SAS Mapping functionality to measure and present the Veracity of Location Data

2 University of Derby, UK Richard J Self, Senior Lecturer in Analytics and Governance Vishal Patel, Final Year Student, University of Derby Daniel Corah, Final Year Student, University of Derby, Viktor Horecny, Final Year Student, University of Derby

3 Objectives  SAS – Exploring Mapping Functionality to Visualise Veracity of Location Data  Lessons Learned about Location Data Veracity and SAS Visualisations

4 Context  Smart Device Locations Services is seen as reliable  May not be true, consequences are many  Retail LBS based marketing  Social network apps  Photo locations in social media and Google Maps  Forensics  Criminal Justice system  Research Question is  To what extent is A-GPS reliable and in what circumstances?

5 Triggers to Research Project

6 Final Year Student Project  12 students researching  3 are co-authors, contributing valuable analyses  7 students contributed data to this presentation (2460 data points)  Daniel Corah  Vishal Patel  Amna Almutawa  Ishwa Khadka  Victor Horecny  Shehzaad kashmiri  Farondeep Bains

7 Critical Questions  Levels of accuracy in different conditions  Indoors / outdoors  Rural / residential / urban  Weather conditions  Stability of indicated location  Differences between devices (make / model / operating system)

8 V Patel – Key Insight – Models Vary phoneNMeanStd DevStd Err Nexus iPhone Diff (1-2) MethodVariancesDFt ValuePr > |t| PooledEqual SatterthwaiteUnequal Proc Univariate – Histogram issues

9 D Corah – Key Insight – Stone Built Houses Proc SGPLOT

10 V Horecny – Key Insight – Chipsets HTC-M8 (blue) modern chipset HTC-Desire S (Pink) early version chipset Uses XL/JMP ®

11 Other Insights  Cloud conditions affect accuracy  Accuracy variable with time

12 Overall Accuracy of LBS 85% <+ 25 metres 2364 out of 2420 <= 500 m

13 Accuracy Variable with Time Start-up of LS max error 360m Uses Annotate coding and macros

14 Accuracy Variable with Time

15 Consolidated Data – 2420 points Red = > 300m

16 Annotate for Time Based Accuracy  Challenges  Auto-scaling and boundaries  Data System  ANNOMAC coding for labels

17 Raw Data Lat_True_Deg Long_True_De gLat_Xif_DegLong_Xif_DegLoc_IndImage_PathDate_Time_StampPhone_type OpenIMG_0464.JPG06/09/ :24:24iPhone 5C OpenIMG_0466.JPG17/08/ :56:51iPhone 5C OpenIMG_0465.JPG17/08/ :52:47iPhone 5C circuit 1IMG_01102.jpg23/03/ :25:46iPhone 5C circuit 1IMG_01103.jpg23/03/ :25:47iPhone 5C circuit 1IMG_01104.jpg23/03/ :25:48iPhone 5C circuit 1IMG_01105.jpg23/03/ :25:49iPhone 5C circuit 1IMG_01106.jpg23/03/ :25:50iPhone 5C circuit 1IMG_01107.jpg23/03/ :25:51iPhone 5C circuit 1IMG_01108.jpg23/03/ :25:52iPhone 5C circuit 1IMG_01109.jpg23/03/ :25:53iPhone 5C Lat_True_Deg and Long_True_Deg found through Google Maps Lat_Xif_Deg, Long_Xif_Deg and Date_Time_Stamp read from images using IrfanView

18 Boundaries  proc means data=work_derby min max noprint;  output out=means_derby;  var x y;  run;  /* deduce and output corner coordinates (in Lat (Y) / Long Degrees (X)) and output using symput */  data _null_;  set means_derby;  if _stat_ = 'MIN' then do;  call symput('min_x', x);  call symput('min_y',y);  end;  if _stat_ = 'MAX' then do;  call symput('max_x', x);  call symput('max_y',y);  end;  run;

19 Auto-Scaling  xsys = '1'; /* using Frame area*/  ysys = '1';  hsys = '3';  dotsize=0.5; /*basic size of plotted error dot % of frame */  /* plot data in centered 90% of Frame Area */  /* min_x etc set from previous section */  x=(90-(x - symget('min_x'))*90 / (symget('max_x') - symget('min_x')))+5;  y=(y - symget('min_y'))*90 / (symget('max_y') - symget('min_y'))+5;

20 Dot Generation – using annomac macros  if error < 1 then do;  dotsize=dotsize*1; /* small dot for high accuracy */  %slice(x,y,0,360,dotsize,darkgreen,solid,3); /* different colors for different errors */  end;  else if error>=1 and error < 10 then do;  dotsize=dotsize*1.5;  %slice(x,y,0,360,dotsize,mediumgreen,solid,3);  end;  else if error >= 10 and error < 100 then do;  dotsize=dotsize*2;  %slice(x,y,0,360,dotsize,mediumyellow,solid,3);  end;  else if error >= 100 and error < 200 then do;  dotsize=dotsize*2.5; /* large dot for big error */  %slice(x,y,0,360,dotsize,darkyellow,solid,3);  end;

21 Adding Sequence Labels  length color $64  number $4  posn 8.  ;  retain posn;  if _n_ = 1 then posn = 0;.  posn = posn + 1;  if posn = 10 then posn = 1; /* similar to using the MOD( ) function base 9 */  if posn=1 then do;  %label(x,y,number,white,0,0,3,times new roman,1); /* Position cannot be added from a variable */  end; /* in the label macro (last macro parameter) */  else if posn=2 then do;  %label(x,y,number,white,0,0,3,times new roman,2);  end; /* etc. */

22 Final Output – Using Proc GANNO  goptions reset=all border cback=black ctitle=white;  proc ganno annotate=workanno; /* from previous Data Step */  run;

23 Conclusions  Mapping relies on using Annotate  Can be displayed in Proc GMAP or GANNO  GANNO allows simple scaling.

24 Session ID #3202