Temporal GIS Design of an Extended Time-geographic Framework for Physical and Virtual Activities

Recent rapid developments of information and communication technologies (ICT) enable a virtual space, which allows people to conduct activities remotely through tele-presence rather than through conventional physical presence in physical space. ICT offer people additional freedom in space and time to carry out their activities; this freedom leads to changes in the spatio-temporal distributions of activities. Given that activities are the reasons for travel, these changes will impact transportation systems. Therefore, a better understanding of the spatial and temporal characteristics of human activities in today’s society will help researchers study the impact of ICT on transportation. Using an integrated space-time system, Hägerstrand’s time geography provides an effective framework for studying the relationships of various constraints and human activities in physical space, but it does not support activities in virtual space. The present study provides a conceptual model to describe the relationships of physical space and virtual space, extending Hägerstrand’s time geography to handle both physical and virtual activities. This extended framework is used to support investigations of spatial and temporal characteristics of human activities and their interactions in physical and virtual spaces. Using a 3D environment (i.e., 2D space + 1D time), a temporal GIS design is developed to accommodate the extended time-geographic framework. This GIS design supports representations of time-geographic objects (e.g., space-time paths, networkbased space-time prisms, and space-time life paths) and a selected set of analysis functions applied to these objects (e.g., temporal dynamic segmentation and spatiotemporal intersection). A prototype system, with customized functions developed in Visual Basic for Applications (VBA) programs with ArcObjects, is implemented in ArcGIS according to the design. Using a hypothetical activity dataset, the system demonstrates the feasibility of the extended framework and the temporal GIS design to explore physical and virtual activities. This system offers useful tools with which to tackle various real problems related to physical and virtual activities

A Microsimulation Approach to the Modelling of Urban Population and Housing Markets Within an Object-Oriented Framework

The structure of a city constantly changes, in accordance to the population’s housing demand and the city’s housing supply. Housing demand depends on several factors, such as changes in household structure caused by demographic events (e.g. mortality, fer

Modélisation spatio-temporelle orientée-objet pour l'étude du comportement de transport basé sur l'activité

Thèse numérisée par la Direction des bibliothèques de l'Université de Montréal

Doctor of Philosophy

dissertationRecent advancements in mobile devices - such as Global Positioning System (GPS), cellular phones, car navigation system, and radio-frequency identification (RFID) - have greatly influenced the nature and volume of data about individual-based movement in space and time. Due to the prevalence of mobile devices, vast amounts of mobile objects data are being produced and stored in databases, overwhelming the capacity of traditional spatial analytical methods. There is a growing need for discovering unexpected patterns, trends, and relationships that are hidden in the massive mobile objects data. Geographic visualization (GVis) and knowledge discovery in databases (KDD) are two major research fields that are associated with knowledge discovery and construction. Their major research challenges are the integration of GVis and KDD, enhancing the ability to handle large volume mobile objects data, and high interactivity between the computer and users of GVis and KDD tools. This dissertation proposes a visualization toolkit to enable highly interactive visual data exploration for mobile objects datasets. Vector algebraic representation and online analytical processing (OLAP) are utilized for managing and querying the mobile object data to accomplish high interactivity of the visualization tool. In addition, reconstructing trajectories at user-defined levels of temporal granularity with time aggregation methods allows exploration of the individual objects at different levels of movement generality. At a given level of generality, individual paths can be combined into synthetic summary paths based on three similarity measures, namely, locational similarity, directional similarity, and geometric similarity functions. A visualization toolkit based on the space-time cube concept exploits these functionalities to create a user-interactive environment for exploring mobile objects data. Furthermore, the characteristics of visualized trajectories are exported to be utilized for data mining, which leads to the integration of GVis and KDD. Case studies using three movement datasets (personal travel data survey in Lexington, Kentucky, wild chicken movement data in Thailand, and self-tracking data in Utah) demonstrate the potential of the system to extract meaningful patterns from the otherwise difficult to comprehend collections of space-time trajectories

A spatiotemporal indexing method for disaggregate transportation data

Time, location, and attributes are three elements of a GIS, but all commercial GIS packages can only handle location and attributes; they are in fact a static GIS. Spatiotemporal GIS has been a hot research topic recentlySpatiotemporal GIS and its application in transportation research are still prematureThis thesis focuses on spatiotemporal query problems on travel data Specifically, It attempts to answer this question during a time period which trips pass through one or more specific streets? To speed up this spatiotemporal query for large data sets, a spatiotemporal index on the trip data is built by combining Avenue, AML, and C+. All the trip origin ends and those last destination ends for each individual on each day are geocoded using Avenue scripts The trip shortest path route system is created based on ArcInfo dynamic segmentation and network analysis functionsAn array of 2-D tree structures based on each trip\u27s beginning time and ending time and each street traversed are then created in C++ and AvenueThis array of 2-D tree structures is stored in memory. Finally, the spatiotemporal query function is performed by examining the array of 2-D tree structures for a given time window using Avenue and C++. A sample trip log data file in the Knoxville Metropolitan Area and Knox county street shape file are used to implement the spatiotemporal query. This thesis is concluded that efficient indexing methods must be developed to handle complicated spatiotemporal queries for large travel data set

Individual accessibility and travel possibilities: A literature review on time geography

In the late 1960s, Torsten Hägerstrand introduced the conceptual framework of time geography which can be deemed an elegant tool for analysing individual movement in space and time. About a decade later, the auspicious time-geographic research has gradually lost favour, mainly due to the unavailability of robust geocomputational tools and the lack of georeferenced individual-level travel data. It was only from the early 1990s that new GISbased research gave evidence of resurgence in popularity of the field. From that time on, several researchers have steadily been publishing work at the intersection of time geography, disaggregate travel modeling, and GI-science. This paper reviews the most important timegeographic contributions. From this exercise, some prevailing research gaps are deduced and a way to deal with these gaps is presented. In particular, we focus on space-time accessibility measures, geovisualisation of activity patterns, human extensibility and fuzzy space-time prisms in relation to CAD

Leveraging data from a smart card automatic fare collection system for public transit planning

RÉSUMÉ Le système de transport en commun est une créature artificielle et complexe. L’interaction spatio-temporelle entre l’offre de service par les opérateurs et la demande des usagers est difficile à mesurer et évolue constamment. C’est dans ce contexte que de nombreux efforts sont mis à la recherche de l’information et de la méthodologie qui peuvent contribuer à révéler et à comprendre cette relation dynamique afin que les services répondent aux besoins des voyageurs. Récemment, des changements aux paradigmes remodèlent ce processus. D’une part, les opérateurs de transport en commun adoptent une orientation axée sur la performance et le client. Ceci demande des données qui ne sont pas recueillies par des enquêtes traditionnelles. D’autre part, l’avancement des systèmes automatiques de collecte de données et leur adoption par les opérateurs génèrent une abondance de données dans un environnement où la collecte de données était auparavant limitée. Les systèmes d’analyse et de planification sont souvent adaptés à ces réalités et sont inadéquats pour exploiter de nouvelles sources de données. Au confluent de ces évolutions, se retrouvent un défi et une opportunité : apprivoiser les nouvelles technologies informationnelles dans le but de les réconcilier avec les besoins grandissants de données dans le domaine de transport en commun. Cette recherche se fonde sur un jeu de données de validation provenant d’un système de perception par carte à puce (CAP). Le but de la recherche est de développer de nouvelles méthodologies d’exploitation des données, notamment au niveau de leur traitement, de leur enrichissement et de leur analyse afin de mieux connaître la demande de transport en commun, d’améliorer la planification opérationnelle, de raffiner la gestion du système et de comprendre les comportements de déplacement. Le jeu de données principal provient du système de perception par CAP de la Société de transport de l’Outaouais (STO). Le système est muni de GPS et le jeu contient toutes les validations désagrégées pour le mois de février 2005. Les technologies informationnelles, incluant la base de données relationnelle, le système d’information géographique (SIG), les statistiques spatiales, le data mining et les visualisations, sont des principaux outils de traitement et d’analyse.----------ABSTRACT Public transit system is an artificial and complex creature. The interaction between operators’ supply and users’ demand is at the same time spatial and temporal. It is also difficult to measure and in constant evolution. There is a continuous quest for information and methodology that can help reveal and facilitate the understanding of this dynamic relationship, so that public transit services can be better organized to suit travelers’ needs. Recent paradigm shifts have contributed the reshaping of this process. On the one hand, public transit service has become more performance-driven and customer-oriented. These require data not covered by traditional survey methods. On the other hand, advances in passive data collection methods and their adoption by transit operators progressively transform the industry from data-poor to data-rich. Traditional analysis and planning tools are adapted to past conditions and are not suited to fully leverage new sources of data. At the confluence of these evolutions lies opportunity and challenge: to embrace the data-rich environment with the view of reconciling with the increasingly demanding data needs in public transit. The research is based on a set of validations data from a smart card automatic fare collection (AFC) system. The goal of the research is to develop new methods in data processing, data enrichment and data analysis in order to better quantify transit demand, enhance operations planning, improve system management and understand travel behaviour. The primary dataset comes from the smart card AFC of the Société de transport de l’Outaouais (STO). The system is equipped with GPS and the dataset contains all fare validations in a disaggregate form for the month of February 2005. Information technologies, including relational database, geographic information system (GIS), spatial statistics, data mining and visualization are the main data processing and analysis tools. Three overall principles guide the research: the information-based (data-driven) approach, the totally disaggregated approach and the object-oriented approach. Combined with multi-day smart card data, these principles lead to the multi-day information approach, a new concept used in the proposed data processing and enrichment procedures. The assumption is that each day of data represent partial information of the universe and may contain errors. By synthesizing the correct information from each day, it is possible to reconstruct complete knowledge. The latter is in turn used as a reference to analyze and interpret multi-day data

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