Cohen SA, Higham JE, Stefan G, Peeters P. Understanding and governing sustainable tourism mobility: Psychological and behavioural approaches. 2014.
McKercher B. The unrecognized threat to tourism: can tourism survive ‘sustainability’? Tourism management. 1993;14(2):131–6.
Article
Google Scholar
Verbeek D, Mommaas H. Transitions to sustainable tourism mobility: The social practices approach. J Sustain Tour. 2008;16(6):629–44.
Article
Google Scholar
Garrigos-Simon FJ, Narangajavana-Kaosiri Y, Lengua-Lengua I. Tourism and sustainability: A bibliometric and visualization analysis. Sustainability. 2018;10(6):1976.
Article
Google Scholar
Bibri SE. The anatomy of the data-driven smart sustainable city: instrumentation, datafication, computerization and related applications. J Big Data. 2019;6(1):59.
Article
Google Scholar
Bibri SE. On the sustainability of smart and smarter cities in the era of big data: an interdisciplinary and transdisciplinary literature review. J Big Data. 2019;6(1):1–64.
Article
Google Scholar
Maeda TN, Shiode N, Zhong C, Mori J, Sakimoto T. Detecting and understanding urban changes through decomposing the numbers of visitors’ arrivals using human mobility data. J Big Data. 2019;6(1):4.
Article
Google Scholar
Nunes N, Ribeiro M, Prandi C, Nisi V. Beanstalk: a community based passive wi-fi tracking system for analysing tourism dynamics. In: Proceedings of the ACM SIGCHI Symposium on Engineering Interactive Computing Systems, 2017;pp. 93–98.
Redin D, Vilela D, Nunes N, Ribeiro M, Prandi C. Vitflow: a platform to visualize tourists flows in a rich interactive map-based interface. In: 2017 Sustainable Internet and ICT for Sustainability (SustainIT), 2017;pp. 1–2. IEEE
Boieiro M, Aguiar AF, Rego C, Borges PA, Serrano AR. The biodiversity of terrestrial arthropods in madeira and selvagens archipelagos. Revista IDE@-SEA 6, 2015;1–20.
Zheng Y, Zhang L, Xie X, Ma W-Y. Mining interesting locations and travel sequences from gps trajectories. In: Proceedings of the 18th International Conference on World Wide Web, 2009;pp. 791–800.
Zheng W, Huang X, Li Y. Understanding the tourist mobility using gps: Where is the next place? Tour Manag. 2017;59:267–80.
Article
Google Scholar
Gabrielli L, Rinzivillo S, Ronzano F, Villatoro D. From tweets to semantic trajectories: mining anomalous urban mobility patterns. In: International Workshop on Citizen in Sensor Networks, 2013. p. 26–35. Springer
Chen Y-Y, Cheng A-J, Hsu WH. Travel recommendation by mining people attributes and travel group types from community-contributed photos. IEEE Trans Multimedia. 2013;15(6):1283–95.
Article
Google Scholar
Bonné B, Barzan A, Quax P, Lamotte W. Wifipi: Involuntary tracking of visitors at mass events. In: 2013 IEEE 14th International Symposium On” A World of Wireless, Mobile and Multimedia Networks”(WoWMoM), 2013. p. 1–6.
Ruiz-Ruiz AJ, Blunck H, Prentow TS, Stisen A, Kjærgaard MB. Analysis methods for extracting knowledge from large-scale wifi monitoring to inform building facility planning. In: 2014 IEEE International Conference on Pervasive Computing and Communications (PerCom), 2014. p. 130–138.
Kjærgaard MB, Wirz M, Roggen D, Tröster G. Mobile sensing of pedestrian flocks in indoor environments using wifi signals. In: 2012 IEEE International Conference on Pervasive Computing and Communications, 2012.p. 95–102.
Nikzad N, Verma N, Ziftci C, Bales E, Quick N, Zappi P, Patrick K, Dasgupta S, Krueger I, Rosing T.Š. et al. Citisense: improving geospatial environmental assessment of air quality using a wireless personal exposure monitoring system. In: Proceedings of the Conference on Wireless Health, 2012. p. 1–8.
Dutta P, Aoki PM, Kumar N, Mainwaring A, Myers C, Willett W, Woodruff A. Common sense: participatory urban sensing using a network of handheld air quality monitors. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, 2009. p. 349–350.
Pousman Z, Stasko J, Mateas M. Casual information visualization: Depictions of data in everyday life. IEEE Transact Visual Comput Graph. 2007;13(6):1145–52.
Article
Google Scholar
Chen M, Ebert D, Hagen H, Laramee RS, Van Liere R, Ma K-L, Ribarsky W, Scheuermann G, Silver D. Data, information, and knowledge in visualization. IEEE Comput Graph Appl. 2008;29(1):12–9.
Article
Google Scholar
Olshannikova E, Ometov A, Koucheryavy Y, Olsson T. Visualizing big data with augmented and virtual reality: challenges and research agenda. J Big Data. 2015;2(1):22.
Article
Google Scholar
Dourish P. Hci and environmental sustainability: the politics of design and the design of politics. In: Proceedings of the 8th ACM Conference on Designing Interactive Systems, 2010. p. 1–10.
DiSalvo C, Sengers P, Brynjarsdóttir H. Mapping the landscape of sustainable hci. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2010. p. 1975–1984.
Paulos E, Honicky R, Hooker B. Citizen science: Enabling participatory urbanism. In: Handbook of Research on Urban Informatics: The Practice and Promise of the Real-time City, 2009. p. 414–436. IGI Global.
Rosi A, Mamei M, Zambonelli F, Dobson S, Stevenson G, Ye J. Social sensors and pervasive services: Approaches and perspectives. In: 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011. p. 525–530.
Moloney J, Spehar B, Globa A, Wang R. The affordance of virtual reality to enable the sensory representation of multi-dimensional data for immersive analytics: from experience to insight. J Big Data. 2018;5(1):53.
Article
Google Scholar
Börner K, Record E. Macroscopes for making sense of science. In: Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, 2017. p. 1–2.
Saleem M, Valle HE, Brown S, Winters VI, Mahmood A. The hiperwall tiled-display wall system for big-data research. J Big Data. 2018;5(1):41.
Article
Google Scholar
Cecaj A, Lippi M, Mamei M, Zambonelli F. Sensing and forecasting crowd distribution in smart cities: Potentials and approaches. IoT. 2021;2(1):33–49.
Article
Google Scholar
Calabrese F, Pereira FC, Di Lorenzo G, Liu L, Ratti C. The geography of taste: analyzing cell-phone mobility and social events. In: International Conference on Pervasive Computing, 2010. p. 22–37. Springer
Jiang S, Ferreira J, González MC. Activity-based human mobility patterns inferred from mobile phone data: A case study of singapore. IEEE Transact Big Data. 2017;3(2):208–19.
Article
Google Scholar
Mamei M, Bicocchi N, Lippi M, Mariani S, Zambonelli F. Evaluating origin-destination matrices obtained from cdr data. Sensors. 2019;19(20):4470.
Article
Google Scholar
Wu Y, Wang L, Fan L, Yang M, Zhang Y, Feng Y. Comparison of the spatiotemporal mobility patterns among typical subgroups of the actual population with mobile phone data: A case study of beijing. Cities. 2020;100:102670.
Article
Google Scholar
Balzotti C, Bragagnini A, Briani M, Cristiani E. Understanding human mobility flows from aggregated mobile phone data. IFAC-PapersOnLine. 2018;51(9):25–30.
Article
Google Scholar
Willberg E, Järv O, Väisänen T, Toivonen T. Escaping from cities during the covid-19 crisis: Using mobile phone data to trace mobility in finland. ISPRS Int J Geo Information. 2021;10(2):103.
Article
Google Scholar
Grantz KH, Meredith HR, Cummings DA, Metcalf CJE, Grenfell BT, Giles JR, Mehta S, Solomon S, Labrique A, Kishore N, et al. The use of mobile phone data to inform analysis of covid-19 pandemic epidemiology. Nature Commun. 2020;11(1):1–8.
Article
Google Scholar
Santamaria C, Sermi F, Spyratos S, Iacus SM, Annunziato A, Tarchi D, Vespe M. Measuring the impact of covid-19 confinement measures on human mobility using mobile positioning data. a european regional analysis. Safety Sci. 2020;132:104925.
Article
Google Scholar
Traunmueller MW, Johnson N, Malik A, Kontokosta CE. Digital footprints: Using wifi probe and locational data to analyze human mobility trajectories in cities. Comput Environ Urban Syst. 2018;72:4–12.
Article
Google Scholar
Zhao F, Shi W, Gan Y, Peng Z, Luo X. A localization and tracking scheme for target gangs based on big data of wi-fi locations. Cluster Comput. 2019;22(1):1679–90.
Article
Google Scholar
Soundararaj B, Cheshire J, Longley P. Estimating real-time high-street footfall from wi-fi probe requests. Int J Geographical Informat Sci. 2020;34(2):325–43.
Article
Google Scholar
Uras M, Cossu R, Ferrara E, Liotta A, Atzori L. Pma: A real-world system for people mobility monitoring and analysis based on wi-fi probes. J Cleaner Prod. 2020;270:122084.
Article
Google Scholar
Potortì F, Crivello A, Girolami M, Barsocchi P, Traficante E. Localising crowds through wi-fi probes. Ad Hoc Networks. 2018;75:87–97.
Article
Google Scholar
Singh U, Determe J-F, Horlin F, De Doncker P. Crowd forecasting based on wifi sensors and lstm neural networks. IEEE Transact Instrument Measur. 2020;69(9):6121–31.
Article
Google Scholar
Zhou Y, Lau BPL, Koh Z, Yuen C, Ng BKK. Understanding crowd behaviors in a social event by passive wifi sensing and data mining. IEEE Internet Things J. 2020;7(5):4442–54.
Article
Google Scholar
Hong H, De Silva GD, Chan MC. Crowdprobe: Non-invasive crowd monitoring with wi-fi probe. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2018;2(3):1–23.
Uras M, Cossu R, Ferrara E, Bagdasar O, Liotta A, Atzori L. Wifi probes sniffing: an artificial intelligence based approach for mac addresses de-randomization. In: 2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2020. p. 1–6.
Redondi AE, Cesana M. Building up knowledge through passive wifi probes. Comput Commun. 2018;117:1–12.
Article
Google Scholar
Cunche M, Kaafar M-A, Boreli R. Linking wireless devices using information contained in wi-fi probe requests. Pervasive Mobile Comput. 2014;11:56–69.
Article
Google Scholar
Andión J, Navarro JM, López G, Álvarez-Campana M, Dueñas JC. Smart behavioral analytics over a low-cost iot wi-fi tracking real deployment. Wireless Commun Mobile Comput. 2018;. https://doi.org/10.1155/2018/3136471.
Article
Google Scholar
Sagl G, Resch B, Hawelka B, Beinat E. From social sensor data to collective human behaviour patterns: Analysing and visualising spatio-temporal dynamics in urban environments. In: Proceedings of the GI-Forum, 2012;p. 54–63. Herbert Wichmann Verlag Berlin
Silva TH, Viana AC, Benevenuto F, Villas L, Salles J, Loureiro A, Quercia D. Urban computing leveraging location-based social network data: a survey. ACM Comput Surveys. 2019;52(1):1–39.
Article
Google Scholar
da Mota VT, Pickering C. Using social media to assess nature-based tourism: Current research and future trends. J Outdoor Recreation Tour. 2020;30:100295.
Article
Google Scholar
Silva TH, De Melo POV, Almeida JM, Loureiro AA. Large-scale study of city dynamics and urban social behavior using participatory sensing. IEEE Wireless Commun. 2014;21(1):42–51.
Article
Google Scholar
Wang D, Al-Rubaie A, Clarke SS, Davies J. Real-time traffic event detection from social media. ACM Trans Internet Technol. 2017;18(1):1–23.
Article
Google Scholar
Ghermandi A, Camacho-Valdez V, Trejo-Espinosa H. Social media-based analysis of cultural ecosystem services and heritage tourism in a coastal region of mexico. Tour Manag. 2020;77:104002.
Article
Google Scholar
Devkota B, Miyazaki H, Witayangkurn A, Kim SM. Using volunteered geographic information and nighttime light remote sensing data to identify tourism areas of interest. Sustainability. 2019;11(17):4718.
Article
Google Scholar
Preis T, Botta F, Moat HS. Sensing global tourism numbers with millions of publicly shared online photographs. Environ Planning A Economy Space. 2020;52(3):471–7.
Article
Google Scholar
Kádár B, Gede M. Tourism flows in large-scale destination systems. Annals Tour Res. 2021;87:103113.
Article
Google Scholar
Ribeiro M, Nunes N, Nisi V, Schöning J. Passive wi-fi monitoring in the wild: a long-term study across multiple location typologies. Personal Ubiquitous Comput. 2020;. https://doi.org/10.1007/s00779-020-01441-z.
Article
Google Scholar
Wellmann T, Lausch A, Andersson E, Knapp S, Cortinovis C, Jache J, Scheuer S, Kremer P, Mascarenhas A, Kraemer R, et al. Remote sensing in urban planning: Contributions towards ecologically sound policies? Landscape Urban Planning. 2020;204:103921.
Article
Google Scholar
Prandi C, Mirri S, Ferretti S, Salomoni P. On the need of trustworthy sensing and crowdsourcing for urban accessibility in smart city. ACM Trans Internet Technol. 2017;18(1):1–21.
Article
Google Scholar
Prandi C, Roccetti M, Salomoni P, Nisi V, Nunes NJ. Fighting exclusion: a multimedia mobile app with zombies and maps as a medium for civic engagement and design. Multimedia Tools Appl. 2017;76(4):4951–79.
Article
Google Scholar
Longo A, Zappatore M, Bochicchio M, Navathe SB. Crowd-sourced data collection for urban monitoring via mobile sensors. ACM Trans Internet Technol. 2017;18(1):1–21.
Article
Google Scholar
Picaut J, Fortin N, Bocher E, Petit G, Aumond P, Guillaume G. An open-science crowdsourcing approach for producing community noise maps using smartphones. Building Environ. 2019;148:20–33.
Article
Google Scholar
Huang J, Duan N, Ji P, Ma C, Ding Y, Yu Y, Zhou Q, Sun W, et al. A crowdsource-based sensing system for monitoring fine-grained air quality in urban environments. IEEE Internet Things J. 2018;6(2):3240–7.
Article
Google Scholar
Golumbic YN, Fishbain B, Baram-Tsabari A. User centered design of a citizen science air-quality monitoring project. Int J Sci Educ Part B. 2019;9(3):195–213.
Article
Google Scholar
Loureiro P, Prandi C, Nunes N, Nisi V. Citizen science and game with a purpose to foster biodiversity awareness and bioacoustic data validation. In: Interactivity, Game Creation, Design, Learning, and Innovation, 2018;p. 245–255. Springer.
Prandi C, Nisi V, Loureiro P, Nunes NJ. Storytelling and remote-sensing playful interventions to foster biodiversity awareness. Int J Arts Technol. 2020;12(1):39–59.
Article
Google Scholar
Niforatos E, Vourvopoulos A, Langheinrich M. Understanding the potential of human-machine crowdsourcing for weather data. Int J Human Comput Stud. 2017;102:54–68.
Article
Google Scholar
Njue N, Kroese JS, Gräf J, Jacobs S, Weeser B, Breuer L, Rufino M. Citizen science in hydrological monitoring and ecosystem services management: State of the art and future prospects. Sci Total Environ. 2019;693:133531.
Article
Google Scholar
Sheppard SA, Turner J, Thebault-Spieker J, Zhu H, Terveen L. Never too old, cold or dry to watch the sky: A survival analysis of citizen science volunteerism. Proceedings of the ACM on Human-Computer Interaction 1(CSCW), 2017;1–21.
Leonardi C, Cappellotto A, Caraviello M, Lepri B, Antonelli F. Secondnose: an air quality mobile crowdsensing system. In: Proceedings of the 8th Nordic Conference on Human-Computer Interaction: Fun, Fast, Foundational, 2014;p. 1051–1054.
Tian R, Dierk C, Myers C, Paulos E. Mypart: Personal, portable, accurate, airborne particle counting. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, 2016;p. 1338–1348.
Kobernus MJ, Berre A-J, Gonzalez M, Liu H-Y, Fredriksen M, Rombouts R, Bartonova A. A practical approach to an integrated citizens’ observatory: The citi-sense framework. 2015.
Luna S, Gold M, Albert A, Ceccaroni L, Claramunt B, Danylo O, Haklay M, Kottmann R, Kyba C, Piera J. et al. Developing mobile applications for environmental and biodiversity citizen science: considerations and recommendations. In: Multimedia Tools and Applications for Environmental & Biodiversity Informatics, 2018;p. 9–30. Springer.
Pejovic V, Skarlatidou A. Understanding interaction design challenges in mobile extreme citizen science. Int J Human Comput Interaction. 2020;36(3):251–70.
Article
Google Scholar
Pataki BA, Garriga J, Eritja R, Palmer JR, Bartumeus F, Csabai I. Deep learning identification for citizen science surveillance of tiger mosquitoes. Scientific Rep. 2021;11(1):1–12.
Google Scholar
Brown C, Chauhan J, Grammenos A, Han J, Hasthanasombat A, Spathis D, Xia T, Cicuta P, Mascolo C. Exploring automatic diagnosis of covid-19 from crowdsourced respiratory sound data. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020;p. 3474–3484.
Wang P, Lin C, Obaidat MS, Yu Z, Wei Z, Zhang Q. Contact tracing incentive for covid-19 and other pandemic diseases from a crowdsourcing perspective. IEEE Internet of Things Journal. 2021.
Valkanova N, Jorda S, Moere AV. Public visualization displays of citizen data: design, impact and implications. Int J Human Comput Stud. 2015;81:4–16.
Article
Google Scholar
Moere AV, Hill D. Designing for the situated and public visualization of urban data. J Urban Technol. 2012;19(2):25–46.
Article
Google Scholar
Valkanova N, Jorda S, Tomitsch M, Vande Moere A. Reveal-it! the impact of a social visualization projection on public awareness and discourse. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2013;p. 3461–3470.
Claes S, Coenen J, Vande Moere A. Empowering citizens with spatially distributed public visualization displays. In: Proceedings of the 2017 ACM Conference Companion Publication on Designing Interactive Systems, 2017;p. 213–217.
Hsu Y-C, Dille P, Cross J, Dias B, Sargent R, Nourbakhsh I. Community-empowered air quality monitoring system. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 2017;p. 1607–1619.
Prandi C, Ceccarini C, Nisi V, Salomoni P. Designing interactive infographics to stimulate environmental awareness: an exploration with a university community. Multimedia Tools and Applications. 2020;1–18.
Ramachandran GS, Bogosian B, Vasudeva K, Sriramaraju SI, Patel J, Amidwar S, Malladi L, Shylaja RD, Kumar NRB, Krishnamachari B. An immersive visualization of micro-climatic data using usc air. In: Proceedings of the 17th Annual International Conference on Mobile Systems, Applications, and Services, 2019;p. 675–676.
Eldin DM, Hassanien AE, Hassanien EE. Challenges of big data visualization in internet-of-things environments. In: International Conference on Innovative Computing and Communications, 2020;p. 873–885. Springer
Protopsaltis A, Sarigiannidis P, Margounakis D, Lytos A. Data visualization in internet of things: tools, methodologies, and challenges. In: Proceedings of the 15th International Conference on Availability, Reliability and Security, 2020;p. 1–11.
Lavalle A, Teruel MA, Maté A, Trujillo J. Improving sustainability of smart cities through visualization techniques for big data from iot devices. Sustainability. 2020;12(14):5595.
Article
Google Scholar
Cairns P. Doing Better Statistics in Human-computer Interaction. Cambridge University Press, 2019.
Grace K, Wasinger R, Ackad C, Collins A, Dawson O, Gluga R, Kay J, Tomitsch M. Conveying interactivity at an interactive public information display. In: Proceedings of the 2nd ACM International Symposium on Pervasive Displays, 2013;p. 19–24.
Peltonen P, Kurvinen E, Salovaara A, Jacucci G, Ilmonen T, Evans J, Oulasvirta A, Saarikko P. It’s mine, don’t touch! interactions at a large multi-touch display in a city centre. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2008;p. 1285–1294.
Müller J, Wilmsmann D, Exeler J, Buzeck M, Schmidt A, Jay T, Krüger A. Display blindness: The effect of expectations on attention towards digital signage. In: International Conference on Pervasive Computing, 2009;p. 1–8. Springer
Parra G, Klerkx J, Duval E. Understanding engagement with interactive public displays: an awareness campaign in the wild. In: Proceedings of The International Symposium on Pervasive Displays, 2014;p. 180–185.
Coenen J, Claes S, Moere AV. The concurrent use of touch and mid-air gestures or floor mat interaction on a public display. In: Proceedings of the 6th ACM International Symposium on Pervasive Displays, 2017;p. 1–9.