1Richard J Self - University of Derby

Smart Device Location Services:- A Reliable Analytics Resource?

CORS/INFORMS, Montreal, June 2015

Richard J SelfSenior Lecturer in Analytics and

GovernanceUniversity of Derby

http://tinyurl.com/ppyg6t8

http://computing.derby.ac.uk

email: r.j.self@derby.ac.uk

2Richard J Self - University of Derby

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

3Richard J Self - University of Derby

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

4Richard J Self - University of Derby

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 )

The critical Vs for A-GPS LS Veracity Variability Verification Visualisation

5Richard J Self - University of Derby

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?

6Richard J Self - University of Derby

Agenda

Identify typical uses of LBS Evaluate accuracy of LBS in smart

devices Identify governance issues of the use

of LBS

7Richard J Self - University of Derby

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

Triggers to Research Project

4900m errorfrom top of Mont-Royal

22km error

Night-time wandering

wandering

Start-up movement

V Patel – Key Insight – Models Vary

phone N Mean Std Dev Std Err

Nexus 54 41.5629 24.1146 3.2816

iPhone 58 85.5101 113.8 14.9403

Method Variances DF t Value Pr > |t|

Pooled Equal 110 -2.78 0.0064

SatterthwaiteUnequal 62.476 -2.870.0055

Proc Univariate – Histogram issues

V Horecny – Key Insight – Chipsets

HTC-M8 (blue) modern chipsetHTC-Desire S (Pink) early version chipset

11Richard J Self - University of Derby

Farondeep Bains – Key Insight –Cars and

Carparks

12Richard J Self - University of Derby

Amna Al-Mutawa – Key Insight – Time Variability

13Richard J Self - University of Derby

Accuracy?

Type of Location Open Rural – most accurate Residential Urban – least accurate

Low rise High rise

Under car very large errors!

Accuracy Variable with Time

15Richard J Self - University of Derby

Consolidated Data – 2420 points

Red = > 300m

Overall Accuracy of LBS

85% <= 25 metres

2364 out of 2420 (97.6%) <= 500 m

Outliers out to 40 to 60 miles!

17Richard J Self - University of Derby

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?

18Richard J Self - University of Derby

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