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Powering the Micromobility Revolution with Spatial Analysis


Published on

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:

Published in: Data & Analytics
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Powering the Micromobility Revolution with Spatial Analysis

  1. 1. Powering the Micromobility Revolution with Spatial Analysis FOLLOW @CARTO ON TWITTER
  2. 2. Introductions Product Marketing Manager Location Intelligence Specialist
  3. 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. 4. ...and micromobility was born connected: Site Monitoring: How are my stores performing?
  5. 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. 6. Micromobility players & cities don’t need to know where. They need to know why.
  7. 7. Scooter Relocation Optimization
  8. 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. 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. 10. Starting points
  11. 11. Stopping points
  12. 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. 13. Demand by downtimes
  14. 14. Results 5000 users benefitted from relocation. 61% decrease in scooter inactivity. 15% increase in the number of scooters in the relocation area.
  15. 15. Micromobility Demand Analysis
  16. 16. What if new data streams could help? Human mobility Road traffic Demographics Environmental Points of interest Global boundaries Financial Housing
  17. 17. 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.
  18. 18. OD Matrix Analysis:
  19. 19. 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
  20. 20. Thanks for listening! Any questions? Request a demo at CARTO.COM Dan Rushton Location Intelligence Specialist // Virginia Diego Product Marketing Manager //