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

Exploring urban visitors' mobilities. A multi-method approach

Aquesta tesi doctoral sorgeix de la necessitat d’aprofundir en el coneixement de les mobilitats dels visitants, entendre les decisions que configuren el seu comportament espacio-temporal i identificar i explorar els efectes que les seves mobilitats tenen sobre les destinacions urbanes. La tesi es desenvolupa entorn a quatre objectius específics que s’emmarquen en l’àmbit de recerca relacionat amb el seguiment de l’activitat dels visitants en destinacions turístiques urbanes. Cadascun d’aquests objectius es desenvolupa en cadascun dels articles científics que conformen aquesta tesi doctoral, publicats tots ells en revistes de revisió per parells. El primer article es proposa com a objectiu identificar els factors, relacionats amb el perfil socioeconòmic dels turistes i amb les característiques de la seva estada, que determinen la selecció d’opcions de transport i mobilitat sostenible per moure’s per la destinació urbana. El segon article pretén analitzar i comprendre com afecta el comportament espacio-temporal dels turistes en els seus patrons de consum econòmic i, per tant, en la generació d’ingressos per a l’economia local. El tercer article es proposa analitzar la influència de l’espai urbà sobre la forma en què els visitants es desplacen per la destinació. I finalment, el quart article té per objectiu reconstruir trajectòries i/o fluxos espacio-temporals a partir de dades geolocalitzades de les xarxes socials per tal de detectar patrons de mobilitat dels visitants de destinacions urbanes. Les fonts de dades i els mètodes utilitzats per complir amb els objectius de partida són diverses. En aquest sentit, la tesi aporta també una àmplia radiografia dels pros i les contres de les diferents fonts de dades disponibles per a l’anàlisi de les mobilitats dels visitants en destinacions turístiques.Esta tesis doctoral surge de la necesidad de profundizar en el conocimiento de las movilidades de los visitantes,entender las decisiones que configuran su comportamiento espaciotemporal e identificar y explorar los efectos que sus movilidades tienen sobre los destinos urbanos. La tesis se desarrolla en torno a cuatro objetivos específicos que se enmarcan en el ámbito de investigación de seguimiento de visitantes, y que se desarrollan en cada uno de los artículos científicos, publicados todos ellos en revistas de revisión por pares, que conforman esta tesis. El primer artículo se propone como objetivo identificar los factores, relacionados con el perfil socioeconómicos de los turistas y con las características de su estancia, que determinan la selección de opciones de transporte y movilidad sostenible para moverse por el destino urbano. El segundo artículo pretende analizar y comprender cómo afecta el comportamiento espaciotemporal de los turistas en sus patrones de consumo económico y, por tanto, en la generación de ingresos para la economía local. El tercer artículo se propone analizar la influencia del espacio urbano sobre la forma en que los visitantes se desplazan por el destino. Y finalmente, el cuarto artículo tiene por objetivo reconstruir trayectorias y / o flujos espaciotemporales a partir de datos geolocalizados de las redes sociales para detectar patrones de movilidad de los visitantes de destinos urbanos. Las fuentes de datos y los métodos utilizados para cumplir con los objetivos de partida son diversos. En este sentido, la tesis aporta también una amplia radiografía de los pros y contras de las diferentes fuentes de datos disponibles para el análisis de las movilidades de los visitantes en destinos turísticos.This dissertation arises from the need to deepen the knowledge of the mobility of visitors, understand the decisions that shape their spatiotemporal behaviour and identify and explore the effects that their mobility has on urban destinations. The thesis is developed around four specific objectives that fall within the scope of visitor tracking research, and that are developed in each of the scientific articles, all of them published in peer-reviewed journals, that make up this thesis. The first article aims to identify the factors, related to the socioeconomic profile of tourists and the characteristics of their stay, that determine the selection of sustainable transport and mobility options to move within the urban destination. The second article aims to analyse and understand how the visitors’ spatiotemporal behaviour affects their patterns of economic consumption and, therefore, the generation of income for the local economy. The third article aims to analyse the influence of the built environment on the visitors’ mobilities at destination. And finally, the fourth article aims to reconstruct trajectories and / or spatiotemporal flows from geolocated data obtained from social networks in order to detect visitors’ mobility patterns at urban destinations. The data sources and methods used to meet the objectives are multiple. In this sense, the thesis also provides an extensive x-ray of the pros and cons of the different data sources available for the analysis of visitors’ mobilities in tourist destinations

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.

Citizen Science and Geospatial Capacity Building

This book is a collection of the articles published the Special Issue of ISPRS International Journal of Geo-Information on “Citizen Science and Geospatial Capacity Building”. The articles cover a wide range of topics regarding the applications of citizen science from a geospatial technology perspective. Several applications show the importance of Citizen Science (CitSci) and volunteered geographic information (VGI) in various stages of geodata collection, processing, analysis and visualization; and for demonstrating the capabilities, which are covered in the book. Particular emphasis is given to various problems encountered in the CitSci and VGI projects with a geospatial aspect, such as platform, tool and interface design, ontology development, spatial analysis and data quality assessment. The book also points out the needs and future research directions in these subjects, such as; (a) data quality issues especially in the light of big data; (b) ontology studies for geospatial data suited for diverse user backgrounds, data integration, and sharing; (c) development of machine learning and artificial intelligence based online tools for pattern recognition and object identification using existing repositories of CitSci and VGI projects; and (d) open science and open data practices for increasing the efficiency, decreasing the redundancy, and acknowledgement of all stakeholders

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

European Handbook of Crowdsourced Geographic Information

"This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research.

Computational socioeconomics

Uncovering the structure of socioeconomic systems and timely estimation of socioeconomic status are significant for economic development. The understanding of socioeconomic processes provides foundations to quantify global economic development, to map regional industrial structure, and to infer individual socioeconomic status. In this review, we will make a brief manifesto about a new interdisciplinary research field named Computational Socioeconomics, followed by detailed introduction about data resources, computational tools, data-driven methods, theoretical models and novel applications at multiple resolutions, including the quantification of global economic inequality and complexity, the map of regional industrial structure and urban perception, the estimation of individual socioeconomic status and demographic, and the real-time monitoring of emergent events. This review, together with pioneering works we have highlighted, will draw increasing interdisciplinary attentions and induce a methodological shift in future socioeconomic studies

Revisiting Urban Dynamics through Social Urban Data:

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities? To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.   After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources. A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics. The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities

Revisiting Urban Dynamics through Social Urban Data

The study of dynamic spatial and social phenomena in cities has evolved rapidly in the recent years, yielding new insights into urban dynamics. This evolution is strongly related to the emergence of new sources of data for cities (e.g. sensors, mobile phones, online social media etc.), which have potential to capture dimensions of social and geographic systems that are difficult to detect in traditional urban data (e.g. census data). However, as the available sources increase in number, the produced datasets increase in diversity. Besides heterogeneity, emerging social urban data are also characterized by multidimensionality. The latter means that the information they contain may simultaneously address spatial, social, temporal, and topical attributes of people and places. Therefore, integration and geospatial (statistical) analysis of multidimensional data remain a challenge. The question which, then, arises is how to integrate heterogeneous and multidimensional social urban data into the analysis of human activity dynamics in cities?  To address the above challenge, this thesis proposes the design of a framework of novel methods and tools for the integration, visualization, and exploratory analysis of large-scale and heterogeneous social urban data to facilitate the understanding of urban dynamics. The research focuses particularly on the spatiotemporal dynamics of human activity in cities, as inferred from different sources of social urban data. The main objective is to provide new means to enable the incorporation of heterogeneous social urban data into city analytics, and to explore the influence of emerging data sources on the understanding of cities and their dynamics.  In mitigating the various heterogeneities, a methodology for the transformation of heterogeneous data for cities into multidimensional linked urban data is, therefore, designed. The methodology follows an ontology-based data integration approach and accommodates a variety of semantic (web) and linked data technologies. A use case of data interlinkage is used as a demonstrator of the proposed methodology. The use case employs nine real-world large-scale spatiotemporal data sets from three public transportation organizations, covering the entire public transport network of the city of Athens, Greece.  To further encourage the consumption of linked urban data by planners and policy-makers, a set of webbased tools for the visual representation of ontologies and linked data is designed and developed. The tools – comprising the OSMoSys framework – provide graphical user interfaces for the visual representation, browsing, and interactive exploration of both ontologies and linked urban data.  After introducing methods and tools for data integration, visual exploration of linked urban data, and derivation of various attributes of people and places from different social urban data, it is examined how they can all be combined into a single platform. To achieve this, a novel web-based system (coined SocialGlass) for the visualization and exploratory analysis of human activity dynamics is designed. The system combines data from various geo-enabled social media (i.e. Twitter, Instagram, Sina Weibo) and LBSNs (i.e. Foursquare), sensor networks (i.e. GPS trackers, Wi-Fi cameras), and conventional socioeconomic urban records, but also has the potential to employ custom datasets from other sources.  A real-world case study is used as a demonstrator of the capacities of the proposed web-based system in the study of urban dynamics. The case study explores the potential impact of a city-scale event (i.e. the Amsterdam Light festival 2015) on the activity and movement patterns of different social categories (i.e. residents, non-residents, foreign tourists), as compared to their daily and hourly routines in the periods  before and after the event. The aim of the case study is twofold. First, to assess the potential and limitations of the proposed system and, second, to investigate how different sources of social urban data could influence the understanding of urban dynamics.  The contribution of this doctoral thesis is the design and development of a framework of novel methods and tools that enables the fusion of heterogeneous multidimensional data for cities. The framework could foster planners, researchers, and policy makers to capitalize on the new possibilities given by emerging social urban data. Having a deep understanding of the spatiotemporal dynamics of cities and, especially of the activity and movement behavior of people, is expected to play a crucial role in addressing the challenges of rapid urbanization. Overall, the framework proposed by this research has potential to open avenues of quantitative explorations of urban dynamics, contributing to the development of a new science of cities