Measuring destination image : a novel approach based on visual data mining. A methodological proposal and an application to European islands

Availability of User Generated Content and the development of Big Data and machine learning algorithms have paved the way to collecting and analysing great volumes of data. We scan imagery data from traveling-related posts on Instagram to identify the key features of the destination image and of its dynamics. Specifically, we exploit a newly introduced Visual Object Recognition tool (Google Cloud Vision) to convert into textual labels the content of about 860,000 travel-related pictures posted on Instagram in Summer 2019 for several European islands. The output, a vector of labels’ frequencies on a very fine-grained scale, is used to proxy the destination image at different points in time. We then introduce the Index of Distance in Destination Image, a metric built on the pictures’ labels ranking, and aimed at providing a quantitative measure of (dis)similarity between destination images. We show that the analysis of labels and the index are fit to compare destinations cross-sectionally and over time, providing a useful tool for researchers, marketers and DMOs. We also deliver evidence on how external shocks (like extreme events linked to climate change) or the organization of events modify the cognitive sphere of the destination image, with repercussions on activities undertaken by tourists and relevant implications for local policies

The development and deployment of walkability assessment models for built environments

To encourage walking behavior, revising a built environment to be walkable is recognized as a necessity for influencing a broader audience while also having a long-term effect. Walkability, which indicates the friendliness of walking in a built environment, helps concerning parties to understand a urban context and make informative decisions when building walkable neighborhoods. Walkability is a fusion of different environment characteristics (e.g. sidewalk quality) influential to walking. Multiple instruments have been developed to measure perceived walkability by conducting surveys. However, this process is expensive and time-consuming. Matured GIS technologies together with extensive accessible data enable analysts to measure walkability objectively. While it is considerably inexpensive and time efficient, measuring walkability objectively has several challenging areas to tackle: the environmental characteristics to be considered, the methods to evaluate these characteristics, and the data availability to conduct the evaluation. To date, no existing model addresses those aspects appropriately. This thesis has developed models to objectively evaluate walkability for neighborhoods and walking routes. Through examining empirical studies that explored the relationship between walking and environment characteristics, this thesis has identified a few characteristics that are influential to walking and incorporated them into the area based walkability evaluation model: population density, destination accessibility, land use mix, walking infrastructure quality, aesthetics, traffic safety and transit accessibility. The importance of these characteristics changes when targeting different walking purposes (recreational or transportation), population groups, geographic locations and cultural contexts. By weighing each characteristic accordingly, the model adapts to different study contexts. The weights should be adjusted based on expert knowledge or by benchmarking empirical studies conducted in similar contexts (e.g. similar urban setting). For evaluating walkability for walking routes, Dijkstra's algorithm is adopted to identify the walkable routes by minimizing the cost associated with the routes. This cost is defined by route distance, street type (e.g. highway, sidewalk), infrastructure quality and facilities along the routes. As a case study, walkability is evaluated for the city of Helsinki. The implementation of the models has two purposes: 1) to provide a benchmark for analysts who intend to apply the model to other contexts, 2) to provide the environment quality information of Helsinki to concerning parties. Data processing, characteristics assessment, and walkability evaluation are described in detail to fulfill the first purpose. Secondly, a web application was developed to provide an accessible service for users to view the environment quality information including walkability. While walkability varies for individuals due to their personal preferences and needs, this service also allows customization by providing functionality to adjust weights of characteristics that are used to define walkability

Harnessing social media data to explore urban tourist patterns and the implications for retail location modelling

The tourism landscape in urban destinations has been spatially expanded in recent years due to the increasing prevalence of sharing economy accommodation and other tourism trends. Tourists now mix with locals to form increasingly intricate population geographies within urban neighbourhoods, bringing new demand into areas which are beyond the conventional tourist locations. How these dispersed tourist demands impact local communities has become an emerging issue in both urban and tourism studies. However, progress has been hampered by the lack of fine granular travel data which can be used for understanding urban tourist patterns at the small-area level. Paying special attention to tourist grocery demand in urban destinations, the thesis takes London as the example to present the various sources of LBSN datasets that can be used as valuable supplements to conventional surveys and statistics to produce novel tourist population estimates and new tourist grocery demand layers at the small area level. First, the work examines the potential of Weibo check-in data in London for offering greater insights into the spatial travel patterns of urban tourists from China. Then, AirDNA and Twitter datasets are used in conjunction with tourism surveys and statistics in London to model the small area tourist population maps of different tourist types and generate tourist demand estimates. Finally, Foursquare datasets are utilised to inform tourist grocery travel behaviour and help to calibrate the retail location model. The tourist travel patterns extracted from various LBSN data, at both individual and collective levels, offer tremendous value to assist the construction and calibration of spatial modelling techniques. In this case, the emphasis is on improving retail location spatial Interaction Models (SIMs) within grocery retailing. These models have seen much recent work to add non-residential demand, but demand from urban tourism has yet to be included. The additional tourist demand layer generated in this thesis is incorporated into a new custom-built SIM to assess the impacts of urban tourism on the local grocery sector and support current store operations and trading potential evaluations of future investments

Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks

[EN] Promoting a tourist destination requires uncovering travel patterns and destination choices, identifying the profile of visitors and analyzing attitudes and preferences of visitors for the city. To this end, tourism-related data are an invaluable asset to understand tourism behaviour, obtain statistical records and support decision-making for business around tourism. In this work, we study the behaviour of tourists visiting top attractions of a city in relation to the tourist influx to restaurants around the attractions. We propose to undertake this analysis by retrieving information posted by visitors in a social network and using an open access map service to locate the tweets in a influence area of the city. Additionally, we present a pattern recognition based technique to differentiate visitors and locals from the collected data from the social network. We apply our study to the city of Valencia in Spain and Berlin in Germany. The results show that, while in Valencia the most frequented restaurants are located near top attractions of the city, in Berlin, it is usually the case that the most visited restaurants are far away from the relevant attractions of the city. The conclusions from this study can be very insightful for destination marketers.This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R.Bustamante, A.; Sebastiá Tarín, L.; Onaindia De La Rivaherrera, E. (2019). Can Tourist Attractions Boost Other Activities Around? A Data Analysis through Social Networks. Sensors. 19(11):1-25. https://doi.org/10.3390/s19112612S1251911Travel and Tourism Competitiveness Report 2017http://reports.weforum.org/travel-and-tourism-competitiveness-report-2017/OECD Datahttps://data.oecd.org/Travel &Tourism: Economic Impact 2019 Worldhttps://www.wttc.org/-/media/files/reports/economic-impact-research/regions-2019/world2019.pdfCohen, S. A., Prayag, G., & Moital, M. (2013). Consumer behaviour in tourism: Concepts, influences and opportunities. Current Issues in Tourism, 17(10), 872-909. doi:10.1080/13683500.2013.850064Yoo, C.-K., Yoon, D., & Park, E. (2018). Tourist motivation: an integral approach to destination choices. Tourism Review, 73(2), 169-185. doi:10.1108/tr-04-2017-0085Cohen, E. (1979). A Phenomenology of Tourist Experiences. Sociology, 13(2), 179-201. doi:10.1177/003803857901300203Decrop, A., & Snelders, D. (2005). A grounded typology of vacation decision-making. Tourism Management, 26(2), 121-132. doi:10.1016/j.tourman.2003.11.011Servidio, R., & Ruffolo, I. (2016). Exploring the relationship between emotions and memorable tourism experiences through narratives. Tourism Management Perspectives, 20, 151-160. doi:10.1016/j.tmp.2016.07.010Prayag, G., Hosany, S., Muskat, B., & Del Chiappa, G. (2016). Understanding the Relationships between Tourists’ Emotional Experiences, Perceived Overall Image, Satisfaction, and Intention to Recommend. Journal of Travel Research, 56(1), 41-54. doi:10.1177/0047287515620567Valls, J.-F., Sureda, J., & Valls-Tuñon, G. (2014). Attractiveness Analysis of European Tourist Cities. Journal of Travel & Tourism Marketing, 31(2), 178-194. doi:10.1080/10548408.2014.873310García-Palomares, J. C., Gutiérrez, J., & Mínguez, C. (2015). Identification of tourist hot spots based on social networks: A comparative analysis of European metropolises using photo-sharing services and GIS. Applied Geography, 63, 408-417. doi:10.1016/j.apgeog.2015.08.002Lu, Y., Wu, H., Liu, X., & Chen, P. (2019). TourSense: A Framework for Tourist Identification and Analytics Using Transport Data. IEEE Transactions on Knowledge and Data Engineering, 31(12), 2407-2422. doi:10.1109/tkde.2019.2894131Buhalis, D. (2000). Marketing the competitive destination of the future. Tourism Management, 21(1), 97-116. doi:10.1016/s0261-5177(99)00095-3Indicators for Measuring Competitiveness in Tourism: A Guidance Documenthttp://dx.doi.org/10.1787/5k47t9q2t923-enLonghi, C., Titz, J.-B., & Viallis, L. (2014). Open Data: Challenges and Opportunities for the Tourism Industry. Tourism Management, Marketing, and Development, 57-76. doi:10.1057/9781137354358_4Open Data in Tourismhttps://www.europeandataportal.eu/en/highlights/open-data-tourismCox, C., Burgess, S., Sellitto, C., & Buultjens, J. (2009). The Role of User-Generated Content in Tourists’ Travel Planning Behavior. Journal of Hospitality Marketing & Management, 18(8), 743-764. doi:10.1080/19368620903235753Lu, W., & Stepchenkova, S. (2014). User-Generated Content as a Research Mode in Tourism and Hospitality Applications: Topics, Methods, and Software. Journal of Hospitality Marketing & Management, 24(2), 119-154. doi:10.1080/19368623.2014.907758Pantano, E., Priporas, C.-V., & Stylos, N. (2017). ‘You will like it!’ using open data to predict tourists’ response to a tourist attraction. Tourism Management, 60, 430-438. doi:10.1016/j.tourman.2016.12.020Hawelka, B., Sitko, I., Beinat, E., Sobolevsky, S., Kazakopoulos, P., & Ratti, C. (2014). Geo-located Twitter as proxy for global mobility patterns. Cartography and Geographic Information Science, 41(3), 260-271. doi:10.1080/15230406.2014.890072Girardin, F., Calabrese, F., Fiore, F. D., Ratti, C., & Blat, J. (2008). Digital Footprinting: Uncovering Tourists with User-Generated Content. IEEE Pervasive Computing, 7(4), 36-43. doi:10.1109/mprv.2008.71Alivand, M., & Hochmair, H. H. (2016). Spatiotemporal analysis of photo contribution patterns to Panoramio and Flickr. Cartography and Geographic Information Science, 44(2), 170-184. doi:10.1080/15230406.2016.1211489Bassolas, A., Lenormand, M., Tugores, A., Gonçalves, B., & Ramasco, J. J. (2016). Touristic site attractiveness seen through Twitter. EPJ Data Science, 5(1). doi:10.1140/epjds/s13688-016-0073-5Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management, 30(12), 3514-3554. doi:10.1108/ijchm-07-2017-0461Francalanci, C., & Hussain, A. (2015). Discovering social influencers with network visualization: evidence from the tourism domain. Information Technology & Tourism, 16(1), 103-125. doi:10.1007/s40558-015-0030-3Williams, N. L., Inversini, A., Ferdinand, N., & Buhalis, D. (2017). Destination eWOM: A macro and meso network approach? Annals of Tourism Research, 64, 87-101. doi:10.1016/j.annals.2017.02.007Salas-Olmedo, M. H., Moya-Gómez, B., García-Palomares, J. C., & Gutiérrez, J. (2018). Tourists’ digital footprint in cities: Comparing Big Data sources. Tourism Management, 66, 13-25. doi:10.1016/j.tourman.2017.11.001Padilla, J. J., Kavak, H., Lynch, C. J., Gore, R. J., & Diallo, S. Y. (2018). Temporal and spatiotemporal investigation of tourist attraction visit sentiment on Twitter. PLOS ONE, 13(6), e0198857. doi:10.1371/journal.pone.0198857Maeda, T., Yoshida, M., Toriumi, F., & Ohashi, H. (2018). Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data. ISPRS International Journal of Geo-Information, 7(3), 99. doi:10.3390/ijgi7030099Wöber, K. W. (2003). Information supply in tourism management by marketing decision support systems. Tourism Management, 24(3), 241-255. doi:10.1016/s0261-5177(02)00071-7Sabou, M., Onder, I., Brasoveanu, A. M. P., & Scharl, A. (2016). Towards cross-domain data analytics in tourism: a linked data based approach. Information Technology & Tourism, 16(1), 71-101. doi:10.1007/s40558-015-0049-5Adamiak, C., Szyda, B., Dubownik, A., & García-Álvarez, D. (2019). Airbnb Offer in Spain—Spatial Analysis of the Pattern and Determinants of Its Distribution. ISPRS International Journal of Geo-Information, 8(3), 155. doi:10.3390/ijgi8030155Padron Municipal de Habitantes [Statistical Report: Residents in Valencia in 2018]https://bit.ly/2JnNNE

Points of interest in tourism : conceptual framework, analytical relevance, methodological proposal, and case studies

La presente tesis doctoral está dedicada al estudio de los puntos de interés turístico, aspecto que ha sido escasamente abordado en la literatura científica sobre el turismo. En concreto, este trabajo se centra en analizar cuatro dimensiones relativas a dichos puntos de interés. De este modo, el objetivo de la investigación se centra en analizar el marco conceptual en el que se inserta el término de punto de interés, identificar cuáles son las implicaciones derivadas de su estudio, realizar una propuesta metodológica que permita identificarlos y, finalmente, aplicar la investigación a un caso de estudio que permita comprender la relevancia de la laguna en la investigación que pretende cubrirse. En lo referente al marco conceptual, esta tesis parte de las definiciones de otros conceptos relacionados y propone una definición de punto de interés que se inserte dentro del marco terminológico existente. Además, la relevancia del trabajo realizado es puesta en valor al establecerse las implicaciones que podrían derivar del análisis de los puntos de interés de cara a mejorar la gestión de los destinos turísticos (especialmente para prevenir problemas relacionados con la congestión de espacios turísticos) y a abrir un nuevo campo de trabajo en la investigación turística. La tesis también se centra en identificar técnicas de rastreo para identificar puntos de interés de los destinos, desarrollar una taxonomía que permite clasificarlos y en estudiar un modo de analizarlos de cara a mejorar la comprensión del comportamiento de los turistas durante sus viajes. Por último, para abordar la dimensión práctica de esta investigación, se ha aplicado el análisis de puntos de interés al caso de Lanzarote. La investigación realizada tiene implicaciones para la mejora de la gestión de destinos turísticos, en concreto relativas a la búsqueda de soluciones a problemas relacionados con la congestión de los espacios turísticos. Esta tesis se ha elaborado bajo la modalidad de compendio de artículos, que exige que se hayan publicado un mínimo de tres artículos en revistas científicas indexadas en índices de prestigio. Por ello, la tesis cuenta con un total de ocho estudios, cada uno de ellos representando un trabajo de investigación ya publicado. En concreto, cuatro de las ocho publicaciones que conforman esta tesis son las que permiten cumplir con el requisito exigido por la normativa de la universidad relativa a esta modalidad de tesis, dado que son artículos publicados en revistas indexadas en bases de datos de prestigio. Otros cuatro trabajos incluidos en este documento ofrecen resultados complementarios y permiten alcanzar una visión más completa de la investigación que hemos realizado a lo largo de los últimos años. En el anexo se incluye la primera página o carta de aceptación de los artículos y capítulos de libro incluidos en esta tesis doctoral.This dissertation is based on the study of ‘points of interest’, which, until recently, have received little attention in tourism research. Specifically, this dissertation focuses on studying four different dimensions related to points of interest. The objectives are to study the conceptual framework in which the concept of points of interest is placed, to describe the implications derived from their study, to propose a methodological approach that identifies and analyses points of interest, and to apply the research to a case study that highlights their relevance and covers some existing gaps in tourism literature. Regarding the conceptual framework, this dissertation analyzes the definitions of other concepts and proposes a definition of points of interest that fits within the existing terminology framework. In addition, the analytical relevance of this research is addressed by indicating the implications that could derive from an analysis of points of interest in order to improve destination management (particularly to prevent problems related to congestion and overtourism) as well as to open a new field of study in tourism research. This dissertation also focuses on proposing tracking techniques as tools to identify points of interest within destinations, as well as a taxonomy for classifying and analyzing these techniques in order to improve understanding of tourists’ behavior during their trips. Finally, to address the practical application of this research, the analysis of points of interest has been applied to the case of the island of Lanzarote, Canary Islands. The study has implications related to the improvement of destination management, particularly to solving problems linked to overtourism in tourist spaces. This dissertation has been prepared under the modality of paper compendium that requires a minimum of three papers to be published in scientific journals in prestige indexes. Therefore, the dissertation has a total of eight studies, each of them representing a publication. Four of these publications fulfill the university requirement regarding the minimum number of papers needed to defend a paper compendium dissertation. The other works offer complementary results to achieve a wider scope of the research carried out. The reader can find the first page or acceptance letter of the papers and book chapters used to complete this dissertation

Modelo espacial integrado das percepções dos turistas no Litoral Alentejano

O turismo é considerado um meio de promover o crescimento económico e de revitalizar economias locais e regionais, proporcionando um conjunto de benefícios. No entanto, a atividade turística quando não bem planeada, pode trazer consigo vários impactos negativos, que prejudicam o ciclo de vida dos destinos turísticos. Neste contexto a necessidade de dados em turismo, é especialmente importante para fins de planeamento, previsão da procura turística, marketing e medição de impactos. Os Big Data surgem como uma oportunidade devido à combinação de dois elementos: a dificuldade de extração de dados sobre o comportamento turístico da estatística oficial e a quantidade de novas fontes da Web 2.0 relacionadas com a atividade turística. Além disso, algumas destas fontes, como as redes sociais, permitem aceder a fotografias georreferenciada com alta resolução espacial e temporal, existindo um elevado número de utilizadores e níveis de participação elevados. Para analisar o comportamento espácio-temporal da procura turística no Alentejo Litoral, foram utilizadas como fonte de dados mais de 40 000 fotografias de turistas, tendo estas sido extraídas das redes sociais Panoramio e Flickr, abrangendo o período de 2007 a 2017. As fotografias foram divididas em turistas e locais, como base nas datas de carregamento nas respetivas redes sociais, sendo o critério utilizado para a distinção a estada média do destino referido pelo Instituto Nacional de Estatística. Nesta dissertação o principal foco foi analisar a localização das fotografias, identificar os padrões espaciais dos turistas e através da contabilização do número de fotos, localizar os locais mais atrativos. Os principais clusters identificados localizam-se ao longo do litoral, correspondendo a centros urbanos e às praias mais próximas do mesmo, como é o caso de Vila Nova de Milfontes, Troia, Sines e Porto Covo. Sendo igualmente pretendido avaliar a relação entre as perceções dos turistas e as perspetivas dos decisores, foi utlizada informação referente aos locais identificados pelos decisores como: locais atrativos, locais com potencial de atratividade e locais menos atrativos, recolhida através da realização do workshop realizado no âmbito do Plano Operacional Estratégico para o Turismo de Sol e Mar do Alentejo (2015). A análise de stakeholders, revelou que os clusters mais identificados pelos decisores coincidem na maioria com os locais de preferência dos turistas. No entanto, existem locais subvalorizados pelos decisores, principalmente no interior da sub-região, onde, apesar de se apresentarem de forma mais dispersa e por vezes pontual, existem registos de atratividade estatisticamente significativos.Tourism is considered a mean to promote economic growth and to revitalize local and regional economies, providing several benefits. However, tourism activity should be well planned, as it can bring with it negative impacts that undermine the tourist destinations' life cycle. In this context, the need for data in tourism is crucial for planning, predicting tourism demand, marketing and impact measurement. The Big Data comes as an opportunity due to the combination of two elements: the difficulty of extracting data on the tourist behavior from official statistics and the number of new sources of Web 2.0 related to the tourist activity. In addition, some of these sources, such as social networks, allow access to georeferenced photographs with high spatial and temporal resolution, with a high number of users and high levels of participation. In order to analyze the spacio-temporal behavior of tourist demand in Alentejo Litoral, more than 40 000 photographs of tourists were used as data. The photos were extracted from Panoramio and Flickr social networks, covering the period from 2007 to 2017. The data was divided into tourists and locals, based on the date that the users uploaded the photos into the social networks. To distinguish it was used the average stay in the destination, referred to by the National Statistics Institute. In this dissertation, the focus was to analyze the location of the photographs, identify the spatial patterns of the tourists and by counting the number of photos, locate the most attractive places. The main clusters identified are located along the coast, corresponding to urban centers and the beaches closest to it, such as Vila Nova de Milfontes, Troia, Sines and Porto Covo. It was also intended to evaluate the relationship between tourists 'perceptions and decision-makers' perspectives. To do that, it was used information regarding the places identified by decision-makers as: attractive places, potentially attractive places and less attractive places, collected through a workshop concerning the Strategic Operational Plan for the Sun and Sea Tourism of Alentejo (2015). Stakeholder analysis revealed that the places most identified by decision-makers, mostly coincide with the places of tourist preference. However, there are places that are undervalued by decision-makers, especially within the subregion, where, despite being more dispersed and sometimes punctual, there are statistically significant attractiveness registers

OPEN SOURCE SOFTWARE AND OPEN EDUCATIONAL MATERIAL ON LAND COVER MAPS INTERCOMPARISON AND VALIDATION

Land Cover (LC) maps represent key resources to understand, model and address many global and local dynamics affecting our planet. They are usually derived from the classification of satellite imagery, after which a validation or intercomparison process is performed to assess their accuracy. This paper presents the project “Capacity Building for High-Resolution Land Cover Intercomparison and Validation”, an educational initiative funded by the International Society for Photogrammetry and Remote Sensing (ISPRS) and mainly targeting developing countries. First, with the help of two open surveys, an analysis of the state of the art was performed which assessed the overall good awareness on LC maps and the needs and requirements for validating and comparing them, as well as the rich availability of educational material on this topic. The second task, currently under finalization, is the development of new educational material, based on open source software and released under an open access license, consisting of: an introduction to the GlobeLand30 (GL30) LC map and its online platform; a desktop GIS procedure showing two use cases on GL30 validation; and an application to collect LC data on the field to be used for validation. Finally, this educational material will be tested in practice in three workshops during the second half of the project, two of which held in developing countries: Dar es Salaam, Tanzania and Nairobi, Kenya

Digital traces and urban research : Barcelona through social media data

Most of the world’s population now resides in urban areas, and it is expected that almost all of the planet’s growth will be concentrated in them for the next 30 years, making the improvement of the quality of life in the cities one of the big challenges of this century. To that end, it is crucial to have information on how people use the spaces in the city, and allows urban planning to successfully respond to their needs. This dissertation proposes using data shared voluntarily by the millions of users that make up social network’s communities as a valuable tool for the study of the complexity of the city, because of its capacity of providing an unprecedented volume of urban information, with geographic, temporal, semantic and multimedia components. However, the volume and variety of data raises important challenges regarding its retrieval, manipulation, analysis and representation, requiring the adoption of the best practices in data science, using a multi-faceted approach in the field of urban studies with a strong emphasis in the reproducibility of the developed methodologies. This research focuses in the case of study of the city of Barcelona, using the public data collected from Panoramio, Flickr, Twitter and Instagram. After a literature review, the methods to access the different services are discussed, along with their available data and limitations. Next, the retrieved data is analyzed at different spatial and temporal scales. The first approximation to data focuses on the origins of users who took geotagged pictures of Barcelona, geocoding the hometowns that appear in their Flickr public profiles, allowing the identification of the regions, countries and cities with the largest influx of visitors, and relating the results with multiple indicators at a global scale. The next scale of analysis discusses the city as a whole, developing methodologies for the representation of the spatial distribution of the collected locations, avoiding the artifacts produced by overplotting. To this end, locations are aggregated in regular tessellations, whose size is determined empirically from their spatial distribution. Two spatial statistics techniques (Moran’s I and Getis-Ord’s G*) are used to visualize the local spatial autocorrelation of the areas with exceptionally high or low densities, under a statistical significance framework. Finally, the kernel density estimation is introduced as a non-parametric alternative. The third level of detail follows the official administrative division of Barcelona in 73 neighborhoods and 12 districts, which obeys to historical, morphological and functional criteria. Micromaps are introduced as a representation technique capable of providing a geographical context to commonly used statistical graphics, along with a methodology to produce these micromaps automatically. This technique is compared to annotated scatterplots to relate picture intensity with different urban indicators at a neighborhood scale. The hypothesis of spatial homogeneity is abandoned at the most detailed scale, focusing the analysis on the street network. Two techniques to assign events to road segments in the street graph are presented (direct by shortest distance or by proxy through the postal addresses), as well as the generalization of the kernel density estimation from the Euclidean space to a network topology. Beyond the spatial domain, the interactions of three temporal cycles are further analyzed using the timestamps available in the picture metadata: daytime/nighttime (daily cycle), work/leisure (weekly cycle) and seasonal (yearly cycle).La major part de la població mundial resideix actualment en àrees urbanes, i es preveu que pràcticament tot el creixement del planeta es concentri en elles en els propers 30 anys, convertint la millora de la qualitat de vida a les ciutats en un dels grans reptes del present segle. És per tant imprescindible disposar d'informació sobre les activitats que les persones desenvolupen en elles, que permetin al planejament donar resposta a les seves necessitats. Aquesta tesi proposa l'ús de dades compartides de manera voluntària pels milions d'usuaris que conformen les comunitats de les xarxes socials com una valuosa eina per a l'estudi de la complexitat de la ciutat, per la seva capacitat de proporcionar un volum d'informació urbana sense precedents, reunint components tant geogràfics, temporals, semàntics i multimèdia. No obstant això, aquest volum i varietat de les dades planteja grans reptes pel que fa a la seva obtenció, tractament, anàlisi i representació, requerint adoptar les millors pràctiques de la ciència de dades, aplicades des de múltiples punts de vista al camp dels estudis urbans, posant sempre l'èmfasi en la reproductibilitat de les metodologies desenvolupades. Aquesta investigació se centra en el cas d'estudi de la ciutat de Barcelona, a partir de les dades públiques obtingudes de Panoramio, Flickr, Twitter i Instagram. Després d'una revisió de l'estat de l'art, es desenvolupa l'operativa d'accés als diferents serveis, revisant les dades disponibles i les seves limitacions. A continuació, s'analitzen les dades obtingudes en diferents escales espacials i temporals. La primera aproximació a les dades es desenvolupa a partir de l'origen dels usuaris que han pres fotografies geolocalitzades de Barcelona, a través de la geocodificació de les ubicacions que apareixen en els seus perfils públics de Flickr, permetent identificar les regions, països i ciutats amb major afluència de visitants i relacionar els resultats amb diferents indicadors a escala global. La següent escala d'anàlisi es centra en la ciutat en el seu conjunt, desenvolupant metodologies per a la representació de la distribució espacial de les localitzacions obtingudes, evitant els artefactes produïts per la superposició de mostres. Per a això s'agreguen les localitzacions en tesselacions regulars, la mida de les quals es determina empíricament a partir de la seva distribució espacial. S'utilitzen dues tècniques d'estadística espacial (I de Moran i G* de Getis-Ord) per a visualitzar l'autocorrelació espacial local dels àmbits amb densitats excepcionalment altes o baixes, seguint un criteri de significança estadística. Finalment s'introdueix com a alternativa no paramètrica l'estimació de la densitat. El tercer nivell de detall coincideix amb la delimitació administrativa oficial de Barcelona en 73 barris i 12 districtes, realitzada a partir de criteris històrics, morfològics i funcionals. S'introdueixen els micromapes com a tècnica de representació capaç d'aportar un context geogràfic a gràfics estadístics d'ús comú, juntament amb una metodologia per produir aquests micromapes de manera automàtica. Es compara aquesta tècnica amb diagrames de dispersió anotats per a relacionar la intensitat de fotografies amb diferents indicadors urbans a escala de barri. En l'escala més detallada s'abandona la hipòtesi d'homogeneïtat espacial i es trasllada l'anàlisi al sistema viari. Es presenten dues tècniques d'atribució de localitzacions a trams de carrer del graf vial (directa per distància o indirecta a través de les adreces postals), així com la generalització de l'estimació de la densitat d'un espai euclidià a una topologia de xarxa. Fora del context espacial, s'analitzen les interaccions de tres cicles temporals a partir de les metadades del moment en què van ser preses les fotografies: diürn/nocturn (cicle diari), treball/oci (cicle setmanal) i estacional (cicle anual).Postprint (published version

Digital traces and urban research : Barcelona through social media data

Most of the world’s population now resides in urban areas, and it is expected that almost all of the planet’s growth will be concentrated in them for the next 30 years, making the improvement of the quality of life in the cities one of the big challenges of this century. To that end, it is crucial to have information on how people use the spaces in the city, and allows urban planning to successfully respond to their needs. This dissertation proposes using data shared voluntarily by the millions of users that make up social network’s communities as a valuable tool for the study of the complexity of the city, because of its capacity of providing an unprecedented volume of urban information, with geographic, temporal, semantic and multimedia components. However, the volume and variety of data raises important challenges regarding its retrieval, manipulation, analysis and representation, requiring the adoption of the best practices in data science, using a multi-faceted approach in the field of urban studies with a strong emphasis in the reproducibility of the developed methodologies. This research focuses in the case of study of the city of Barcelona, using the public data collected from Panoramio, Flickr, Twitter and Instagram. After a literature review, the methods to access the different services are discussed, along with their available data and limitations. Next, the retrieved data is analyzed at different spatial and temporal scales. The first approximation to data focuses on the origins of users who took geotagged pictures of Barcelona, geocoding the hometowns that appear in their Flickr public profiles, allowing the identification of the regions, countries and cities with the largest influx of visitors, and relating the results with multiple indicators at a global scale. The next scale of analysis discusses the city as a whole, developing methodologies for the representation of the spatial distribution of the collected locations, avoiding the artifacts produced by overplotting. To this end, locations are aggregated in regular tessellations, whose size is determined empirically from their spatial distribution. Two spatial statistics techniques (Moran’s I and Getis-Ord’s G*) are used to visualize the local spatial autocorrelation of the areas with exceptionally high or low densities, under a statistical significance framework. Finally, the kernel density estimation is introduced as a non-parametric alternative. The third level of detail follows the official administrative division of Barcelona in 73 neighborhoods and 12 districts, which obeys to historical, morphological and functional criteria. Micromaps are introduced as a representation technique capable of providing a geographical context to commonly used statistical graphics, along with a methodology to produce these micromaps automatically. This technique is compared to annotated scatterplots to relate picture intensity with different urban indicators at a neighborhood scale. The hypothesis of spatial homogeneity is abandoned at the most detailed scale, focusing the analysis on the street network. Two techniques to assign events to road segments in the street graph are presented (direct by shortest distance or by proxy through the postal addresses), as well as the generalization of the kernel density estimation from the Euclidean space to a network topology. Beyond the spatial domain, the interactions of three temporal cycles are further analyzed using the timestamps available in the picture metadata: daytime/nighttime (daily cycle), work/leisure (weekly cycle) and seasonal (yearly cycle).La major part de la població mundial resideix actualment en àrees urbanes, i es preveu que pràcticament tot el creixement del planeta es concentri en elles en els propers 30 anys, convertint la millora de la qualitat de vida a les ciutats en un dels grans reptes del present segle. És per tant imprescindible disposar d'informació sobre les activitats que les persones desenvolupen en elles, que permetin al planejament donar resposta a les seves necessitats. Aquesta tesi proposa l'ús de dades compartides de manera voluntària pels milions d'usuaris que conformen les comunitats de les xarxes socials com una valuosa eina per a l'estudi de la complexitat de la ciutat, per la seva capacitat de proporcionar un volum d'informació urbana sense precedents, reunint components tant geogràfics, temporals, semàntics i multimèdia. No obstant això, aquest volum i varietat de les dades planteja grans reptes pel que fa a la seva obtenció, tractament, anàlisi i representació, requerint adoptar les millors pràctiques de la ciència de dades, aplicades des de múltiples punts de vista al camp dels estudis urbans, posant sempre l'èmfasi en la reproductibilitat de les metodologies desenvolupades. Aquesta investigació se centra en el cas d'estudi de la ciutat de Barcelona, a partir de les dades públiques obtingudes de Panoramio, Flickr, Twitter i Instagram. Després d'una revisió de l'estat de l'art, es desenvolupa l'operativa d'accés als diferents serveis, revisant les dades disponibles i les seves limitacions. A continuació, s'analitzen les dades obtingudes en diferents escales espacials i temporals. La primera aproximació a les dades es desenvolupa a partir de l'origen dels usuaris que han pres fotografies geolocalitzades de Barcelona, a través de la geocodificació de les ubicacions que apareixen en els seus perfils públics de Flickr, permetent identificar les regions, països i ciutats amb major afluència de visitants i relacionar els resultats amb diferents indicadors a escala global. La següent escala d'anàlisi es centra en la ciutat en el seu conjunt, desenvolupant metodologies per a la representació de la distribució espacial de les localitzacions obtingudes, evitant els artefactes produïts per la superposició de mostres. Per a això s'agreguen les localitzacions en tesselacions regulars, la mida de les quals es determina empíricament a partir de la seva distribució espacial. S'utilitzen dues tècniques d'estadística espacial (I de Moran i G* de Getis-Ord) per a visualitzar l'autocorrelació espacial local dels àmbits amb densitats excepcionalment altes o baixes, seguint un criteri de significança estadística. Finalment s'introdueix com a alternativa no paramètrica l'estimació de la densitat. El tercer nivell de detall coincideix amb la delimitació administrativa oficial de Barcelona en 73 barris i 12 districtes, realitzada a partir de criteris històrics, morfològics i funcionals. S'introdueixen els micromapes com a tècnica de representació capaç d'aportar un context geogràfic a gràfics estadístics d'ús comú, juntament amb una metodologia per produir aquests micromapes de manera automàtica. Es compara aquesta tècnica amb diagrames de dispersió anotats per a relacionar la intensitat de fotografies amb diferents indicadors urbans a escala de barri. En l'escala més detallada s'abandona la hipòtesi d'homogeneïtat espacial i es trasllada l'anàlisi al sistema viari. Es presenten dues tècniques d'atribució de localitzacions a trams de carrer del graf vial (directa per distància o indirecta a través de les adreces postals), així com la generalització de l'estimació de la densitat d'un espai euclidià a una topologia de xarxa. Fora del context espacial, s'analitzen les interaccions de tres cicles temporals a partir de les metadades del moment en què van ser preses les fotografies: diürn/nocturn (cicle diari), treball/oci (cicle setmanal) i estacional (cicle anual)