Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics

The impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics.This work would not have been accomplished without the financial support of CONICYT-PFCHA/DOCTORADO BECAS CHILE/2019-21190345. The last author received research funds from the Basque Government as the head of the Grupo de Inteligencia Computacional, Universidad del Pais Vasco, UPV/EHU, from 2007 until 2025. The current code for the grant is IT1689-22. Additionally, the author participates in Elkartek projects KK-2022/00051 and KK-2021/00070. The Spanish MCIN has also granted the author a research project under code PID2020-116346GB-I00

(Un)filial daughters and digital feminisms in China: The stories of awakening, resisting, and finding comrades

This thesis sets out to understand Chinese feminist struggles in a so-called digital era by looking at the experiences and practices of an emerging generation of digital feminists that came into light in Chinese feminist movements. Conceptually and methodologically, this research took inspirations from an interdisciplinary body of literature including feminist theory, sociology, media and cultural studies, girlhood studies and gender studies. Inspired by online ethnography and feminist participatory methodologies, it combined an online tracking of feminist events on Weibo with semi-structured interviews and social media diary study with 21 Chinese girls and young women. This thesis explores the embedded and embodied experiences of these participants as they discover and learn about feminism, resist and challenge gender and sexual inequalities, and try to build connections with like-minded people within and beyond the digital sphere. By charting feminist responses and resistance to familial discourses and norms around girlhood and young femininity, I show the emergence of feminist subjectivities of (un)filial daughters that arises from but also comes to reconfigure gender and sexuality within a neoliberal and postsocialist context of patriarchal familism in China. I build upon the concepts of networked counterpublics and networked affects to explore how these (un)filial daughters are networked to carve out spaces for feminist discussion in social media. Employing an affective-discursive analysis, I also tune into how networked feminist resistance and alliances are formed not merely on the basis of how women and feminists talk about these issues but also how they feel

Assessing the Potential of Ride-Sharing Using Mobile and Social Data

Ride-sharing on the daily home-work-home commute can help individuals save on gasoline and other car-related costs, while at the same time it can reduce traffic and pollution. This paper assesses the potential of ride-sharing for reducing traffic in a city, based on mobility data extracted from 3G Call Description Records (CDRs, for the cities of Barcelona and Madrid) and from Online Social Networks (Twitter, collected for the cities of New York and Los Angeles). We first analyze these data sets to understand mobility patterns, home and work locations, and social ties between users. We then develop an efficient algorithm for matching users with similar mobility patterns, considering a range of constraints. The solution provides an upper bound to the potential reduction of cars in a city that can be achieved by ride-sharing. We use our framework to understand the different constraints and city characteristics on this potential benefit. For example, our study shows that traffic in the city of Madrid can be reduced by 59% if users are willing to share a ride with people who live and work within 1 km; if they can only accept a pick-up and drop-off delay up to 10 minutes, this potential benefit drops to 24%; if drivers also pick up passengers along the way, this number increases to 53%. If users are willing to ride only with people they know ("friends" in the CDR and OSN data sets), the potential of ride-sharing becomes negligible; if they are willing to ride with friends of friends, the potential reduction is up to 31%.Comment: 11 page

Characterization of behavioral patterns exploiting description of geographical areas

Abstract The enormous amount of recently available mobile phone data is providing unprecedented direct measurements of human behavior. Early recognition and prediction of behavioral patterns are of great importance in many societal applications like urban planning, transportation optimization, and health-care. Understanding the relationships between human behaviors and location's context is an emerging interest for understanding human-environmental dynamics. Growing availability of Web 2.0, i.e. the increasing amount of websites with mainly user created content and social platforms opens up an opportunity to study such location's contexts. This paper investigates relationships existing between human behavior and location context, by analyzing log mobile phone data records. First an advanced approach to categorize areas in a city based on the presence and distribution of categories of human activity (e.g., eating, working, and shopping) found across the areas, is proposed. The proposed classification is then evaluated through its comparison with the patterns of temporal variation of mobile phone activity and applying machine learning techniques to predict a timeline type of communication activity in a given location based on the knowledge of the obtained category vs. land-use type of the locations areas. The proposed classification turns out to 1 arXiv:1510.02995v1 [cs.SI] 11 Oct 2015 be more consistent with the temporal variation of human communication activity, being a better predictor for those compared to the official land use classification

Visual Social Media and Vernacular Responses to Environmental Issues in China

This thesis investigates the role of visual social media in providing ordinary Chinese with an alternative space to articulate their opinions on environmental issues. By studying three notable environmental cases, this thesis explores how ordinary Chinese adopt visual social media practices as a response to environmental issues, and to aid in the fight for environmental justice. This thesis provides a new perspective to understand China’s visual social media practices and its networked civic engagement

Sina Weibo and its political implications: a case study of the Zhou Yongkang incident

After the Chinese government blocked Facebook in 2008 and Twitter in 2009 in mainland China, perspicacious Chinese Internet service providers have invented alternative social media platforms. Sina Weibo is one of them. Scholars studying the Internet and social media in Western countries have suggested that social media have the potential to construct a unique online public sphere and contribute to a much deeper social change. However, social media and its social and political implications in such a populous developing country with 710 million Internet users have not been thoroughly addressed due to the linguistic estrangement and the firm historical association of the new communication technology with democratic discourse. Furthermore, among the literature studying Chinese Internet, there is a lack of empirical research. The number of studies that look directly into the Chinese social media content is still relatively small. Therefore, this study is an effort to fill this gap through an empirical case study to map out the distinct dynamics in China’s online public sphere facilitated by Sina Weibo. This thesis strives to examine Weibo’s role in facilitating public discussion and constructing an online public sphere in China. To this end, it analyses Sina Weibo users’ discussion about the Zhou Yongkang incident. The theoretical framework applied in this study originates from Habermas’s conception of the public sphere and Warner’s notion of publics. Since these theories formed in Anglophone context, this study focuses on extrapolating the theories into Chinese context. This study uses mixed research methods. It uses both quantitative content analysis and qualitative critical discourse analysis. A wide range of political, social and historical perspectives are also employed to explore the diverse discourse and dynamic interaction on Weibo. Drawing from the public discussion in Zhou’s case, the thesis paints a relatively promising picture of the social media as a platform for personal expression in public discussion on political issues, comparatively jumping out of the discourse agenda set by the government and state media. The interaction among users indicates that rational-critical debate has become a part of China’s online public sphere

Investigating the Potential of Ridesharing to Reduce Vehicle Emissions

As urban populations grow, cities need new strategies to maintain a good standard of living while enhancing services and infrastructure development. A key area for improving city operations and spatial layout is the transportation of people and goods. While conventional transportation systems (i.e., fossil fuel based) are struggling to serve mobility needs for growing populations, they also represent serious environmental threats. Alternative-fuel vehicles can reduce emissions that contribute to local air pollution and greenhouse gases as mobility needs grow. However, even if alternative-powered vehicles were widely employed, road congestion would still increase. This paper investigates ridesharing as a mobility option to reduce emissions (carbon, particulates and ozone) while accommodating growing transportation needs and reducing overall congestion. The potential of ridesharing to reduce carbon emissions from personal vehicles in Changsha, China, is examined by reviewing mobility patterns of approximately 8,900 privately-owned vehicles over two months. Big data analytics identify ridesharing potential among these drivers by grouping vehicles by their trajectory similarity. The approach includes five steps: data preprocessing, trip recognition, feature vector creation, similarity measurement and clustering. Potential reductions in vehicle emissions through ridesharing among a specific group of drivers are calculated and discussed. While the quantitative results of this analysis are specific to the population of Changsha, they provide useful insights for the potential of ridesharing to reduce vehicle emissions and the congestion expected to grow with mobility needs. Within the study area, ridesharing has the potential to reduce total kilometers driven by about 24% assuming a maximum distance between trips less than 10 kilometers, and schedule time less than 60 minutes. For a more conservative maximum trip distance of 2 kilometers and passenger schedule time of less than 40 minutes, the reductions in traveled kilometers could translate to the equivalent of approximately 4.0 tons CO2 emission reductions daily