1. bookVolume 26 (2022): Issue 4 (October 2022)
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Journal
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2084-6118
First Published
01 Jan 1984
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English
Open Access

Variability of mobile phone network logins in the Białowieża National Park during the 2019 and 2020 summer holiday periods in the context of the COVID-19 pandemic

Published Online: 31 Oct 2022
Volume & Issue: Volume 26 (2022) - Issue 4 (October 2022)
Page range: 169 - 177
Received: 17 Mar 2022
Accepted: 16 Sep 2022
Journal Details
License
Format
Journal
eISSN
2084-6118
First Published
01 Jan 1984
Publication timeframe
4 times per year
Languages
English
Introduction

The mobile phone is an integral part of almost everyone's life. The development of communication technologies means that mobile phones are usually smartphones, which not only serve as telephones but also as mobile devices that can send emails, play movies, send photos, and much more. Many of these activities happen automatically, such as receiving messages or connecting to the cellular network to download updates. The digital data collected from mobile phones allows for an increase in knowledge about the location and movement of people in time and space.

The objective of this study is to analyse the variability of the number of mobile phone network users in the Białowieża National Park (BNP) during the summer holidays in 2019 and 2020 in the context of the epidemiological and socio-economic conditions prevailing in Poland during the study period. The obtained results attest to the mobility of Poles during the SARS-CoV-2 pandemic and can be used as a model of tourist traffic in national park areas during subsequent phases of the epidemic.

Background

The mobile telecommunications network has been in use for over 40 years globally. In that time, it has undergone many changes that allow not only phone calls but also the sending of text messages and data transfer.

The cellular telephone system supports two types of device location. Active location is the forcing of cellular location by mobile applications that explicitly use GPS or network location services in their operation; in this case, location data is collected with the consent of the user. Passive location, on the other hand, is based on data from a telephone network that is automatically registered by the operator for technical and billing purposes. Passive data is collected for each telephone connected to a network, so they cover a large population (Smoreda et al. 2013). However, this method of acquiring locations has a certain drawback – the location accuracy is not constant, as it depends on the use of telecommunications services and the distribution of antennas, which is irregular.

When using telecommunications services (making calls, sending text messages, accessing the Internet and other applications), each user generates individual data (a Call Detail Record, CDR), including spatial data about the location of the mobile phone relative to telecommunications antennas (Tizzoni et al. 2014; Vazquez-Prokopec et al. 2013; Song et al. 2010). Data collected from mobile phones is much less accurate than from GPS technology, but in densely populated areas accuracy of up to 100 m can be assumed, while in rural areas it covers a radius of several kilometres (Smoreda et al. 2013). The advantage of CDR data over GPS data is that it requires no special application or devices to be in use in order to be generated, and thus provides more data due to the omnipresence of phones. The use of data obtained from CDRs is gaining in popularity, and allows particular patterns of mobility to be described in space and time that had previously been undetectable (Song et al. 2010; Wesolowski et al. 2012; Bengtssonet al. 2011). Spatiotemporal data from phones is also useful for comparability.

The use of CDRs for analyses is associated with a number of problems. First, when using information from a single operator the question of representativeness arises, because the operator holds data about its own subscribers only, so it covers only part of the population. This may create difficulties in interpretation (Sakarovitch et al. 2018). Eliminating this error requires additional data from operators (e.g. local coverage, number of users in a given area), which is often impossible because it is considered to be confidential commercial information that is not to be shared. Additionally, the degree of telephone use and the use of a given operator's services may differ between social groups and between territories (Wesolowski et al. 2013). Furthermore, the distribution of telecommunications towers depends on population density and does not map onto, for example, administrative divisions, national park boundaries, etc. Consequently, the location of origin of the data is only approximate to the studied objects (Ricciato et al. 2015).

CDR information is generated during connection with a telecommunications tower, and this allows real-time analysis (Steenbruggen et al. 2014). It should thus be considered that:

the obtained results reflect anomalies in the real data, although the nature of CDRs means that no definitive conclusions can be drawn as to their causes (Steenbruggen et al. 2016),

location data can be processed, and the anonymisation of data or user consent for processing (which is attainable only for much smaller groups) allows many aspects of everyday life to be reconstructed, such as most-visited places and the time and frequency of such visits (Ahas 2011; Ahas et al. 2010). Research by Montjoye et al. (2013) shows that in 95% of cases, four spatiotemporal points suffice to identify such aspects. Analysing these spatiotemporal point records helps generate algorithms to determine the probable home of a mobile phone owner (Vanhoof et al. 2017; Bojic et al. 2015).

CDR information is the property of mobile operators, and its use requires that appropriate consent be obtained, which is often difficult for formal reasons. Nevertheless, the use of advanced IT systems and the analysis of spatiotemporal data is becoming increasingly important for researchers (Steenbruggen et al. 2014). Mobile phones create a spatially recognisable network of sensors that collects traces of individual and social behaviour on an unprecedented scale (Zhang et al. 2010) and in great detail (Goodchild 2007).

Gathered CDR information has facilitated many quantitative analyses characterising everyday public life in the regions covered by research (Blondel et al. 2015). It has been widely applicable because it describes population movements from the micro to the macro scale (Calabrese et al. 2015). It is used in urban planning as a spatiotemporal tool to improve spatial planning and increase the functional efficiency of contemporary urban systems (Steenbruggen et al. 2014) through, for example, the analysis of urban transport in Paris (Larijani et al. 2015), and statistical modelling and the elaboration of detailed mobility matrices (Aguiléra et al. 2014; Demissie et al. 2016). At the macro scale, CDRs have been used, for example, to determine mobility patterns for France, Portugal and Spain (Tizzoni et al. 2014).

Regular mapping of mobile phone users makes it possible to determine not only their location in space, but also their movement (Sakarovitch et al. 2018). Therefore, CDRs have been used to monitor human displacement during natural disasters (Lu et al. 2012). Data on migration has also been extremely valuable during malaria outbreaks (Wesolowski et al. 2012; Tatem et al. 2009; Lynch & Roper 2011). The current epidemiological situation, the second year of the global SARS-CoV-2 pandemic, has made spatiotemporal data even more important. It is used to create mobility patterns that can help to evaluate the spread of epidemics (Tizzoni et al. 2014; Wesolowski et al. 2012; Panigutti et al. 2017). CDR information collected during the SARS-CoV-2 pandemic is used to, among other things, assess the impact of the lockdown imposed in France (Pullano et al. 2020), to assess the impact of COVID-19 on intercity movements during the Chinese New Year (Ye et al. 2020) and to analyse mobility in, for example, Belgium (The Data Against Corona Taskforce 2020), Germany (COVID-19 Mobility Project in Germany 2022), Spain (Data Analytics@IFISC 2020) and the USA (Gao et al. 2020).

Oliver et al. (2020) show how mobile phone data can be used to coordinate a COVID-19 pandemic response. Previously, similarly obtained information was used during the Ebola epidemic in West Africa (EVD) in 2014 (Halloran et al. 2014; Wesolowski et al. 2014) and other epidemics such as MERS-CoV in 2013 and the Zika virus in 2016. At that time, the use of data characterised population movements in affected areas in order to limit transmission to other areas (Poletto et al. 2014; Bogoch et al. 2016).

The analysis of data from mobile phones has also been used to determine tourist traffic. Traditional tourist traffic data collection methods are becoming increasingly insufficient (Silm & Ahas 2010; Vanhoof et al. 2017). One example of this, according to Bel et al. (2014), relates to agritourism: it was not possible to differentiate people choosing this form of tourism from other travellers, and surveys were often limited in range to the local (Bel et al. 2014). It is also difficult to measure short trips and seasonal migrations (Silm & Ahas 2010). Data from GPS receivers (Grinberger et al. 2014; Hallo et al. 2012) is usually used to track movements, but other data from mobile phones can also be used to locate a user. These include data from social networks, Bluetooth, radio frequency identification or even photos containing landmarks, which all allow locations to be determined and tourist patterns to be identified (Hawelka et al. 2014; Nielsen 2011). In tourism studies, too, CDRs are used (Raun et al. 2016; Nilbe et al. 2014). They allow the users of more than one network at a time to be analysed. Research on foreign tourists with a foreign operator's SIM card was carried out, for example, in Estonia (Ahas et al. 2007). It allowed this data to be correlated with conventional tourist accommodation statistics, which reached 0.99, and also allowed the trip plans of individual nationalities to be developed (Ahas et al. 2008a). In the 2000s in Estonia, the web-based tourism management and monitoring system ‘Positium Barometer’ was created on the basis of passive data. This system was developed to analyse the space-time movement of tourists (Ahas et al. 2008b). Passive mobile positioning data was also used in the study of tourist loyalty in Estonia. Thanks to the research, a model was established that matched repeated visits based on the time interval (Kuusik et al. 2009). This data was also used to analyse the long-term impact of the events on the marketing of particular destinations (Kuusik et al. 2014). In the Dolomites, quantitative data from mobile phones was used to monitor tourist traffic, one-day visits and their impact on the destination (Bertocchi et al. 2021). The use of data from mobile phones is gaining importance in tourism. One of the leaders in this field is Estonia where data and methodology have been used by Eesti Bank (the central bank of Estonia) since 2008 to calculate the national balance of payments and public tourism statistics (Saluveer et al. 2020).

Method

In this study, spatiotemporal data (CDR) was used that covered each user's activity, namely voice calls, SMS sending and GPRS / EDGE transmission, on a network (Steenbruggen et al. 2014). The data was provided by Orange Polska, one of Poland's leading telecommunications service providers. Orange Polska provided fully anonymised quantitative CDR data for the July–August holiday periods in 2019 and 2020 for Białowieża National Park (BNP) and the surrounding area. It included only the numbers of users for a specific time in the study area. This was determined using 10 GSM antennas directed at the park. A minimum of one connection to an antenna by a mobile phone user in an analysed time interval indicated presence in the study area. The inclusion of data from the area around the park in the analysis is due to the small number of telecommunications antennas and the technical impossibility of limiting the study to the boundaries of the park only. Quantitative data was made available at one-day resolution. The data gathered was statistically analysed and was presented using the main descriptive statistics and tests of intergroup comparisons, especially between the 2019 and 2020 data. Descriptive statistics were used to describe the characteristics of the study period: means, medians, standard deviations (SD), interquartile ranges (IQR) and full ranges. The normal distributions of the dependent variable, which was the daily number of users, was tested using the Shapiro-Wilka test, as this is the assumption of the t-Student test. When a normal distribution was confirmed, the Student's t-test was used, and when it was disconfirmed, the Mann–Whitney non-parametric U-test was used (Stanisz 2006). The tests allowed for the comparison of numerical variables between two groups of observations. The statistically significant results thus obtained attest to a difference in the distribution of a given variable between these groups. For the data on the number of users and the number of cases of COVID-19 from 2020, the strength of the correlation was measured due to the lack of normal distribution; the Spearman test was used (Stanisz 2006). The level of significance was p=0.05. Calculations were made in the R statistical package version 4.0.2 and Statistica 13. Charts were produced in Excel.

In Poland, the length of the summer holidays depends on the last day of the school year, which is usually the first Friday after 20 June, but in 2019 it was 19 June. Meanwhile, the school year begins on the first weekday in September. The holidays thus lasted for 74 days in 2019, and 65 days in 2020 (Table 1). Therefore, for better comparability of data, analyses were conducted for the full months of July and August (Table 1).

Number of daily mobile phone users in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasons

2019 2020 test p-value
N 74 65 U Mann-Whitney <0.001
Mean (SD) 3778.5 (378.97) 4454.65 (426.16)
Median (IQR) 3765 (3550.25 – 3984) 4565 (4199 – 4714)
Range 2428 – 4972 3054 – 5362

Source: own study

Case study

National parks are among the most-visited tourist destinations in Poland and in the world. The Bialowieża National Park (BNP) is in the Podlaskie Voivodeship of central-eastern Poland, where it occupies the central part of the Białowieża old-growth forest [Puszcza Białowieska]. Already in 1932, the outstanding natural values of this area saw it granted national park status – it is one of the oldest areas of this type not only in Poland but in Europe. The best-preserved part of the forest – the last old-growth forest in the European lowlands, and one of great diversity – is a protected area. The Białowieża old-growth forest extends into Belarus, where it also constitutes a national park. In 1979, the Polish part of the forest was added to the UNESCO World Cultural Heritage List, and in 1992 the Belarusian territories were also added. The Polish part of the park covers about 105.2 km2. In 2009–19, the BNP was visited by between 82,300 people annually (in 2009) and 248,700 people annually (in 2017).

The first 1G cellular network in Poland was introduced in 1992; currently, the standard is 4G, which aside from basic telecommunications services also allows for the free transmission of data packets. Over 99% of the country's population has 4G network coverage (Od 1G do 5G… 2020). Since 2020, 5G technology exists in Poland, and it is intended to serve as much for communication between people as between devices. Currently, it covers major cities. In 2019, 52.2 million SIM cards were active in Poland, and 54.1 million in 2020, covering a population of approximately 38 million. The largest operators in the market share are: P4 (29.0% in 2019 and 2020), Orange (27.0% in 2020; 27.3% in 2019), Polkomtel (21.5% and 21.3%) and T-Mobile (18.7% and 18.9%) (UKE 2021). Similarly, in the entire European Union, the number of active SIM cards exceeds the number of citizens, and 67% of the global population used telephones in 2020 (GSMA Intelligence 2021).

Socio-economic conditions and the epidemiological situation during the research period

At the beginning of 2020, an unprecedented phenomenon took place – a new strain of coronavirus (SARS-CoV-2) that caused COVID-19 respiratory disease began to spread globally. In March, WHO declared a pandemic and, by April, measures to reduce the spread of the virus were affecting 81% of the global workforce (ILO 2020). At the time of writing (January 2022), most countries in the world are struggling with the numerous problems caused by the pandemic's successive waves.

Following 2019 – a record year for tourist traffic in Poland when the number of tourists amounted to 35.67 million, 2020 saw a decrease in tourist figures to 17.88 million, constituting a drop of 49.9% (Cierpiał-Wolan 2020, 2021). This was influenced by the prevailing global situation because safety and epidemic issues are among the main factors in making decisions to travel (Blondel et al. 2015; Calabrese et al. 2015; Bengtsson et al. 2011). The holiday period (July and August) is the peak tourist season in Poland, accounting for about 25% of total tourist traffic. The global epidemiological situation in 2020 caused a drastic 59.4% decrease in foreign trips among Poles. The most popular tourist holiday destinations for Poles recorded decreases of 40.9% (Croatia) and 74.1% (Turkey) compared with the same period in 2019 (Cierpiał-Wolan 2020, 2021). Outside the Schengen area, declines were even more drastic and exceeded 70% (Cierpiał-Wolan 2020, 2021), which resulted from the additional restrictions on tourists leaving the area.

Because of the SARS-CoV-2 coronavirus pandemic, during the holidays in 2020, the Polish government maintained the restrictions that had been previously introduced, aimed at limiting the spread of the disease. The state of epidemic lasted throughout the summer, and people were required to cover their noses and mouths indoors, while limits were introduced on the numbers of people permissible in public places and on means of transport. There were also informational campaigns about maintaining special safety conditions and safe distances, and disinfecting rooms, devices and hands. To stimulate internal tourist traffic, the Polish government introduced a ‘tourist voucher’ for each child in the amount of PLN 500 (about EUR 120).

During the summer holidays of 2020, 193 to 933 daily new cases of SARS-CoV-2 were recorded in Poland (on May 28 and Aug 21, respectively), with the average for August being 514 (Fig. 1). The severe outbreak of the pandemic was yet to come, as the highest number of COVID cases in Poland took place in March 2021 and accounted for 35,100 cases daily. However, the restrictions that were applied in Poland were one of the strictest in the world to protect the population from rapid increase of cases. This included the fact that the entrance to the forests and national parks was prohibited between March and April 2020.

Figure 1

Number of daily new cases of COVID-19 in the 2020 holiday season

Source: own study based on Medicover 2021

Results

The daily average number of Orange network users in the study area during the holiday period was 3,778.5 in 2019, while in 2020 it increased by 17.9% to 4,454.65; the tests revealed statistically significant differences between the two periods. For 2019, the annual mean for the daily number of mobile phone users was 3,178.75, which is 17.9% below the mean for the holiday season, while the analogous difference for 2020 was 28.0% (3,209.77). In 2019, the minimum number of daily users was 2,428 (13 July). By contrast, the maximum of 4,972 was recorded on 16 August, when there is a long weekend in Poland associated with a national holiday that falls on 15 August. In 2020, the minimum (3,054) was recorded on Saturday, 29 August, while the maximum of 5,362 was recorded, as in the previous year, on 15 August during the long weekend (Fig. 2).

Figure 2

Number of daily mobile phone users in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasons

Source: own study

The statistical analyses revealed statistically significant differences between 2019 and 2020 for July, as well as for August. In July 2019, the average number of users was 3,690.77, which was 16.8% lower than in 2020, when it was 4,435.71. The case was similar for August 2019, when the average number of users of 3,965.06 was 13.5% lower than in the following year (Table 2).

Daily mobile phone users in the Białowieża National Park and its vicinity in July and August, comparison between 2019 and 2020

Month Notch statistics 2019 2020 test p-value
July N 31 31 U Mann-Whitney <0.001
Mean (SD) 3690.77 (320.21) 4435.71 (366.32)
Median (IQR) 3749 (3556 – 3862.5) 4500 (4263 – 4677.5)
Range 2428 – 4115 3583 – 4965
August N 31 31 U Mann-Whitney <0.001
Mean (SD) 3965.06 (375.25) 4530.45 (460.55)
Median (IQR) 3872 (3741.5 – 4102.5) 4659 (4326.5 – 4810)
Range 3486 – 4972 3054 – 5362

Source: own study

The statistical tests for specific days of the week during the 2019 and 2020 summer holidays showed statistically significant differences in user numbers for each day of the week (Table 3). The average number of users for individual days of the week was lower in 2019 than in 2020, with differences ranging from 16.0% on Mondays to 20.0% on Tuesdays (Fig. 3). During the summer holidays of 2019, the average number of users was highest for Fridays, amounting to 3,947.09 records, and lowest on Tuesdays, with 3,610.80 records. In 2020, however, the day with the highest number of records was Sunday (4,636.60), and the lowest was Thursday (4,331.0).

Daily mobile phone users on specific days of the week in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasons

Zmienna Parameter 2019 2020 test p-value
Monday N 10 10 U Mann-Whitney <0.001
Mean (SD) 3699.6 (285.70) 4293.2 (365.81)
Median (IQR) 3674.50 (3507 – 3805) 4370.50 (4021 – 4659)
Range 3392 – 4371 3580 – 4681
Tuesday N 10 9 U Mann-Whitney <0.001
Mean (SD) 3610.80 (250.73) 4334.00 (371.85)
Median (IQR) 3612.50 (3377.00 – 3811.00) 4384.00 (4199.00 – 4450.00)
Range 3273 – 4015 3591 – 4965
Wednesday N 10 9 U Mann-Whitney <0.001
Mean (SD) 3665.80 (248.99) 4364.44 (332.92)
Median (IQR) 3646.50 (3491.00 – 381.00) 4449.00 (4145.00 – 4614.00)
Range 3222 – 4058 3694 – 4684
Thursday N 11 9 T test <0.001
Mean (SD) 3729.18 (382.58) 4331.00 (463.78)
Median (IQR) 3735 (3491 – 3858) 4605 (3924 – 4642)
Range 3203 – 4706 3583 – 4809
Friday N 11 9 T test <0.001
Mean (SD) 3947.09 (427.06) 4599.33 (331.20)
Median (IQR) 3962 (3616 – 4125) 4714.00 (4596 – 4812)
Range 3365 – 4972 3999 – 4856
Saturday N 11 10 U Mann-Whitney <0.001
Mean (SD) 3839.81 (576.29) 4517.50 (670.57)
Median (IQR) 3934 (3632 – 4115) 4718.50 (4216 – 4920)
Range 2428 – 4744 3054 – 5362
Sunday N 11 10 U Mann-Whitney <0.001
Mean (SD) 3924.55 (305.13) 4636.60 (392.14)
Median (IQR) 3872 (3765 – 4071) 4703 (4417 – 4894)
Range 3322 – 4434 3838 – 5176

Source: own study

Figure 3

Average number of daily mobile phone users on specific days of the week in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasons

Source: own study

The Spearman statistical test was used to check the correlation between the number of cases and the number of mobile phone users. The obtained result for the entire holiday period (0.439105) is statistically significant (p = 0.05). According to J. Guilford's (1965) classification, it is at an average level (0.3 <| r | ≤0.5). The same test for July showed a high correlation (0.625), while in August the correlation is not statistically significant (p> 0.05).

Discussion and conclusion

Despite limitations with regard to precision, availability (coming from a single operator) and recording frequency, the analysed CDR information has many advantages. Its greatest advantage is its abundance, which results from the large number of mobile phone users. The collected data provided important information about public mobility in the BNP and its vicinity during the 2019 and 2020 summer holidays. The importance of using CDRs increased in the initial pandemic period, when a significant proportion of the public limited face-to-face contacts, often drastically, for fear of SARS-CoV-2 infection, thus reducing the opportunity for data collection using interview-based surveys. Such detailed spatiotemporal research on such a large population would have been impossible without access to CDRs.

The travel industry is prone to crises, including COVID-19 (Khalid et al. 2021; Zhang et al. 2021). According to the World Tourism Organization, comparing 2020 to 2019, international tourism decreased by 74% in 2020. A similar situation took place in Poland, where the decrease was 49.9%.

In the analysed period, according to the Central Statistical Office, the number of tourists dropped by 31% in July and by 24% in August (Cierpiał-Wolan 2021). The conducted research shows that in the holiday season of 2020, as compared with 2019, the number of mobile network users was 20.2% higher for July and 14.3% higher for August, which is the month with the highest tourist traffic. The increase in the number of tourists in the BNP and nearby locations, despite high declines throughout the country, probably results from the change in travel patterns (tourists chose isolated ‘safe’ places, the number of long-distance trips decreased, etc.) as well as the development of regional, rural, health (Wang et al. 2021) or sustainable tourism (Lama, Rai 2021; Purcell et al. 2021). The BNP area is especially predestined to suit these forms of tourism. The correlation test for the whole period under study shows an average correlation between the number of users and the COVID-19 statistics. Correlation tests for individual months are differentiated – in July the correlation is high, and in August it is not statistically significant. The high correlation in July and the increase in the number of users, but also the increase in the number of COVID cases in the whole country, is probably due to two factors. The first is that BNP and the surrounding area is considered safe (isolated, with low population density and high forest cover), and the second is that the analysed period was already a subsequent stage of the pandemic during which the sense of panic among Poles decreased (Kalinowski & Wyduba 2020). On the other hand, the increase in the number of COVID-19 cases in August 2020 did not reduce the mobility of the population.

The maximum number of mobile networks users during the holiday period occurred on the long August weekend (around 15 August), when Poland celebrates a public holiday, which is an additional day off work. The smallest number of users, however, did not show at a specific time and, in the analysed years, took place at various moments, which is probably due to local conditions – for example, the weather.

During all the days of the week in the holiday season of 2020, compared with 2019, an increase in the number of records was observed from 16.0% on Mondays to 20.0% on Tuesdays, with the highest number during weekends (from Friday to Sunday) in both analysed holiday periods, which is characteristic of tourist traffic in Poland.

The BNP's location in a peripheral area of Poland, and its relatively small number of tourists had a positive impact on its attractiveness, increasing tourist mobility, which is reflected in the number of cell phone users in the pandemic year of 2020. This is confirmed by previous research by the author, which shows that, in 2020, tourists were looking for remote places that would offer them protection from SARS-CoV-2 infection.

Figure 1

Number of daily new cases of COVID-19 in the 2020 holiday seasonSource: own study based on Medicover 2021
Number of daily new cases of COVID-19 in the 2020 holiday seasonSource: own study based on Medicover 2021

Figure 2

Number of daily mobile phone users in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasonsSource: own study
Number of daily mobile phone users in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasonsSource: own study

Figure 3

Average number of daily mobile phone users on specific days of the week in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasonsSource: own study
Average number of daily mobile phone users on specific days of the week in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasonsSource: own study

Daily mobile phone users on specific days of the week in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasons

Zmienna Parameter 2019 2020 test p-value
Monday N 10 10 U Mann-Whitney <0.001
Mean (SD) 3699.6 (285.70) 4293.2 (365.81)
Median (IQR) 3674.50 (3507 – 3805) 4370.50 (4021 – 4659)
Range 3392 – 4371 3580 – 4681
Tuesday N 10 9 U Mann-Whitney <0.001
Mean (SD) 3610.80 (250.73) 4334.00 (371.85)
Median (IQR) 3612.50 (3377.00 – 3811.00) 4384.00 (4199.00 – 4450.00)
Range 3273 – 4015 3591 – 4965
Wednesday N 10 9 U Mann-Whitney <0.001
Mean (SD) 3665.80 (248.99) 4364.44 (332.92)
Median (IQR) 3646.50 (3491.00 – 381.00) 4449.00 (4145.00 – 4614.00)
Range 3222 – 4058 3694 – 4684
Thursday N 11 9 T test <0.001
Mean (SD) 3729.18 (382.58) 4331.00 (463.78)
Median (IQR) 3735 (3491 – 3858) 4605 (3924 – 4642)
Range 3203 – 4706 3583 – 4809
Friday N 11 9 T test <0.001
Mean (SD) 3947.09 (427.06) 4599.33 (331.20)
Median (IQR) 3962 (3616 – 4125) 4714.00 (4596 – 4812)
Range 3365 – 4972 3999 – 4856
Saturday N 11 10 U Mann-Whitney <0.001
Mean (SD) 3839.81 (576.29) 4517.50 (670.57)
Median (IQR) 3934 (3632 – 4115) 4718.50 (4216 – 4920)
Range 2428 – 4744 3054 – 5362
Sunday N 11 10 U Mann-Whitney <0.001
Mean (SD) 3924.55 (305.13) 4636.60 (392.14)
Median (IQR) 3872 (3765 – 4071) 4703 (4417 – 4894)
Range 3322 – 4434 3838 – 5176

Number of daily mobile phone users in the Białowieża National Park and its vicinity in the 2019 and 2020 holiday seasons

2019 2020 test p-value
N 74 65 U Mann-Whitney <0.001
Mean (SD) 3778.5 (378.97) 4454.65 (426.16)
Median (IQR) 3765 (3550.25 – 3984) 4565 (4199 – 4714)
Range 2428 – 4972 3054 – 5362

Daily mobile phone users in the Białowieża National Park and its vicinity in July and August, comparison between 2019 and 2020

Month Notch statistics 2019 2020 test p-value
July N 31 31 U Mann-Whitney <0.001
Mean (SD) 3690.77 (320.21) 4435.71 (366.32)
Median (IQR) 3749 (3556 – 3862.5) 4500 (4263 – 4677.5)
Range 2428 – 4115 3583 – 4965
August N 31 31 U Mann-Whitney <0.001
Mean (SD) 3965.06 (375.25) 4530.45 (460.55)
Median (IQR) 3872 (3741.5 – 4102.5) 4659 (4326.5 – 4810)
Range 3486 – 4972 3054 – 5362

Aguiléra, V, Allio, S, Benezech, V, Combes, F & Milion, C 2014, ‘Using cell phone data to measure quality of service and passenger flows of Paris transit system’, Transportation Research Part C: Emerging Technologies, vol. 43, pp. 198–211. AguiléraV AllioS BenezechV CombesF MilionC 2014 ‘Using cell phone data to measure quality of service and passenger flows of Paris transit system’ Transportation Research Part C: Emerging Technologies 43 198 211 10.1016/j.trc.2013.11.007 Search in Google Scholar

Ahas, R, 2011, ‘Mobile positioning’ in Mobile Methods, eds M Büscher, J Urry & K Witchger, Routledge, New York, pp. 183–199. AhasR 2011 ‘Mobile positioning’ in Mobile Methods eds BüscherM UrryJ WitchgerK Routledge New York 183 199 Search in Google Scholar

Ahas, R, Aasa, A, Mark, Ü, Pae, T, Kull, T 2007, ‘Seasonal tourism spaces in Estonia: Case study with mobile positioning data’, Tourism Management, vol. 28, no. 3, pp. 898–910. AhasR AasaA MarkÜ PaeT KullT 2007 ‘Seasonal tourism spaces in Estonia: Case study with mobile positioning data’ Tourism Management 28 3 898 910 10.1016/j.tourman.2006.05.010 Search in Google Scholar

Ahas, R, Aasa, A, Roosea, A, Mark, Ü, Silm, S 2008a, ‘Evaluating passive mobile positioning data for tourism surveys: An Estonian case study’, Tourism Management, vol. 29, no. 3, pp. 469–486. AhasR AasaA RooseaA MarkÜ SilmS 2008a ‘Evaluating passive mobile positioning data for tourism surveys: An Estonian case study’ Tourism Management 29 3 469 486 10.1016/j.tourman.2007.05.014 Search in Google Scholar

Ahas, R, Saluveer, E, Tiru, M, Silm, S 2008b, ‘Mobile Positioning Based Tourism Monitoring System: Positium Barometer’ in Information and Communication Technologies in Tourism, eds P O’Connor, W Höpken & U Gretzel, Springer Computer Science. Springer-Verlag, pp. 475–485, Innsbruck. AhasR SaluveerE TiruM SilmS 2008b ‘Mobile Positioning Based Tourism Monitoring System: Positium Barometer’ in Information and Communication Technologies in Tourism eds O’ConnorP HöpkenW GretzelU Springer Computer Science. Springer-Verlag 475 485 Innsbruck 10.1007/978-3-211-77280-5_42 Search in Google Scholar

Ahas, R, Silm, S, Järv, O, Saluveer, E & Tiru, M 2010, ‘Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones’, Journal of Urban Technology, vol. 17, pp. 3–27. AhasR SilmS JärvO SaluveerE TiruM 2010 ‘Using Mobile Positioning Data to Model Locations Meaningful to Users of Mobile Phones’ Journal of Urban Technology 17 3 27 10.1080/10630731003597306 Search in Google Scholar

Bekhor, S, Cohen, Y & Solomon, C 2013, ‘Evaluating long-distance travel patterns in Israel by tracking cellular phone positions’, Journal of Advanced Transportation, vol. 47, pp. 435–446. BekhorS CohenY SolomonC 2013 ‘Evaluating long-distance travel patterns in Israel by tracking cellular phone positions’ Journal of Advanced Transportation 47 435 446 10.1002/atr.170 Search in Google Scholar

Bel, F, Lacroix, A, Lyser, S, Rambonilaza, T & Turpin, N 2014, ‘Domestic demand for tourism in rural areas: Insights from summer stays in three French regions’, Tourism Management, vol. 46, pp. 562–570. BelF LacroixA LyserS RambonilazaT TurpinN 2014 ‘Domestic demand for tourism in rural areas: Insights from summer stays in three French regions’ Tourism Management 46 562 570 10.1016/j.tourman.2014.07.020 Search in Google Scholar

Bengtsson, L, Lu, X, Thorson, A, Garfield, R & von Schreeb, J 2011, ‘Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: apost-earthquake geospatial study in Haiti’, PLoS Med, vol. 8, no 8. BengtssonL LuX ThorsonA GarfieldR von SchreebJ 2011 ‘Improved response to disasters and outbreaks by tracking population movements with mobile phone network data: apost-earthquake geospatial study in Haiti’ PLoS Med 8 8 10.1371/journal.pmed.1001083316887321918643 Search in Google Scholar

Bertocchi, D, , Camatti, N & van der Borg, J 2021, ‘Tourism Peaks on the Three Peaks. Using big data to monitor where, when and how many visitors impact the Dolomites UNESCO World Heritage Site’, Rivista Geografica Italiana - Open Access, vol. 3, pp. 59–81. BertocchiD CamattiN van der BorgJ 2021 ‘Tourism Peaks on the Three Peaks. Using big data to monitor where, when and how many visitors impact the Dolomites UNESCO World Heritage Site’ Rivista Geografica Italiana - Open Access 3 59 81 10.3280/rgioa3-2021oa12532 Search in Google Scholar

Blondel, VD, Decuyper, A & Krings, G 2015, ‘A survey of results on mobile phone datasets analysis’, EPJ DataScience, vol. 4, p. 10. BlondelVD DecuyperA KringsG 2015 ‘A survey of results on mobile phone datasets analysis’ EPJ DataScience 4 10 10.1140/epjds/s13688-015-0046-0 Search in Google Scholar

Bogoch, II, Brady, OJ, Kraemer, MU, German, M, Creatore, MI, Kulkarni, MA, Brownstein, JS, Mekaru, SR, Hay, SI, Groot, E, Watts, A & Khan, K 2016, ‘Anticipating the international spread of Zika virus from Brazil’, Lancet, vol. 387(10016), pp. 335–336. BogochII BradyOJ KraemerMU GermanM CreatoreMI KulkarniMA BrownsteinJS MekaruSR HaySI GrootE WattsA KhanK 2016 ‘Anticipating the international spread of Zika virus from Brazil’ Lancet 387 10016 335 336 10.1016/S0140-6736(16)00080-5487315926777915 Search in Google Scholar

Bojic, I, Massaro, E, Belyi, A, Sobolevsky, S & Ratti, C 2015, ‘Choosing the Right Home Location Definition Method for the given Dataset’, in Social Informatics, Proceedings of the 7th International Conference, SocInfo 2015, eds T-Y Liu, C Scollon & W Zhu, Springer International Publishing, Beijing, pp. 194–208. BojicI MassaroE BelyiA SobolevskyS RattiC 2015 ‘Choosing the Right Home Location Definition Method for the given Dataset’ in Social Informatics, Proceedings of the 7th International Conference, SocInfo 2015 eds LiuT-Y ScollonC ZhuW Springer International Publishing Beijing 194 208 10.1007/978-3-319-27433-1_14 Search in Google Scholar

Calabrese, F, Ferrari, L & Blondel, VD 2015, ‘Urban sensing using mobile phone network data: a survey of research’, ACM ComputSurveys, vol. 47, pp. 1–20. CalabreseF FerrariL BlondelVD 2015 ‘Urban sensing using mobile phone network data: a survey of research’ ACM ComputSurveys 47 1 20 10.1145/2655691 Search in Google Scholar

Cierpiał-Wolan, M (ed.) 2020, ‘Tourism in 2019’, Główny Urząd Statystyczny, Warszawa, Rzeszów. Cierpiał-WolanM (ed.) 2020 ‘Tourism in 2019’ Główny Urząd Statystyczny Warszawa, Rzeszów Search in Google Scholar

Cierpiał-Wolan, M (ed.) 2021, ‘Tourism in 2020’, Główny Urząd Statystyczny, Warszawa, Rzeszów. Cierpiał-WolanM (ed.) 2021 ‘Tourism in 2020’ Główny Urząd Statystyczny Warszawa, Rzeszów Search in Google Scholar

Covid-19 Mobility Project in Germany 2022, Mobility monitor. Available from: <https://www.covid-19-mobility.org/>. [12 December 2021]. Covid-19 Mobility Project in Germany 2022 Mobility monitor Available from: <https://www.covid-19-mobility.org/>. [12 December 2021]. Search in Google Scholar

Data Analytics@IFISC 2020, Mobility reduction in Spain after the adoption of COVID confinement measures. Available from: <https://analytics.ifisc.uib-csic.es>. [28 March 2021]. Data Analytics@IFISC 2020 Mobility reduction in Spain after the adoption of COVID confinement measures Available from: <https://analytics.ifisc.uib-csic.es>. [28 March 2021]. Search in Google Scholar

Demissie, MG, Phithakkitnukoon, S, Sukhvi-bul, T, Antunes, F, Gomes, R & Bento, C 2016, ‘Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal’, IEEE Transactions on Intelligent Transportation Systems, vol. 17, pp. 2466–2478. DemissieMG PhithakkitnukoonS Sukhvi-bulT AntunesF GomesR BentoC 2016 ‘Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal’ IEEE Transactions on Intelligent Transportation Systems 17 2466 2478 10.1109/TITS.2016.2521830 Search in Google Scholar

Gao, S, Rao, J, Kang, Y, Liang, Y & Kruse, J 2020, ‘Mapping county-level mobility pattern changes in the United States in response to COVID-19’, SSRN, 2 April. Available from: <https://papers.ssrn.com/>. [9 October 2021]. GaoS RaoJ KangY LiangY KruseJ 2020 ‘Mapping county-level mobility pattern changes in the United States in response to COVID-19’ SSRN 2 April Available from: <https://papers.ssrn.com/>. [9 October 2021]. 10.2139/ssrn.3570145 Search in Google Scholar

Goodchild, MF 2007, ‘Citizens as voluntary sensors: Spatial data infrastructure in the world of web 2.0’, International Journal of Spatial Data Infrastructures Research, vol. 2, pp. 24–32. GoodchildMF 2007 ‘Citizens as voluntary sensors: Spatial data infrastructure in the world of web 2.0’ International Journal of Spatial Data Infrastructures Research 2 24 32 Search in Google Scholar

Grinberger, AY, Shoval, N & McKercher, B 2014, ‘Typologies of tourists’ time–space consumption: a new approach using GPS data and GIS tools’, Tourism Geographies, vol. 16, pp. 105–123. GrinbergerAY ShovalN McKercherB 2014 ‘Typologies of tourists’ time–space consumption: a new approach using GPS data and GIS tools’ Tourism Geographies 16 105 123 10.1080/14616688.2013.869249 Search in Google Scholar

GSMA Intelligence 2021, Research & Analysis, Available from: <https://www.gsmaintelligence.com>. [15 December 2021]. GSMA Intelligence 2021 Research & Analysis Available from: <https://www.gsmaintelligence.com>. [15 December 2021]. Search in Google Scholar

Guilford, JP 1965, Fundamental Statistics in Psychology and Education, McGraw-Hill, New York. GuilfordJP 1965 Fundamental Statistics in Psychology and Education McGraw-Hill New York Search in Google Scholar

Hallo, JC, Beeco, JA, Goetcheus, C, McGee, J, McGehee, NG & Norman, WC 2012, ‘GPS as a Method for Assessing Spatial and Temporal Use Distributions of Nature-Based Tourists’, Journal of Travel Research, vol. 51, pp. 591–606. HalloJC BeecoJA GoetcheusC McGeeJ McGeheeNG NormanWC 2012 ‘GPS as a Method for Assessing Spatial and Temporal Use Distributions of Nature-Based Tourists’ Journal of Travel Research 51 591 606 10.1177/0047287511431325 Search in Google Scholar

Halloran, ME, Vespignani, A, Bharti, N, Feldstein, LR, Alexander, KA, Ferrari, M, Shaman, J, Drake, JM, Porco, T, Eisenberg, JNS, Del Valle, SY, Lofgren, E, Scarpino, SV, Eisenberg, MC, Gao, D, Hyman, JM, Eubank, S & Longini, IM 2014, ‘Ebola: Mobility data’, Science, vol. 346(6208), p. 433. HalloranME VespignaniA BhartiN FeldsteinLR AlexanderKA FerrariM ShamanJ DrakeJM PorcoT EisenbergJNS Del ValleSY LofgrenE ScarpinoSV EisenbergMC GaoD HymanJM EubankS LonginiIM 2014 ‘Ebola: Mobility data’ Science 346 6208 433 10.1126/science.346.6208.433-a440860725342792 Search in Google Scholar

Hawelka, B, Sitko, I, Beinat, E, Sobolevsky, S, Kazakopoulos, P & Ratti, C 2014, ‘Geo-located Twitter as proxy for global mobility patterns’, Cartography and Geographic Information Science, vol. 41, pp. 260–271. HawelkaB SitkoI BeinatE SobolevskyS KazakopoulosP RattiC 2014 ‘Geo-located Twitter as proxy for global mobility patterns’ Cartography and Geographic Information Science 41 260 271 10.1080/15230406.2014.890072478682927019645 Search in Google Scholar

ILO 2020, COVID-19 and the world of work: Updated estimates and analysis. Available from: < https://www.ilo.org/>. [13 December 2021]. ILO 2020 COVID-19 and the world of work: Updated estimates and analysis Available from: < https://www.ilo.org/>. [13 December 2021]. Search in Google Scholar

Kalinowski, S, Wyduba, W. 2020, ‘Moja sytuacja w okresie koronawirusa. Raport końcowy z badan [My situation in the coronavirus period. The final report of the study]’, Wyd. IRWiR PAN, Warszawa. KalinowskiS WydubaW. 2020 ‘Moja sytuacja w okresie koronawirusa. Raport końcowy z badan [My situation in the coronavirus period. The final report of the study]’ Wyd. IRWiR PAN Warszawa 10.53098/9788389900609 Search in Google Scholar

Khalid, U, Okafor, LE, Burzynska, K 2021, ‘Does the size of the tourism sector influence the economic policy response to the COVID-19 pandemic?’, Current Issues in Tourism, vol. 24, no. 19, pp. 2801–2820. KhalidU OkaforLE BurzynskaK 2021 ‘Does the size of the tourism sector influence the economic policy response to the COVID-19 pandemic?’ Current Issues in Tourism 24 19 2801 2820 10.1080/13683500.2021.1874311 Search in Google Scholar

Kuusik, A, Ahas, R, Tiru, M 2009, ‘Analysing Repeat Visitation on Country Level with Passive Mobile Positioning Method: an Estonian Case Study’, Estonian Discussions on Economic Policy, vol. 17, pp. 140–155. KuusikA AhasR TiruM 2009 ‘Analysing Repeat Visitation on Country Level with Passive Mobile Positioning Method: an Estonian Case Study’ Estonian Discussions on Economic Policy 17 140 155 Search in Google Scholar

Kuusik, A, Nilbe, K, Mehine, T, Ahas, R 2014, ‘Country as a free sample: the ability of tourism events to generate repeat visits. Case study with mobile positioning data in Estonia’, KuusikA NilbeK MehineT AhasR 2014 ‘Country as a free sample: the ability of tourism events to generate repeat visits. Case study with mobile positioning data in Estonia’ 10.1016/j.sbspro.2014.07.042 Search in Google Scholar

Lama, R, Rai, A 2021, ‘Challenges in developing sustainable tourism post COVID-19 pandemic’ in Tourism destination management in a post-pandemic context (Tourism security-safety and post conflict destinations) eds VG Gowreesunkar, SW Maingi, H Roy and R Micera, Emerald Publishing LamaR RaiA 2021 ‘Challenges in developing sustainable tourism post COVID-19 pandemic’ in Tourism destination management in a post-pandemic context (Tourism security-safety and post conflict destinations) eds GowreesunkarVG MaingiSW RoyH MiceraR Emerald Publishing 10.1108/978-1-80071-511-020211016 Search in Google Scholar

Larijani, AN, Olteanu-Raimond, AM, Perret, J, Brédif, M & Ziemlicki, C 2015, ‘Investigating the mobile phone data to estimate the origin destination flow and analysis; case study: Paris region’, Transportation Research Procedia, vol. 6, pp. 64–78. LarijaniAN Olteanu-RaimondAM PerretJ BrédifM ZiemlickiC 2015 ‘Investigating the mobile phone data to estimate the origin destination flow and analysis; case study: Paris region’ Transportation Research Procedia 6 64 78 10.1016/j.trpro.2015.03.006 Search in Google Scholar

Lu, X, Bengtsson, L & Holme, P 2012, ‘Predictability of population displacement after the 2010 Haiti earthquake’, PNAS, pp. 11576–11581. LuX BengtssonL HolmeP 2012 ‘Predictability of population displacement after the 2010 Haiti earthquake’ PNAS 11576 11581 10.1073/pnas.1203882109340687122711804 Search in Google Scholar

Lynch, C & Roper, C 2011, ‘The Transit Phase of Migration: Circulation of Malaria and Its Multidrug-Resistant Forms in Africa’, PLoS Med, vol. 8(5). LynchC RoperC 2011 ‘The Transit Phase of Migration: Circulation of Malaria and Its Multidrug-Resistant Forms in Africa’ PLoS Med 8 5 10.1371/journal.pmed.1001040310497721655316 Search in Google Scholar

Medicover 2021, Koronawirus [Coronavirus]. Available from: <www.medicover.pl/koronawirus/statystyki/>. [30 June 2021] Medicover 2021 Koronawirus [Coronavirus] Available from: <www.medicover.pl/koronawirus/statystyki/>. [30 June 2021] Search in Google Scholar

Montjoye, YA, Hidalgo, CA, Verleysen, M & Blondel, VD 2013, ‘Unique in the Crowd: The privacy bounds of human mobility’, Science Report, vol. 3, p. 1376. MontjoyeYA HidalgoCA VerleysenM BlondelVD 2013 ‘Unique in the Crowd: The privacy bounds of human mobility’ Science Report 3 1376 10.1038/srep01376360724723524645 Search in Google Scholar

Nielsen, NC 2011, ‘Tourist Mobility and Advanced Tracking Technologies’, Tourism Management, vol. 32, pp. 461–462. NielsenNC 2011 ‘Tourist Mobility and Advanced Tracking Technologies’ Tourism Management 32 461 462 10.1016/j.tourman.2010.02.004 Search in Google Scholar

Nilbe, K, Ahas, R & Silm, S 2014, ‘Evaluating the Travel Distances of Events Visitors and Regular Visitors Using Mobile Positioning Data: The Case of Estonia’, Journal of Urban Technology, vol. 21, pp. 91–107. NilbeK AhasR SilmS 2014 ‘Evaluating the Travel Distances of Events Visitors and Regular Visitors Using Mobile Positioning Data: The Case of Estonia’ Journal of Urban Technology 21 91 107 10.1080/10630732.2014.888218 Search in Google Scholar

Od 1G do 5G, czyli historia technologii mobilnej [From 1G to 5G, or the history of mobile technology] 2020. Available from: <https://www.orange.pl/poradnik/siec-komorkowa/od-1g-do-5g-czyli-historia-technologii-mobilnej>. [4 March 2020]. Od 1G do 5G, czyli historia technologii mobilnej [From 1G to 5G, or the history of mobile technology] 2020 Available from: <https://www.orange.pl/poradnik/siec-komorkowa/od-1g-do-5g-czyli-historia-technologii-mobilnej>. [4 March 2020]. Search in Google Scholar

Oliver, N, Lepri, B, Sterly, H, Lambiotte, R, Deletaille, S, De Nadai, M, Letouzé, E, Salah, AA, Benjamins, R, Cattuto, C, Colizza, V, de Cordes, N, Fraiberger, SP, Koebe, T, Lehmann, S, Murillo, J, Pentland, A, Pham, PN, Pivetta, F, Saramäki, J, Scarpino, SV, Tizzoni, M, Verhulst, S & Vinck, P 2020, ‘Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle’, Science Advances, vol. 6, no. 23. OliverN LepriB SterlyH LambiotteR DeletailleS De NadaiM LetouzéE SalahAA BenjaminsR CattutoC ColizzaV de CordesN FraibergerSP KoebeT LehmannS MurilloJ PentlandA PhamPN PivettaF SaramäkiJ ScarpinoSV TizzoniM VerhulstS VinckP 2020 ‘Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle’ Science Advances 6 23 10.1126/sciadv.abc0764727480732548274 Search in Google Scholar

Panigutti, C, Tizzoni, M, Bajardi, P, Smoreda, Z & Colizza, V 2017, ‘Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models’, Royal Society Open Science, 17 May. Available from: <https://royalsocietypublishing.org/>. [1 December 2021]. PaniguttiC TizzoniM BajardiP SmoredaZ ColizzaV 2017 ‘Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models’ Royal Society Open Science 17 May Available from: <https://royalsocietypublishing.org/>. [1 December 2021]. 10.1098/rsos.160950545179128572990 Search in Google Scholar

Poletto, C, Pelat, C, Levy-Bruhl, D, Yazdanpanah, Y, Boelle, PY & Colizza, V 2014, ‘Assessment of the Middle East respiratory syndrome coronavirus (MERS-CoV)epidemic in the Middle East and risk ofinternational spread using a novel maximum likelihood analysis approach’, Eurosurveillance, vol. 19, no. 23, p. 23. PolettoC PelatC Levy-BruhlD YazdanpanahY BoellePY ColizzaV 2014 ‘Assessment of the Middle East respiratory syndrome coronavirus (MERS-CoV)epidemic in the Middle East and risk ofinternational spread using a novel maximum likelihood analysis approach’ Eurosurveillance 19 23 23 10.2807/1560-7917.ES2014.19.23.20824 Search in Google Scholar

Pullano, G, Valdano, E, Scarpa, N, Rubrichi, S & Colizza, V 2020, ‘Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study’, The Lancet Digital Health, vol. 2, pp. e638–e649. PullanoG ValdanoE ScarpaN RubrichiS ColizzaV 2020 ‘Evaluating the effect of demographic factors, socioeconomic factors, and risk aversion on mobility during the COVID-19 epidemic in France under lockdown: a population-based study’ The Lancet Digital Health 2 e638 e649 10.1016/S2589-7500(20)30243-0759836833163951 Search in Google Scholar

Purcell, WM, Burns, O, Voss, A 2021, ‘COVID-19 and sustainable tourism’ in COVID-19: Paving the Way for a More Sustainable World, eds W Leal Filho, World Sustainability Series. Springer, Cham. PurcellWM BurnsO VossA 2021 ‘COVID-19 and sustainable tourism’ in COVID-19: Paving the Way for a More Sustainable World eds Leal FilhoW World Sustainability Series. Springer Cham 10.1007/978-3-030-69284-1_9 Search in Google Scholar

Raun, J, Ahas, R & Tiru, M 2016, ‘Measuring tourism destinations using mobile tracking data’, Tourism Management, vol. 57, pp. 202–212. RaunJ AhasR TiruM 2016 ‘Measuring tourism destinations using mobile tracking data’ Tourism Management 57 202 212 10.1016/j.tourman.2016.06.006 Search in Google Scholar

Ricciato, F, Widhalm, P, Craglia, M & Pantisano, F 2015, ‘Estimating Population Density Distribution from Network-based Mobile Phone Data’, Joint Research Centre. RicciatoF WidhalmP CragliaM PantisanoF 2015 ‘Estimating Population Density Distribution from Network-based Mobile Phone Data’ Joint Research Centre Search in Google Scholar

Sakarovitch, B, Bellefon, M, Givord, P & Vanhoof, M 2018, ‘Estimating the Residential Population from Mobile Phone Data, an Initial Exploration’, Economie et Statistique, vol. 505–506, pp. 109–132. SakarovitchB BellefonM GivordP VanhoofM 2018 ‘Estimating the Residential Population from Mobile Phone Data, an Initial Exploration’ Economie et Statistique 505–506 109 132 10.24187/ecostat.2018.505d.1968 Search in Google Scholar

Saluveer, E, Raunb, J, Tirua M, Altinb L, Kroonc, J, Snitsarenkoc, T, Aasab, A, Silmb, S 2020, ‘Methodological framework for producing national tourismstatistics from mobile positioning data’, Annals of Tourism Research, vol. 81, 102895. SaluveerE RaunbJ TiruaM AltinbL KrooncJ SnitsarenkocT AasabA SilmbS 2020 ‘Methodological framework for producing national tourismstatistics from mobile positioning data’ Annals of Tourism Research 81 102895 10.1016/j.annals.2020.102895 Search in Google Scholar

Silm, S & Ahas, R 2010, ‘The Seasonal Variability of Population in Estonian Municipalities’, Environment and Planning A: Economy and Space, vol. 42, pp. 2527–2546. SilmS AhasR 2010 ‘The Seasonal Variability of Population in Estonian Municipalities’ Environment and Planning A: Economy and Space 42 2527 2546 10.1068/a43139 Search in Google Scholar

Smoreda, Z, Olteanu-Raimond, AM & Couronné, T 2013, ‘Spatiotemporal data from mobile phones for personal mobility assessment’ in Transport Survey Methods: Best Practice for Decision Making, eds J Zmud, Emerald Group Publishing Limited, Bingley, pp. 745–768. SmoredaZ Olteanu-RaimondAM CouronnéT 2013 ‘Spatiotemporal data from mobile phones for personal mobility assessment’ in Transport Survey Methods: Best Practice for Decision Making eds ZmudJ Emerald Group Publishing Limited Bingley 745 768 10.1108/9781781902882-041 Search in Google Scholar

Song, C, Qu, Z, Blumm, N & Barabási, AL 2010, ‘Limits of predictability in human mobility’, Science, vol. 327, pp. 1018–1021. SongC QuZ BlummN BarabásiAL 2010 ‘Limits of predictability in human mobility’ Science 327 1018 1021 10.1126/science.117717020167789 Search in Google Scholar

Stanisz, A 2006, Przystępny kurs statystyki z zastosowaniem STATISTICA PL na przykładach z medycyny, Tom 1. Statystyki podstawowe [An accessible course in statistics from STATISTICA PL on examples from medicine, Vo. 1. Basic statistics], StatSoft Polska, Kraków. StaniszA 2006 Przystępny kurs statystyki z zastosowaniem STATISTICA PL na przykładach z medycyny, Tom 1. Statystyki podstawowe [An accessible course in statistics from STATISTICA PL on examples from medicine, Vo. 1. Basic statistics] StatSoft Polska Kraków Search in Google Scholar

Steenbruggen, J, Tranos, E & Nijkamp, P 2014, ‘Data from mobile phone operators: A tool for smarter cities?’, Telecommunications Policy, vol. 39, pp. 335–346. SteenbruggenJ TranosE NijkampP 2014 ‘Data from mobile phone operators: A tool for smarter cities?’ Telecommunications Policy 39 335 346 10.1016/j.telpol.2014.04.001 Search in Google Scholar

Steenbruggen, J, Tranos, E & Rietveld, P 2016, ‘Can Motorway Traffic Incidents be detected by Mobile Phone Usage Data? An Empirical Application in the Netherlands’, Journal of Transport Geography, vol. 54, pp. 81–90. SteenbruggenJ TranosE RietveldP 2016 ‘Can Motorway Traffic Incidents be detected by Mobile Phone Usage Data? An Empirical Application in the Netherlands’ Journal of Transport Geography 54 81 90 10.1016/j.jtrangeo.2016.05.008 Search in Google Scholar

Tatem, AJ, Qiu, Y, Smith, DL, Sabot, O, Ali, AS & 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 Journal, vol. 8(287). TatemAJ QiuY SmithDL SabotO AliAS MoonenB 2009 ‘The use of mobile phone data for the estimation of the travel patterns and imported Plasmodium falciparum rates among Zanzibar residents’ Malaria Journal 8 287 10.1186/1475-2875-8-287280011820003266 Search in Google Scholar

The Data Against Corona Taskforce 2020, Covid-19: Belgium analyses telecom data to measure the impact of confinement. 2020. Available from:<https://press.telenet.be>. [16 July 2021]. The Data Against Corona Taskforce 2020 Covid-19: Belgium analyses telecom data to measure the impact of confinement 2020 Available from:<https://press.telenet.be>. [16 July 2021]. Search in Google Scholar

Tizzoni, M, Bajardi, P, Decuyper, A, Kon Kam King, G, Schneider, CM, Blondel, V, Smoreda, Z, González, MC & Colizza, V 2014, ‘On the use of human mobility proxy for the modeling of epidemics’, PLoS Computational Biology, vol. 10, no. 7. TizzoniM BajardiP DecuyperA Kon Kam KingG SchneiderCM BlondelV SmoredaZ GonzálezMC ColizzaV 2014 ‘On the use of human mobility proxy for the modeling of epidemics’ PLoS Computational Biology 10 7 10.1371/journal.pcbi.1003716409170625010676 Search in Google Scholar

UKE 2021, Raport o stanie rynku telekomunikacyjnego w Polsce w 2020 r. [Report on the condition of the telecommunications market in Poland in 2020] Available from: UKE [30 June 2021]. UKE 2021 Raport o stanie rynku telekomunikacyjnego w Polsce w 2020 r. [Report on the condition of the telecommunications market in Poland in 2020] Available from: UKE [30 June 2021]. Search in Google Scholar

Vanhoof, M, Combes, S & de Bellefon, MP 2017, ‘Mining mobile phone data to detect urban areas’, Proceedings of the Conference of the Italian Statistical Society, eds A Petrucci & R. Verde, Firenze University Press, Florence. VanhoofM CombesS de BellefonMP 2017 ‘Mining mobile phone data to detect urban areas’ Proceedings of the Conference of the Italian Statistical Society eds PetrucciA VerdeR. Firenze University Press Florence Search in Google Scholar

Vanhoof, M, Hendrickx, L, Puussaar, A, Verstraeten, G, Ploetz, T & Smoreda, Z 2017, ‘Exploring the use of mobile phone data for domestic tourism trip analysis’, Netcom, vol. 31, pp. 335 – 372. VanhoofM HendrickxL PuussaarA VerstraetenG PloetzT SmoredaZ 2017 ‘Exploring the use of mobile phone data for domestic tourism trip analysis’ Netcom 31 335 372 10.4000/netcom.2742 Search in Google Scholar

Vazquez-Prokopec, GM, Bisanzio, D, Stoddard, ST, Paz-Soldan, V & Morrison, AC 2013, ‘Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment’, PLoS ONE, vol. 8, no. 4, e58802. Vazquez-ProkopecGM BisanzioD StoddardST Paz-SoldanV MorrisonAC 2013 ‘Using GPS Technology to Quantify Human Mobility, Dynamic Contacts and Infectious Disease Dynamics in a Resource-Poor Urban Environment’ PLoS ONE 8 4 e58802 10.1371/journal.pone.0058802362011323577059 Search in Google Scholar

Wang, X, Lai, IKW, Zhou, Q, Pang, YH 2021, ‘Regional travel as an alternative form of tourism during the COVID-19 pandemic: impacts of a low-risk perception and perceived benefits’ International Journal of Environmental Research and Public Health, vol. 18, 9422. WangX LaiIKW ZhouQ PangYH 2021 ‘Regional travel as an alternative form of tourism during the COVID-19 pandemic: impacts of a low-risk perception and perceived benefits’ International Journal of Environmental Research and Public Health 18 9422 10.3390/ijerph18179422843124334502008 Search in Google Scholar

Wesolowski, A, Buckee, CO, Bengtsson, L, Wetter, E, Lu, X & Tatem, AJ 2014, ‘Commentary: Containing the Ebola Outbreak - the potential and Challenge of Mobile Network Data, PLoSCurr, 29 September. WesolowskiA BuckeeCO BengtssonL WetterE LuX TatemAJ 2014 ‘Commentary: Containing the Ebola Outbreak - the potential and Challenge of Mobile Network Data PLoSCurr 29 September 10.1371/currents.outbreaks.0177e7fcf52217b8b634376e2f3efc5e420512025642369 Search in Google Scholar

Wesolowski, A, Eagle, N, Noor, AM, Snow, RW & Buckee, CO 2013, ‘The impact of biases in mobile phone ownership on estimates of human mobility’, Journal of The Royal Society Interface, vol. 10(81). WesolowskiA EagleN NoorAM SnowRW BuckeeCO 2013 ‘The impact of biases in mobile phone ownership on estimates of human mobility’ Journal of The Royal Society Interface 10 81 10.1098/rsif.2012.0986362710823389897 Search in Google Scholar

Wesolowski, A, Eagle, N, Tatem, AJ, Smith, DL, Noor, AM, Snow, RW & Buckee, CO 2012, ‘Quantifying the impact of human mobility on malaria’, Science, vol. 338, pp. 267–270. WesolowskiA EagleN TatemAJ SmithDL NoorAM SnowRW BuckeeCO 2012 ‘Quantifying the impact of human mobility on malaria’ Science 338 267 270 10.1126/science.1223467367579423066082 Search in Google Scholar

Ye, J, Hu, Q, Ji, P & Barthelemy, M 2020, ‘The effect of interurban movements on the spatial distribution of population in China’, March. Available from: <https://hal-cea.archives-ouvertes.fr>. [20 July 2021]. YeJ HuQ JiP BarthelemyM 2020 ‘The effect of interurban movements on the spatial distribution of population in China’ March Available from: <https://hal-cea.archives-ouvertes.fr>. [20 July 2021]. Search in Google Scholar

Zhang, D, Guo, B, Li, B & Yu, Z 2010, Extracting social and community intelligence from digital footprints: an emerging research area, in Ubiquitous Intelligence and Computing, Proceedings of the 7th international conference on Ubiquitous Intelligence and Computing, Berlin, pp. 4–18. ZhangD GuoB LiB YuZ 2010 Extracting social and community intelligence from digital footprints: an emerging research area, in Ubiquitous Intelligence and Computing Proceedings of the 7th international conference on Ubiquitous Intelligence and Computing Berlin 4 18 10.1007/978-3-642-16355-5_4 Search in Google Scholar

Zhang, H, Song, H, Wen, L, Liu, C 2021, ‘Forecasting tourism recovery amid COVID-19’, Annals of Tourism Research, vol. 87, pp. 103–149. ZhangH SongH WenL LiuC 2021 ‘Forecasting tourism recovery amid COVID-19’ Annals of Tourism Research 87 103 149 10.1016/j.annals.2021.103149975476536540616 Search in Google Scholar

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