Use of Geolocated Twitter Data in Mobility Pattern Analysisof Foreign Tourists
Transport Engineering Laboratory
Isaac Alfonso Garcia Garcia
1. INTRODUCTION
Explaining leisure travel is challenging the field of transport planning in both quantitative and qualitative
senses [1]. This is mainly because it is driven by different, less constant factors.
Tourism related studies have relied either on aggregated and temporally-sparse official statistics or on
selective small-scale observations and surveys [2]. However, with the surge of smartphones and social networks
like Twitter, the use of the digital trace that people leave behind while travelling has become possible. This typeof big data is called “tourism big data” and has the potential to provide higher detail into patterns of tourists at alower cost than traditional surveys.
Previous research has found that mobility measures obtained from geolocated Twitter data have the same
behavior as other human mobility related datasets. Moreover, trips extracted from this type of data were found to
be closely related to leisure activities, validating the use of Twitter as a data source for tourism related statistics.
Additionally, previous studies on filter methods for identifying foreign tourists in Twitter data haveconcentrated in calculating external parameters as a form to detect foreign tourists. However, the current
research explores the use of existing parameters within the data in order to achieve a higher accuracy.
2. METHODOLOGY
The study proposed a new data filter method to identify foreign tourists by the utilization of internalparameters from geolocated Twitter data. The method was tested with data from Japan, and validated withofficial tourism statistics. Several spatial-temporal analyses were conducted to investigate: travel patterns, trip
purpose, and tourist group behavior. Finally, a case study was performed by using data from Mexico.
3. RESULTS AND DISCUSSION
3.1 Proposed Method for Data Filter
The data filter method was tested with summer season data from Japan. From the results of studying thecomposition of each subgroup, shown in Figure 1, user language parameter was found to be the best indicator ofwhether a user is foreigner. The same trend was found when analyzing the total geolocated tweets by eachsubgroup.

Figure 1. Total Identified Visitors by Subgroup1

3.2 Validation Results
The accuracy level of the filter method was calculated by evaluating a random sample of each dataset.
Accuracy results are shown in Table 1. Additional validation was performed by correlating the most accurate
datasets (G2 and G3) to official statistics, presented in Table 2.
Table 1. Accuracy of Filter Method

Table 2. G3 and G2 Correlation to Official Statistics

3.3 Spatial-Temporal Analysis
Travel Patterns
From the visitor Trajectory Analysis, flows of tourists to and from Narita International Airport were found tofollow the trends in departure and arrival international flight times. An additional study in Nara prefecture
investigated a trend for tourists to visit during the day and avoid staying the night. This had already been
identified in Japanese tourists by local authorities. However, from the results the phenomenon was confirmed toalso be present with foreigner tourists. Therefore, the movements of visitors were confirmed to be captured by
the filtered data and were validated.
Trip Purpose
Land use data from Tokyo was use to infer the trip purpose of visitors. From the results, on average 77% of
the activity was identified to happen in commercial districts. Nevertheless, insight on trip purpose was found tobe limited from this data. From further studying the spatial distribution of the identified visitors, a strong
correlation was found to tourist attractions. Moreover, popularity was identified to be a factor. Finally, thepreferences to certain types of tourist attractions could also be observed in the data.

2Tourist Group Behavior
In the language distribution analysis, the correlation between the filtered data and different types of tourist
attractions was studied. In the case of the English group no particular trends were observed. On the contrary, in
the Tagalog group specific trends were found. This is considered to be due to English being widely used bypeople of different nationalities, whereas Tagalog is a more regional language.
3.4 Case Study in Mexico
The adaptability of the proposed method was studied by applying it to one week of data from Mexico. From
the results, the data filter can be applied. However, improvements to filter out bot accounts are necessary toincrease the obtained accuracy (71%).
4. CONCLUSIONS
The findings of the present study are summarized as follows: User language was found to be the best indicator of whether a user is foreigner.
 Accuracy was found to increase with the increase in the parameters fulfilled by the data. Additionally, filtereddata was highly correlated to official tourism statistics.
 Movements and trends in mobility were found to be captured by the filtered data and can be validated.
 Land use data provided limited insight on the trip purpose of visitors. However, trends were identified whencorrelating to tourist attractions.
 Region-specific languages were found to better provided insight on the trip purpose of visitors.
 Finally, the data filter method can be applied to other cases. Nonetheless, improvements in the bot accountfilter are suggested.
REFERENCES
1. UrryJohn. (2011). Mobilities: New Perspectives on Transport and Society.
2. BartoszHawelka, Izabela Sitko, EuroBeinat, Stanislav Sobolevsky, Pavlos Kazakopoulos, CarloRatti. (2012).
Geo-located Twitter as the proxy for global mobility patterns.

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