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Cli-Migration.com!
Where to live in a changing climate.
Thomas C. Moran
Insight Data Science
Moving is a big decision.
In NYC alone!
• 50,000 people migrate from New York City each year
• Assume 20% choose where to go (not forced for job, etc.)
• About 50% of these are homeowners, and assume they’ll also buy in their new
location
• Typical NYC home is worth $600,000
(5,000 home buying decisions) * ($600,000) !
~ $3B/yr in relocation decisions for NYC alone
People typically move to places with familiar climate.
cooler warmer
wetterdrier
Migration records show a
tendency to choose
destinations with similar
climate as origin.
Annual temperature increase of +3 to +5 F by mid-century
Where can you go to keep what you like about your city, including climate?
3
3.5
4
4.5
5
5.5
T increase (F)
Cli-migration.com
1. Learn what sorts of cities
and climates you like
2. Recommend comparable
cities in future climate
Content-based recommender
Similarity comparison: euclidean.
If you like Boston now… Portland, ME might be a
good long-term fit.
Climate migration: movement to most similar cities in the future climate.
Changing just climate
features, which cities
are most comparable
to those we like today? !
!
1. Northward to cooler
temperatures.!
2. Coastward to
tempered climate
change near the sea.
!
Content-based
recommender
Climate and sea !
level predictions
Climate !
records
Pre-processing
Geospatial analysis
City
features
Migration
User input and
feedback
Backend Front-end
Re:
UC Berkeley
MIT Lincoln Laboratory
Bon Savants
e to the i pi
Migration modeling
Example: migration from San Francisco
R2 = 0.85
Objective: match historical migration
patterns for user’s home city as a function
of city features (Zillow) and climate.!
!
1. Select features!
• reduce dimensionality via PCA
2. Select model!
• linear regression found to capture
migration patterns, easily interpreted
• R2 = 0.74 ± 0.15, p-value < 0.05
3. Learn from user
• “dislikes” penalize migration scores
• re-run regression, re-rank cities
Observed annual migration [log(movers)]
Predictedannualmigration[log(movers)]
A city recommender for the new climate.
New York
San Francisco
Los Angeles
Boston
Seattle
San Francisco
Content-based city
recommender
Similar cities
in future
climate
Climate
Predictions
Cities you like today

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Climate Migration Recommendations

  • 1. Cli-Migration.com! Where to live in a changing climate. Thomas C. Moran Insight Data Science
  • 2. Moving is a big decision. In NYC alone! • 50,000 people migrate from New York City each year • Assume 20% choose where to go (not forced for job, etc.) • About 50% of these are homeowners, and assume they’ll also buy in their new location • Typical NYC home is worth $600,000 (5,000 home buying decisions) * ($600,000) ! ~ $3B/yr in relocation decisions for NYC alone
  • 3. People typically move to places with familiar climate. cooler warmer wetterdrier Migration records show a tendency to choose destinations with similar climate as origin.
  • 4. Annual temperature increase of +3 to +5 F by mid-century Where can you go to keep what you like about your city, including climate? 3 3.5 4 4.5 5 5.5 T increase (F)
  • 5. Cli-migration.com 1. Learn what sorts of cities and climates you like 2. Recommend comparable cities in future climate Content-based recommender Similarity comparison: euclidean. If you like Boston now… Portland, ME might be a good long-term fit.
  • 6. Climate migration: movement to most similar cities in the future climate. Changing just climate features, which cities are most comparable to those we like today? ! ! 1. Northward to cooler temperatures.! 2. Coastward to tempered climate change near the sea.
  • 7. ! Content-based recommender Climate and sea ! level predictions Climate ! records Pre-processing Geospatial analysis City features Migration User input and feedback Backend Front-end
  • 8. Re: UC Berkeley MIT Lincoln Laboratory Bon Savants e to the i pi
  • 9. Migration modeling Example: migration from San Francisco R2 = 0.85 Objective: match historical migration patterns for user’s home city as a function of city features (Zillow) and climate.! ! 1. Select features! • reduce dimensionality via PCA 2. Select model! • linear regression found to capture migration patterns, easily interpreted • R2 = 0.74 ± 0.15, p-value < 0.05 3. Learn from user • “dislikes” penalize migration scores • re-run regression, re-rank cities Observed annual migration [log(movers)] Predictedannualmigration[log(movers)]
  • 10.
  • 11.
  • 12.
  • 13.
  • 14. A city recommender for the new climate. New York San Francisco Los Angeles Boston Seattle San Francisco Content-based city recommender Similar cities in future climate Climate Predictions Cities you like today