Continuous Representation of Location for Geolocation and Lexical Dialectology using Mixture Density Networks

We propose a method for embedding two-dimensional locations in a continuous vector space using a neural network-based model incorporating mixtures of Gaussian distributions, presenting two model variants for text-based geolocation and lexical dialectology. Evaluated over Twitter data, the proposed model outperforms conventional regression-based geolocation and provides a better estimate of uncertainty. We also show the effectiveness of the representation for predicting words from location in lexical dialectology, and evaluate it using the DARE dataset.Comment: Conference on Empirical Methods in Natural Language Processing (EMNLP 2017) September 2017, Copenhagen, Denmar

Urban Dreams of Migrants: A Case Study of Migrant Integration in Shanghai

Unprecedented human mobility has driven the rapid urbanization around the world. In China, the fraction of population dwelling in cities increased from 17.9% to 52.6% between 1978 and 2012. Such large-scale migration poses challenges for policymakers and important questions for researchers. To investigate the process of migrant integration, we employ a one-month complete dataset of telecommunication metadata in Shanghai with 54 million users and 698 million call logs. We find systematic differences between locals and migrants in their mobile communication networks and geographical locations. For instance, migrants have more diverse contacts and move around the city with a larger radius than locals after they settle down. By distinguishing new migrants (who recently moved to Shanghai) from settled migrants (who have been in Shanghai for a while), we demonstrate the integration process of new migrants in their first three weeks. Moreover, we formulate classification problems to predict whether a person is a migrant. Our classifier is able to achieve an F1-score of 0.82 when distinguishing settled migrants from locals, but it remains challenging to identify new migrants because of class imbalance. This classification setup holds promise for identifying new migrants who will successfully integrate into locals (new migrants that misclassified as locals).Comment: A modified version. The paper was accepted by AAAI 201

Estimating mobility of tourists. New Twitter-based procedure

Twitter has been actively researched as a human mobility proxy. Tweets can contain two classes of geographical metadata: the location from which a tweet was published, and the place where the tweet is estimated to have been published. Nevertheless, Twitter also presents tweets without any geographical metadata when querying for tweets on a specific location. This study presents a methodology which includes an algorithm for estimating the geographical coordinates to tweets for which Twitter doesn't assign any. Our objective is to determine the origin and the route that a tourist followed, even if Twitter doesn't return geographically identified data. This is carried out through geographical searches of tweets inside a defined area. Once a tweet is found inside an area, but its metadata contains no explicit geographical coordinates, its coordinates are estimated by iteratively performing geographical searches, with a decreasing geographical searching radius. This algorithm was tested in two touristic villages of Madrid (Spain) and a major city in Canada. A set of tweets without geographical coordinates in these areas were found and processed. The coordinates of a subset of them were successfully estimated.Agencia Estatal de Investigación | Ref. PID2020-116040RB-I00Universidade de Vigo/CISU

Analisando padrões de mobilidade a partir de redes sociais e de dados sócio demográficos abertos.

A demanda constante por melhorias na qualidade de vida dos habitantes das grandes cidades, somado à crescente urbanização desses centros, torna imprescindível a utilização de meios tecnológicos para um melhor entendimento da dinâmica dos centros urbanos e como seus habitantes interagem nesses ambientes. Nesse sentido, o aumento na utilização de dispositivos eletrônicos equipados com sistemas GPS e o constante anseio da humanidade por comunicação e, mais atualmente, por conexão à internet, vem criando novas oportunidades de estudo e também grandes desafios, especialmente no que tange a grande quantidade de dados gerados pelas redes sociais. Diversas pesquisas vêm utilizando esses dados para realizar estudos que buscam compreender traços do comportamento humano, especialmente no que diz respeito à mobilidade urbana e trajetórias. Porém, grande parte das pesquisas que utilizam dados georreferenciados se restringem às dimensões espaciais e temporais, desconsiderando outros aspectos que podem influenciar na mobilidade humana. Este trabalho propõe um método computacional capaz de extrair padrões de mobilidade oriundos de mensagens georreferenciadas de redes sociais e correlacioná-los com indicadores sociais, econômicos e demográficos fornecidos por órgãos governamentais, buscando assim, analisar quais possíveis fatores poderiam exercer alguma influência sobre a mobilidade dos moradores de uma grande cidade. Para validar o método proposto, foram utilizadas mensagens postadas no Twitter e um conjunto de indicadores sociais, ambos oriundos da cidade de Londres. Os resultados mostraram a existência de correlações entre padrões de mobilidade e indicadores sociais, especialmente os relacionados com condições de emprego e renda, como também com características étnico-religiosas dos indivíduos em estudo.The constant need for improvements in life quality of inhabitants of big cities, together with the increasing urbanization of these centers, demands the use of technological means for a better understanding of the dynamics of urban centers and how their inhabitants interact in these environments. In this sense, the adoption of electronic devices equipped with GPS systems, the human need for communication and, more recently, for Internet connection, have brought new research opportunities and great challenges, especially due to the huge amount of data generated by social networks. Several studies have used this data to carry out research that seek to understand traces of human behavior, especially with respect to urban mobility and trajectories. However, much of the research that uses georeferenced data are restricted to spatial and temporal dimensions, disregarding other aspects that may influence human mobility. This work proposes a model capable of extracting mobility patterns from georeferenced messages of social networks and correlating them with social, economic and demographic indicators provided by government agencies, seeking to analyze which factors may impact in urban mobility. To evaluate the model, we used messages posted on Twitter and a set of social indicators, both related to the city of London. The results revealed the existence of correlations between mobility patterns and social indicators, especially those related to employment and income conditions, as well as ethnic and religious characteristics of the individuals under study.Cape

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