Spatial Representation and Destination Choice: What are...

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TransLAB / School of Geography & Earth SciencesMcMaster University

Hamilton, Ontario, CanadaL8S 4K1

E-mail: scottdm@mcmaster.ca

Spatial Representation and Destination Choice: What are the

Consequences of Modifying Reality?

Darren M. Scott

IVT ETH Seminar, June 10, Zürich, CH 2

Outline

A. Research programB. Research objectives &

questionsC. Some basic conceptsD. Comparison of PPA

algorithmsE. Evaluation of MAUP effectsF. Scale effect & grocery

shoppingG. Lessons learnedH. Future research

IVT ETH Seminar, June 10, Zürich, CH 3

A. Research Program

Goals Enhance our understanding of decision

processes underlying activity/travel behavior Incorporate said understanding in policy-

sensitive activity/travel demand models (e.g., MUSCAMAGS Project)

Tools Statistical and econometric techniques GIS (realistic travel environment, variable

generation, software applications)

IVT ETH Seminar, June 10, Zürich, CH 4

A. Research Program

Data GIS layers (e.g., street networks, opportunities,

land uses) Existing activity/travel surveys (e.g., TTS for

GTA, TDS for Louisville KY) New data collection techniques (e.g., Halifax

STAR Project & GPS)

IVT ETH Seminar, June 10, Zürich, CH 5

B. Research Objectives & Questions

Objectives Incorporate spatio-temporal constraints into

destination choice Estimate constrained destination choice

models Sylvia He (2004-06) – shopping location

Questions How do I do this? How does spatial representation impact choice

sets?

IVT ETH Seminar, June 10, Zürich, CH 6

C. Some Basic Concepts

Space-time prism (STP) Time geography (Hägerstrand, 1970) Spatial and temporal limits for decisions

concerning out-of-home activity participation Constrained destination choice sets 3D

Potential path area (PPA) 2D representation of STP Implemented via GIS

C. Some Basic Concepts

IVT ETH Seminar, June 10, Zürich, CH 7

Time

Space

Work

Home

PPA

STP

ljli

STP

PPA

aij

vij

Space (x,y)

Time (t)tj

ti

Tim

e

Win

dow

x

y

C. Some Basic Concepts

IVT ETH Seminar, June 10, Zürich, CH 8

C. Representation

Objects: 3 primitives: 0-dimensional points, 1-

dimensional lines, 2-dimensional areas Vector data model in GIS

Fields: Continuous surface (values change at all

locations) Raster data model in GIS

IVT ETH Seminar, June 10, Zürich, CH 9

C. Points

IVT ETH Seminar, June 10, Zürich, CH 10

C. Areas

IVT ETH Seminar, June 10, Zürich, CH 11

12

IVT ETH Seminar, June 10, Zürich, CH 12

C. Some Basic Concepts

Modifiable Areal Unit Problem (MAUP) Well-known spatial analytical issue that has yet

to be solved Arises due to the fact that an infinite number of

zoning systems can be constructed to subdivide space into smaller areal units

Scale effects Level of spatial resolution

Zoning effects Configuration of the zoning system given a fixed level

of spatial resolution

C. MAUP: Scale Effects

IVT ETH Seminar, June 10, Zürich, CH 13

128

C. MAUP: Zoning Effects

IVT ETH Seminar, June 10, Zürich, CH 14

A

BC

A

BC

IVT ETH Seminar, June 10, Zürich, CH 15

D. Comparison of PPA Algorithms

Objective Compare two GIS-based algorithms for

generating space-time prisms (specifically PPAs) to assess which one will offer more realistic results if implemented within the framework of an activity/travel demand model

Algorithm 1, based on the work of Kwan and Hong (1997), is implemented within ArcView GIS

Algorithm 2, developed by Scott, is implemented within ArcGIS

IVT ETH Seminar, June 10, Zürich, CH 16

D. Algorithm 1: Overlay Approach

i j

A.

i j

B.

C.

ji

+

+

i ji j

INCREMENT = 5 minutes

515C.

1010B.

155A.

ToDestination (j)

FromOrigin (i)

SPEED = 60 km/hr

TTB = 20 minutes

=

D.

0 5 10 155Kilometers

PPA

IVT ETH Seminar, June 10, Zürich, CH 17

D. Algorithm 2: Shortest Path Approach

A. Shortest Path

0 5 10 155Kilometers

SPEED = 60 km/hr

TTB = 20 minutes

i j12.5 min.

B. Subset of Links

i j

10 km

D. PPA

i j

C. Selection of Links

i j

ev1 v2

e is added to PPA if the shortest path containing it has a time ≤ TTB

Junction Flag

IVT ETH Seminar, June 10, Zürich, CH 18

D. Study Area and Data Activity/travel

Synthetic data set Random sampling

program (GAUSS) Normal distributions of

coordinate pairs for origins and destinations of trips

Observations Unique identifier Origin latitude Origin longitude Destination latitude Destination longitude 20-minute travel time

budget

IVT ETH Seminar, June 10, Zürich, CH 19

D. Original Data Set

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20 0 20 Kilom eters

MS A20 K ilom eter Buffe r10 K ilom eter Buffe r

# PPA D es tin ations# PPA O rig ins

N

IVT ETH Seminar, June 10, Zürich, CH 20

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20 0 20 Kilom eters

MS A20 K ilom eter Buffe r10 K ilom eter Buffe r

# PPA Des tin ations# PPA O rig ins

N

D. Reduced Data Set

IVT ETH Seminar, June 10, Zürich, CH 21

D. Opportunities

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20 0 20 Kilom eters

MS A# Urb an O pp ortunities

N

IVT ETH Seminar, June 10, Zürich, CH 22

D. Street Network

20 0 20 Kilom eters

MS AStree ts

N

IVT ETH Seminar, June 10, Zürich, CH 23

D. Definition of Variables

IVT ETH Seminar, June 10, Zürich, CH 24

D. Buffer Area Comparison

IVT ETH Seminar, June 10, Zürich, CH 25

D. Algorithm 1: INT-1

IVT ETH Seminar, June 10, Zürich, CH 26

D. Algorithm 1: INT-5

IVT ETH Seminar, June 10, Zürich, CH 27

D. Algorithm 1: INT-10

IVT ETH Seminar, June 10, Zürich, CH 28

D. Algorithm 2

IVT ETH Seminar, June 10, Zürich, CH 29

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10

Time Interval (minutes)

Are

a (s

quar

e km

s)

Algorithm 1

Algorithm 2

D. Buffer Area Comparison: Averages

IVT ETH Seminar, June 10, Zürich, CH 30

D. Opportunity Comparison

IVT ETH Seminar, June 10, Zürich, CH 31

D.9. Urban Opportunity Comparison: Averages

0

1,000

2,000

3,000

4,000

5,000

6,000

1 2 3 4 5 6 7 8 9 10

Time Interval (minutes)

Num

ber o

f Opp

ortu

nitie

sf

Algorithm 1

Algorithm 2

IVT ETH Seminar, June 10, Zürich, CH 32

D. Conclusions

PPAs, which delimit “realistic” or constrained destination choice sets, should be implemented within activity-based travel demand models

As demonstrated, however, the GIS-based algorithms available to generate them can significantly impact results

If accuracy is to be ensured, a shortest path approach should be used instead of an overlay approach

IVT ETH Seminar, June 10, Zürich, CH 33

E. Evaluation of MAUP Effects

How should destinations for out-of-home activities be represented? Points vs. polygons Implications for “constrained” destination

choice modeling

Outside the MATSIM world, points present challenges when developing activity-based travel demand models Traffic assignment models Updating urban opportunities through time Computational cost (lots of opportunities)

IVT ETH Seminar, June 10, Zürich, CH 34

E. Evaluation of MAUP Effects

Area-based (i.e., zonal) representation of space is typical MAUP effects: scale effect (resolution) and

zoning effect (configuration) Effects will inflate the number of urban

opportunities contained within space-time prisms (network-based prisms become grid-based) and their PPA 2D equivalents

To what degree?

IVT ETH Seminar, June 10, Zürich, CH 35

E. Evaluation of MAUP Effects

Challenge Use a zoning system that incorporates spatio-

temporal constraints in such a way that MAUP effects are minimized

Objective Evaluate MAUP effects on space-time prisms,

and therefore, destination choice sets

IVT ETH Seminar, June 10, Zürich, CH 36

E. Data and Methods

Primary data Travel diary survey

KIPDA, 2000 4,383 households 200 randomly selected

for analysis Opportunity database

infoUSA 34,440 opportunities

Street network Dynamap/Transportati

on 4.0 > 92,000 links

IVT ETH Seminar, June 10, Zürich, CH 37

E. Data and Methods Derived data:

289 network-based PPAs Actual travel behavior of household members Criteria: ≥ 16 years, automobile Shortest path approach (software written in ArcGIS)

144,500 grid-based PPAs 500 randomly generated grids

50 for each of 10 pre-specified grid sizes (1 to 10 sq. km) Random horizontal shift (up to resolution), random vertical

shift (up to resolution), random rotation (0 to 360 degrees) Network-based PPAs intersected with each grid

Comparative measures: number of opportunities (total and by type), weights of opportunities (total and by type) and segment length (km) Number of all opportunities used in analysis

IVT ETH Seminar, June 10, Zürich, CH 38

E. Data and Methods

A. Shortest Path

0 5 10 155Kilometers

SPEED = 60 km/hr

TTB = 20 minutes

i j12.5 min.

B. Subset of Links

i j

10 km

D. PPA

i j

C. Selection of Links

i j

ev1 v2

e is added to PPA if the shortest path containing it has a time ≤ TTB

Junction Flag

IVT ETH Seminar, June 10, Zürich, CH 39

E. Evaluation of Zoning Effects: Example PPA

IVT ETH Seminar, June 10, Zürich, CH 40

E. Evaluation of Scale Effects: Example PPA

IVT ETH Seminar, June 10, Zürich, CH 41

E. Evaluation of MAUP Effects: Example PPA (3)

10,000

11,000

12,000

13,000

14,000

15,000

16,000

0 1 2 3 4 5 6 7 8 9 10 11

Grid size (sq. km)

Num

ber 

of o

ppor

tuni

ties

IVT ETH Seminar, June 10, Zürich, CH 42

E. Evaluation of Zoning Effect: Data Set

0

10

20

30

40

50

PPAs ranked by time budget

Coe

ffici

ent o

f var

iatio

n (%

)

Mean

0

10

20

30

40

50

PPAs ranked by time budget

Coe

ffici

ent o

f var

iatio

n (%

)

Mean

0

10

20

30

40

50

PPAs ranked by time budget

Coe

ffici

ent o

f var

iatio

n (%

)

Mean

0

10

20

30

40

50

PPAs ranked by time budget

Coe

ffici

ent o

f var

iatio

n (%

)

Mean

1 Sq. Km

4 Sq. Km

7 Sq. Km

10 Sq. Km

IVT ETH Seminar, June 10, Zürich, CH 43

E. Evaluation of Scale Effect: Data Set

7,000

7,500

8,000

8,500

9,000

9,500

1 2 3 4 5 6 7 8 9 10

Grid size (sq. km)

Mea

n nu

mbe

r of

 opp

ortu

nitie

s

0

5

10

15

20

25

30

Cha

nge 

from

 gri

d si

ze o

f 1 s

q. k

m (%

)

Mean number ofopportunities

Change (%)

IVT ETH Seminar, June 10, Zürich, CH 44

E. Comparison of Grid and Network PPAs

1,000

1,500

2,000

2,500

3,000

3,500

1 2 3 4 5 6 7 8 9 10

Grid size (sq. km)

Mea

n di

ffere

nce 

(num

ber 

of 

oppo

rtun

ities

)

0

10

20

30

40

50

60

Mea

n di

ffere

nce 

(%)

Mean difference (number ofopportunities)

Mean difference (%)

IVT ETH Seminar, June 10, Zürich, CH 45

E. Conclusions

Both zoning and scale effects are present in the grid-based PPAs Zoning effect tends to be less severe than the

scale effect Suggests that at any scale, different zoning

configurations should produce similar results when estimating constrained destination choice models

All zoning configurations “distort” reality Suggests that even at small grid sizes, more

information will be built into constrained destination choice models than exists in reality

F. Scale Effect & Grocery Shopping

Objective: Investigate the scale effect on constrained

destination choice models for shopping (general, grocery, non-grocery)

Research conducted by Sylvia He (2004-06)

Only results for grocery shopping are reported

IVT ETH Seminar, June 10, Zürich, CH 46

F. Data and Methods

11 zoning systems (TAZ plus 1 km2 to 10 km2 grids in 1 km2 increments)

295 single-purpose grocery shopping trips for KIPDA travel diary survey valid for all zoning systems

IVT ETH Seminar, June 10, Zürich, CH 47

F. Data and Methods

Multinomial logit model estimated for each zoning system Random sampling of alternatives (chosen plus 9 others) Independent variables: number of opportunities (by SIC),

activity duration, store diversity index

IVT ETH Seminar, June 10, Zürich, CH 48

Zone Universal Choice Set

Constrained Choice Set (Mean

for General Shopping)TAZ 818 338

1 km2 5,713 563

4 km2 1,493 163

7 km2 872 99

10 km2 625 73

F. Model Results

IVT ETH Seminar, June 10, Zürich, CH 49

­1.5

­1.0

­0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0Coefficient

Grocery stores 0.3713 0.2006 0.1772 0.1374 0.1404 0.0848 0.0253 0.0381 0.0458 0.0421 0.0119

Meijer 2.3582 2.5796 2.2325 1.9036 0.9405 2.0338 0.5236 1.4401 0.6389 2.0147 2.0008

Department stores 0.1709 0.0925 0.1404 ­0.0163 0.0972 0.0434 0.0555 0.0263 0.0300 0.0404 0.0000

Variety stores ­0.8145 0.1810 0.6525 0.4026 0.4144 0.6917 0.5805 0.5623 0.4794 0.5274 0.4041

Store diversity 3.6061 3.4863 2.7781 2.7978 1.6443 1.3525 1.9090 1.7684 0.9298 1.4005 2.5305

Activity duration 0.1613 0.1721 0.1546 0.1546 0.1551 0.1549 0.1451 0.1585 0.1661 0.1678 0.1440

TAZ 1 km2 2 km2 3 km2 4 km2 5 km2 6 km2 7 km2 8 km2 9 km2 10 km2

IVT ETH Seminar, June 10, Zürich, CH 50

G. Lessons Learned

ESRI, ESRI, ESRI…

Time, and lots of it!

Approach works, as shown in the case of grocery shopping destination choice

Must consider scale effects if using a zoning system

IVT ETH Seminar, June 10, Zürich, CH 51

H. Future Work

Fixity vs. flexibility

Implementation of approach in MUSCAMAGS project PCASPS

Point-based representation

IVT ETH Seminar, June 10, Zürich, CH 52

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