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.uk
Richard J Self - University of Derby
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Based on Final Year Student
Project
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12 students researching
7 students contributed data to this analysis (2460 data points)
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Daniel Corah
Vishal Patel
Amna Almutawa
Ishwa Khadka
Victor Horecny
Shehzaad kashmiri
Farondeep Bains
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Context (1)
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GPS accuracy claim: 95% of all fixes to be <=10m
Thinknear identify the fact that
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46% of reported locations are accurate <= 1000m (Q1 2015
Location Score report)
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10% error > 100,000m (60 miles)
My students’ research indicates (2420 data points)
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85% are accurate to <= 25m
2.5% are >= 500m
Outliers 1km to 80km
Richard J Self - University of Derby
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The Vs of Big Data and Analytics
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Big Data Veracity
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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 )
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The critical Vs for A-GPS LS
Veracity
 Variability
 Verification
 Visualisation
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Richard J Self - University of Derby
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Critical Governance Questions
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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
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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
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Marketing
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Recreational
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Social media
Photo tagging
European e-Call
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Geo-fencing?
Car crash reporting (required max error of 100 – 200m)
Crime prevention services
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GPS tagging
Richard J Self - University of Derby
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Triggers to Research Project
wandering
22km
error
Nighttime
wandering
4900m error
from top of
Mont-Royal
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
62.476
-2.87
0.0055
SatterthwaiteUnequal
Proc Univariate – Histogram issues
V Horecny – Key Insight –
Chipsets
HTC-M8 (blue) modern chipset
HTC-Desire S (Pink) early
version chipset
Farondeep Bains – Key Insight –
Cars and Carparks
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Amna Al-Mutawa – Key Insight –
Time Variability
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Accuracy?
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Type of Location
Open Rural – most accurate
 Residential
 Urban – least accurate
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Low rise
 High rise
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Under car very large errors!
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Accuracy Variable with Time
Consolidated Data – 2420 points
Red = >
300m
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Overall Accuracy of LBS
85% <= 25 metres
2364 out of 2420
(97.6%) <= 500 m
Outliers out to 40
to 60 miles!
Key Governance Questions
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What level of accuracy do you need or can you
accept?
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10m, 50m, 100m, 0.5km, 1km, 10km?
What are consequences of uncertain veracity?
To your organisation
 To your customers and clients
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EU Data Protection regime implications?
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Consequences of storing when lacking veracity and
accuracy?
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Further Research
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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
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