potential.bib

@article{araujo2019,
  title = {A Critical Review of the Issue of Cigarette Butt Pollution in Coastal Environments},
  author = {Ara{\'u}jo, Maria Christina B. and Costa, Monica F.},
  year = {2019},
  month = may,
  journal = {Environmental Research},
  volume = {172},
  pages = {137--149},
  issn = {0013-9351},
  doi = {10.1016/j.envres.2019.02.005},
  urldate = {2023-03-22},
  abstract = {Beach anthropogenic litter is a worldwide problem and has been discussed in the specialized literature for decades. Cigarette butts (CB) are the most frequent form of personal item found on beaches. Yearly, 6 trillion cigarettes are smoked worldwide, and 4.5 trillion cigarettes are littered in the environment. The objective of our review was to assess the relevant literature on the issue of CB in costal environments, including urban areas. We compile and discuss studies (1998\textendash 2018) of CB sources for coastal environments, composition/degradability, quantification on beaches, toxicity to aquatic organisms and existing strategies to abate the problem. The literature shows that despite the growing interest in marine litter, this specific issue remains little studied and information is limited in time and space. Studies have been undertaken on islands, continental coasts, estuaries and coastal cities. There area wide variety of approaches to classification; for example, CB are considered plastic in 19\% of studies and placed in an isolated category in another 16\%. It was possible to identify the main sources of CB in coastal environments and access to the marine biota. In conclusion, we list and discuss proposals for reducing smoking, littering and marine pollution as a contribution to reduce the problems caused by CB in coastal and marine environments. Capsule Cigarette butts are a pervasive, toxic and recalcitrant type of marine litter that requires urgent attention from manufacturers, users, authorities and the public to prevent the ingestion of cigarette butts by biota and water pollution from its leachate.},
  langid = {english},
  keywords = {Anthropogenic litter,Beach pollution,Marine debris,Smoking},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\HXVCVFLK\\Araújo and Costa - 2019 - A critical review of the issue of cigarette butt p.pdf;C\:\\Users\\jodyh\\Zotero\\storage\\IJVNQRTP\\S0013935119300787.html}
}

@techreport{caponlitter2021,
  title = {Regional {{Stakeholders}}' {{Workshop}} \#2 ({{Spain}})},
  author = {CAPonLITTER},
  year = {2021},
  urldate = {2023-03-22},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\AMQD84U4\\file_1621971369.pdf}
}

@techreport{caponlitter2022,
  title = {{{GreenBook}}},
  author = {CAPonLITTER},
  year = {2022},
  urldate = {2023-03-22},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\E6WL7K4N\\file_1674214145.pdf}
}

@article{chen2021,
  title = {Harnessing Social Media to Understand Tourist Mobility: The Role of Information Technology and Big Data},
  shorttitle = {Harnessing Social Media to Understand Tourist Mobility},
  author = {Chen, Jinyan and Becken, Susanne and Stantic, Bela},
  year = {2021},
  month = jan,
  journal = {Tourism Review},
  volume = {77},
  number = {4},
  pages = {1219--1233},
  publisher = {{Emerald Publishing Limited}},
  issn = {1660-5373},
  doi = {10.1108/TR-02-2021-0090},
  urldate = {2023-03-22},
  abstract = {Purpose This paper aims to examine key parameters of scholarly context and geographic focus and provide an assessment of theoretical underpinnings of studies in the field of social media and visitor mobility. This review also summarised the characteristics of social media data, including how data are collected from different social media platforms and their advantages and limitations. The stocktake of research in this field was completed by examining technologies and applied methods that supported different research questions. Design/methodology/approach This literature review applied a mix of methods to conduct a literature review. This review analysed 82 journal articles on using social media to track visitors' movements between 2014 and November 2020. The literature compared the different social media, discussed current applied theories, available technologies, analysed the current trend and provided advice for future directions. Findings This review provides a state-of-the-art assessment of the research to date on tourist mobility analysed using social media data. The diversity of scales (with a dominant focus on the city-scale), platforms and methods highlight that this field is emerging, but it also reflects the complexity of the tourism phenomenon. This review identified a lack of theory in this field, and it points to ongoing challenges in ensuring appropriate use of data (e.g. differentiating travellers from residents) and the ethics surrounding them. Originality/value The findings guide researchers, especially those with no computer science background, on the different types of approaches, data sources and methods available for tracking tourist mobility by harnessing social media. Depending on the particular research interest, different tools for processing and visualization are available. 利用社交媒体了解游客的流动性：信息技术和大数据的作用 摘要 目的 本综述审查了学术背景的关键参数和案例调查的地理焦点, 并评估了社交媒体和访客流动领域研究的理论基础。本文章还总结了社交媒体数据的特征, 包括如何从不同社交媒体平台收集数据及其优势和局限。 此外本论文通过研究不同的应用方法和总结相关技术来完成的。 结果 本研究提供了最新的使用社交媒体数据分析游客流动性研究的评估。比如案例分析的地理大小（主要集中在城市尺度）、社交媒体平台和方法的多样性突出了该领域的新兴, 但复杂旅游流动现象。审查发现利用社交媒体进行的研究缺乏理论贡献, 并指出在确保适当使用数据（例如区分旅行者与居民）和围绕他们的道德规范方面存在持续挑战。 原创性/价值\textemdash\textemdash{} 研究结果指导研究人员, 尤其是那些没有计算机科学背景的研究人员, 了解不同类型的方法、数据来源和方法, 可用于通过利用社交媒体来跟踪旅游流动性。根据特定的研究兴趣, 可以使用不同的处理和可视化工具。 关键词：旅游模式; 游客流动; 游客轨迹; 社交媒体; 信息技术; 大数据 文章类型： 文献评论 Uso de las redes sociales para comprender la movilidad tur\'istica: el papel de la tecnolog\'ia de la informaci\'on y los macrodatos Resumen Objetivo En este estudio se examinan los par\'ametros clave en el contexto acad\'emico y enfoque geogr\'afico y se eval\'uan los fundamentos te\'oricos de estudios en el campo de las redes sociales y la movilidad de los visitantes. Se resumen tambi\'en las caracter\'isticas de datos de las redes sociales, incluidos los m\'etodos de recopilaci\'on de datos de las diferentes plataformas de redes sociales as\'i como sus ventajas y limitaciones. Finalmente, se examinan tecnolog\'ias y m\'etodos aplicados que respaldan las diferentes cuestiones de la investigaci\'on. Resultados El estudio proporciona una evaluaci\'on avanzada del conocimiento hasta la fecha sobre la movilidad tur\'istica analizada utilizando datos de redes sociales. La diversidad de escalas (con un enfoque dominante en la escala de la ciudad), plataformas y m\'etodos indica que este campo est\'a en auge, pero tambi\'en refleja la complejidad del fen\'omeno tur\'istico. En este estudio se identifica una falta de teor\'ia en este campo y se se\~nalan los cont\'unios desaf\'ios para garantizar el uso apropiado de datos (por ejemplo, diferenciar a los viajeros de los residentes) y la \'etica que los rodea. Originalidad / valor los resultados gu\'ian a los investigadores, especialmente a aquellos sin formaci\'on en inform\'atica, sobre los diferentes tipos de enfoques, fuentes de datos y m\'etodos disponibles para rastrear la movilidad tur\'istica mediante el uso de las redes sociales. Existen diferentes herramientas de procesamiento y visualizaci\'on disponibles dependiendo del inter\'es particular de la investigaci\'on. Palabras clave: Patrones de viaje; Movilidad tur\'istica; Movimientos de visitantes; Redes sociales; Tecnolog\'ias de la informaci\'on; Macrodatos.},
  keywords = {Big data,Information technology,Social media,Tourist mobility,Travel patterns,Visitor movements},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\QML53X32\\Chen et al. - 2021 - Harnessing social media to understand tourist mobi.pdf}
}

@misc{exceltur2019,
  title = {{Exceltur | MONITUR}},
  author = {Exceltur},
  year = {2019},
  urldate = {2023-03-22},
  langid = {spanish},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\N3QHPT8A\\monitur.html}
}

@article{giglio2020,
  title = {Machine Learning and Points of Interest: Typical Tourist {{Italian}} Cities},
  shorttitle = {Machine Learning and Points of Interest},
  author = {Giglio, Simona and Bertacchini, Francesca and Bilotta, Eleonora and Pantano, Pietro},
  year = {2020},
  month = jul,
  journal = {Current Issues in Tourism},
  volume = {23},
  number = {13},
  pages = {1646--1658},
  publisher = {{Routledge}},
  issn = {1368-3500},
  doi = {10.1080/13683500.2019.1637827},
  urldate = {2023-03-22},
  abstract = {Today georeferenced images posted on the social network provide a lot of information about people behaviours and movements. Using social media platforms users upload photos, share locations and post comments about their activities, influencing other people. In this research, we examine the relationship between human mobility and touristic attractions through geo-located images provided by Flickr users. A sample of 26,392 pictures related to 6 Italian cities has been collected and analysed applying cluster analysis. In our work, the function of the clustering analysis, employed in Wolfram Mathematica Machine Learning, allows one to automatically identify clusters surrounding points of interest (POIs). Findings show that social media datasets are valuable data to understand tourist behaviour and mobility within a location. The scope is to delineate famous or unpopular places and propose new touristic scenarios, highlighting how the social part covers the main role in the POIs' recommendation process in the touristic field. Furthermore, we aim to promote the machine learning approach as a useful support in human behaviour research.},
  keywords = {Big Data analytics,machine learning techniques,points of interest,tourist attractiveness,users’ behaviour},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\BTIBGRAU\\Giglio et al. - 2020 - Machine learning and points of interest typical t.pdf}
}

@article{gonzalez-gil,
  title = {Preliminary Results of the Accumulation of Marine Debris on the Shoreline of {{Fuerteventura}} and {{El Hierro}} Islands ({{Canary Islands}}, {{Spain}})},
  author = {{Gonz{\'a}lez-Gil}, S and Tello, O and Jim{\'e}nez, S and Tel, E},
  langid = {english},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\X7S2ANIF\\González-Gil et al. - Preliminary results of the accumulation of marine .pdf}
}

@article{heidbreder2019,
  title = {Tackling the Plastic Problem: {{A}} Review on Perceptions, Behaviors, and Interventions},
  shorttitle = {Tackling the Plastic Problem},
  author = {Heidbreder, Lea Marie and Bablok, Isabella and Drews, Stefan and Menzel, Claudia},
  year = {2019},
  month = jun,
  journal = {Science of The Total Environment},
  volume = {668},
  pages = {1077--1093},
  issn = {0048-9697},
  doi = {10.1016/j.scitotenv.2019.02.437},
  urldate = {2023-03-22},
  abstract = {The excessive production and consumption of plastic has serious consequences on the environment and human health. The reduction of plastic has therefore become a major global challenge. As technical solutions might be insufficient to curb the problem, a perspective highlighting the impact of human behavior is needed. The current literature review provides an overview of the existing social-scientific literature on plastic, ranging from risk awareness, consumers' preferences, and predictors of usage behavior to political and psychological intervention strategies. By reviewing the literature, we aim to identify potential factors for future interventions to reduce plastic consumption. The 187 studies reviewed show that people much appreciate and routinely use plastic, despite a pronounced awareness of the associated problems. Habits, norms, and situational factors seem to be especially predictive for plastic consumption behavior. Both political and psychological interventions are potentially effective, although long-term effects are often uncertain. The review closes with implications for behavior-based solutions and future research, which should combine interdisciplinary approaches and take into account cultural differences.},
  langid = {english},
  keywords = {Behavior-based solutions,Consumer behavior,Environmental psychology,Plastic pollution,Problem awareness},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\FJ98WJWU\\Heidbreder et al. - 2019 - Tackling the plastic problem A review on percepti.pdf;C\:\\Users\\jodyh\\Zotero\\storage\\WUBJK3UT\\S0048969719309519.html}
}

@article{kedzierski2020,
  title = {Why Is There Plastic Packaging in the Natural Environment? {{Understanding}} the Roots of Our Individual Plastic Waste Management Behaviours},
  shorttitle = {Why Is There Plastic Packaging in the Natural Environment?},
  author = {Kedzierski, Mika{\"e}l and Fr{\`e}re, Dominique and Le Maguer, Gw{\'e}na{\"e}l and Bruzaud, St{\'e}phane},
  year = {2020},
  month = oct,
  journal = {Science of The Total Environment},
  volume = {740},
  pages = {139985},
  issn = {0048-9697},
  doi = {10.1016/j.scitotenv.2020.139985},
  urldate = {2023-03-22},
  abstract = {Plastic waste is now a classic contaminant of the natural environment and the origins of the contamination need to be well understood. The transition from a useful object to a waste product is a fundamental moment that, from the point of view of the scientific literature, remains poorly understood. This review therefore aims to highlight some factors controlling this intentionality, but also those that influence individual waste management behaviours. For this purpose, an original approach involving the study of the amount of knowledge within different disciplinary fields of research has been employed. The results underline that the low direct impact of the consequences on their users of the discarding of plastic packaging seems to be an important reason for individual mismanagement. Furthermore, the modern individual behaviours of the discarding of plastics are often deeply rooted in the past of the populations. Policies to reduce waste disposal come up against strong individual behavioural constraints that limit the proper management of plastic waste. Thus, incivilities, difficulty in enforcing sanctions, or public opposition to changes in waste management are all factors that contribute to the maintenance waste discarding behaviour. The reuse behaviour of objects that have become useless is also historically attested, but has tended to disappear with the rise of the consumer society. This type of behaviour, whose valorisation is a way of reducing plastic waste abandonment behaviour, remains, however, less scientifically studied than other ways such as recycling.},
  langid = {english},
  keywords = {Environment,Plastic waste,Waste management behaviours,Waste management history},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\5RHKG3TD\\Kedzierski et al. - 2020 - Why is there plastic packaging in the natural envi.pdf;C\:\\Users\\jodyh\\Zotero\\storage\\RCYDWHZB\\S0048969720335051.html}
}

@article{krelling2017,
  title = {Differences in Perception and Reaction of Tourist Groups to Beach Marine Debris That Can Influence a Loss of Tourism Revenue in Coastal Areas},
  author = {Krelling, Allan Paul and Williams, Allan Thomas and Turra, Alexander},
  year = {2017},
  month = nov,
  journal = {Marine Policy},
  volume = {85},
  pages = {87--99},
  issn = {0308-597X},
  doi = {10.1016/j.marpol.2017.08.021},
  urldate = {2023-03-22},
  abstract = {Marine debris is the most conspicuous pollutant that makes beaches aesthetically unappealing to users. The perceptions and reactions of beach users to stranded litter were compared between second-home owners and users (SHOU) and non-recurrent tourists (T). A questionnaire was applied to obtain socio-economic characteristics; assessment of the overall beach quality and perception of beach litter pollution (perception); hypothetical scenarios of marine litter pollution and deterrence (reaction); and potential alternative destinations in the case of deterrence (economic effect). Questionnaires (n = 319) were applied at two Brazilian subtropical beaches, with different physiographical settings (Pontal do Sul, PS, estuarine beach; Ipanema, I, open-ocean beach). Beach users' groups differed regarding daily expenses (T {$>$} SHOU), period of permanence per trip (SHOU {$>$} T) and trip frequency (SHOU {$>$} T). The open-ocean beach (I) was rated the worst regarding overall beach quality. Marine debris generation was mainly attributed to local ``beach users'', in the open-ocean beach (I). ``Marine'' (or non-local) sources were four times more frequently cited in the estuarine beach (PS). Perception on actual litter pollution and litter deterrence scenarios, did not vary between beaches or groups. More than 85\% of beachgoers would avoid a beach visit if a worst scenario ({$>$} 15items/m2) occurred and most users would choose a neighboring state beach destination. Stranded litter may potentially reduce local tourism income by 39.1\%, representing losses of up to US\$ 8.5 million per year. These figures are proxies to support the trade-off local authority's make between investments to prevent/remove beach litter and the potential reduction in income from a tourist destination change.},
  langid = {english},
  keywords = {Economic effects,Marine debris,Public perception,Second home,Tourism},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\EQLHFIQQ\\Krelling et al. - 2017 - Differences in perception and reaction of tourist .pdf;C\:\\Users\\jodyh\\Zotero\\storage\\F4WN5TFT\\S0308597X17301689.html}
}

@article{li2018,
  title = {Big Data in Tourism Research: {{A}} Literature Review},
  shorttitle = {Big Data in Tourism Research},
  author = {Li, Jingjing and Xu, Lizhi and Tang, Ling and Wang, Shouyang and Li, Ling},
  year = {2018},
  month = oct,
  journal = {Tourism Management},
  volume = {68},
  pages = {301--323},
  issn = {0261-5177},
  doi = {10.1016/j.tourman.2018.03.009},
  urldate = {2023-03-22},
  abstract = {Even at an early stage, diverse big data have been applied to tourism research and made an amazing improvement. This paper might be the first attempt to present a comprehensive literature review on different types of big data in tourism research. By data sources, the tourism-related big data fall into three primary categories: UGC data (generated by users), including online textual data and online photo data; device data (by devices), including GPS data, mobile roaming data, Bluetooth data, etc.; transaction data (by operations), including web search data, webpage visiting data, online booking data, etc. Carrying different information, different data types address different tourism issues. For each type, a systematical analysis is conducted from the perspectives of research focuses, data characteristics, analytic techniques, major challenges and further directions. This survey facilitates a thorough understanding of this sunrise research and offers valuable insights into its future prospects.},
  langid = {english},
  keywords = {Big data,Literature review,Tourism management,Tourism research,Tourist behavior},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\KKNGRT6J\\Li et al. - 2018 - Big data in tourism research A literature review.pdf;C\:\\Users\\jodyh\\Zotero\\storage\\D9KAWGUL\\S0261517718300591.html}
}

@article{martin2017,
  title = {Regional {{Spanish Tourism Competitiveness}}. {{A DEA-MONITUR}} Approach},
  author = {Mart{\'i}n, Juan Carlos and Mendoza, Cira and Rom{\'a}n, Concepci{\'o}n},
  year = {2017},
  month = nov,
  journal = {REGION},
  volume = {4},
  number = {3},
  pages = {153},
  issn = {2409-5370},
  doi = {10.18335/region.v4i3.145},
  urldate = {2023-03-22},
  abstract = {The aim of this paper is to analyse the regional tourist competitiveness performance in Spain. We use the seven pillars of tourism from a very detailed and complete database carried out by the Spanish Government \textendash{} MoniTUR 2010 as primary data. Thus, we calculate several regional tourist competitiveness indices using data envelopment analysis (DEA) to analyse the robustness of the results obtained in the ranking of the tourist competitiveness for the 17 Spanish Autonomous Communities. Our results are robust to the use of two different modelling strategies: (1) input and output variables selection; and (2) virtual and super efficiency DEA models. Madrid and La Rioja are found to be the most competitive regions; meanwhile other inland regions of Spain like Extremadura and Arag\textasciiacute on are the least competitive. The position of each of the laggard Autonomous Communities should be analysed by their respective destination management organizations (DMOs) in order to envisage adequate corrective measures.},
  langid = {english},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\39HY9L37\\Martín et al. - 2017 - Regional Spanish Tourism Competitiveness. A DEA-MO.pdf}
}

@article{reif2020,
  title = {Exploring New Ways of Visitor Tracking Using Big Data Sources: {{Opportunities}} and Limits of Passive Mobile Data for Tourism},
  shorttitle = {Exploring New Ways of Visitor Tracking Using Big Data Sources},
  author = {Reif, Julian and Schm{\"u}cker, Dirk},
  year = {2020},
  month = dec,
  journal = {Journal of Destination Marketing \& Management},
  volume = {18},
  pages = {100481},
  issn = {2212-571X},
  doi = {10.1016/j.jdmm.2020.100481},
  urldate = {2023-03-22},
  abstract = {Passive mobile data (PMD) are event data recorded by mobile network operators (MNOs) in the course of a consumer's use of mobile phones connected to public voice and data networks. Increasingly, MNOs provide such data for research and applications in tourism, anonymised according to national regulations and aggregated based on the technical and economic interests of the MNO. Alongside mobility research, it is evident that tourism research has been one of the early adopters of this data source. Possible applications of PMD in tourism research include the identification of tourists, the detection of temporal and spatial distribution patterns, and the analysis of spatial and temporal relations. However, a number of drawbacks have been identified. These include the results of anonymisation and aggregation procedures, and, most of all, the inability to identify tourist activities properly, as opposed to everyday or other non-tourist types of mobility. This paper analyses and aggregates the results of different research projects on different spatial levels in Germany in order to build a conceptual framework for the specific strengths and weaknesses of the use of PMD in tourism research. The study found that, at the current state of research, PMD can measure the mobility of people in space and time but are not suitable for correctly identifying tourists and distinguishing them from non-tourists. Destination management organisations (DMOs) that are working with PMD should be aware of these barriers and adapt their research questions accordingly. However, PMD can be a powerful instrument, particularly because of its high temporal and spatial granularity.},
  langid = {english},
  keywords = {Big data,Identification of tourists,Passive mobile data,Spatio-temporal behaviour,Tourist tracking},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\IUH97LDB\\Reif and Schmücker - 2020 - Exploring new ways of visitor tracking using big d.pdf;C\:\\Users\\jodyh\\Zotero\\storage\\2XEGBAL7\\S2212571X20301037.html}
}

@article{santos2005,
  title = {Influence of Socio-Economic Characteristics of Beach Users on Litter Generation},
  author = {Santos, Isaac Rodrigues and Friedrich, Ana Cl{\'a}udia and {Wallner-Kersanach}, M{\^o}nica and Fillmann, Gilberto},
  year = {2005},
  journal = {Ocean \& Coastal Management},
  volume = {48},
  number = {9},
  pages = {742--752},
  issn = {0964-5691},
  doi = {10.1016/j.ocecoaman.2005.08.006},
  urldate = {2023-03-22},
  abstract = {Marine litter is now recognized as a major form of marine pollution and key factor for coastal managers. The aims of this paper are to: (a) investigate the perception of beach users on aspects related to solid waste pollution and (b) quantify the input of tourism-related litter to the southern Brazilian coastal ecosystem in areas occupied by beach users with different socio-economic characteristics. Interview results indicated that beach users normally do not admit littering on the beach; believe that hazards to humans are the main problem caused by litter; suggest the conduction of education activities and more trash bins for reducing beach contamination. Results indicated that daily litter input to the beach was higher (p{$<$}0.01) in the region frequented by people with lower annual income and literacy degree, evidencing the influence of educational level on people environmental awareness and behavior in relation to its own residues. Cigarette butts, followed by plastics are the main kind of litter generated. Strong correlations between beach visitor density and litter generation showed that (1) tourism is the main source of marine debris and (2) beach contamination depends on beach visitor density. The use of southern Brazilian coastal zone has increased very quickly, but environmental awareness of people has not accompanied it. It is evident that litter input and impacts in the oceans will increase if no preventive actions were taken.},
  langid = {english},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\I2HYFKFK\\Santos et al. - 2005 - Influence of socio-economic characteristics of bea.pdf}
}

@article{solazzo2021,
  title = {Extracting Insights from Big Social Data for Smarter Tourism Destination Management},
  author = {Solazzo, Gianluca and Maruccia, Ylenia and Lorenzo, Gianluca and Ndou, Valentina and Del Vecchio, Pasquale and Elia, Gianluca},
  year = {2021},
  month = jan,
  journal = {Measuring Business Excellence},
  volume = {26},
  number = {1},
  pages = {122--140},
  publisher = {{Emerald Publishing Limited}},
  issn = {1368-3047},
  doi = {10.1108/MBE-11-2020-0156},
  urldate = {2023-03-22},
  abstract = {Purpose This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image. Design/methodology/approach This work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study. Findings Results show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination. Originality/value The originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.},
  keywords = {Big data analytics,DMO,Tourism destination management,Tourist behaviour},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\9UQALKEI\\Solazzo et al. - 2021 - Extracting insights from big social data for smart.pdf}
}

@article{torre-bastida2018,
  title = {Big {{Data}} for Transportation and Mobility: Recent Advances, Trends and Challenges},
  shorttitle = {Big {{Data}} for Transportation and Mobility},
  author = {{Torre-Bastida}, Ana Isabel and Del Ser, Javier and La{\~n}a, Ibai and Ilardia, Maitena and Bilbao, Miren Nekane and {Campos-Cordob{\'e}s}, Sergio},
  year = {2018},
  journal = {IET Intelligent Transport Systems},
  volume = {12},
  number = {8},
  pages = {742--755},
  issn = {1751-9578},
  doi = {10.1049/iet-its.2018.5188},
  urldate = {2023-03-22},
  abstract = {Big Data is an emerging paradigm and has currently become a strong attractor of global interest, specially within the transportation industry. The combination of disruptive technologies and new concepts such as the Smart City upgrades the transport data life cycle. In this context, Big Data is considered as a new pledge for the transportation industry to effectively manage all data this sector required for providing safer, cleaner and more efficient transport means, as well as for users to personalize their transport experience. However, Big Data comes along with its own set of technological challenges, stemming from the multiple and heterogeneous transportation/mobility application scenarios. In this survey we analyze the latest research efforts revolving on Big Data for the transportation and mobility industry, its applications, baselines scenarios, fields and use case such as routing, planning, infrastructure monitoring, network design, among others. This analysis will be done strictly from the Big Data perspective, focusing on those contributions gravitating on techniques, tools and methods for modeling, processing, analyzing and visualizing transport and mobility Big Data. From the literature review a set of trends and challenges is extracted so as to provide researchers with an insightful outlook on the field of transport and mobility.},
  langid = {english},
  keywords = {Big Data,data analysis,data visualisation,disruptive technologies,heterogeneous transportation application scenario,infrastructure monitoring,mobility application scenario,mobility big data analysis,mobility big data modeling,mobility big data processing,mobility big data visualisation,mobility industry,mobility pattern detection,network design,planning,resource scheduling,routing,smart cities,smart city,traffic engineering computing,transport big data analysis,transport big data modeling,transport big data processing,transport big data visualisation,transport data life cycle,transportation industry},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\CBYT7WNA\\Torre-Bastida et al. - 2018 - Big Data for transportation and mobility recent a.pdf;C\:\\Users\\jodyh\\Zotero\\storage\\44ZYDJ6X\\iet-its.2018.html}
}

@article{williams2016,
  title = {Litter Impacts on Scenery and Tourism on the {{Colombian}} North {{Caribbean}} Coast},
  author = {Williams, Allan Thomas and {Rangel-Buitrago}, Nelson Guillermo and Anfuso, Giorgio and Cervantes, Omar and Botero, Camilo Mateo},
  year = {2016},
  month = aug,
  journal = {Tourism Management},
  volume = {55},
  pages = {209--224},
  issn = {0261-5177},
  doi = {10.1016/j.tourman.2016.02.008},
  urldate = {2023-03-22},
  abstract = {This paper provides the location, scenery and litter evaluation of 35 Colombian Caribbean beaches (9 remote, 9 village, 14 urban and 3 resort). Four litter grades were found. A: excellent (5); B: good (8); C: fair (19) and D: poor (3). A Decision Value parameter (D), for scenery gave: Class I \textendash{} extremely attractive/natural, D~{$>~$}0.85, 6 sites; Class II \textendash{} attractive/natural sites, D~=~0.85 \textendash 0.65, 2 sites; Class III \textendash{} mainly natural sites, few outstanding features, D~=~0.65\textendash 0.4, 1 site; Class IV \textendash{} mainly unattractive sites, D~=~0.4 to zero, 6 sites; Class V \textendash{} very unattractive sites, D~=~{$<$}0, 20 sites. Litter amounts placed most beaches into a poor scenic category and many scenic beaches could jump a grade by means of clean-ups. A graphic methodology highlighted beaches with contradictory results for litter/scenic grades. Tourists abhor littered beaches and clean-ups would improve scenery scores.},
  langid = {english},
  keywords = {Beach management,Colombia,Litter,Scenery,Tourism},
  file = {C\:\\Users\\jodyh\\Zotero\\storage\\JGVCJWKH\\Williams et al. - 2016 - Litter impacts on scenery and tourism on the Colom.pdf;C\:\\Users\\jodyh\\Zotero\\storage\\V5UL8VFG\\S0261517716300218.html}
}