In this webinar, we discuss: - How innovators are using spatial data and analysis to make mobility planning decisions. - Why spatial data is becoming essential in data science workflows to understand and predict patterns. - How mobility companies use thousands of routes and data points, such as weather data, in machine learning models to optimize their scooter fleet usage. Watch it now at: https://go.carto.com/webinars/micromobility-revolution-recorded

1. Powering the Micromobility
Revolution with Spatial
Analysis
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2. Introductions
Product Marketing Manager Location Intelligence Specialist

3. Multi-billion dollar industries are being
built on location data
80%Of all data collected has a
location element on it
10%Is actually used to Power
Business Decisions

4. ...and micromobility was born connected:
Site Monitoring:
How are my
stores
performing?

5. ...and the market is booming:
$5.7bn
In investments in
micromobility start-ups
since 2015.
85%
of investments made in
Chinese market.
3x
faster user acquisition than
car sharing or ride hailing
apps.
Source: McKinsey, Jan 2019

6. Micromobility players & cities
don’t need to know where.
They need to know why.

7. Scooter Relocation
Optimization

8. Challenge:
● Free floating services need to be able to
balance supply (scooters available) and
demand (riders wanting to book a
vehicle).
● Riders are satisfied if they find a vehicle
within an acceptable walking distance
(typically 230 metres).
● It’s not about increasing the number of
vehicles only, it’s about increasing vehicle
availability in high demand areas.
● How can we rotate scooters from low
demand areas to high demand areas?

9. Options for relocation?
User-driven
Users contribute to relocate in
exchange for free rides.
Operator-driven
Space range using a van to
move scooters. Not scalable.

10. Starting points

11. Stopping points

12. Creating a
downtime
metric
● Downtime = the time a scooter is inactive
between one ride and the next.
● Thinking about the problem spatially, we
can use spatial features to drive
decisions about user-driven relocation.

13. Demand by downtimes

14. Results
5000
users benefitted from
relocation.
61%
decrease in scooter
inactivity.
15%
increase in the number of
scooters in the relocation
area.

15. Micromobility Demand Analysis

17. What if new data streams could help?
Human mobility
Road traffic
Demographics
Environmental
Points of interest
Global boundaries
Financial
Housing

18. Challenge:
● A micromobility company wants to enter
a new market, but is ensure of which
cities / areas of larger cities to launch in.
● Census data is outdated, making it
difficult to use that as accurate predictor
of potential success.
● The company wants to be able to see
how demand evolves over time and
changes from season to season.

19. OD Matrix Analysis:

20. Key Takeaways
● New data streams unlock insights by setting the context and elevating
models to their full potential
● Spatial analysis allows micromobility players to go beyond observing
where things happen to uncover why, and drive business outcomes
● Mobility companies are very well positioned to tap into new data and
analysis due to their digital-born nature and established data science
teams

21. Thanks for listening!
Any questions?
Request a demo at CARTO.COM
Dan Rushton
Location Intelligence Specialist // drushton@carto.com
Virginia Diego
Product Marketing Manager // vdiego@carto.com