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Passive Anonymous Mobile Positioning Data for Tourism Statistics
Margus Tiru1, Rein Ahas
2
1Positium LBS, Estonia, [email protected]
2Department of Geography, University of Tartu, Estonia, [email protected]
Introduction
The 21st century began in many regions with the rising spatial mobility of both individuals
and goods, and with opening borders. The rising spatial mobility of individuals is connected
to the diversity of such motivations as leisure, shopping, work and business, and social
networks. This new societal mobility is empowered by rising cross-border information flows,
ICT use and the expanding meaning of virtual and mental travel (Sheller & Urry 2006). Due
to the diversity of different forms of travel and the motivations behind travelling, there is a
lack of border statistics and data concerning individuals’ space-time mobility in many
countries and regions.
The objective of this study is to introduce a methodology for generating tourism statistics
from passive mobile positioning data. The authors use the mobile operator’s Call Detail
Records (CDR) for the purposes of this study, as an example of the flow of inbound foreign
visitors. The questions of our research focus on the following issues:
1) What kind of tourism statistics can be generated using CDR?
2) How to define those new statistics and relate them to existing statistics?
3) What kind of data processing steps are necessary for generation such statistics?
4) How to evaluate the quality of mobile positioning based on tourism related statistics?
The study of phone movements in and between mobile networks enables measuring the flows
of people in and between countries, which is why mobile positioning has become useful in
different fields of statistics. Mobile telephones are widespread in most countries and they can
be used for collecting data for different purposes. For example, mobile data has been used for
studying transportation and urban development (Asakura & Hato 2004; Calabrese et al. 2007;
Shoval 2007; Ahas et al 2010), tourism (Shoval & Isaacson 2006; Ahas et al. 2008; Tiru et al.
2010), migration (Silm & Ahas 2010), and emergency management (Bengtsson et al. 2011).
Our experience with mobile positioning based statistics and geographical analyses in Estonia
go back to 2002 and we have been collecting approximately 45% of incoming tourism related
data in Estonia since 2004 in cooperation with the largest mobile operator, EMT. Eesti Pank
(Estonian Central Bank) has been using mobile positioning statistics generated by Positium
LBS since 2008 (Positium 2009). The current paper describes the statistics developed in this
framework in Estonia.
The production of tourism statistics is internationally coordinated by several methodological
frameworks, such as the United Nations’ International Recommendations for Tourism
Statistics 2008 (UN 2008) and the Eurostat 1996 document “Applying the Eurostat
methodological guidelines in basic tourism and travel statistics” (Eurostat 2006). The
European initiatives are connected to the Council Directive 95/57/EC issued on 23 November
1995 regarding the collection of statistical information in the field of tourism (Council
Directive 95/57/EC), and Regulation (EU) 692/2011 issued by the European Parliament
concerning tourism related statistics in Europe (Regulation (EU) 692/2011). Our methodology
is composed based on the terminology and principles used in those documents.
Like any data, using mobile positioning has several limitations, for instance difficulty
accessing the data, sampling issues connected to the different data formats used in mobile
networks and differences in phone use in different cultures. These aspects will be discussed
later in the paper.
Privacy and data protection issues are extremely important in this matter and have to be
strictly followed according to the regulations and ethical principles. We point out here the
requirements specified in the EU directives for processing personal data (Directive 95/46/EC)
and the protection of privacy in the electronic communications sector (Directive 2002/58/EC).
The phone numbers used for the purposes of our methodology in Estonia were made
anonymous in the mobile operator’s system by a methodology developed by Positium LBS
(Positium 2009). The main principle followed was to keep the identity of all of the
respondents unknown and impossible to decode. The current paper does not focus on privacy
and data protection issues as these provide material for an entire separate paper.
The authors would like to thank EMT and Eesti Pank for their support in the form of the
provided data and their experiences with this topic. We would also like to thank all the
anonymous mobile subscribers, whose data was used in this study. The methodological and
theoretical development of mobile positioning based research in the University of Tartu has
been supported by the Estonian Information Technology Foundation (EITSA), the Target
Funding Project no. SF0180052s07 of the Estonian Ministry of Education, the EU Regional
Development Foundation, project TERIKVANT 3.2.0802.11-0043 of the Environmental
Conservation & Technology R&D Program and Research grant no. ETF7562 of the Estonian
Science Foundation.
2. Defining mobile positioning based statistics
2.1. Mobile positioning based data sources in the mobile operators’ system
Passive mobile positioning data refers to location data that is automatically recorded in the
mobile operators’ memory files as the locations of the telephones or as the network’s call
activities (Ahas & Mark 2005; Ahas et al. 2008). Passive mobile positioning data can be
collected by means of various methods from the mobile operator’s core network. The most
common method is to collect Call Detail Record (CDR) information from an invoice database
or a data warehouse. It is possible to collect CDR information in real-time from data
mediation services or to store real-time data from a radio access network (e.g. A-bis
interface).
Figure 1. Sources for passive mobile positioning data in the operators’ system.
The data formats and description levels of different passive mobile positioning data are
diverse. We evaluated 3 major sources of data for the purposes of this paper.
Table1. Comparison of different data sources.
Type of data Individual or
aggregated
Accessibility Major features
of the statistics
Adequacy for
tourism
statistics
Erlang Aggregated Easy, standard
outlet from the
operator’s
system
Phone use
intensity in the
antenna
Low
CDR Individual Privacy
problem,
software
development
needed
Call activities,
personal
features from
the operator
High
MPS Individual Easy, contract
from respondent
needed
Positioning
frequencies
determined by
the researcher;
questionnaire
possible with the
respondent
High
A-BIS probe-
based
Individual Privacy
problem,
software
development
needed
Call activities
and handover
logs; personal
features from
the operator
High
Anonymous
Bulk Location
Data
Individual Privacy
problem,
software
development
needed
Call activities
and handover
logs; personal
features from
the operator
High
Active mobile positioning (tracking) data refers to collecting phone location data according to
specially initiated queries. A number of systems have been developed for positioning
individual phones, such as friend-finders, sport-trackers, car-trackers etc. All the
aforementioned systems can be used for tracing individual phones to acquire tourism related
data. The listed data collection and storage systems can be found from such open sources as
Google, First Location Bank etc. In most cases the data collection initiatives are limited by the
pool of persons or the timeframe, or the data might not be suitable for statistical purposes.
2.2. Defining statistical units for CDR based statistics
Call Activity – any active use (incoming or outgoing voice, text, internet or services) of a
mobile phone generated from the data warehouse or billing databases.
Handover between cells – data that enters from the mobile station or base station subsystem
and is concerned with the movement logs of the phones within the network.
The country of origin or nationality of visitors - is determined here on the basis of the
country the mobile phone is registered in. I.e. a phone registered in Estonia may be used by a
person of any nationality. Regardless, the registration of a mobile phone indicates the place,
where the person spends most of their time or is strongly connected to.
Random ID – a non-identifiable but certain phone number is always given the same ID by
the operator.
Visitor – a unique person (mobile phone user) that has travelled to another country and has
performed call activities there.
Visit – a unique visit to another country by an individual. One visit is normally composed of
two trips: one to the destination and a second back home from that destination. One person
can make a number of visits. We use the visits that have been made by Estonians directly
from Estonia to Finland and by Finns directly from Finland to Estonia, without transit or a
stop in a third country.
Trip – a unique one way trip to another country by an individual, for example from Estonia to
Finland or from Finland to Estonia.
Number of days – the duration of one visit in days.
Number of nights – the duration of one visit in nights. The formula for calculating the
number of nights in one visit is: nights = days – 1.
We have used different segments for the visits/visitors based on the duration of the visit, the
number of visits per year and the number of days spent in another country per year.
Visits. We have divided the visits based on the length of the stay. 1) Transit visits – visits to
another country (Finland/Estonia) for a short period of time (may be <3...<12 hours) to leave
to a third country on the same day. 2) Visits to the destination are divided based on the
duration of the visit in days: one day visits, 2–4 day visits, 5 or more day visits.
Visitors. Based on the number of visits to another country, we divide the visitors as follows:
one time visitors, 2–4 time visitors, 5 or more time visitors. Based on the total number of days
spent in another country, the visitors are divided into 1 day visitors, 2–30 day visitors, 31+
day visitors and 183+ day visitors. According to the common definition (WTO), a visitor who
stays in another country for 183 or more days is considered a foreign labourer.
Geographically the analysis of Estonia has been divided according to the territory of city of
Tallinn, Harju County and Estonia as a whole. Finnish visitors in Estonia are studied
according to call activity locations. The home and work district locations of Estonian phone
users are measured using the anchor point model (Ahas et al. 2010).
3. Conclusions
Using mobile positioning data in scientific research has several positive aspects as speed of
data collection, digital format of data, large sample and high penetration of phones in most of
societies. Because of a lack of border statistics in today’s world mobile positioning is often
easiest way to collect statistical data about travel. Mobile data has also several shortcomings
that we have to keep in mind when interpreting the results. One of the weaknesses of such
quantitative statistical data is that we do not know the exact motivations and relations lying
behind those visits. The most important question is related to sampling: Who have phones?
Are they using phones during travels? How often do visitors use phones in a foreign country?
As roaming calls are expensive, it is likely that wealthy tourists and businessmen use their
phones more often than less active people with a lower income (children, students,
pensioners). This means that sampling issues are also related to lower income and age groups.
Calling is also connected with cultural differences, such as calling regulations and traditions.
Another problem that arises in case of using mobile positioning data is its quantitative
structure – we know the locations of calls (dots), but we do not know who is really making the
calls, what kind of visit he/she is on, and what kind of transportation he/she is using. The huge
amount of quantitative data also poses a problem for data processing and cleaning; the
databases are too large to enable using traditional software and data preparation options.
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