- Informacje o czasopiśmie
- Format
- Czasopismo
- eISSN
- 2084-6118
- Pierwsze wydanie
- 01 Jan 1984
- Częstotliwość wydawania
- 4 razy w roku
- Języki
- Angielski
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.
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).
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
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
| N | 74 | 65 | U Mann-Whitney | |
| Mean (SD) | 3778.5 (378.97) | 4454.65 (426.16) | ||
| Median (IQR) | 3765 (3550.25 – 3984) | 4565 (4199 – 4714) | ||
| Range | 2428 – 4972 | 3054 – 5362 |
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 [
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).
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
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
| N | 31 | 31 | U Mann-Whitney | ||
| 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 | |||
| N | 31 | 31 | U Mann-Whitney | ||
| 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 |
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
| N | 10 | 10 | U Mann-Whitney | ||
| 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 | |||
| N | 10 | 9 | U Mann-Whitney | ||
| 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 | |||
| N | 10 | 9 | U Mann-Whitney | ||
| 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 | |||
| N | 11 | 9 | T test | ||
| Mean (SD) | 3729.18 (382.58) | 4331.00 (463.78) | |||
| Median (IQR) | 3735 (3491 – 3858) | 4605 (3924 – 4642) | |||
| Range | 3203 – 4706 | 3583 – 4809 | |||
| N | 11 | 9 | T test | ||
| Mean (SD) | 3947.09 (427.06) | 4599.33 (331.20) | |||
| Median (IQR) | 3962 (3616 – 4125) | 4714.00 (4596 – 4812) | |||
| Range | 3365 – 4972 | 3999 – 4856 | |||
| N | 11 | 10 | U Mann-Whitney | ||
| Mean (SD) | 3839.81 (576.29) | 4517.50 (670.57) | |||
| Median (IQR) | 3934 (3632 – 4115) | 4718.50 (4216 – 4920) | |||
| Range | 2428 – 4744 | 3054 – 5362 | |||
| N | 11 | 10 | U Mann-Whitney | ||
| Mean (SD) | 3924.55 (305.13) | 4636.60 (392.14) | |||
| Median (IQR) | 3872 (3765 – 4071) | 4703 (4417 – 4894) | |||
| Range | 3322 – 4434 | 3838 – 5176 |
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).
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
Figure 2
Figure 3
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
| N | 10 | 10 | U Mann-Whitney | ||
| 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 | |||
| N | 10 | 9 | U Mann-Whitney | ||
| 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 | |||
| N | 10 | 9 | U Mann-Whitney | ||
| 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 | |||
| N | 11 | 9 | T test | ||
| Mean (SD) | 3729.18 (382.58) | 4331.00 (463.78) | |||
| Median (IQR) | 3735 (3491 – 3858) | 4605 (3924 – 4642) | |||
| Range | 3203 – 4706 | 3583 – 4809 | |||
| N | 11 | 9 | T test | ||
| Mean (SD) | 3947.09 (427.06) | 4599.33 (331.20) | |||
| Median (IQR) | 3962 (3616 – 4125) | 4714.00 (4596 – 4812) | |||
| Range | 3365 – 4972 | 3999 – 4856 | |||
| N | 11 | 10 | U Mann-Whitney | ||
| Mean (SD) | 3839.81 (576.29) | 4517.50 (670.57) | |||
| Median (IQR) | 3934 (3632 – 4115) | 4718.50 (4216 – 4920) | |||
| Range | 2428 – 4744 | 3054 – 5362 | |||
| N | 11 | 10 | U Mann-Whitney | ||
| 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
| N | 74 | 65 | U Mann-Whitney | |
| 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
| N | 31 | 31 | U Mann-Whitney | ||
| 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 | |||
| N | 31 | 31 | U Mann-Whitney | ||
| 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 |
Long-term changes in circulation conditions over southern Poland for the period 1874–2020Towards sustainable development exemplified by monitoring land use efficiency in Europe using SDG 11.3.1Variability 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 pandemicModelling Net Ecosystem Exchange in the Biebrza Wetlands using satellite and meteorological dataThe Iranian Housing System under the Islamic Republic Government (1990–2021)Surge-type Uisu glacier and its undisturbed forefield relief, Eastern Pamir, TajikistanRelated versus unrelated variety and per employee income regional disparities: A case of Polish regionsRegional and temporal changes in demand for geography teachers in Poland. Results of job advert tracking in the years 2019–2020