1. Richard J Self - University of Derby 1 Smart Device Location Services:- A Reliable Analytics Resource? CORS/INFORMS, Montreal, June 2015 Richard J Self Senior Lecturer in Analytics and Governance University of Derby http://tinyurl.com/ppyg6t8 http://computing.derby.ac.uk email: r.j.self@derby.ac.ukr.j.self@derby.ac.uk

2. Based on Final Year Student Project 12 students researching 7 students contributed data to this analysis (2460 data points) Daniel Corah Vishal Patel Amna Almutawa Ishwa Khadka Victor Horecny Shehzaad kashmiri Farondeep Bains 2Richard J Self - University of Derby

3. Context (1) GPS accuracy claim: 95% of all fixes to be <=10m Thinknear identify the fact that 46% of reported locations are accurate <= 1000m (Q1 2015 Location Score report) 10% error > 100,000m (60 miles) My students’ research indicates (2420 data points) 85% are accurate to <= 25m 2.5% are >= 500m Outliers 1km to 80km 3Richard J Self - University of Derby

4. The Vs of Big Data and Analytics Big Data Veracity Over 80% of all data (small, large and big) is of uncertain veracity (J Easton, IBM, 2012, http://www.thebigdatainsightgroup.com/site/system/files/private_1 ) http://www.thebigdatainsightgroup.com/site/system/files/private_1 The critical Vs for A-GPS LS Veracity Variability Verification Visualisation 4Richard J Self - University of Derby

5. Critical Governance Questions What is the reliability of A-GPS in smart devices? What are the consequences of uncertain veracity of A-GPS based Location Services to relevant stakeholders? 5Richard J Self - University of Derby

6. Agenda Identify typical uses of LBS Evaluate accuracy of LBS in smart devices Identify governance issues of the use of LBS 6Richard J Self - University of Derby

7. Some Uses for LBS Marketing Geo-fencing? Recreational Social media Photo tagging European e-Call Car crash reporting (required max error of 100 – 200m) Crime prevention services GPS tagging 7Richard J Self - University of Derby

8. Triggers to Research Project 4900m error from top of Mont-Royal 22km error Night- time wandering wandering Start-up movement

9. V Patel – Key Insight – Models Vary phoneNMeanStd DevStd Err Nexus5441.562924.11463.2816 iPhone5885.5101113.814.9403 MethodVariancesDFt ValuePr > |t| PooledEqual110-2.780.0064 SatterthwaiteUnequal62.476-2.870.0055 Proc Univariate – Histogram issues

10. V Horecny – Key Insight – Chipsets HTC-M8 (blue) modern chipset HTC-Desire S (Pink) early version chipset

11. Farondeep Bains – Key Insight – Cars and Carparks 11Richard J Self - University of Derby

12. Amna Al-Mutawa – Key Insight – Time Variability 12Richard J Self - University of Derby

13. Accuracy? Type of Location Open Rural – most accurate Residential Urban – least accurate Low rise High rise Under car very large errors! 13Richard J Self - University of Derby

14. Accuracy Variable with Time

15. Consolidated Data – 2420 points 15Richard J Self - University of Derby Red = > 300m

16. Overall Accuracy of LBS 85% <= 25 metres 2364 out of 2420 (97.6%) <= 500 m Outliers out to 40 to 60 miles!

17. Key Governance Questions What level of accuracy do you need or can you accept? 10m, 50m, 100m, 0.5km, 1km, 10km? What are consequences of uncertain veracity? To your organisation To your customers and clients EU Data Protection regime implications? Consequences of storing when lacking veracity and accuracy? 17Richard J Self - University of Derby

18. Further Research Replicate the research with a standardised set of parameters and values, based on this year’s exploratory research Control for GPS / Cell based / WiFi / Bluetooth Widen the participation to a world-wide team Extend list of devices / generations / OS / etc. Analyse with IBM’s Watson Analytics (100k data points + needed) – please volunteer!! Extend to High School projects 18Richard J Self - University of Derby