The document discusses retail catchment areas using mobility data, emphasizing differences between drive time and mobility-based trade areas. It highlights how SafeGraph’s mobility data provides more accurate insights into customer demographics and behaviors compared to traditional drive time data. Key use cases include marketing, site selection, and business forecasting using enriched trade area data.

1. Understanding Retail
Catchment Areas with Human
Mobility Data
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2. Introductions
Solutions Engineer at CARTO VP of Partnerships at SafeGraph

3. Agenda
● Background on trade areas and catchments
● Quick review of drive time trade areas
● Creating trade areas using mobility data
● Trade area enrichment notebook demo
● Diving deeper into SafeGraph’s mobility data
● Use cases for SafeGraph’s data
● Q&A Read more at:
carto.com/blog

4. ● Know the difference between drive time vs. mobility
data trade areas
● Be able to generate your own mobility data trade
areas
● Understand what makes SafeGraph data different
and how to leverage that data properly
● Know where to go for more information, ideas, and
tools
Key Takeaways

5. ● Catchments and Trade Areas
are used somewhat
interchangeably
● Trade Areas allow us to
understand the audience or
area that a location serves
● Based loosely on the idea of a
gravity model
Catchment and
Trade Areas

6. Poll
What data are you currently using
to identify trade areas?

7. 3.0 - Mobility Data
Buffers → Drive Times → Mobility Data
1.0 - Buffers 2.0 - Drive Times
How it started How it’s going

8. Drive Time Trade Area
Enrichment Primer
● Generate the drive time polygons
(ideally using CARTO)
● Enrich the drivetime polygon using a
spatial join in PostGIS or
CARTOframes enrichment()
● We commonly use Census data for
this but it could be any dataset
● Now we have additional attributes
on those polygons -- like the median
income of the people in that trade
area

9. Demo: Drive Time
Polygon Enrichment
● Drive time polygons can be
generated with CARTO Builder,
CARTO’s SQL API, or CARTOframes
● Enrichment using data from the
CARTO data observatory is very easy
using the enrichment() methods or
can also be done using SQL

10. What Mobility Data
Offers
● Buffers and drive times are based on
the idea that customers tend to live
close to the store or within driving
distance
● The real world is messier than this
and mobility data allows us to get a
much more accurate picture
measure
● For every POI, SafeGraph gives us the
block group IDs and number of
visitors that came from that block
group

11. Trade Areas with
Mobility Data
● SafeGraph gives us visitor home block
group IDs and the number of people who
came from that block group
● We can do a spatial intersection of those
visitor block groups with the ACS to enrich
them (with a weighted average)
● We now have a much closer proxy for the
real visitors/customers - something like
the ACS-derived median income should be
much more accurate

12. Demo: Bay Area
Costcos Analysis
● Costco median income for the trade areas
differs by as much as 30% (between the
trade area types)
● Drive times might be telling you one thing
but mobility data should ultimately be the
more accurate data source
● Median income is just the beginning and
purposefully simple for this example
● There could be all sorts of differences
between drivetime trade areas vs. mobility
data trade areas

13. ● Drive time trade areas might be giving you an inaccurate
idea of who your customers and competitors are
● Mobility data can tell you where customers come from,
where else they go, how far they travel for one store vs.
another, and what time of day they tend to shop
● This can inform all sorts of product and marketing
decisions
Why Does This Matter?

14. Poll
What business decisions are your
trade area analyses powering?

15. Our Mission:
The Source of Truth for Physical
Places

16. Core Places
(POI)
Geometry Neighborhood
Patterns
Core attributes of +7MM POI Building footprints with
spatial hierarchy for POI
Census Block Group visit
patterns and mobility
insights
Over 7MM points of interest across 6,200 major brands in the U.S, Canada, & UK
SafeGraph’s Physical Places Data
Places
Patterns
Foot traffic insights for
places derived from
anonymized mobile
devices

17. Retail,
Real Estate, &
Logistics
Marketing &
Advertising
Visit
Attribution
Location-
Based Ads
Financial
Services
Business
Forecasting
Site
Selection
Trade
Area
Public
Equities
Private
Equity
Out-Of-
Home Ads
Real
Estate
Places Data is Relevant Across Industries

18. SafeGraph is in the Spatial Data Catalog

19. Thanks for listening!
Any questions?
Request a demo at CARTO.COM
Inspect POI and mobility data at shop.safegraph.com
Jonathan Wolf
VP of Partnerships at Safegraph // jonathan@safegraph.com
Kyle Pennell
Solutions Engineer at CARTO // kpennell@carto.com