Near Field Communication (NFC) approach may be seen as a perspective way to improve user experience of quick data access with mobile devices to various services (secure payments, information exchange between users, etc.) in a city. This thesis concerns the topic of challenges which may be faced by interaction designers using this approach for creating a context-aware mobile solution for personalized data access in service-intensive urban environments. This research is based on considerations from researches, which explored which mobile information needs in particular seemed to be relevant for a modern user. Moreover, affordances and design blends concept are discussed in this work through a prism of the human-computer interaction in a city. ‘Research through design’ concept allowed performing the investigation of the given problem, starting from a user research, followed by prototyping an alternative solution and after - user evaluation of the prototype. Finally, a critical overview of the performed research gives suggestions for further improvement of the project.

abstract: As urban populations become increasingly dense, massive amounts of new 'big' data that characterize human activity are being made available and may be characterized as having a large volume of observations, being produced in real-time or near real-time, and including a diverse variety of information. In particular, spatial interaction (SI) data - a collection of human interactions across a set of origins and destination locations - present unique challenges for distilling big data into insight. Therefore, this dissertation identifies some of the potential and pitfalls associated with new sources of big SI data. It also evaluates methods for modeling SI to investigate the relationships that drive SI processes in order to focus on human behavior rather than data description. A critical review of the existing SI modeling paradigms is first presented, which also highlights features of big data that are particular to SI data. Next, a simulation experiment is carried out to evaluate three different statistical modeling frameworks for SI data that are supported by different underlying conceptual frameworks. Then, two approaches are taken to identify the potential and pitfalls associated with two newer sources of data from New York City - bike-share cycling trips and taxi trips. The first approach builds a model of commuting behavior using a traditional census data set and then compares the results for the same model when it is applied to these newer data sources. The second approach examines how the increased temporal resolution of big SI data may be incorporated into SI models. Several important results are obtained through this research. First, it is demonstrated that different SI models account for different types of spatial effects and that the Competing Destination framework seems to be the most robust for capturing spatial structure effects. Second, newer sources of big SI data are shown to be very useful for complimenting traditional sources of data, though they are not sufficient substitutions. Finally, it is demonstrated that the increased temporal resolution of new data sources may usher in a new era of SI modeling that allows us to better understand the dynamics of human behavior. / Dissertation/Thesis / Doctoral Dissertation Geography 2017

京都大学 / 0048 / 新制・課程博士 / 博士(情報学) / 甲第20737号 / 情博第651号 / 新制||情||112(附属図書館) / 京都大学大学院情報学研究科社会情報学専攻 / (主査)教授 吉川 正俊, 教授 石田 亨, 教授 美濃 導彦, 准教授 馬 強 / 学位規則第4条第1項該当 / Doctor of Informatics / Kyoto University / DFAM

This is a project to design digital technologies to promote uses of public spaces challenging the social religion of productivism + consumerism. Instead I celebrate participative leisure, free time, political involvement and social relationships. Digital artefacts for what I'm calling the "unproductive city". The goal is to incorporate a different set of values where the “paid work” is not as relevant in our life as it is today.The project is focused on life in cities and works with the integration of computing technologies into everyday urban settings and lifestyles. What it is called “urban informatics”.Participative processes as well as user center design have guided the design. It comprehends different services and activities: A collaborative urban jukebox, exercises with locative media, participative design as a leisure activity, technological infrastructures for meetings and game design for public spacesThese activities are examples and explorations to find future challenges and different ways to design technologies for the unproductive city.

Urban Informatics är ett forskningsvetenskapligt fält som berör sig med att försöka förstå människors upplevelse av offentliga platser när teknologi är inblandat. Då det är många faktorer som påverkar människors upplevelser av platser finns det utmaningar i Urban Informatics-processer. Det visas på förståelse inom Urban Informatics på ett behov av en designvetenskaplig process som ett svar på de utmaningar som kan uppstå. User Experience Design är ett designvetenskapligt forskningsfält där processer ämnar bibehålla ett användarcentrerat perspektiv under arbetets gång. Att ta bort fokuset från datakunskapen som krävs för teknologi och istället titta på engagemang tillåter oss att se teknologi som artefakter som kan upplevas. Olika engagemangsmodeller har gjorts tidigare där vissa har ett teknologiskt fokus på engagemang eller ett urbant fokus på engagemang. Det visar att det är viktigt att undersöka hur en sammanfattad modell kan se ut för att förklara interrelationen i människors engagemang till platser och mobilteknologi. Detta kan bidra till att minska bryggan till de forskningsfält som undersöker människor, teknologi och platser. Resultatet av studien blev en engagemangsmodell på hur ett engagemangstillfälle kan se ut med dess olika engagemangstillstånd som kan uppnås i plats och/eller mobilteknologi. / Urban Informatics is a research field that involves itself with understanding the interrelation of people, places and technology. Due to its many factors that affect people’s experiences of places when technology is involved it faces several challenges. It is understood within the field of Urban Informatics that there is a need for a research-based process based on design thinking to face these challenges. User Experience Design is a research field focuses on user-centered design processes. When the focus is removed from computing and shifted to engagement, technology can be seen as artefacts that can be experienced. Different engagement models have been made in the past where they focus either on technology or places. It shows the importance to explore how a engagement model can be interpreted and suited to explain the interrelation of peoples engagement to places using technology. The findings may contribute to lessening the gap of the research fields involved in understanding people and their engagement to places and technology. The result of this study resulted in a proposed engagement model in how different conditions of mobile engagement and place engagement may appear in one engagement session.

Designers of information communication technologies (ICTs) in public space often fall into the trap of designing only for the "flaneur," an unembedded mobile subject in the generic global city. They deracinate the experience of space and support the global flâneur as the paradigmatic deracinated subject. In this thesis I propose a specific vision of "en-racinating" media, that is media that takes the specificity of place seriously. A careful consideration of public art can help us in this endeavor by leveraging the artistic notion of "site specificity" in the most culturally grounded meaning of the term. I examining three public digital media/information-based public art works through the lens of urban informatics in order to see how the works do or do not en-racinate experience in a specific city: Atlanta

<p dir="ltr">Urban traffic networks encompass the collection and interlinking of urban entities, including but not limited to road networks, congested segments, mobile populations, and emergency occurrences. These entities facilitate daily human activities, support economic endeavors, and influence the trajectory of societal advancement. Comprehending the characteristics and anticipating the evolution of dynamic urban traffic networks have been fundamental building blocks in urban science. Typical examples include the primal and dual representations of road networks, the macroscopic fundamental diagram applied to congested roads, and models on the spread of diseases. Current seminal studies either devise physics metrics and models to elucidate universal traits of urban traffic networks, or exploit data-driven approaches to depict the urban landscape using vast amounts of urban data. However, these physics and data-driven methods primarily function separately, resulting in a lack of a comprehensive framework to accurately and interpretably (1) characterize the topology and dynamics of urban traffic networks; and (2) forecast the evolution of dynamics within urban traffic networks.</p><p dir="ltr">In this dissertation, we develop physics-informed graph learning methods to learn and forecast urban traffic networks in manners that are accurate, interpretable, adaptable, and applicable, aiming to advance urban science theories and support urban decision-making processes.</p><p dir="ltr">In Chapters 3 and 4, we explore novel physics knowledge of urban traffic networks in terms of new metrics and equations. In Chapter 3, we define new morphological metrics for urban road networks. Specifically, we present a network metric called spatial homogeneity (SH), which gauges the topological similarities among urban road networks using graph neural networks. Employing this metric, we analyze 11,790 urban road networks across 30 cities worldwide. Our findings reveal the inherent correlations between innercity SH, gross domestic product, and population growth. Furthermore, we quantify learning trajectories between cities from intercity SH and connect them with existing qualitative urban studies. In Chapter 4, we establish new differential equations governing dynamic urban traffic. Through a symbolic regression-based learning approach, we come up with network-level dynamic traffic equations (NDTEs), which capture time-of-day traffic flow and traffic occupancy dynamics. The advantages of NDTEs are twofold: (1) all input variables are easily obtainable; (2) they incorporate vehicle count-related variables. Our experiments on road networks in Zurich and Toronto demonstrate that the generated NDTEs offer enhanced fitting accuracy compared to the baseline model while maintaining a moderate level of equation complexity.</p><p dir="ltr">In Chapters 5, 6, and 7, we harness physics knowledge to devise graph learning approaches for urban prediction and imputation. In Chapter 5, we present NMFD-GNN, a physics-informed machine learning method that integrates the network macroscopic fundamental diagram and the graph neural network for traffic state imputation. Our approach is the first physics-informed machine learning model specifically designed for real-world traffic networks with multiple roads, while existing studies have primarily focused on individual road corridors. In Chapter 6, we develop the spatio-temporal physics ordinary differential equation (ST-PODE), which connects PODEs with spatio-temporal neural networks. ST-PODE is composed of the spatio-temporal neural network module, the PODE module, and the state transition module. We downscale our focus to the prediction of morning traffic patterns and evaluate our models using datasets from the Bay Area and Los Angeles. In Chapter 7, we address the multiwave COVID-19 prediction challenge on urban mobility networks. The proposed social awareness-based graph neural network (SAB-GNN) models the evolution of public awareness across multiple pandemic waves as an exponential function with learnable parameters. We employ the mobility, web search, and infection data in Tokyo from April 2020 to May 2021 to validate its performance. </p><p dir="ltr">The intended audiences of this dissertation comprise colleagues in the fields of artificial intelligence, urban science, transportation engineering, and network science. Our goal is to offer instructive insights to the community to (1) explore universal properties, (2) foresee future evolution, and (3) interpret models and results using massive graph-structured data in urban traffic networks.</p>

A Bicycle-sharing system (BSS) is a popular service scheme deployed in cities of different sizes around the world. Although docked bike systems are its most popular model used, it still experiences a number of weaknesses that could be optimized by investigating bike sharing network properties and evolution of obtained patterns.Efficiently keeping bicycle-sharing system as balanced as possible is the main problem and thus, predicting or minimizing the manual transportation of bikes across the city is the prime objective in order to save logistic costs for operating companies.The purpose of this thesis is two-fold; Firstly, it is to visualize bike flow using data exploration methods and statistical analysis to better understand mobility characteristics with respect to distance, duration, time of the day, spatial distribution, weather circumstances, and other attributes. Secondly, by obtaining flow visualizations, it is possible to focus on specific directed sub-graphs containing only those pairs of stations whose mutual flow difference is the most asymmetric. By doing so, we are able to use graph mining and machine learning techniques on these unbalanced stations.Identification of spatial structures and their structural change can be captured using Convolutional neural network (CNN) that takes adjacency matrix snapshots of unbalanced sub-graphs. A generated structure from the previous method is then used in the Long short-term memory artificial recurrent neural network (RNN LSTM) in order to find and predict its dynamic patterns.As a result, we are predicting bike flows for each node in the possible future sub-graph configuration, which in turn informs bicycle-sharing system owners in advance to plan accordingly. This combination of methods notifies them which prospective areas they should focus on more and how many bike relocation phases are to be expected. Methods are evaluated using Cross validation (CV), Root mean square error (RMSE) and Mean average error (MAE) metrics. Benefits are identified both for urban city planning and for bike sharing companies by saving time and minimizing their cost. / Lånecykel avser ett system för uthyrning eller utlåning av cyklar. Systemet används främst i större städer och bekostas huvudsakligen genom tecknande av ett abonnemang.Effektivt hålla cykel andelssystem som balanseras som möjligt huvud problemand därmed förutsäga eller minimera manuell transport av cyklar över staden isthe främsta mål för att spara logistikkostnaderna för drift companies.Syftet med denna avhandling är tvåfaldigt.För det första är det att visualisera cykelflödet med hjälp av datautforskningsmetoder och statistisk analys för att bättre förstå rörlighetskarakteristika med avseende på avstånd, varaktighet, tid på dagen, rumsfördelning, väderförhållanden och andra attribut.För det andra är det vid möjliga flödesvisualiseringar möjligt att fokusera på specifika riktade grafer som endast innehåller de par eller stationer vars ömsesidiga flödesskillnad är den mest asymmetriska.Genom att göra det kan vi anvnda grafmining och maskininlärningsteknik på dessa obalanserade stationer, och använda konjunktionsnurala nätverk (CNN) som tar adjacency matrix snapshots eller obalanserade subgrafer.En genererad struktur från den tidigare metoden används i det långa kortvariga minnet artificiella återkommande neurala nätverket (RNN LSTM) för att hitta och förutsäga dess dynamiska mönster.Som ett resultat förutsäger vi cykelflden för varje nod i den eventuella framtida underkonfigurationen, vilket i sin tur informerar cykeldelningsägare om att planera i enlighet med detta.Denna kombination av metoder meddelar dem vilka framtida områden som bör inriktas på mer och hur många cykelflyttningsfaser som kan förväntas.Metoder utvärderas med hjälp av cross validation (CV), Root mean square error (RMSE) och Mean average error (MAE) metrics.Fördelar identifieras både för stadsplanering och för cykeldelningsföretag genom att spara tid och minimera kostnaderna.