The Evolution Of Travel In The Urban LandscapeExtrapolating Spatio Temporal Behavioral‐ Trends With Longitudinal Data

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

Keywords: Behavioral Geography, Migration, Central Place Theory, GIS , Spatio-Temporal

• 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?

1. Trends in individual spatial behavior- What changes about how you travel as you

age? As you start a family? How does this aggregate?

2. Trends in aggregate spatial interactions- How does the urban area change as it grows?

Study Background

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

HOME

Δ Spatial behavior = Δ Activity 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

1996 1997

HOME

North North

• Unique destinations 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 Δ directionality (space)– 1997(Σdx, Σdy) – 1996 (Σdx, Σdy)

Δ Time– 1997(distance) – 1996 (distance)

Δ Spatial behavior = Δ Activity spaces

HOME

1996

1997

HOMEHOME

HOME

(0,0)

(0,0)(2,-3)

(1,-1)

(3,4)(-1,2)

(4,-4)

(Σdx, Σdy) = (3,5)

(Σdx, Σdy) = (6,-7)

(0,0)

Δ Space = (3,-2)

Δ Time = 11 mi Distance = 9 mi

Distance = 20 mi

Outcome: Δ Activity SpaceΔ Space (1, 2, 3, 4) where 1 = Δ NE, 2 = SE…Δ Time 11 miles = Δ Distance

NE

SW SE

NW

Size of circle indicates intensity of changeColor of circle indicates direction of change

NW

NE

SE

SW

0.01

1

Log valueΔ Time

Δ Space

Δ Spatial behavior = Δ Activity spaces

NE

SW SE

NW

NW

NE

SE

SW

0.01

1

Log valueΔ Time

Δ Space

Changes in activity spaces show time-variant spatial behavior in individuals

1990-1992

New spatial patterns + new localized economic change

1993-19941994-19961996-1997

Persons with unique destinations Change in number of businesses for home zip code

Spatial association between new spatial patterns and change in activity spaceThe LISA statistic: Local Indicators of Spatial Association

LISA indicator

Δ Spatial Travel Pattern

Δ # of Businesses

High-High increase increase Cluster

Low-Low small increase decrease Cluster

High-Low increase decrease Outlier

Low-High small increase increase Outlier

Mean, Change vector Mean, change estab

High-High 18.0339 16.0085

Low-Low 12.3438 -15.9244

High-Low 29.1778 -15.1245

Low-High 14.3424 16.4432

-15

-5

5

15

25

35

Summary statistics from bivariate LISA, 1999-2000

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

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…

Two of three ideas for future work:1) 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?

2) 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?)

The big ideaWhat 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?