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Work by: Nathaniel Royal, Pamela Dalal , Kostas G. Goulias

The Evolution Of Travel In The Urban Landscape Extrapolating Spatio ‐Temporal Behavioral Trends With Longitudinal Data. Keywords: Behavioral Geography, Migration, Central Place Theory, GIS , Spatio -Temporal. Work by: Nathaniel Royal, Pamela Dalal , Kostas G. Goulias.

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Work by: Nathaniel Royal, Pamela Dalal , Kostas G. Goulias

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  1. The Evolution Of Travel In The Urban LandscapeExtrapolating Spatio‐Temporal Behavioral Trends With Longitudinal Data Keywords: Behavioral Geography, Migration, Central Place Theory, GIS , Spatio-Temporal Work by: Nathaniel Royal, Pamela Dalal, Kostas G. Goulias

  2. Study Background What are the spatio-temporal behavioral trends of persons in an environment? Over time, how do the human-environment interactions evolve in an urban setting? Trends in individual spatial behavior What changes about how you travel as you age? As you start a family? How does this aggregate? Trends in aggregate spatial interactions - How does the urban area change as it grows?

  3. Study region and data • Puget Sound • 3.7 million people in 6,290 sq miles (2010) • GDP: $22.9 billion (2009, BEA) • Largest city: Seattle, population 612,000, 142.5 sq miles (2010) • Greece • 11.3 million people in 50,944 sq miles (2010) • GDP: $311.3 billion (2011 estimate) • Largest city: Athens, population 3.1 million, 15 sq. mi

  4. Δ Spatial behavior = ΔActivity spaces • How does behavioral change over space and time • Change in spatial patterns though observed activity spaces • The environment in which an individual travels for activity participation • Δ Space: x y coordinates of home and destinations from activity diaries • Δ Time: Total travel distance North North HOME HOME 1996 1997

  5. Δ Spatial behavior = ΔActivity spaces • ΔTime = 11 mi Distance = 9 mi • Uniquedestinations per person between two time points Δ Space • Create vectors of directionality 1. Normalize x y coordinates • Home xy – Home xy (0,0) • Destination xy – Home xy (dx,dy) 2. Calculate dominate direction • dx1+ dx2 + dx3 = Σdx • dy1 +dy2 +dy3 = Σdy • Direction from home based on (Σdx, Σdy) 3. Calculate Δdirectionality (space) • 1997(Σdx, Σdy) – 1996 (Σdx, Σdy) Δ Time • 1997(distance) – 1996 (distance) (-1,2) (3,4) HOME HOME (0,0) (0,0) (1,-1) Δ Space = (3,-2) (Σdx, Σdy) = (3,5) 1996 Distance = 20 mi HOME HOME (0,0) • Outcome: Δ Activity Space • Δ Space (1, 2, 3, 4) where 1 = Δ NE, 2 = SE… • Δ Time 11 miles = Δ Distance (2,-3) (4,-4) (Σdx, Σdy) = (6,-7) 1997

  6. Δ Spatial behavior = Δ Activity spaces NW NE • Δ Time Log value NW NE SE SW 0.01 1 SW SE • ΔSpace Size of circle indicates intensity of change Color of circle indicates direction of change

  7. NW NE • Δ Time Log value NW NE SE SW 0.01 1 SW SE • ΔSpace Changes in activity spaces show time-variant spatial behavior in individuals

  8. New spatial patterns + new localized economic change 1993-1994 1990-1992 1996-1997 1994-1996 Persons with unique destinations Change in number of businesses for home zip code

  9. Spatial association between new spatial patterns and change in activity space The LISA statistic: Local Indicators of Spatial Association

  10. Thoughts so far… Extrapolating behavioral trends using longitudinal travel data seems to work. • Spatial behavior of individuals • How is their behavior changing over time can be studied • Next step: Extrapolate trends in behavioral change • Link to changes in the person or household, i.e. turning points • Spatial interactions in an urban environment • Correlate spatial outcomes of travel behavior and built environment • Next step: include other spatial factors that affect travel behavior, i.e. work-based accessibility

  11. Futures • Data’s at the individual level • Lots of peripheral data (age, marriage stats, etc.) • But, only a few hundred surveyed in a city of millions…

  12. Two of three ideas for future work: Lifestyle changes: Marriage and kids- can these very specific turning points in a persons life be gleaned from the travel behavior.? If so, can we then say something about how a persons travel behavior changes when they do; are there patterns that stand out? Change in the city: can patterns in the cities lifestyle be predicted? by residents travel behavior? Vice versa? (does change in city = change in behavior or does change in behavior = change in city or both and in what cases?)

  13. The big idea What is the most engaging question we can look at with this sort of data? I say it’s the sprawl… How did cities become there modern versions of themselves? How’d did the suburbs become where everyone wanted to live? Wanting a yard, a house to call your own, and a quiet neighborhood is not a new want. Was it a want that was only achievable recently? Did “good” transportation create the sprawl?

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