not nearby parallel streets.
2. Store near a major intersection (e.g., corner location in city center)
- Catchment: 50m
- Method: Maximum
Why: The busiest footfall might be at the intersection, not exactly at your door. Maximum helps capture that peak opportunity.
3. Shopping mall store (indoor location)
- Catchment: 30m
- Method: Weighted average
Why: Footfall is more evenly distributed inside malls. You want a realistic average around your unit.
4. Suburban store with imprecise coordinates
- Catchment: 50m+
- Method: Weighted average
Why: If your location is slightly off, a larger radius avoids misleading low values.
5. Benchmarking multiple locations across a city
- Catchment: 50m+ (consistent across all)
- Method: Maximum or Weighted (but keep consistent)
Why: Consistency matters more than precision when comparing locations.
Key tip
- Use smaller radius + weighted average for precision
- Use larger radius + maximum to capture peak potential
If you're unsure, start with:
- 30m + weighted average
Then adjust based on how your locations behave (street vs area-driven).