Lab 2 variable transformations lisa statistics getis ord statistics constructing spider diagrams
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Lab #2: - variable transformations - LISA statistics - Getis-Ord statistics - constructing spider diagrams. Spatial statistics in practice Center for Tropical Ecology and Biodiversity, Tunghai University & Fushan Botanical Garden.

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Spatial statistics in practice

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Lab 2 variable transformations lisa statistics getis ord statistics constructing spider diagrams

Lab #2:- variable transformations- LISA statistics - Getis-Ord statistics- constructing spider diagrams

Spatial statistics in practice

Center for Tropical Ecology and Biodiversity, Tunghai University & Fushan Botanical Garden


Sas code for identifying a box cox type of response variable transformation

SAS code for identifying a Box-Cox type of response variable transformation

FILENAME INDATA 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-AREAS-COMPETITION.TXT';

TITLE 'SW-BASED TRANSFORMATIONS FOR THE PR AREA DATA';

************************************************

* READ IN GEOREFERENCED DATA; *

* THEN CENTER THE SELEDTED ATTRIBUTE VARIABLE *

************************************************;

DATA STEP1;

INFILE INDATA LRECL=1024;

INPUT ID AREAM AREACT MCT_RATIO TRMCT_RATIO AREAPT MPT_RATIO TRMPT_RATIO NAME$;

Y = AREAM;

X0=1;

RUN;

PROC UNIVARIATE NORMAL; VAR Y; RUN;

PROC UNIVARIATE NOPRINT; VAR Y; OUTPUT OUT=TEMP MEDIAN=XM MAX=YMAX MIN=YMIN; RUN;

DATA TEMP(REPLACE=YES);

SET TEMP;

IF YMIN>0 THEN DEC = YMAX/YMIN; ELSE DEM=YMAX;

RUN;

PROC PRINT; VAR DEC YMAX YMIN XM; RUN;

PROC RANK DATA=STEP1 OUT=STEP1 (REPLACE=YES) NORMAL=BLOM; VAR Y; RANKS NY; RUN;

PROC RANK DATA=STEP1 OUT=STEP1 (REPLACE=YES); VAR Y; RANKS RY; RUN;

DATA STEP1 (REPLACE=YES);

SET STEP1;

IF _N_=1 THEN SET TEMP(KEEP=YMIN YMAX);

Y=Y-YMIN;

RUN;


Spatial statistics in practice

exponent of 0

****************************************************

* A JACOBIAN TERM MAY BE NEEDED HERE IF N IS SMALL *

****************************************************

* LOG TRANSFORMATION (POWER OF 0) WITH TRANSLATION *

****************************************************;

PROC NLIN DATA=STEP1 NOITPRINT MAXITER=5000 METHOD=MARQUARDT;

PARMS A=0 B=1 D=0.01;

BOUNDS 0<D;

MODEL NY = A + B*LOG(Y+D);

OUTPUT OUT=TEMP1 PARMS=A B D;

RUN;

DATA TEMP1(REPLACE=YES); SET TEMP1;

D=D-YMIN;

RUN;

PROC MEANS; VAR D; RUN;

******************************************************

* POWER TRANSFORMATION (POWER <> 0) WITH TRANSLATION *

******************************************************;

PROC NLIN DATA=STEP1 NOITPRINT MAXITER=1000 METHOD=MARQUARDT;

PARMS A=0 B=1 C=0.5; D=1.0E-10;

BOUNDS 0<D;

MODEL NY = A + B*((Y + D)**C - 1)/C;

OUTPUT OUT=TEMP2 PARMS=A B D;

RUN;

DATA TEMP2(REPLACE=YES); SET TEMP2;

D=D-YMIN;

RUN;

PROC MEANS; VAR C D; RUN;

******************************

* EXPONENTIAL TRANSFORMATION *

******************************;

PROC NLIN DATA=STEP1 NOITPRINT MAXITER=5000 METHOD=MARQUARDT;

PARMS A=0 B=1 C=0.01;

MODEL NY = A + B*EXP(-C*(Y+YMIN) );

OUTPUT OUT=TEMP PARMS=A B C;

RUN;

PROC MEANS; VAR C; RUN;

exponent of < 0, > 0

exponentiation (Manley transformation)


Transformation possibilities example

Transformation possibilities example


Sas code for normality variance equality tests

SAS code for normality & variance equality tests

FILENAME INDATA 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-AREAS-COMPETITION.TXT';

OPTIONS LINESIZE=72;

TITLE 'SW-BASED TRANSFORMATIONS FOR THE PR AREA DATA';

************************************************

* READ IN GEOREFERENCED DATA; *

* THEN CENTER THE SELEDTED ATTRIBUTE VARIABLE *

************************************************;

DATA STEP1;

INFILE INDATA LRECL=1024;

INPUT ID AREAM AREACT MCT_RATIO TRMCT_RATIO AREAPT MPT_RATIO TRMPT_RATIO NAME$;

Y = MCT_RATIO;

TRY = LOG(Y – 0.20);

X0=1;

RUN;

PROC UNIVARIATE NORMAL; VAR Y TRY; RUN;

PROC MEANS DATA=STEP1 NOPRINT; VAR Y; OUTPUT OUT=YMEAN MEAN=YMEAN; RUN;

DATA STEP1(REPLACE=YES); SET STEP1; IF _N_=1 THEN SET YMEAN(KEEP=YMEAN);

IF Y>YMEAN THEN IYMEAN=1; ELSE IYMEAN=0;

RUN;

PROC GLM; CLASS IYMEAN; MODEL Y=IYMEAN; MEANS IYMEAN/HOVTEST=BARTLETT; RUN;

PROC GLM; CLASS IYMEAN; MODEL Y=IYMEAN; MEANS IYMEAN/HOVTEST=LEVENE; RUN;

PROC MEANS DATA=STEP1 NOPRINT; VAR TRY; OUTPUT OUT=TRYMEAN MEAN=TRYMEAN; RUN;

DATA STEP1(REPLACE=YES); SET STEP1; IF _N_=1 THEN SET TRYMEAN(KEEP=TRYMEAN);

IF TRY>TRYMEAN THEN ITRYMEAN=1; ELSE ITRYMEAN=0;

RUN;

PROC GLM; CLASS ITRYMEAN; MODEL TRY=ITRYMEAN; MEANS ITRYMEAN/HOVTEST=BARTLETT; RUN;

PROC GLM; CLASS ITRYMEAN; MODEL TRY=ITRYMEAN; MEANS ITRYMEAN/HOVTEST=LEVENE; RUN;


Sas code for g i lisa and z lisa

SAS code for Gi, LISA and zLISA

FILENAME INDATA1 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-AREAS-COMPETITION.TXT';

FILENAME INDATA2 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-DEM&QUAD-DATA.TXT';

FILENAME CONN 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-CON.TXT';

FILENAME OUTFILE 'D:\JYU-SUMMERSCHOOL2006\LAB#2\PR-LISA-OUT.TXT';

OPTIONS LINESIZE=72;

TITLE 'GI AND LISA FOR THE PR AREA DATA';

**************************************************************************************

* READ IN THE N-BY-N GEOGRAPHIC CONNECTIVITY MATRIX; MAKE SURE NOT TO MISREAD THE ID *

**************************************************************************************;

DATA STEP1; INFILE CONN; INPUT IDC C1-C73; RUN;

****************************************************************************

* READ IN GEOREFERENCED DATA; THEN CENTER THE SELECTED ATTRIBUTE VARIABLE *

****************************************************************************;

DATA STEP2A; INFILE INDATA1 LRECL=1024;

INPUT POLYGON AREAM AREACT MCT_RATIO TRMCT_RATIO AREAPT MPT_RATIO TRMPT_RATIO NAME$;

* Y = AREAM;

* Y = MCT_RATIO;

X0=1;

RUN;

PROC SORT DATA=STEP2A OUT=STEP2A(REPLACE=YES); BY NAME; RUN;

DATA STEP2B; INFILE INDATA2 LRECL=1024; INPUT IDDEM MELEV SELEV U V QUAD NAME$;

U=U/1000; V=V/1000;

Y=LOG(MELEV+17.5);

* Y=(SELEV-25)**0.5;

YO=Y;

RUN;

PROC SORT DATA=STEP2B OUT=STEP2B(REPLACE=YES); BY NAME; RUN;

DATA STEP2; MERGE STEP2A STEP2B; BY NAME; RUN;


Spatial statistics in practice

PROC STANDARD MEAN=0 STD=1 OUT=STEP2(REPLACE=YES); VAR Y; RUN;

PROC MEANS DATA=STEP2 NOPRINT; VAR X0; OUTPUT OUT=NOUT SUM=N; RUN;

PROC MEANS DATA=STEP2 NOPRINT; VAR YO; OUTPUT OUT=YOOUT MEAN=YOBAR USS=YOUSS; RUN;

DATA STEP2(REPLACE=YES); SET STEP2; SET STEP1;

ARRAY CONN{73} C1-C73;

ARRAY YCONN{73} CY1-CY73;

ARRAY YOCONN{73} CYO1-CYO73;

CSUM = 0;

DO I=1 TO 73;

CSUM = CSUM + CONN{I};

IF _N_=I THEN CONN{I}=0;

YCONN{I} = Y*CONN{I};

YOCONN{I} = YO*CONN{I};

END;

RUN;

PROC MEANS DATA=STEP2 NOPRINT; VAR CY1-CY73; OUTPUT OUT=CYOUT1 SUM=CY1-CY73; RUN;

PROC TRANSPOSE DATA=CYOUT1 PREFIX=CY OUT=CYOUT2; VAR CY1-CY73; RUN;

PROC MEANS DATA=STEP2 NOPRINT; VAR CYO1-CYO73; OUTPUT OUT=CYOOUT1 SUM=CYO1-CYO73; RUN;

PROC TRANSPOSE DATA=CYOOUT1 PREFIX=CYO OUT=CYOOUT2; VAR CYO1-CYO73; RUN;

DATA STEP3;

SET STEP2(KEEP=POLYGON Y YO CSUM);

SET CYOUT2(KEEP=CY1);

SET CYOOUT2(KEEP=CYO1);

IF _N_=1 THEN SET NOUT(KEEP=N);

IF _N_=1 THEN SET YOOUT(KEEP=YOBAR YOUSS);

LISA=Y*CY1/((N-1)/N);

ELISA=-CSUM/(N-1);

GI=(CYO1-((N*YOBAR-YO)/(N-1))*CSUM)/

( SQRT((YOUSS-YO**2-((N*YOBAR-YO)**2)/(N-1))/(N-1))*SQRT(((N-1)*CSUM-CSUM**2)/(N-2)) );

DROP CY1;

RUN;

PROC PRINT; VAR LISA GI; RUN;

would be (n-2)

for an unbiased

estimate


Spatial statistics in practice

*****************************

* *

* CONDITIONAL RANDOMIZATION *

* *

*****************************;

PROC TRANSPOSE DATA=STEP3 PREFIX=N OUT=SAMPLE1; VAR CSUM; RUN;

PROC TRANSPOSE DATA=STEP3 PREFIX=Y OUT=SAMPLE2; VAR Y; RUN;

DATA SAMPLE3; SET SAMPLE1; SET SAMPLE2;

ARRAY TY{73} Y1-Y73;

ARRAY NI{73} N1-N73;

DO I=1 TO 73;

DO J=1 TO 10000;

DO K=1 TO 73;

YI=TY{I}; YJ=TY{K};

PROB = RANUNI(0);

IF K=I THEN PROB=0;

NUMI=NI{I};

OUTPUT;

END;

END;

END;

DROP K Y1-Y73 N1-N73;

RUN;

PROC RANK DATA=SAMPLE3 OUT=SAMPLE3 (REPLACE=YES) DESCENDING; VAR PROB; RANKS RPROB; BY I J; RUN;

DATA SAMPLE3(REPLACE=YES); SET SAMPLE3; IF _N_=1 THEN SET NOUT(KEEP=N); IF RPROB > NUMI THEN DELETE;

YIYJ=YI*YJ/((N-1)/N);

RUN;

PROC MEANS NOPRINT; VAR YIYJ; BY I J; OUTPUT OUT=SAMPLE4 SUM=CYIYJ; RUN;

PROC MEANS NOPRINT; VAR CYIYJ; BY I; OUTPUT OUT=SAMPLE5 MEAN=MCYIYJ STD=SCYIYJ; RUN;

reduce to 100 for class purposes


Spatial statistics in practice

DATA FINAL; SET STEP3(KEEP=POLYGON LISA ELISA N); SET SAMPLE5(KEEP=MCYIYJ SCYIYJ);

ZLISA = (LISA-MCYIYJ)/SCYIYJ;

IF ZLISA> 0 THEN PROBLISA = 1 - PROBNORM(ZLISA);

ELSE PROBLISA = PROBNORM(ZLISA);

BON1 = 0.05/N; BON2 = 1 - 0.01/N;

SIDAK1 = 1 - (1 - 0.05)**(1/N); SIDAK2 = (1 - 0.05)**(1/N);

IF PROBLISA < BON1 OR PROBLISA < SIDAK1 OR PROBLISA > BON2 OR PROBLISA > SIDAK2 THEN IEXTREME=1; ELSE IEXTREME=0;

RUN;

PROC PRINT; VAR POLYGON LISA ZLISA PROBLISA IEXTREME BON1 BON2 SIDAK1 SIDAK2; RUN;

DATA EXTREMES; SET FINAL; IF IEXTREME=0 THEN DELETE; RUN;

PROC PRINT; VAR POLYGON LISA ZLISA PROBLISA IEXTREME BON1 BON2 SIDAK1 SIDAK2; RUN;

PROC CLUSTER DATA=FINAL NOPRINT SIMPLE METHOD=COMPLETE NOEIGEN OUTTREE=LISATREE;

VAR ZLISA;

ID POLYGON;

RUN;

****************************

* SET THE NUMBER OF GROUPS *

****************************;

PROC TREE DATA=LISATREE OUT=TREEOUT NCLUSTERS=5; ID POLYGON; RUN;

PROC SORT OUT=TREEOUT; BY POLYGON; RUN;

DATA FINAL(REPLACE=YES); MERGE FINAL TREEOUT; BY POLYGON; RUN;

PROC SORT OUT=FINAL(REPLACE=YES); BY CLUSTER ZLISA; RUN;

PROC ANOVA; CLASS CLUSTER; MODEL ZLISA=CLUSTER; RUN;

PROC MEANS DATA=FINAL NOPRINT; VAR ZLISA; BY CLUSTER; OUTPUT OUT=TEMP MIN=ZMIN MAX=ZMAX; RUN;

PROC PRINT; RUN;

PROC UNIVARIATE DATA=FINAL NOPRINT; VAR ZLISA; BY CLUSTER; OUTPUT OUT=TEMP2 PROBN=SW; RUN;

PROC PRINT; RUN;

PROC DISCRIM DATA=FINAL POOL=TEST; CLASS CLUSTER; VAR ZLISA; RUN;

DATA _NULL_; SET FINAL; FILE OUTFILE;

PUT CLUSTER POLYGON ZLISA;

RUN;


Sas code for moran scatterplot regression diagnostics

SAS code for Moran scatterplot regression diagnostics

FILENAME INDATA 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-DEM&QUAD-DATA.TXT';

FILENAME CONN 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-CON.TXT';

FILENAME OUTFILE 'D:\JYU-SUMMERSCHOOL2006\LAB#2\PR-REGRESSION-DIAGNOSTICS.TXT';

TITLE 'MORAN SCATTERPLOT REGRESSION DIAGNOSTICS FOR THE PR DATA';

**************************************************************

* READ IN THE N-BY-N GEOGRAPHIC CONNECTIVITY MATRIX; *

**************************************************************;

DATA STEP1;

INFILE CONN LRECL=512;

INPUT IDC C1-C73;

ARRAY CONN{73} C1-C73;

CSUM = 0;

DO I=1 TO 73;

CSUM = CSUM + CONN{I};

END;

RUN;

PROC MEANS DATA=STEP1 NOPRINT; VAR CSUM; OUTPUT OUT=CSUM SUM=SUMCSUM; RUN;

PROC PRINT; RUN;

****************************************************************************

* READ IN GEOREFERENCED DATA; THEN CENTER THE SELECTED ATTRIBUTE VARIABLE *

****************************************************************************;

DATA STEP2;

INFILE INDATA;

INPUT IDDEM MELEV SELEV U V QUAD NAME$;

U=U/1000; V=V/1000;

Y=LOG(MELEV+17.5);

* Y=(SELEV-25)**0.5;

X0=1;

RUN;

PROC SORT OUT=STEP2(REPLACE=YES); BY IDDEM; RUN;

PROC STANDARD MEAN=0 STD=1 OUT=STEP2(REPLACE=YES); VAR Y; RUN;

PROC MEANS NOPRINT; VAR X0; OUTPUT OUT=OUTN SUM=N; RUN;

IDDEM is

the unique

id


Spatial statistics in practice

DATA STEP2(REPLACE=YES); SET STEP2; SET STEP1;

ARRAY CONN{73} C1-C73;

ARRAY YCONN{73} CY1-CY73;

DO I=1 TO 73;

YCONN{I} = Y*CONN{I};

END;

RUN;

PROC MEANS NOPRINT; VAR CY1-CY73; OUTPUT OUT=CYOUT1 SUM=CY1-CY73; RUN;

PROC TRANSPOSE DATA=CYOUT1 PREFIX=CY OUT=CYOUT2; VAR CY1-CY73; RUN;

DATA STEP3; SET STEP2(KEEP=X0 Y CSUM); SET CYOUT2(KEEP=CY1);

CZY=CY1;

ZY=Y;

RUN;

PROC REG OUTEST=OUTREG; MODEL CZY=ZY/NOINT PRESS; OUTPUT OUT=MCPRED P=CZYHAT H=HI RSTUDENT=RSTUDENTI DFFITS=DFFITSI; RUN;

DATA OUTREG(REPLACE=YES); SET OUTREG; SET OUTN(KEEP=N);

RMSEPRESS=SQRT(_PRESS_/N);

RUN;

PROC PRINT DATA=OUTREG; VAR _RMSE_ RMSEPRESS; RUN;

DATA _NULL_;

SET MCPRED; SET STEP2(KEEP=IDDEM NAME);

FILE OUTFILE;

PUT IDDEM HI RSTUDENTI DFFITSI NAME;

RUN;

DATA MCPRED(REPLACE=YES); SET MCPRED; IF _N_=1 THEN SET OUTN(KEEP=N);

IF HI<2*1/N THEN HI='.';

IF ABS(RSTUDENTI) < 2 THEN RSTUDENTI='.';

IF DFFITSI < 2/SQRT(1/N) THEN DFFITSI='.';

RUN;

DATA CHECK; SET MCPRED; SET STEP2(KEEP=IDDEM NAME);

IF HI='.' AND RSTUDENTI='.' AND DFFITSI='.' THEN DELETE;

RUN;

PROC PRINT DATA=CHECK; VAR IDDEM HI RSTUDENTI DFFITSI NAME; RUN;


Sas code for local statistics eigenvector covariates

SAS code for local statistics eigenvector covariates

FILENAME EVECS 'D:\JYU-SUMMERSCHOOL2006\LAB#1\PR-EIGENVECTORS.TXT';

FILENAME LISA 'D:\JYU-SUMMERSCHOOL2006\LAB#2\PR-LISA-OUT.TXT';

FILENAME REGD 'D:\JYU-SUMMERSCHOOL2006\LAB#2\PR-REGRESSION-DIAGNOSTICS.TXT';

FILENAME OUTFILE 'D:\JYU-SUMMERSCHOOL2006\LAB#2\PR-SELECTED-EVECS.TXT';

TITLE 'SPATIAL AUTOCORRELATION IN PR MORAN SCATTERPLOT DIAGNOSTICS';

******************************************************

* READ IN EIGENVECTORS, LISA, REGRESSION DIAGNOSTICS *

******************************************************;

DATA STEP1; INFILE EVECS LRECL=1024; INPUT IDE E1-E73; RUN;

PROC SORT OUT=STEP1(REPLACE=YES); BY IDE; RUN;

DATA STEP2; INFILE LISA; INPUT CLUSTER IDL ZLISA; RUN;

PROC SORT OUT=STEP2(REPLACE=YES); BY IDL; RUN;

DATA STEP3; INFILE REGD; INPUT IDR HI RSTUDENTI DFFITSI; RUN;

PROC SORT OUT=STEP3(REPLACE=YES); BY IDR; RUN;

DATA FINAL; SET STEP1; SET STEP2; SET STEP3; RUN;

PROC PRINT; VAR IDE IDL IDR; RUN;

PROC REG; MODEL ZLISA = E1-E73/SELECTION=STEPWISE SLE=0.01; OUTPUT OUT=OUTLISA P=ZLISAHAT; RUN;

PROC GPLOT; PLOT ZLISA*ZLISAHAT ZLISA*ZLISA/OVERLAY; RUN;

PROC REG; MODEL HI = E1-E73/SELECTION=STEPWISE SLE=0.01; OUTPUT OUT=OUTHI P=HIHAT; RUN;

PROC GPLOT; PLOT HI*HIHAT HI*HI/OVERLAY; RUN;

PROC REG; MODEL RSTUDENTI = E1-E73/SELECTION=STEPWISE SLE=0.01; OUTPUT OUT=OUTRST P=RSTHAT; RUN;

PROC GPLOT; PLOT RSTUDENTI*RSTHAT RSTUDENTI*RSTUDENTI/OVERLAY; RUN;

PROC REG; MODEL DFFITSI = E1-E73/SELECTION=STEPWISE SLE=0.01; OUTPUT OUT=OUTDFFITS P=DFFITSHAT; RUN;

PROC GPLOT; PLOT DFFITSI*DFFITSHAT DFFITSI*DFFITSI/OVERLAY; RUN;

DATA _NULL_; SET FINAL(KEEP=IDE E2); FILE OUTFILE; PUT IDE E2; RUN;


Arcgis 9 1 lisa getis ord g i

ArcGIS 9.1: LISA & Getis-Ord Gi

This is to be fully functional in ArcGIS 9.2!


Constructing spider diagrams in arcview

Constructing spider diagrams in ArcView

  • Enable Geoprocessing extension

  • Use Geoprocessing Wizard to disolve areal units on the basis of some grouping

  • Enable “Add true X,Y Centroid” extension

  • Compute centroids of disolved *.shp map

  • RUN spider diagram script to add “event themes” from original map (centers) and disolved map (points)


What you should have learned in today s lab

What you should have learned in today’s lab:

  • Identify power transformation for variables

  • Calculate normality and variance equality (attribute and geographic) diagnostics

  • Reconstruct the Moran scatterplots and the semivariogram plots

  • Calculate and map Gi, LISA and regression diagnostic statistics

  • The ability to construct spider diagrams


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