Discovering Latent Patterns of Urban Cultural Interactions in WeChat for Modern City Planning

Cultural activity is an inherent aspect of urban life and the success of a modern city is largely determined by its capacity to o er gen- erous cultural entertainment to its citizens. To this end, the optimal allocation of cultural establishments and related resources across urban regions becomes of vital importance, as it can reduce nan- cial costs in terms of planning and improve quality of life in the city, more generally. In this paper, we make use of a large longitudinal dataset of user location check-ins from the online social network WeChat to develop a data-driven framework for culture planning in the city of Beijing. We exploit rich spatio-temporal representations on user activity at cultural venues and use a novel extended version of the traditional latent Dirichlet allocation model that incorporates temporal information to identify latent patterns of urban cultural interactions. Using the characteristic typologies of mobile user cul- tural activities emitted by the model, we determine the levels of demand for di erent types of cultural resources across urban areas. We then compare those with the corresponding levels of supply as driven by the presence and spatial reach of cultural venues in local areas to obtain high resolution maps that indicate urban re- gions with lack or oversupply of cultural resources, and thus give evidence and suggestions for further urban cultural planning and investment optimisation.Cambridge Trus

Spatio-temporal patterns of human mobility from geo-social networks for urban computing: Analysis, models & applications

The availability of rich information about fine-grained user mobility in urban environments from increasingly geographically-aware social networking services and the rapid development of machine learning applications greatly facilitate the investigation of urban issues. In this setting, urban computing emerges intending to tackle a variety of challenges faced by cities nowadays and to offer promising approaches to improving our living environment. Leveraging massive amounts of data from geo-social networks with unprecedented richness, we show how to devise novel algorithmic techniques to reveal underlying urban mobility patterns for better policy-making and more efficient mobile applications in this dissertation. Building upon the foundation of existing research efforts in urban computing field and basic machine learning techniques, in this dissertation, we propose a general framework of urban computing with geo-social network data and develop novel algorithms tailored for three urban computing tasks. We begin by exploring how the transition data recording human movements between urban venues from geo-social networks can be aggregated and utilised to detect spatio-temporal changes of local graphs in urban areas. We further explore how this can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing areas by supervised machine learning methods. We then study how to extract latent patterns from collective user-venue interactions with the help of a spatio-temporal aware topic modeling approach for the benefit of urban infrastructure planning. After that, we propose a model to detect the gap between user-side demand and venue-side supply levels for certain types of services in urban environments to suggest further policymaking and investment optimisation. Finally, we address a mobility prediction task, the application aim of which is to recommend new places to explore in the city for mobile users. To this end, we develop a deep learning framework that integrates memory network and topic modeling techniques. Extensive experiments indicate that the proposed architecture can enhance the prediction performance in various recommendation scenarios with high interpretability. All in all, the insights drawn and the techniques developed in this dissertation make a substantial step in addressing issues in cities and open the door to future possibilities in the promising urban computing area

A Social Citizen Dashboard for Participatory Urban Planning in Berlin: Prototype and Evaluation

Participatory urban planning enables citizens to make their voices heard in the urban planning process. The resulting measures are more likely to be accepted by the community. However, the parti-cipation process becomes more effortful and time-consuming. New approaches have been developed using digital technologies to facilitate citizen participation, such as topic modeling based on social media. Using Twitter data for the city of Berlin, we explore how social media and topic modeling can be used to classify and analyze citizen opinions. We develop a Social Citizen Dashboard allowing for a better understanding of changes in citizens’ priorities and incorporating constant cycles of feedback throughout planning phases. Evaluation interviews indicate the dashboard’s potential usefulness and implications as well as point to limitation in data quality and spur further research potentials

Forecasting of the Urban Area State Using Convolutional Neural Networks

Active development of modern cities requires not only efficient monitoring systems but furthermore forecasting systems that can predict future state of the urban area with high accuracy. In this work we present a method for urban area prediction based on geospatial activity of users in social network. One of the most popular social networks, Instagram, was taken as a source for spatial data and two large cities with different peculiarities of online activity – New York City, USA, and Saint Petersburg, Russia – were taken as target cities. We propose three different deep learning architectures that are able to solve a target problem and show that convolutional neural network based on three-dimensional convolution layers provides the best results with accuracy of 99%

Digital social norms and mobile-based social networking applications : a study of urban Chinese young people's use of WeChat

Ph.D. ThesisToday, the advent of mobile-based social networking applications is dramatically changing how urban Chinese young people socialise with each other, as well as how they experience the world. In particular, WeChat – the most popular Chinese mobile-based social networking application – has been launched onto the market, attracting millions of young users in urban China. The ways in which young people use this application are inextricably linked to the dynamics of their urban living experiences, forming the digital social norms to which they adhere in their everyday lives. In this thesis, I develop an interdisciplinary approach which synthesises affect/new materialism and traditional cultural studies (e.g. symbolic interactionism) in order to understand the digital social norms emerging with urban Chinese young people’s everyday use of WeChat. In particular, Chinese college students are a representative group of young people, who are early adopters of WeChat and lead the trend of its usage in China. Through a year-long netnographic enquiry with 19 college students recruited from a chosen university in China, the research uncovers: 1) how the affective design of WeChat attracts urban Chinese young people’s attention and influences their everyday practices; 2) how these young people practise self-presentation through their personalisation of space; 3) how these young people socialise with close-by strangers; as well as 4) how these young people preserve their spatial privacy. The outcomes of the discussion not only help to understand the digital social norms emerging with this particular form of technology among urban Chinese younger generation but also develop an in-depth understanding of the relationship between culture and technology that speaks to a broader audience

Contextual Affordances of Social Media, Clinical Prosess Changes and Health Service Outcomes

Never had consumers been empowered by information technologies such as social media-enabled portals that permit them to access and conduct all aspects of life and work activities through a mobile phone at any time from anywhere. WeChat, with over 963 million active monthly users, represents such a revolutionary platform. In healthcare, patients can use WeChat to make doctor appointments, access health and lab results, consult with doctors, and check on the queuing status and parking conditions in the health clinics and hospitals. Such social-media-enabled systems have transformed the relationships between consumers and businesses into a new paradigm in which the supply-side is driven by the demand-side. As a result, the new technology is fundamentally changing; not only the context in which business is conducted but also the business itself. The extant literature on technology acceptance, however, has mostly focused on technical functionalities and user characteristics without adequately considering the specific context in which the technology is used. Although these affordance concepts have advanced our knowledge about the interactions between technology and users, the specific contexts in which such interactions occur have been largely ignored. There is a critical literature gap that hinders our ability to understand and provide guidelines to help organizations deal with the complex challenges they face in managing social mediaenabled technologies in today’s changing environment. Our research attempts to bridge this critical literature gap by conceptualizing the concept of contextual affordance, and by examining its determinants and consequences in healthcare services. We use a combination of qualitative method and quantitative method. Research sites are in China across multiple healthcare facilities. The anticipated findings include validated dimensions of contextual affordance and relationships between contextual affordance and its determinants and impacts on clinical process changes and health service outcomes. Theoretically, this study extends the current understanding of affordance by considering contextual dimensions of affordance, and by examining the relationships between contextual affordance and its determinants and consequences. Practically, this study sheds new lights on how organizations should go beyond the out-of-context interactions between technologies and users by considering users’ perceived affordance of technology within the specific contexts of use

Dwelling in a “living museum of old Beijing”: a study of the cultural heritage discourse in residential everyday life

This study explores the relationship between cultural heritage discourse and the people who inhabit heritage spaces, through research conducted in an urban residential neighbourhood of conservation importance in Beijing, China. With the aim of improving the implementation of heritage conservation, which has been an established discourse of preserving community culture and history and gets popular based on values attached to old buildings, the study departs from the conventional heritage-centred perspective and foregrounds an everyday perspective, to demonstrate the way that heritage conservation is integrated into the inhabitants’ social lives and personal histories. The thesis is organised into two empirical parts: Firstly, it examines how heritage is discursively constituted and how heritage conservation has become the mainstream approach to preserving and representing local culture in Beijing. Secondly, it describes and examines inhabitants' attitudes, understandings of heritage discourse, and their practices of everyday life in these spaces based on data collected from ethnographic fieldwork. Drawing on these two aspects, the study argues that the cultural heritage discourse and corresponding framings of local culture in urban China form a type of social knowledge that is constantly reproduced in social practices and has been legitimized and popularized among citizens. As a result, the urban space is defined by the hegemonic social knowledge with limited public doubts, while other spatial practices are marginalized. Inhabitants of conservation spaces navigate this situation by using the knowledge in various ways to their everyday lives according to their individual needs for a better life. However, this common yet individualized approach does not change their marginalized positions in defining and using the urban space that was their home before the heritage discourse arrived. By identifying the practical issues arising from the implementation of heritage conservation, this study offers an alternative perspective for understanding the social impacts of cultural heritage conservation and inspires further ideas for possible solutions

Geographies of online social interaction: a big data analytics approach to social media platform Sina Weibo

Social media has revolutionized many aspects of people’s social life. However, few studies have utilized massive individual-level data from social media to examine the effects of geography. In this study a program was developed to collect and analyze data from Sina Weibo in ten selected Chinese cities. Four geographic concepts, i.e., borders, distance, places, and urban system hierarchy were chosen to measure the geographic effects by investigating geographical distribution of people’s connections and comparing tweets similarity between different cities. The results show that these geographic concepts are playing an important role in the formation of new online connections and shaping people’s interests. Social media users still tend to establish connections and share more common interests with people who live in the same city or close to them. People who live in the first-tier cities have more opportunities to establish connections across the country and their interests cover a broader range

Mining Behavioral Patterns from Mobile Big Data

Mobile devices connected to the Internet are a ubiquitous platform that can easily record a large amount of data describing human behavior. Specifically, the data collected from mobile devices --- referred to as mobile big data reveal important social and economic information. Therefore, analyzing mobile big data is valuable for several stakeholders, ranging from smartphone manufacturers to network operators and app developers. This thesis aims to discover and understand behavioral patterns from mobile big data based on large real-world datasets. Specifically, this thesis reveals patterns from three domains: people, time, and location. First, we explore mobile big data from the people domain and propose a framework to discover users' daily activity patterns from their mobile app usage. By applying the framework to a real-world dataset consisting of 653,092 users, we successfully extract five common patterns among millions of people, including commuting, pervasive socializing, nightly entertainment, afternoon reading, and nightly socializing. Second, still from the people domain, we derive group health conditions by using their smartphone usage data. In particular, we collect mobile usage records of 452 users in North America. We then demonstrate the potential for inferring group health conditions (i.e., COVID-19 outbreak stages) by leveraging less privacy-sensitive smartphone data, including CPU usage, memory usage, and network connections. Third, we mine the behavior patterns from the time domain. We reveal the evolution of mobile app usage by conducting a longitudinal study on 1,465 users from 2012 to 2017. The results show that users' app usage significantly changes over time. However, the evolution in app-category usage and individual app usage are different in terms of popularity distribution, usage diversity, and correlations. Last, with respect to the location domain, we leverage city-scale spatiotemporal mobile app usage data to reveal urban land usage patterns. We prove the strong correlation between mobile usage behavior and location features, which brings a new angle to urban analytics.Internetiin kytketyt mobiililaitteet ovat kaikkialla läsnä oleva alusta, joka voi helposti tallentaa suuren määrän tietoja, jotka kuvaavat ihmisen käyttäytymistä. Erityisesti mobiililaitteista kerätyt tiedot, joita kutsutaan mobiiliksi massadataksi (big data), paljastavat tärkeitä sosiaalisia ja taloudellisia tietoja. Siksi mobiilin massadatan analysointi on arvokasta useille sidosryhmille älypuhelinvalmistajista verkko-operaattoreihin ja sovelluskehittäjiin. Tämän väitöskirjan tavoitteena on löytää ja ymmärtää käyttäytymismalleja mobiilista massadatasta, joka perustuu suuriin reaalimaailman tietojoukkoihin. Erityisesti tämä väitöskirja tuottaa malleja kolmelta eri alueelta: ihmisiin, aikaan ja sijaintiin liittyen. Ensinnäkin tutkimme mobiilia massadataa ihmisiin liittyen ja ehdotamme viitekehystä, jonka avulla voidaan löytää käyttäjien päivittäisiä toimintamalleja heidän mobiilisovellustensa käytön perusteella. Soveltamalla tätä viitekehystä tosielämän tietojoukkoon, joka koostuu 653 092 käyttäjästä, löysimme onnistuneesti viisi yleistä mallia miljoonien ihmisten tiedoista, joihin kuuluivat mm. tiedot työmatkoista, sosiaalisista kontakteista, yöllisestä viihteestä, iltapäivän lukemisesta ja yöllisestä seurustelusta. Toiseksi, edelleen ihmisiin liittyen, johdamme tietoja ryhmien terveysolosuhteista käyttämällä heidän älypuhelintensa käyttötietoja. Keräsimme erityisesti 452 käyttäjän mobiilikäyttötietoja Pohjois-Amerikassa. Sitten osoitamme, että on mahdollista päätellä ryhmän terveysolosuhteet (eli COVID-19-epidemiavaiheet) hyödyntämällä vähemmän yksityisyyden kannalta arkoja älypuhelintietoja, mukaan lukien suorittimen käyttö, muistin käyttö ja verkkoyhteydet. Kolmanneksi louhimme käyttäytymismalleja aikaan liittyen. Paljastamme mobiilisovellusten käytön kehityksen tekemällä pitkittäistutkimuksen 1 465 käyttäjälle vuosina 2012–2017. Tulokset osoittavat, että käyttäjien sovellusten käyttö muuttuu merkittävästi ajan myötä. Sovellusluokan käytön ja yksittäisten sovellusten käytön kehitys on kuitenkin erilainen niiden suosion jakautumisen, käytön moninaisuuden ja korrelaatioiden suhteen. Lopuksi liittyen sijaintitietoihin hyödynnämme spatiotemporaalisten mobiilisovellusten käyttötietoja suurkaupunkitasolla paljastaaksemme kaupunkien maankäyttömallit. Todistamme vahvan korrelaation mobiililaitteiden käyttöön liittyvän käyttäytymisen ja sijaintiominaisuuksien välillä, mikä tuottaa uuden näkökulman kaupunkianalytiikkaan

市民への電子行政サービスのプロモーション -中国地方電子政府の事例から

早大学位記番号:新7939早稲田大