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Commuting and Gateway Communities

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Problem:

Commuting causes:

    • Traffic and accidents
    • Decreased quality of life
    • Higher cost of living
    • Change in community character
    • Emissions and pollutants
    • Displaced infrastructure and housing costs

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Experience:

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Quantitative evidence:

Commuting data:

  • Census data (LODES)
  • Location of Worker Origin
  • Destination of Employment (Statistics)
  • Where do people live and work?

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The evidence:

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Research Questions

    • Which gateway communities have the highest proportion of workers who commute from outside the community? Why?
    • What influences commuting patterns and rates?
    • What is the spatial extent of these “spillover” effects?
    • What is the relationship between workforce housing and commuting?

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Datasets

Data:

  • 1600 rural western communities
    • Housing characteristics
    • Growth rates
    • Second home ownership
    • Available land to be developed
    • Proximity to amenities
  • LODES data
  • On the Map: https://onthemap.ces.census.gov/

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  • Mapped 32 gateway communities
  • Visualizing the regional nature of commuting
  • Developed online viewing tool
  • Developing a replicable methodology

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Analysis

Mapping Commute-sheds

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  • “Sink” communities: more commuters coming in than out.
  • “Source” communities: More commuters leaving than coming in.
  • “Internal Capture”: majority of workers live and work in a city (less than 10 cities)

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Analysis

Commute patterns:

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  • Spatial linear regressions:
    • Unexplained variance (~70%)
    • Higher rent communities have higher in-commuting
    • Higher cost of housing associated with more in-commuting
  • Multi-level models
    • Accounting for regional or county characteristics
  • Creating sub-sets of communities
    • High second home ownership communities
    • High proportions of jobs in tourism communities

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Analysis

Explaining these patterns

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Questions?