Geo Data Science for Tourism

This reprint describes the recent challenges in tourism seen from the point of view of data science. Thanks to the use of the most popular Data Science concepts, you can easily recognise trends and patterns in tourism, detect the impact of tourism on the environment, and predict future trends in tourism. This reprint starts by describing how to analyse data related to the past, then it moves on to detecting behaviours in the present, and, finally, it describes some techniques to predict future trends. By the end of the reprint, you will be able to use data science to help tourism businesses make better use of data and improve their decision making and operations.

Studium sídelních systémů pomocí lokalizačních dat mobilních telefonů

Settlement systems, the large-scale manifestation of the spatial organization of human society, are the frequent object of human geography studies. Mobile positioning data have been used in recent decades as a unique source on human activities in space, but robust methodologies to generate datasets from them that would be comparable in accuracy to conventional sources such as census data or travel diaries have still been lacking. This thesis aims to bridge the gap by describing in detail how to generate population distribution and commuting datasets from mobile positioning sources with the mentioned accuracy using ma- chine learning and ancillary geospatial data, including proper treatment of data privacy and artifacts inherent to mobile positioning, and to illustrate their ca- pabilities by building hierarchical settlement system models from them. Several further possible refinements are presented and discussed. 1Sídelní systémy jsou jakožto významná složka makrogeografické organizace lidské společnosti častým objektem studia sociální geografie. Lokalizační data mobilních telefonů jsou v posledních dekádách oblíbená jako unikátní zdroj o lidských aktivitách v prostoru, stále však chybí spolehlivé metody, jak z nich vytvořit datové sady s přesností porovnatelnou s konvenčními zdroji, jako jsou data ze sčítání lidu nebo cestovní deníky. Tato práce si klade za cíl zaplnit tuto mezeru detailním popisem tvorby datových sad o počtech obyvatel a dojížďce z lokalizačních dat mobilních telefonů se zmíněnou úrovní přesnosti za pomoci strojového učení a pomocných geografických dat, včetně správného ošetření anonymity dat i artefaktů plynoucích z principů fungování mobilní sítě, a ilustrovat možnosti těchto výsledků tvorbou hierarchického modelu sídelního systému. Jsou představeny a diskutovány další možnosti rozvoje. 1Department of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Sustainable Smart Cities and Smart Villages Research

ca. 200 words; this text will present the book in all promotional forms (e.g. flyers). Please describe the book in straightforward and consumer-friendly terms. [There is ever more research on smart cities and new interdisciplinary approaches proposed on the study of smart cities. At the same time, problems pertinent to communities inhabiting rural areas are being addressed, as part of discussions in contigious fields of research, be it environmental studies, sociology, or agriculture. Even if rural areas and countryside communities have previously been a subject of concern for robust policy frameworks, such as the European Union’s Cohesion Policy and Common Agricultural Policy Arguably, the concept of ‘the village’ has been largely absent in the debate. As a result, when advances in sophisticated information and communication technology (ICT) led to the emergence of a rich body of research on smart cities, the application and usability of ICT in the context of a village has remained underdiscussed in the literature. Against this backdrop, this volume delivers on four objectives. It delineates the conceptual boundaries of the concept of ‘smart village’. It highlights in which ways ‘smart village’ is distinct from ‘smart city’. It examines in which ways smart cities research can enrich smart villages research. It sheds light on the smart village research agenda as it unfolds in European and global contexts.

CLADAG 2021 BOOK OF ABSTRACTS AND SHORT PAPERS

The book collects the short papers presented at the 13th Scientific Meeting of the Classification and Data Analysis Group (CLADAG) of the Italian Statistical Society (SIS). The meeting has been organized by the Department of Statistics, Computer Science and Applications of the University of Florence, under the auspices of the Italian Statistical Society and the International Federation of Classification Societies (IFCS). CLADAG is a member of the IFCS, a federation of national, regional, and linguistically-based classification societies. It is a non-profit, non-political scientific organization, whose aims are to further classification research