1 statistical bases for map reconstructions and comparisons jerry platt may 2005
TRANSCRIPT
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Statistical Bases for Map Reconstructions and Comparisons
Jerry Platt
May 2005
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Preliminaries
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Outline• Motivation
– Do Different Maps “Differ”?
• Methods
– Singular-Value Decomposition
Multidimensional Scaling and PCA
Mantel Permutation TestProcrustean Fit and Permu. TestBidimensional Regression
• Working Example– Locational Attributes of Eight URSB Campuses
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Motivation• Comparing Maps Over Time
Accuracy of a 14th Century MapLeader Image Change in Great BritainWhere IS Wall Street, post-9/11?
• Comparing Maps Among Sub-samplesThings People Fear, M v. F Face-to-Face Comparisons
• Comparing Maps Across AttributesCompetitive Positioning of FirmsChinese Provinces & Human Dev. Indices
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Accuracy of a 14th Century Map
http://www.geog.ucsb.edu/~tobler/publications/pdf_docs/geog_analysis/Bi_Dim_Reg.pdf
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6http://www.mori.com/pubinfo/rmw/two-triangulation-models.pdf
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7http://igeographer.lib.indstate.edu/pohl.pdf
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http://www.analytictech.com/borgatti/papers/borgatti%2002%20-%20A%20statistical%20method%20for%20comparing.pdf
Things People Fear, F v. M
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9http://www.multid.se/references/Chem%20Intell%20Lab%20Syst%2072,%20123%20(2004).pdf
Face-to-Face Comparisons
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10http://www.gsoresearch.com/page2/map.htm
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MethodsEigen-Analysis and Singular-Value Decomposition
Multidimensional Scaling & Principal Comps.
Mantel Permutation Test
Procrustean Fit and Permutation Test
Bidimensional Regression
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Eigen-analysis
• C = an NxN variance-covariance matrix
• Find the N solutions to C = = the N Eigenvalues, with 1 ≥ 2 ≥ …
= the N associated Eigenvectors
• C = LDL’, where
L = matrix of s
D = diagonal matrix of s
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Singular Value Decomposition
• Every NxP matrix A has a SVD
• A = U D V’
• Columns of U = Eigenvectors of AA’
• Entries in Diagonal Matrix D = Singular Values
= SQRT of Eigenvalues of either AA’ or A’A
• Columns of V = Eigenvectors of A’A
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SVD
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Principal Component Analysis
• A is a column-centered data matrix
• A = U D V’
• V’ = Row-wise Principal Components
• D ~ Proportional to variance explained
• UD = Principal Component Scores
• DV’ = Principle Axes
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Multidimensional Scaling• A is a column-centered dissimilarity matrix
• B =
• B = U D V’
• B = XX’, where X = UD1/2
• Limit X to 2 Columns Coordinates to 2d MDS
'
1'
1
2
1 2 iiN
IAiiN
I
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A RandomPermutation
Test
Given DissimilarityMatrices A and B:
N! Permutations37! = 1.4*E+43 8! = 40,320
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Permutation Tests
PermuteList & rerun
ObservedTestStatisticTS = 25# CorrectOf 37 SB.
Is 25Significantly> 18.5?
Ho: TS = 18.5HA: TS > 18.5
P = .069P > .05Do NotReject Ho
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21http://www.entrenet.com/~groedmed/greekm/mythproc.html
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http://www.zoo.utoronto.ca/jackson/pro2.html
Centering &Scaling
MirrorReflection
Rotation &Dilation toMin ∑(є2)
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Procrustean Analysis
• Two NxP data configurations, X and Y• X’Y = U D V’• H = UV• OLS Min SSE = tr ∑(XH-Y)’(XH-Y)
= tr(XX’) + tr(YY’) -2tr(D)
= tr(XX’) + tr(YY’) – 2tr(VDV’)
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OLS Regression
• Y = X + • Y = Xb + e• X = UDV’• b = VrD-1Ur’Y, where r = first r columns (N>P)
• b = (X’X)-1X’Y
• b = VrVr’ • Estimated Y values = Ur Ur’Y
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Bidimensional Regression• (Y,X) = Coordinate pair in 2d Map 1
Y = 0 + 0X
• (A,B) = Coordinate pair in 2d Map 2
E[A] 1 1 -2 X 1
E[B] 1 2 1 Y 2
1 = Horizontal Translation
2 = Vertical Translation
= Scale Transformation = SQRT(12
+ 22)
= Angle Transformation = TAN-1(2 / 1 ) +1800
= + +
Iff 1 < 0
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Althoughr = 1,differ inlocation,scale, andangles ofrotationaroundorigin (0,0)
Horizontal& VerticalTranslation
Angle ofrotationaroundorigin (0,0)
Scaletransform,with < 1 ifcontration,& > 1 ifexpansion
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Working Example
• Eight URSB Campuses– RD, BK, TO, RC, SA, RV, SD, TA
• Data Sources– Locations– Housing Attributes– Tapestry Attributes
• Data Analyses
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Eight URSB Campuses
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87.5 miles
88.1 miles
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…
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EXAMPLE: Eight URSB Campuses
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SD
TA
RDRVRCBK
TOSA
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… and if DISTANCES available, but COORDINATES Unavailable?
• Treat Distance Matrix as Dissimilarity Matrix
• Apply Multidimensional Scaling
• Apply the two-dimension solution “as if” it represents latitude and longitude coordinates
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Distance Estimates Vary
… But Not “Significantly”
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MDS RepresentationInput = D; Output = 2d
D8x8
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SD
TA
RD
RVRC
BK
TO
SA
Errors“appear”
to bequitesmall
…BUT
is therea wayto test
if errorsare
“STATSIGNIF”
?
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Mantel Test
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Procrustean Test:MDS Map Recreation
CONCLUDE: Near-perfect Map Recreation
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Driving Distances
Do these differ “significantly” from linear distances?
STATISTICAL PRACTICAL
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DriveD = Driving DistancesEight URSB Locations
Multidimensional Scaling,with 2-dimension solution
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SD
TA
RD
RVRC
BK
TO
SA
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Bidimensional Regression:AB on YX
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PROTEST Comparison
BidimensionalRegression
ProcrusteanRotation
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Housing
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Tapestry (ESRI)
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Map Coordinates as Explanatory Variables in Linear Models
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Incremental Tests
So Map Coordinates seem sufficient as predictors
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Proxy Measures of lat-longin Linear Model
Translations& Transforms
Reduce 8
And ↑ R2
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Robust criterionwould help here:
Min (Med(є2))
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Bidimensional Regression
Is There a Linear RelationshipBetween Housing and Tapestry
Data?
r = 0.5449
MustStandardize
Data
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It’s Still a 3-d World
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