Catchments

A catchment describes who lives and works around a point. Where a trade area models where a store’s customers come from, a catchment simply draws rings and summarizes everything inside them. It is the standard first look at a site and the table landlords and brokers expect.

Inputs

Input	Description
lng, lat	Center of the rings
rings	1 to 4 positive values, e.g. [0.5, 1, 3]
unit	km, mi or min

Each ring’s radius must be at most 50 km after conversion. In the app: Site selection → Catchment. Via the API: POST /api/v1/sdss/catchment (reference). Each run counts as one analysis.

Ring units

Unit	Radius
km	the value in kilometers
mi	value × 1.609344 km
min	value × city average speed ÷ 60

About drive-time rings

Drive-time rings in MapDove are a speed-based radius approximation, not a road-network isochrone. Minutes are converted to a straight-line radius using the average driving speed of the city containing the point:

City	Average speed	10-minute ring radius
Austin	42 km/h	7.0 km (4.3 mi)
Houston	45 km/h	7.5 km (4.7 mi)
Phoenix	50 km/h	8.3 km (5.2 mi)
Miami	35 km/h	5.8 km (3.6 mi)
Dubai	55 km/h	9.2 km

The result is a circle. It does not account for the road network, highways, bridges, water bodies, congestion by time of day or turn restrictions. In practice a true 10-minute isochrone is elongated along highways and clipped by barriers; the approximation over-states reach across a bay or river and under-states it along a freeway. Use minute rings for consistent comparisons between candidates in the same city, and commission a network-based drive-time study before a final commitment.

What each ring reports

Field	How it is computed
label, radius_km, area_km2	Ring label (e.g. "1 mi"), radius and area π·r²
population, households, daytime_pop	Area-weighted sums over intersecting hexes
median_income, median_age, pct_college, pct_family, growth_rate	Population-weighted averages (using area-weighted population)
traffic_index, rent_index	Simple averages over intersecting hexes
segments	Population and share by lifestyle segment
places_by_category	Count of places inside the ring, by category
geometry	GeoJSON polygon of the ring

Rings are cumulative discs, not donuts: a 3-mile ring includes the population of the 1-mile ring. Subtract to get the band between them.

Area-weighted aggregation

Hex cells rarely sit entirely inside a ring. For each hex h that intersects ring R, MapDove computes the fraction of the hex’s area inside the ring:

f_h = min(1, |h ∩ R| / |h|)

Counts are apportioned by that fraction, and averages are weighted by the apportioned population:

population_R = Σ_h population_h · f_h
income_R     = Σ_h income_h · population_h · f_h  /  Σ_h population_h · f_h

• |h ∩ R| — area of the part of hex h inside the ring, |h| — area of the hex (about 0.8 km²).
• This assumes people are spread evenly within each hex — the usual assumption for areal interpolation.

Income in a ring is therefore a population-weighted average of hex medians, not a true median of all households in the ring. Over a few hexes the two are close; treat it as a representative central value. The full set of formulas is in Methodology.

Tips

• Pick rings that match the category. Convenience: 0.5/1/2 mi or 5/10 min. Destination formats: 3/5/10 mi or 10/20 min.
• Compare daytime vs resident population. A ring with daytime_pop far above population is a work or tourism district: weekday lunch, weak evenings.
• Read the competitive context. places_by_category is a quick saturation check before you open the full Huff model.
• Small rings near the edge of the study area may cover few hexes; very large rings can extend beyond the modeled city and will under-count population there.

All demographics are synthetic (Demo data).