This is a supplementary materials to the paper "Exploring Georeferenced Mobile Phone Datasets - A Survey and Reference Framework" by Y. Yuan and M. Raubal

1. Table A-1. A summary of the datasets
Dataset Sample Size Area Covered
Temporal
Duration
User Type
Location
Technique
Location
Accuracy
Service Usage
Type
Addtional
Attributes
Reality Mining
Dataset1
100
User oriented,
Not specified
9 months
between
2004-2005
75 students
and faculty
at MIT; 25:
incoming
students
GSM-
based,
GPS-
based
Antenna
level (no
specific
number)
Voice call, SMS,
Bluetooth,
Applications
Questionnair
es about
social
activities
AirSage Data
(Phithakkitnukoo
n and Ratti 2011)
1’000’000
Massachusetts,
USA
07/30/2009-
09/12/2009
Anonymous
GSM-
based
Antenna
level (320
meters in
average)
Voice call,
Cellular Data,
SMS
N/A
1
http://reality.media.mit.edu/dataset.php

2. 2
Northeast China
Dataset
(Kang, et al. 2010)
3’509’280
8 cities in
northeast
China
07/21/2007-
07/29/2007
Anonymous
GSM-
based
Antenna
level (300-
500m)
Voice call Age; Gender
Rome Dataset
(Reades, et al.
2007)
N/A, Erlang
data2
A sub region
of Rome
Four
months in
late 2006
Anonymous
GSM-
based
Antenna
level
Voice call N/A
Anonymous
dataset 1 (Reades,
et al. 2007)
1.4 Million N/A
Four years
(2005-2008)
Anonymous
GSM-
based
Antenna
level
Voice call N/A
Anonymous
dataset
100,000 N/A 6 months Anonymous
GSM-
based
Antenna
level
Voice call N/A
2
An Erlang is one person-hour of phone use, so 1 Erlang can represent one person talking for an hour, two people talking for half hour each, 30 people
speaking for two minutes each, and so on.

3. 3
2(Gonzalez, et al.
2008)
Real-Time Graz
dataset (Ratti, et
al. 2005)
N/A Graz
10/01/2005 –
01/08/2006
Registered
users to a
location
application
GSM-
based;
(Paging
every 5
Minutes)
Antenna
level (100-
300m)
Voice call N/A
Milan dataset
(Liebig, et al. 2009)
N/A
Milan
(69 cells)
One week in
autumn
2008
Anonymous
GSM-
based
Antenna
level
Voice call N/A
Portugal data
(Calabrese, et al.
2011)
1 million Portugal
12 months
between
2006 and
2007
Anonymous
GSM-
based
Antenna
level
Voice call N/A

4. 4
Orange GSM
dataset
(Couronne, et al.
2011)
4 million France
1 day
(weekday)
Anonymous
GSM-
based
Antenna
level
Voice call,
SMS
N/A
Haiti dataset
(Gething and
Tatem 2011)
2.8 million Haiti
6 weeks
before the
earthquake3
to 5 months
after
Anonymous
GSM-
based
Antenna
level
Voice call,
SMS
N/A
Zanzibar dataset
(Tatem, et al.
2009)
770,369 Zanzibar
10/2008-
12/2008
Anonymous
GSM-
based
Antenna
level
Voice call,
SMS
N/A
3
http://en.wikipedia.org/wiki/2010_Haiti_earthquake

5. 5
Anonymous
dataset 3 (Traag,
et al. 2011)
5.75 million
Anonymous
European
country
14 months Anonymous
GSM-
based
Antenna
level
Voice call,
SMS
N/A
Nokia data
Challenge4
200 Lausanne > 1 year Anonymous
GSM-
based;
GPS-
based; IP
Based
Antenna
level for
GSM
tracking; 5-
10m for
GPS
tracking
Voice call, SMS,
Applications, IP,
Bluetooth
Rich
attributes
including
age, gender,
occupation,
income, etc.
Estonia dataset 1
(Ahas, et al. 2010)
200
Estonia (46,000
km2)
11/01/2006 –
10/31/2007
Anonymous
GSM-
based
Antenna
level
Voice call
N/A
4
http://research.nokia.com/page/12123

6. 6
Ivory Coast
Dataset (Liu, et al.
2014)
5'000'000 N/A
12/01/2011 –
04/28/ 2012
Anonymous
GSM-
based
Antenna
level
Voice call, SMS N/A
Estonia Dataset
2(Silm and Ahas
2014)
12500 (6'250
Estonian
and 6,250
Russian-
speaking)
Tallin 2010 Anonymous
GSM-
based
Antenna
level
Voice call, SMS
Gender, birth
year,
language
preference
Telecom Italia
Dataset
(Manfredini, et al.
2014)
N/A
Lombardy,
Italy
09/2009-
04/2010
Anonymous
GSM-
based
Antenna
level
Erlang data;
SMS traffic;
Number of
switched-on cell
phones
Nationalities,
Age

7. 7
Chinese Dataset
(Pu, et al. 2014)
3'600'000
Anonymous
Chinese city
01/01/2008-
12/31/2008
Anonymous GPRS
Antenna
level
Voice call N/A
Northern Italy
data (Sagl, et al.
2014)
N/A Udine, Italy
07/20/2009 -
09/302009
Anonymous
GSM-
based
Antenna
level
Voice call, SMS N/A
Chinese dataset 3
(Ma, et al. 2014)
15
User oriented,
Not specified
30 days
15 college
students,
teachers, or
white-collar
workers
GPS-
based, IP-
based
3m to tens
of meters
Voice call, SMS,
accelerometer
records
Acceleromete
r records
such as
walking,
running, etc.
Tokyo GPS
Dataset (Horanont
et al 2013)
31'855 Tokyo
08/01/2010-
07/31/2011
Anonymous GPS N/A
Location-based
service (LBS)
applications
N/A

8. 8
European data
(Liu, et al. 2013)
80
Anonymous
European city
2009-2011 Anonymous
GSM-
based
Antenna
level
Voice call, SMS
NTT
Docomo/Zenrin
Dataset (Hayano
and Adachi 2013)
N/A
Fukushima,
Japan
03/10/2011-
03/18/2011
Anonymous
GPS-
based
N/A LBS applications N/A
Kenya data
(Wesolowski, et
al. 2013)
14'816'521 Kenya
06/2008 –
06/2009
Anonymous
GSM-
based
Antenna
level
Voice call, SMS N/A
Shanghai data (Jia,
et al. 2013)
139'978
Shanghai,
China
07/01/2010 -
07/05/2010
Anonymous
GSM-
based
Antenna
level
Various types of
signaling
including
location update,
N/A

9. 9
Paging
Response, etc.)
Friends and
Family Dataset
(Bogomolov, et al.
2013)
117 N/A
02/21/2010-
07/16/2011
married
graduate
student of a
major U.S.
university
N/A N/A
Voice call, SMS,
nearby
Bluetooth
devices
Self-reports
about
personality
traits and
happiness
level.
Paris data (Iovan,
et al. 2013)
4'000'000 Parisian region 04/02/2009 Anonymous
GSM-
based
Antenna
level
Voice call, SMS N/A
China "Real
World" dataset
965'434
Anonymous
mid-size
Chinese city
05/01/2008 -
12/31/2008
Anonymous
GSM-
based
Antenna
level
Voice call N/A

10. 10
(Zheng, et al.
2013)
Portugese dataset
(Phithakkitnukoo
n, et al. 2012,
Phithakkitnukoon,
et al. 2012)
22'696 Lisbon
04/2006-
03/2007
Anonymious
GSM-
based
Antenna
level
Voice call N/A
OpenCellID
database
(Oxendine, et al.
2012)
16'597 New York city N/A Anonymous
GSM-
based
Antenna
level
N/A N/A

11. 11
MIT data (Farrahi,
et al. 2012)
72
User oriented,
Not specified
10/2008 –
06/2009
Residents of
an
undergradu
ate residence
GSM-
based; IP
Based
N/A
Voice call, SMS,
Bluetooth,
WLAN
User
relationship
status
Chinese dataset 4
(Xu, et al. 2011)
N/A
Anonymous
small Chinese
city
20 days Anonymous
GSM-
based
Antenna
level
Mobile
switching data
N/A
U.S. Presidential
election data
(Madan, et al.
2011)
N/A
Anonymous
US university
3 months in
2008
Volunteers
IP-based
Bluetooth
-based
N/A
Voice call, SMS,
Bluetooth, Wi-Fi
Survey on
political
opinions
TRL’s MOLA
software data
N/A Kent, UK N/A
Anonoymou
s
GSM-
based
Antenna
level
Voice call
Vehicle
information
such as

12. 12
(White and Wells
2002)
speed, map
display
Singapore
aggregated
dataset (Pei, et al.
2014)
5500+ base
towers
Singapore
03/28/11-
04/03/11
Anonymous
GSM-
based
Antenna
level
Hourly
aggregatefd
voice calls
N/A
Telecom Italia
dataset 2
(Dashdorj, et al.
2013)
N/A Italy N/A Anonymous
GSM-
based
Antenna
level
Voice call N/A
Spain dataset
(Noulas, et al.
2013)
100‘000 base
towers;
Spain 09/2009 Anonymous
GSM-
based
Antenna
level
Voice call;
SMS
N/A

13. 13
20 Million
Voice calls;
12 Million
users
Boston dataset
(Toole, et al. 2012)
600‘000 Boston three weeks Anonymous
GSM-
based;
Signal
triangulat
ion
Higher
than
Antenna
level
Voice call; N/A
Telecom Italia
dataset 3
(Manfredini, et al.
2011)
4.5 million Italy
09/2009;
04/2010
Anonymous
GSM-
based;
Antenna
level
Mobile
Switching
Center Data
(number of
N/A

14. 14
phones turned
on)
AT&T dataset
(Becker, et al.
2011)
475‘000
Morristown,
New Jersey
11/29/2009-
01/27/2010
Anonymous
GSM-
based;
Antenna
level
Voice call, SMS N/A
CDMA2000 dataset 10,000 N/A 03/2004 Anonymous
GSM-
based;
Antenna
level
Packet data calls
(e.g., Cellular
plan usage)
data rate and
duration for
each data call

15. References
Ahas R, Silm S, Jarv O, Saluveer E, and Tiru M (2010). Using Mobile Positioning Data to Model
Locations Meaningful to Users of Mobile Phones. J Urban Technol 17 3-27
Becker RA, Caceres R, Hanson K, Loh JM, Urbanek S, Varshavsky A, and Volinsky C (2011). A
Tale of One City: Using Cellular Network Data for Urban Planning. Ieee Pervas Comput
10 18-26
Bogomolov A, Lepri B, Pianesi F, Sci ICSA, and Engn (2013). Happiness Recognition from
Mobile Phone Data. 2013 Ase/IEEE International Conference on Social Computing (Socialcom)
790-795
Calabrese F, Smoreda Z, Blondel VD, and Ratti C (2011). Interplay between
Telecommunications and Face-to-Face Interactions: A Study Using Mobile Phone Data.
Plos One 6 e20814
Couronne T, Olteanu AM, and Smoreda Z (2011). Urban Mobility: Velocity and Uncertainty in
Mobile Phone Data. Proceedings of the 2011 IEEE Third International Conference on Privacy,
Security, Risk and Trust and IEEE Third International Conference on Social Computing
(PASSAT/SocialCom 2011)
Dashdorj Z, Serafini L, Antonelli F, and Larcher R (2013). Semantic enrichment of mobile phone
data records. Proceedings of the 12th International Conference on Mobile and Ubiquitous
Multimedia, Luleå, Sweden
Farrahi K, Emonet R, Ferscha A, and Ieee (2012). Socio-Technical Network Analysis from
Wearable Interactions. 2012 16th International Symposium on Wearable Computers (Iswc) 9-
16
Gething PW, and Tatem AJ (2011). Can Mobile Phone Data Improve Emergency Response to
Natural Disasters? Plos Medicine 8 e1001085
Gonzalez MC, Hidalgo CA, and Barabasi AL (2008). Understanding individual human mobility
patterns. Nature 453 779-782
Hayano RS, and Adachi R (2013). Estimation of the total population moving into and out of the
20 km evacuation zone during the Fukushima NPP accident as calculated using "Auto-
GPS" mobile phone data. Proceedings of the Japan Academy Series B-Physical and Biological
Sciences 89 196-199
Iovan C, Olteanu-Raimond A-M, Couronne T, and Smoreda Z (2013). Moving and Calling:
Mobile Phone Data Quality Measurements and Spatiotemporal Uncertainty in Human
Mobility Studies. Geographic Information Science at the Heart of Europe 247-265
Jia F, Cheng X, and Duan Z (2013). Analyzing the Activity Areas of Non-resident Tourists of
Shanghai Expo using Cellular Phone Data. Intelligent and Integrated Sustainable
Multimodal Transportation Systems Proceedings from the 13th Cota International Conference of
Transportation Professionals (Cictp2013) 96 1136-1145
Kang CG, Gao S, Lin X, Xiao Y, Yuan YH, Liu Y, and Ma XJ (2010). Analyzing and Geo-
visualizing Individual Human Mobility Patterns Using Mobile Call Records. 2010 18th
International Conference on Geoinformatics

16. 16
Liebig T, Koumlrner C, and May M (2009). Fast visual trajectory analysis using spatial Bayesian
networks. 2009 IEEE International Conference on Data Mining Workshops (ICDMW 2009)
Liu F, Janssens D, Cui JX, Wang YP, Wets G, and Cools M (2014). Building a validation measure
for activity-based transportation models based on mobile phone data. Expert Syst Appl 41
6174-6189
Liu F, Janssens D, Wets G, and Cools M (2013). Annotating mobile phone location data with
activity purposes using machine learning algorithms. Expert Syst Appl 40 3299-3311
Ma Y, Xu B, Bai Y, Sun G, and Zhu R (2014). Infer Daily Mood using Mobile Phone Sensing. Ad
Hoc & Sensor Wireless Networks 20 133-152
Madan A, Farrahi K, Gatica-Perez D, and Pentland A (2011). Pervasive Sensing to Model
Political Opinions in Face-to-Face Networks. Pervasive Computing 6696 214-231
Manfredini F, Pucci P, and Tagliolato P (2014). Toward a Systemic Use of Manifold Cell Phone
Network Data for Urban Analysis and Planning. J Urban Technol 21 39-59
Manfredini F, Tagliolato P, and Di Rosa C (2011). Monitoring Temporary Populations through
Cellular Core Network Data. In: Murgante B, Gervasi O, Iglesias A, Taniar D and
Apduhan BO eds Computational Science and Its Applications - Iccsa 2011, Pt Ii, 151-161
Noulas A, Mascolo C, and Frias-Martinez E (2013). Exploiting Foursquare and Cellular Data to
Infer User Activity in Urban Environments. 2013 Ieee 14th International Conference on
Mobile Data Management (Mdm 2013), Vol 1 167-176
Oxendine C, Sonwalkar M, and Waters N (2012). A Multi-Objective, Multi-Criteria Approach to
Improve Situational Awareness in Emergency Evacuation Routing Using Mobile Phone
Data. Transactions in GIS 16 375-396
Pei T, Sobolevsky S, Ratti C, Shaw S-L, Li T, and Zhou C (2014). A new insight into land use
classification based on aggregated mobile phone data. Int J Geogr Inf Sci 28 1988-2007
Phithakkitnukoon S, Leong TW, Smoreda Z, and Olivier P (2012). Weather Effects on Mobile
Social Interactions: A Case Study of Mobile Phone Users in Lisbon, Portugal. Plos One 7
Phithakkitnukoon S, and Ratti C (2011). Inferring Asymmetry of Inhabitant Flow using Call
Detail Records. Journal of Advances in Information Technology 2 1-12
Phithakkitnukoon S, Smoreda Z, and Olivier P (2012). Socio-Geography of Human Mobility: A
Study Using Longitudinal Mobile Phone Data. Plos One 7
Pu J, Liu S, Xu P, Qu H, and Ni LM (2014). MViewer: mobile phone spatiotemporal data viewer.
Front Comput Sci-Chi 8 298-315
Ratti C, Sevtsuk A, Huang S, and Pailer R (2005). Mobile landscapes: Graz in real time. The 3rd
Symposium on LBS & TeleCartography, Vienna, Austria
Reades J, Calabrese F, Sevtsuk A, and Ratti C (2007). Cellular census: Explorations in urban data
collection. Ieee Pervas Comput 6 30-38
Sagl G, Delmelle E, and Delmelle E (2014). Mapping collective human activity in an urban
environment based on mobile phone data. Cartography and Geographic Information Science
41 272-285
Silm S, and Ahas R (2014). Ethnic Differences in Activity Spaces: A Study of Out-of-Home
Nonemployment Activities with Mobile Phone Data. Ann Assoc Am Geogr 104 542-559

17. 17
Tatem AJ, Qiu YL, Smith DL, Sabot O, Ali AS, and Moonen B (2009). The use of mobile phone
data for the estimation of the travel patterns and imported Plasmodium falciparum rates
among Zanzibar residents. Malaria J 8 287
Toole JL, Ulm M, and González MC (2012). Inferring land use from mobile phone activity. ACM
SIGKDD International Workshop on Urban Computing, Beijing 1-8
Traag VA, Browet A, Calabrese F, and Morlot F (2011). Social Event Detection in Massive
Mobile Phone Data Using Probabilistic Location Inference. Proceedings of the 2011 IEEE
Third International Conference on Privacy, Security, Risk and Trust and IEEE Third
International Conference on Social Computing (PASSAT/SocialCom 2011)
Wesolowski A, Eagle N, Noor AM, Snow RW, and Buckee CO (2013). The impact of biases in
mobile phone ownership on estimates of human mobility. J R Soc Interface 10
White J, and Wells I (2002). Extracting origin destination information from mobile phone data.
Eleventh International Conference on Road Transport Information and Control (IEE Conf Publ
No486),
Xu D, Song G, Gao P, Cao R, Nie X, and Xie K (2011). Transportation Modes Identification from
Mobile Phone Data Using Probabilistic Models. Advanced Data Mining and Applications
7121 359-371
Zheng J, Liu S, Ni LM, and Ieee (2013). Effective Routine Behavior Pattern Discovery from
Sparse Mobile Phone Data via Collaborative Filtering. 2013 IEEE International Conference
on Pervasive Computing and Communications (Percom) 29-37