## 5 Discussion, Conclusions, and Implications
Accordingly, it can be used by local and regional authorities, as well as by planners and urban designers, to deal with urban complexity, especially in successful tourist cities with contradictions and conflicts generated by overtourism [92]. From this perspective, the managerial implications of the study are diverse. Using these kinds of analytical tools, DMOs and tourism service providers could be able to offer the most personalized services and information, to attract specific types of tourists to certain points of interest [37], to propose the visit of new under-visited attractions to certain market segments, to create new routes or to optimize the existing ones, to enhance public transport services, to develop new POIs or tourist services for the busiest routes [93] or to create tourism development plans for the least visited areas [60]. In addition, it will allow destinations to encourage smart development, overcoming some of the existing gaps in the level of achievement of their objectives [7]. This includes the improvement of smart tourism developments such as the creation of differentiated attractive travel packages [86], the adaptation of the marketing and communication tactics to the preferences of visitors [10; 11], the improvement of the satisfaction of tourists [5; 14; 15], and the co-creation of a more positive tourist destination image [94]. It will even have a major impact on travel recommendation systems, as shown in a previous study [39]. Finally, from the place management perspective, the detection of visitor flow patterns would help to regulate the carrying capacity of the visitors' points of interest avoiding overcrowding, improving allocation of visitor services and reducing tensions produced by the different tourist and residential uses of the city areas, infrastructures, and services around the points of interest. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12125834/s1](https://www.mdpi.com/article/10.3390/app12125834/s1)]([https://www.mdpi.com/article/10.3390/app12125834/s1](https://www.mdpi.com/article/10.3390/app12125834/s1)). Conceptualization, A.H. and S.A.C. ; methodology, J.A.O., J.B. and A.M.; software, J.A.O. ; validation, J.B. and A.M.; formal analysis, J.A.O. ; investigation, J.A.O. ; resources, J.B. and A.M.; data curation, J.A.O. and J.B.; writing--original draft preparation, J.A.O. and A.H.; writing--review and editing, A.H., A.M. and S.A.C. ; visualization, J.A.O. ; supervision, J.B. and A.M.; project administration, J.B. and A.M.; funding acquisition, J.B. and A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not Applicable. Data Availability Statement: 3 rd Party Data Restrictions apply to the availability of these data. Data were obtained from Twitter and are available at [[https://twitter.com](https://twitter.com)]([https://twitter.com](https://twitter.com)) (accessed on 1 January 2019) with the permission of Twitter. Research is allowed to provide Tweet IDs to other researchers to download using Twitter's API. The Tweet IDs of data presented in this paper are included in the supplementary material. [PERSON] is a fellow of Eurecat's \"[PERSON]\" PhD grant program. The authors declare no conflict of interest.