trb planning applications conference
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Necessary or Nice? Mapping the Perceptual Distance between Current & Ideal Location Attributes in Utah. TRB Planning Applications Conference. RSG, Inc Åsa Bergman Elizabeth Greene. WFRC Jon Larsen. Alternative Title. - PowerPoint PPT PresentationTRANSCRIPT
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Necessary or Nice? Mapping the Perceptual Distance between Current & Ideal Location Attributes in Utah
Wednesday May 8, 201310:30AM-12:00PM
TRB Planning Applications Conference
RSG, IncÅsa BergmanElizabeth GreeneWFRCJon Larsen
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Alternative Title
“Necessary or nice? Exploring Utah Residential Preference Data with Multidimensional Scaling”
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Overview The Utah Residential
Choice Stated Preference Survey– Study overview– Our research questions
What is MDS?– Multi-dimensional
scaling MDS results Lessons learned Next analysis steps
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Survey Context: Utah Residential Choice Stated Preference Survey
2012 Utah Statewide Household Travel Diary– 9,100 households– 1 HH member from 2,800 households ALSO completed the
Residential Choice Stated Preference Survey 2012 Utah Residential Choice Stated Preference
Survey– Survey design inputs:
TCRP H-31 (How Individuals Make Travel & Location Decisions) Community Preference Survey (National Association of Realtors) Growth & Transportation Survey for National Association of
Realtors & SmartGrowth America Residential Choice Survey Resulting Data:
– Current & ideal home location (transit, shopping, parks, etc.)• Area type (downtown, city residential, suburban, small town, rural)
– Ideal home location stated preference experiments– Household & individual demographics
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Our Segmentation VariableSelf-Reported Home Area “Type”Home Location
HH Diary
Res Choice
This Study:
Res Choice Wasatch Front
City downtown 4% 5% 6%City residential 27% 26% 27%Suburban mixed 18% 21% 25%Suburban residential 31% 33% 36%Small town 15% 10% 4%Rural area 6% 4% 1%
9,155 HHs
2,795 responde
nts
1,972responde
nts Focus greater Salt Lake City region,
more comparable and relevant from planning perspective
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Research Objectives Evaluate Multi-Dimensional Scaling (MDS) as
analysis technique to answer our research questions…
Our Research Questions: Compare “ideals” to “current” for residents of different area types:– What location attributes do residents of different area
types (downtown, suburban, et c) prioritize?– How do the area types differ from one another in terms of
priorities/values of residents?– How well do existing amenities associated with the area
types align with the preferences of residents?– How do reported distances to services (e.g. grocery store)
compare to stated ideals?– How do walk, bike, & transit offerings compare to stated
ideals?
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What is Multi-Dimensional Scaling (MDS)?
An exploratory data reduction technique to visualize differences between a set of objects where the difference between each pair of objects can be thought of as a distance
– Origin in psychometrics, commonly used in market research
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Why Try Multi-Dimensional Scaling (MDS)? Cross-tabulations are a good start to answer research questions But it can be difficult to simultaneously visualize all differences
City downto
wn
City resident
ial
Suburban
mixed
Suburban
residential
Small town
Rural area
Row Total
City downtown
44% 18% 18% 4% 7% 10% 100%
City residential
10% 33% 24% 15% 12% 7% 100%
Suburban mixed
6% 10% 50% 16% 11% 6% 100%
Suburban residential
5% 5% 31% 35% 13% 10% 100%
Small town
3% 1% 12% 14% 38% 32% 100%
Rural area
0% 4% 15% 11% 4% 67% 100%
Ideal Home Location
CurrentHomeLocation
(Small town n=73, rural area n=27)
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The MDS Components Simplified, MDS is a 3-step process:
– Input, iteration, & output Step 1: Formatting matrix input
– Distances, frequencies, means, ratings, rankings, proportions, correlations
– Matrix with differences between pairs of objects (e.g. area types)
Rows = Objects to map
Columns = VariablesCity
downtownCity
residentialSuburban
mixedSuburban residential
Small town Rural area Row Sum
City downtown 44% 18% 18% 4% 7% 10% 100%City residential 10% 33% 24% 15% 12% 7% 100%Suburban mixed 6% 10% 50% 16% 11% 6% 100%Suburban residential 5% 5% 31% 35% 13% 10% 100%Small town 3% 1% 12% 14% 38% 32% 100%Rural area 0% 4% 15% 11% 4% 67% 100%n <10
Curr
ent L
ocati
on
Ideal Location
City downtown
City residential
Suburban mixed
Suburban residential
Small town
City residential 0.3964846Suburban mixed 0.5203845 0.3498571Suburban residential 0.5344156 0.3560899 0.2771281Small town 0.5959027 0.5018964 0.5424942 0.4394315Rural area 0.7381057 0.6857113 0.7135825 0.6471476 0.4916299
MDS input:Difference between objects(Euclidean)
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The MDS Components
Use input matrix and output map to interpret locations of points relative to each other. Points closer = More similar
Clusters
Step 2: Iterate to find arrangement of objects in space– Closely matching distances in matrix & preserving rank
order (non-metric MDS) Step 3: Plot and interpret outputX Y
City downtown -1.301775 0.4952946City residential -0.600017 -0.146982Suburban mixed -0.616132 -0.901549Suburban residential -0.018231 -0.652253Small town 0.7834467 0.1067196Rural area 1.7527082 1.0987697
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Simple Example: Ideal Residence Type by Current Location Type
Single-family house Town-house
Multi-family house 3 or fewer units 4 or more units
City downtown 66% 6% 2% 3% 22%City residential 89% 4% 1% 2% 4%Suburban mixed 89% 5% 1% 2% 3%Suburban residential 93% 3% 1% 1% 2%Small town 96% 1% 0% 1% 1%Rural area 96% 4% 0% 0% 0%
Building w Apt's or Condos
Overwhelming preference for single family houses.
Cross-tab tells the story:
– MDS does not add value
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City downtown
City residential
Suburban mixed
Suburban residential
Small town Rural area Row Sum
City downtown 44% 18% 18% 4% 7% 10% 100%City residential 10% 33% 24% 15% 12% 7% 100%Suburban mixed 6% 10% 50% 16% 11% 6% 100%Suburban residential 5% 5% 31% 35% 13% 10% 100%Small town 3% 1% 12% 14% 38% 32% 100%Rural area 0% 4% 15% 11% 4% 67% 100%n <10
Curr
ent L
ocati
on
Ideal Location
Simple Example: Current vs Ideal Location Type
As expected, differences in preferences match differences in area type
– People largely live in the area type they want to live
Most satisfied:1. Rural (67%)2. Suburban mixed
(50%)3. City downtown
(44%)4. Small town (38%)5. Suburban
residential (35%)6. City residential
(33%)
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‘Flip’ the matrix; Map ideal location attributes– Glean location
types from the attributes
– Dimensions emerge
Primary Reason Chose Current Home
City downtown
City residential
Suburban mixed
Suburban residential
Small town Rural area N
Home Price 5% 26% 30% 34% 4% 1% 466Commute 9% 38% 20% 31% 1% 0% 359NearFamilyFriends 1% 26% 24% 41% 7% 2% 227MoreLivingSpace 3% 15% 24% 54% 4% 1% 185WalktoService 25% 41% 26% 7% 0% 0% 68TransitAccess 19% 47% 28% 6% 0% 0% 68Quality of schools 0% 23% 19% 56% 2% 0% 62LotSize 2% 14% 24% 42% 15% 3% 59ParkRecAccess 0% 13% 29% 48% 6% 3% 31LowCrime 3% 13% 33% 43% 7% 0% 30
Rural residents are fundamentally different (46% chose “Other reason”)
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“Very Important” Reasons for Choosing Current Home
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“Very Important” Reasons for Choosing Current Home
Now, having removed the extremes:– Glean location types from the attributes
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Current Commute Distance
– Caution: Small town, rural, small sample size
Commute distance primary reason chose home:1. Downtown 27%2. City residential 27%3. Suburban mixed 16%4. Suburban residential
17%5. Small town 6%6. Rural area 4%
1/2 mile or less 1/2-1 mi 1 -2 mi 2-5 mi 5-10 mi 10-20 mi 20 -30 mi 30-50 mi >50 miles
City downtown 12% 8% 12% 18% 18% 20% 3% 5% 2%City residential 8% 5% 11% 23% 19% 20% 6% 8% 1%Suburban mixed 6% 3% 5% 12% 21% 29% 14% 10% 1%Suburban residential 6% 1% 2% 6% 16% 32% 20% 13% 2%Small town 8% 0% 6% 2% 12% 20% 25% 25% 2%Rural area 0% 0% 5% 0% 19% 5% 38% 29% 5%
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Downtown residents wish to walk more, or expressing their values in the survey?
Desire to Walk More by Location Type
Strongly Disagree Disagree
Somewhat Disagree Neutral
Somewhat Agree Agree
Strongly Agree
City downtown 2% 1% 2% 10% 17% 22% 46%City residential 3% 4% 5% 14% 23% 22% 29%Suburban mixed 3% 4% 6% 14% 30% 19% 25%Suburban residential 3% 5% 7% 16% 27% 19% 22%Small town 3% 8% 4% 21% 25% 23% 16%Rural area 4% 7% 15% 15% 26% 11% 22%
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Not all of them, and not exhaustively, but we learned something.
Did MDS help Answer Our Research Questions?
– What location attributes do residents of different area types (downtown, suburban, et c) prioritize?
– How do the area types differ from one another in terms of priorities/values of residents?
– How well do existing amenities associated with the area types align with the preferences of residents?
– How do reported distances to services (e.g. grocery store) compare to stated ideals?
– How do walk, bike, & transit offerings compare to stated ideals?
MDS useful
MDS useful
Want land-use and secondary data to really get at this.
Better done with more disaggregate and secondary actual distance (miles) data. Want to quantify this, MDS is not sufficient. But learned something about the survey question (‘desire to walk more’).
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Considerations for Using MDSStrengths Excellent for exploring ordinal data (e.g.,
attitude/opinion/judgment) for groups/objects/segments of interest before further analysis
Flexible: Any difference measure accepted– Compare to e.g. factor analysis (requires correlations)– Does well with ranking or rating or single choice data
Quickly reveals clusters and extremes Evaluate not only the answers, but the question itself
– Useful to evaluate answer options in a pilot surveyChallenges, Limitations Want ~10 or more objects to map, sufficiently large
matrix for MDS to really add value Exploratory technique ≠ simple:
– Are differences shown statistically significant? To control for other variables:
– Regression analysis better suited To answer questions about relative priorities, trade-offs:
– Choice modeling better suited (but requires stated preference experiments)
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Next Analysis Steps
Involve land-use and secondary access variables to verify and supplement the self-reported;
– More refined area type variables than the six included here– Population density– Walk score– Transit and other access measures
Introduce socio-economic variables;– Education, income, age group, presence of children
Perform analysis on larger dataset, but geographically focused
– Allows for more segmentation while still comparing ‘apples to apples’
Move beyond MDS (it is an exploratory technique, after all)– Regression analysis
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Acknowledgements
Thank You
– Wasatch Front Regional Council– Mountainland Association of Govermnents– Dixie Metropolitan Planning Organization– Cache Metropolitan Planning Organization– Utah Department of Transportation– Utah Transit Authority
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MDS References
Kruskal & Wish (1978) Borg & Groenen (2005) Takane (2007) Borgatti (1997) isoMDS() from R library MASS
Examples:– Psychometrics: Judge similarity between facial expressions– Marketing research: Map differences between car brands from subjects’
ratings – Communication studies: Create organizational chart from the flows of
email between staff– Animal studies: How genetically close are populations of turtles relative
to their spatial locations?