How much data is enough to track tourists? The tradeoff between data granularity and storage costs

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn the increasingly technology-dependent world, data is one of the key strategic resources for organizations. Often, the challenge that many decision-makers face is to determine which data and how much to collect, and what needs to be kept in their data storage. The challenge is to preserve enough information to inform decisions but doing so without overly high costs of storage and data processing cost. In this thesis, this challenge is studied in the context of a collection of mobile signaling data for studying tourists’ behavioral patterns. Given the number of mobile phones in use, and frequency of their interaction with network infrastructure and location reporting, mobile data sets represent a rich source of information for mobility studies. The objective of this research is to analyze to what extent can individual trajectories be reconstructed if only a fraction of the original location data is preserved, providing insights about the tradeoff between the volume of data available and the accuracy of reconstructed paths. To achieve this, a signaling data of 277,093 anonymized foreign travelers is sampled with different sampling rates, and the full trajectories are reconstructed, using the last seen, linear, and cubic interpolations completion methods. The results of the comparison are discussed from the perspective of data management and implications on the research, especially the results of research with lower time-density mobile phone data

Event management in the era of big data and advanced analytics: an empirical investigation in Portugal

This work project can be considered as anexample of how big data and advanced analytics can be utilized to inform event management. Knowing what event visitors do, where they goandwhat they spend before and after their visitto a special eventis highly important for event managers. With this in mind,this investigation analyzed and studied mobile call detail record data of tourists visiting a certain music event in Portugal in August 2017. The results successfully showedpractical insights on how tourists movedand behaved.These results were thentranslatedintohow theycan inform event management

SMSM: a similarity measure for trajectory stops and moves

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Ciência da Computação, Florianópolis, 2019.Medidas de similaridade são a base para a maioria dos métodos de mineração de dados e extração de conhecimento. Na área de trajetórias de objetos móveis, por muitos anos a pesquisa em similaridade de trajetórias focou nas trajetórias brutas, considerando somente a informação de espaço e tempo. Com o enriquecimento das trajetórias com informações semânticas, como o nome e a categoria dos locais visitados, meio de transporte utilizado durante o movimento, o nome das ruas percorridas, etc, emergiu a necessidade por medidas de similaridade que suportem espaço, tempo e semântica. Apesar de algumas medidas de similaridade para trajetórias lidarem com todas estas dimensões, elas consideram somente os locais onde o objeto móvel faz paradas, denominados stops, ignorando o movimento que ocorre entre as paradas, denominado move. Acredita-se que, para algumas aplicações, o movimento entre os stops é tão importante quanto o stop em si, e ele deve ser levado em consideração na análise da similaridade, como em sistemas de transporte público, turismo, planejamento urbano, entre outros. Nesta dissertação é proposta a medida Similarity Measure for trajectory Stops and Moves (SMSM), um nova medida de similaridade para trajetórias semânticas que considera tanto os stops quanto os moves. O SMSM é avaliado em três conjuntos de dados: (i) um conjunto de dados de trajetórias sintéticas criadas com o gerador de trajetórias semânticas Hermoupolis, (ii) um conjunto de trajetórias reais de táxis do projeto CRAWDAD, e (iii) o conjunto de dados de trajetórias reais chamado Geolife, com trajetórias de pessoas na cidade de Pequim. Os resultados mostram que o SMSM supera as medidas de similaridade do estado da arte desenvolvidas tanto para trajetórias brutas quanto semânticas.Abstract : For many years trajectory similarity research has focused on raw trajectories, considering only space and time information. With the trajectory semantic enrichment, using information as the name and type of the visited places, the transportation mean, the name of the streets, etc, emerged the need for similarity measures that support space, time, and semantics. Although some trajectory similarity measures deal with all these dimensions, they consider only the places where the moving object stays for a certain time, called stop, ignoring the movement between stops. We claim that, for some applications, as traffic management systems, urban planning, public transportation, etc, the movement between stops is as important as the stops, and it must be considered in the similarity analysis. In this thesis we propose the similarity measure called Similarity Measure for trajectory Stops and Moves(SMSM), a novel similarity measure for semantic trajectories that considers both stops and moves. We evaluate SMSM with three trajectory datasets: (i) a synthetic trajectory dataset generated with the Hermoupolis semantic trajectory generator, (ii) a real trajectory dataset of taxis from the CRAWDAD project, and (iii) the Geolife trajectory dataset, with raw trajectories of persons around Beijing. The results show that SMSM overcomes state-of-the-art measures developed for both raw and semantic trajectories

Utilizing Call Detail Records for Travel Mode Discovery in Urban Areas

Mobile network operators often bill their customers based on their network usage. For this purpose, operators collect information about billable events, such as calls, text messages, and data usage. In recent years, operators have realized that they can monetize these billing records by selling insights extracted from them. In this thesis, a multi-stage data analysis algorithm is presented that uses these billing records for travel mode classification. This algorithm identifies whether a mobile phone user has traveled using a public transportation bus or using another transportation mode. The billing records collected by a network operator contain the time at which a billable event happened, as well as the network cell from which the event originated. The coverage area of each network cell is known to the operator. Therefore, the billing records of a mobile phone user give an overview of that user’s approximate location at different times. This data can be used to discover the sequence of network cells that the user has traveled through during a trip. Travel mode classification algorithms in literature analyze long-distance or medium- distance trips. The data analysis algorithm presented in this thesis is novel for analyzing and classifying short-distance, intra-city trips. To classify mobility traces, it uses publicly available bus timetable data and road network infrastructure data. The accuracy of the classification algorithm is evaluated using a two-fold cross-validation analysis

Uncovering population dynamics using mobile phone data : the case of Helsinki Metropolitan Area

Understanding the whereabouts of people in time and space is necessary for unraveling how our societies function. Regardless, our understanding of human presence is predominantly based on static residential population data, which is often outdated and excludes certain population groups, such as commuters or tourists. In the light of development towards 24-hour societies and the needs for promoting sustainable and equitable urban planning, reliable data of population dynamics are needed. To this end, ubiquitous mobile phones provide an attractive source for estimating the spatiotemporal digital footprints of people. In this study, I set out to investigate 1) the feasibility of three different aggregated network-based mobile phone data – the number of voice calls, data transmission and general network connection attempts – as a proxy for human presence, 2) how does the population distribution vary in Helsinki Metropolitan Area over the course of a regular weekday and 3) the role of temporally-sensitive population data when analysing dynamic accessibility to grocery stores and transport hubs. To my best knowledge, this is the first attempt when mobile phone data is used to reveal population dynamics for scientific purposes in Finland. Mobile phone data collected by the mobile network operator Elisa in 2017–2018 and ancillary data about land cover, buildings and a time use survey were used to estimate the 24-hour population distribution of the Helsinki Metropolitan Area. The mobile phone data were allocated to statistical 250 m x 250 m grid cells using an advanced dasymetric interpolation method and validated against population register data from Statistics Finland. The resulting 24-hour population was used to map the pulse of the city and to introduce the first fully dynamic accessibility model in the study area. The results show that data use is a good proxy for people and outperforms voice calls or overall network connection attempts. During daytime, the static population overestimates the population in residential areas and underestimates the population in work and service areas. In general, the 24-hour population reveals the pulse of a city, which is highlighted especially in the inner city of Helsinki, where the relative share of population of the study area increases by 50 % from the share at night-time to its peak at noon. The results of the case study suggest that integrating dynamic population data to location-based accessibility analysis provides more realistic results compared to static population data, but the significance of dynamic population data depends on the study context and research questions. In summary, aggregated network-driven mobile phone data is a feasible alternative for dynamic population modelling, however, different mobile phone data types vary in representativeness, which should be taken into account when using mobile phone data in research. To this end, critical evaluation of data and transparent data description are essential. Overall, understanding 24-hour societies and supporting sustainable urban planning necessitates dynamic population data, but advancements in data policy and availability are needed to harvest these possibilities. The results of this study also provide new empirical insights of the population dynamics in the study area, which can be used to advance planning and decision making.Ymmärrys väestön alueellisen jakautumisen ajallisesta vaihtelusta on keskeistä yhteiskuntamme toiminnan ymmärtämiseksi. Tästä huolimatta ymmärrys ihmisten läsnäolosta on vähäistä ja perustuu pääasiassa staattisiin asuinpaikkakohtaisiin väestötietoihin, jotka ovat usein vanhentuneita ja saattavat johtaa eräiden väestöryhmien, kuten työmatkalaisten tai turistien, sivuuttamiseen. Kehityksen kohti ympärivuorokautista yhteiskuntaa ja kestävän ja tasa-arvoisen kaupunkisuunnittelun edistämisen tarpeiden valossa tarvitaan luotettavia tietoja väestön dynamiikasta. Tässä tutkimuksessa tarkastelin 1) kolmen eri verkkopohjaisen matkapuhelinaineiston – puheluiden, tiedonsiirtoyhteyksien ja verkkoyhteyksien muodostusyritysten lukumäärän – soveltuvuutta ihmisen läsnäolon kuvaajana, 2) miten väestöjakauma vaihtelee pääkaupunkiseudulla säännöllisen arkipäivän aikana ja 3) temporaalisten väestötietojen käytön roolia saavutettavuusmallinnuksessa tarkasteltaessa ruokakauppojen ja liikenteen solmukohtien saavutettavuutta joukkoliikenteellä. Parhaan tietämykseni mukaan tämä on ensimmäinen kerta, kun matkapuhelinaineistoja käytetään väestön dynamiikan tarkasteluun tieteellisiin tarkoituksiin Suomessa. Matkapuhelinoperaattori Elisan keräämiä matkapuhelinaineistoja (2017–2018) sekä aineistoja maankäytöstä, rakennuksista ja ajankäyttötutkimuksen tuloksia käytettiin pääkaupunkiseudun 24 tunnin väestöjakauman arvioimiseen. Matkapuhelimen tiedot allokoitiin 250 m x 250 m tilastoruutuihin käyttäen edistynyttä dasymetristä interpolointimenetelmää ja validoitiin Tilastokeskuksen väestörekisteritietoja käyttäen. Tuloksena saatua 24 tunnin väestöaineistoa käytettiin kaupungin pulssin analysointiin ja ensimmäisen täysin dynaamisen saavutettavuusmallin toteuttamiseen tutkimusalueella. Tutkimuksen tulokset osoittavat, että matkapuhelinten tiedonsiirto on hyvä kuvaaja ihmisten sijainnille ja parempi kuin puhelut tai verkkoyhteyksien muodostusyritykset. Päivän aikana staattinen väestöaineisto yliarvioi väestöä erityisesti asuinalueilla samalla aliarvioiden väestöä alueilla, joilla on työpaikka- tai palvelukeskittymiä. Yleisesti katsottuna 24 tunnin väestö paljastaa kaupungin pulssin, mikä korostuu erityisesti Helsingin keskustassa, jossa tutkimusalueen väestön suhteellinen osuus kasvaa 50 %:lla yöstä sen huippuun keskipäivällä. Tapaustutkimuksen tulokset havainnollistavat kuinka dynaamisen väestötietojen integroiminen sijaintipohjaiseen saavutettavuustarkasteluun tarjoaa realistisempia tuloksia verrattuna staattiseen väestöaineistoon, mutta dynaamisten väestötietojen integroimisen merkitys riippuu tutkimuksen kontekstista ja tutkimuskysymyksistä. Yhteenvetona voidaan todeta, että aggregoitu verkkopohjainen matkapuhelinaineisto on hyvä vaihtoehto dynaamisen väestön mallintamiseen, mutta soveltuvuus vaihtelee aineistojen välillä, mikä on tärkeä huomioida käytettäessä matkapuhelinaineistoja tutkimuksessa. Tätä vasten aineiston kriittinen tarkastelu ja läpinäkyvä aineiston dokumentointi on olennaista. Kaiken kaikkiaan 24 tunnin yhteiskuntien ymmärtäminen ja kestävän kaupunkisuunnittelun tukeminen edellyttävät dynaamisia väestötietoja, mutta tietopolitiikan ja aineistojen saatavuuden edistäminen on välttämätöntä tämän toteutumiseksi. Tämä työ tarjoaa myös uutta empiiristä tietoa väestön dynamiikasta pääkaupunkiseudulla, jota voidaan käyttää suunnittelun ja päätöksenteon tukena