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: [email protected]
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
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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
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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
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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?
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Agenda
Identify typical uses of LBS Evaluate accuracy of LBS in smart
devices Identify governance issues of the use
of LBS
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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
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Farondeep Bains – Key Insight –Cars and
Carparks
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Amna Al-Mutawa – Key Insight – Time Variability
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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
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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!
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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?
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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