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Users’ country of residence accuracy combining crossing median centre, time zone, and self-reported home location methods_
| Country | Accuracy (%) |
|---|---|
| Portugal | 95% |
| Spain | 94% |
| United Kingdom | 85% |
| United States | 91% |
| France | 84% |
| Italy | 83% |
| Germany | 70% |
| Brazil | 96% |
| Netherlands | 62% |
| Canada | 79% |
Number of visits by Flickr users’ country of residence (2010–2022)
| Nr. Visits | Total | Spain | United Kingdom | France | United States | Italy |
|---|---|---|---|---|---|---|
| 1 | 79% | 69% | 77% | 81% | 83% | 87% |
| 2 | 12% | 17% | 13% | 13% | 9% | 9% |
| 3 | 4% | 6% | 3% | 2% | 4% | 3% |
| 4 | 2% | 3% | 2% | 2% | 2% | 1% |
| 5 | 1% | 1% | 1% | 1% | 1% | 1% |
Users’ country of residence using centrographic measures (mean and median centre)
| Countries | Total | Median - Accuracy (%) | Media - Accuracy (%) |
|---|---|---|---|
| Portugal | 2659 | 88% | 63% |
| Spain | 2001 | 83% | 56% |
| United Kingdom | 1477 | 65% | 32% |
| Italy | 771 | 64% | 29% |
| France | 772 | 66% | 36% |
| United States | 808 | 53% | 62% |
| Germany | 654 | 56% | 21% |
| Brazil | 541 | 55% | 53% |
| Netherlands | 455 | 48% | 24% |
| Canada | 252 | 45% | 58% |
Methods used to distinguish visitors and residents through Flickr photos_
| Geographic scale | Authors | Study areas | Method |
|---|---|---|---|
| Urban | (De Choudhury et al., 2010) | Barcelona, Spain; Paris, France; London, UK; San Francisco, New York City, USA. | A time span of 21 days between the first and last photo taken and at least two POIs visited in the same city to be identified as a tourist |
| (Straumann et al., 2014) | Zurich | A semi-automated methodology to classify the user location attribute of Flickr user profiles was employed (extract the countries of residence) | |
| (Kádár & Gede, 2013) | Budapest | Used a threshold of 5 days. If this difference is smaller than 5 days, the user can be considered a visitor; otherwise, he/she is a local. | |
| (Yan et al., 2017) | Central Philippines islands region | Classification of tourist vs. non-tourist based on user profiles. | |
| Regional | (Kádár & Gede, 2022) | Danube Bend | Locals if they have an interval at least 30 days long or have at least 4 intervals visit; space–time patterns, user data and profile analysis |
| (Girardin et al., 2007, 2008) | Province of Florence | Visitor if all photos are taken within a period of 30 days, and inhabitant if photos have an interval greater than 30 days. | |
| (Chen et al., 2019b) | China | Data screening, text data similarity calculation, geographical location clustering, and time series data modelling. | |
| National | (Önder, 2017) | Austria | A time span of 30 days between the first and last photo taken to identify tourists. |
| (Stepchenkova & Zhan, 2013) | Peru | Photos with the tags “Peru” and “travel” considered indicators of images taken by travelers rather than residents | |
| Global | (Wood et al., 2013) | 836 sites in 31 countries around the world | User’s current location was the origin of each trip and used this information to calculate the proportion of photographers originating from each country. |
Flickr users’ activity (2010-2022)
| Number Photos per User | total of Users (%) | total of Visitors (%) | total of Locals (%) |
|---|---|---|---|
| 1 | 26 | 26 | 20 |
| 2 | 11 | 12 | 10 |
| 3 | 7 | 7 | 6 |
| 4 | 5 | 6 | 4 |
| 5 | 4 | 4 | 3 |
| 6 | 3 | 3 | 3 |
| 7 | 3 | 3 | 2 |
| 8 | 2 | 2 | 2 |
| 9 | 2 | 2 | 2 |
| 10 | 2 | 2 | 2 |
| 11 | 2 | 2 | 1 |
| 12 | 1 | 1 | 1 |
Algorithm for Flick users’ classification
| User type | Nr. | % | Rules |
|---|---|---|---|
| Local/resident | 2659 | 9 | if(location(user) equal Portugal) then type = Local |
| Visitor | 12144 | 41 | if(location(user) not equal Portugal) then type = Visitor OR if(timezone(user) not equal Portugal) then type = Visitor |
| Unknown | 15087 | 50 | - |
| Total | 29890 | 100 | - |
Flick users by nationality and Guests in Portugal
| Country of residence | Number (#) of Flickr users (2010-22) | Users (2010-22) % | Number (#) of Flickr users (2018-22) | Users (2018-22) % | Guests (2018-2022) % |
|---|---|---|---|---|---|
| Spain | 2001 | 21 | 349 | 20 | 10,5 |
| United Kingdom | 1477 | 15 | 306 | 18 | 7,9 |
| France | 772 | 8 | 161 | 9 | 6,9 |
| United States | 808 | 8 | 167 | 10 | 5 |
| Italy | 771 | 8 | 89 | 5 | 3 |
| Germany | 654 | 7 | 147 | 9 | 5,4 |
| Brazil | 541 | 6 | 70 | 4 | 4,8 |
| Netherlands | 455 | 5 | 103 | 6 | 2,6 |
| Canada | 252 | 3 | 41 | 2 | 1,4 |
| Belgium | 201 | 2 | 32 | 2 | 1,4 |
| Switzerland | 174 | 2 | 36 | 2 | 1,3 |
| Russia | 125 | 1 | 18 | 1 | 0,6 |
| Ireland | 115 | 1 | 21 | 1 | 1,8 |
| Sweden | 103 | 1 | 18 | 1 | 0,6 |
| Australia | 108 | 1 | 19 | 1 | 0,5 |
| Austria | 93 | 1 | 16 | 1 | 0,5 |
| Poland | 77 | 1 | 16 | 1 | 1 |
| Norway | 74 | 1 | 15 | 1 | 0,3 |
| Denmark | 70 | 1 | 9 | 1 | 0,5 |
| Finland | 73 | 1 | 15 | 1 | 0,3 |
| China | 51 | 1 | 9 | 1 | 1,1 |
| Argentina | 46 | 0 | 9 | 1 | 0,3 |