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The COVID-19 outbreak implied many changes in the daily life of most of the world's population for a long time, prompting severe restrictions on sociality. The Behavioral Immune System (BIS) suggests that when facing pathogens, a psychological mechanism would be activated that, among other things, would generate an increase in prejudice and discrimination towards marginalized groups, including immigrants. This study aimed to test if people tend to enhance their rejection of minorities and foreign groups under the threat of contagious diseases, using the users' attitudes towards migrants in Twitter data from Chile, for pre-pandemic and pandemic contexts. Our results appear to be mostly against the BIS hypothesis, with some faint exceptions, since threatened users increased their tweet production in the pandemic period, compared to empathetic users, but the latter grew in number and also increased the reach of their tweets between the two periods. We also found differences in the use of language between these types of users. Alternative explanations for these results may be context-dependent. [ABSTRACT FROM AUTHOR]

Futbol Club Barcelona operates the largest stadium in Europe (with a seating capacity of almost one hundred thousand people) and manages recurring sports events. These are influenced by multiple conditions (time and day of the week, weather, adversary) and affect city dynamics—e.g., peak demand for related services like public transport and stores. We study fine grain audience entrances at the stadium segregated by visitor type and gate to gain insights and predict the arrival behavior of future games, with a direct impact on the organizational performance and productivity of the business. We can forecast the timeline of arrivals at gate level 72 h prior to kickoff, facilitating operational and organizational decision-making by anticipating potential agglomerations and audience behavior. Furthermore, we can identify patterns for different types of visitors and understand how relevant factors affect them. These findings directly impact commercial and business interests and can alter operational logistics, venue management, and safety. [ABSTRACT FROM AUTHOR]

Understanding public opinion towards immigrants is key to prevent acts of violence, discrimination and abuse. Traditional data sources, such as surveys, provide rich insights into the formation of such attitudes; yet, they are costly and offer limited temporal granularity, providing only a partial understanding of the dynamics of attitudes towards immigrants. Leveraging Twitter data and natural language processing, we propose a framework to measure attitudes towards immigration in online discussions. Grounded in theories of social psychology, the proposed framework enables the classification of users' into profile stances of positive and negative attitudes towards immigrants and characterisation of these profiles quantitatively summarising users' content and temporal stance trends. We use a Twitter sample composed of 36 K users and 160 K tweets discussing the topic in 2017, when the immigrant population in the country recorded an increase by a factor of four from 2010. We found that the negative attitude group of users is smaller than the positive group, and that both attitudes have different distributions of the volume of content. Both types of attitudes show fluctuations over time that seem to be influenced by news events related to immigration. Accounts with negative attitudes use arguments of labour competition and stricter regulation of immigration. In contrast, accounts with positive attitudes reflect arguments in support of immigrants' human and civil rights. The framework and its application can inform policy makers about how people feel about immigration, with possible implications for policy communication and the design of interventions to improve negative attitudes. [ABSTRACT FROM AUTHOR]

In this paper, the Camp Nou stadium is used as a testbed for City Physiology, a theoretical framework for urban digital twins. With this case study, the modularity and adaptability of the framework, originally intended for city-scale simulations, are tested on a large facility venue. As a proof of concept, several statistical techniques and an agent-based simulation platform are coupled to simulate a crowd in the stadium, and a process of four steps is followed to build the case study. Both the conceptual (interdomain) and technical (domain specific) layers of the digital twin are defined and connected in a nonlinear process so that they represent the complexity of the object to be simulated. The result obtained is a strategy to build a digital twin from the domain point of view, paving the way for more complex, more ambitious simulators. [ABSTRACT FROM AUTHOR]

As cities expand, human mobility has become a central focus of urban planning and policy making to make cities more inclusive and sustainable. Initiatives such as the "15-minutes city" have been put in place to shift the attention from monocentric city configurations to polycentric structures, increasing the availability and diversity of local urban amenities. Ultimately they expect to increase local walkability and increase mobility within residential areas. While we know how urban amenities influence human mobility at the city level, little is known about spatial variations in this relationship. Here, we use mobile phone, census, and volunteered geographical data to measure geographic variations in the relationship between origin-destination flows and local urban accessibility in Barcelona. Using a Negative Binomial Geographically Weighted Regression model, we show that, globally, people tend to visit neighborhoods with better access to education and retail. Locally, these and other features change in sign and magnitude through the different neighborhoods of the city in ways that are not explained by administrative boundaries, and that provide deeper insights regarding urban characteristics such as rental prices. In conclusion, our work suggests that the qualities of a 15-minutes city can be measured at scale, delivering actionable insights on the polycentric structure of cities, and how people use and access this structure. [ABSTRACT FROM AUTHOR]

This paper explores the misogynistic abuse against female Chilean politicians who openly supported a pro-choice bill that allowed the access to abortion in limited circumstances. We analysed the verbal abuse targeted at these politicians during the legislation of the abortion bill (2015–2017) and the linguistic and discursive patterns of online abuse. To that end, we collected tweets from this legislation period and created a subset with specific milestones of the parliamentary debate. Further, we undertook a corpus-assisted analysis of the data, focusing on collocations and keywords, which were then analysed in the light of van Leeuwen's (2008) framework on the representation of social actors and legitimation strategies. Results evidence that violence against women in power can take forms other than the explicit sexual, physical, and psychological threats that are commonly identified. Violence targets these women as it is claimed that they are unsuitable to legislate for allegedly having tolerated and protected crime. Therefore, the corrective function of abuse takes the form of legal actions against their crimes. [ABSTRACT FROM AUTHOR]

Pokémon Go, a location-based game that uses augmented reality techniques, received unprecedented media coverage due to claims that it allowed for greater access to public spaces, increasing the number of people out on the streets, and generally improving health, social, and security indices. However, the true impact of Pokémon Go on people's mobility patterns in a city is still largely unknown. In this paper, we perform a natural experiment using data from mobile phone networks to evaluate the effect of Pokémon Go on the pulse of a big city: Santiago, capital of Chile. We found significant effects of the game on the floating population of Santiago compared to movement prior to the game's release in August 2016: in the following week, up to 13.8% more people spent time outside at certain times of the day, even if they do not seem to go out of their usual way. These effects were found by performing regressions using count models over the states of the cellphone network during each day under study. The models used controlled for land use, daily patterns, and points of interest in the city. Our results indicate that, on business days, there are more people on the street at commuting times, meaning that people did not change their daily routines but slightly adapted them to play the game. Conversely, on Saturday and Sunday night, people indeed went out to play, but favored places close to where they live. Even if the statistical effects of the game do not reflect the massive change in mobility behavior portrayed by the media, at least in terms of expanse, they do show how 'the street' may become a new place of leisure. This change should have an impact on long-term infrastructure investment by city officials, and on the drafting of public policies aimed at stimulating pedestrian traffic. [ABSTRACT FROM AUTHOR]

Mobile data has allowed us to sense urban dynamics at scales and granularities not known before, helping urban planners to cope with urban growth. A frequently used kind of dataset are Call Detail Records (CDR), used by telecommunication operators for billing purposes. Being an already extracted and processed dataset, it is inexpensive and reliable. A common assumption with respect to geography when working with CDR data is that the position of a device is the same as the Base Transceiver Station (BTS) it is connected to. Because the city is divided into a square grid, or by coverage zones approximated by Voronoi tessellations, CDR network events are assigned to corresponding areas according to BTS position. This geolocation may suffer from non negligible error in almost all cases. In this paper we propose “Antenna Virtual Placement” (AVP), a method to geolocate mobile devices according to their connections to BTS, based on decoupling antennas from its corresponding BTS according to its physical configuration (height, downtilt, and azimuth). We use AVP applied to CDR data as input for two different tasks: first, from an individual perspective, what places are meaningful for them? And second, from a global perspective, how to cluster city areas to understand land use using floating population flows? For both tasks we propose methods that complement or improve prior work in the literature. Our proposed methods are simple, yet not trivial, and work with daily CDR data from the biggest telecommunication operator in Chile. We evaluate them in Santiago, the capital of Chile, with data from working days from June 2015. We find that: (1) AVP improves city coverage of CDR data by geolocating devices to more city areas than using standard methods; (2) we find important places (home and work) for a 10% of the sample using just daily information, and recreate the population distribution as well as commuting trips; (3) the daily rhythms of floating population allow to cluster areas of the city, and explain them from a land use perspective by finding signature points of interest from crowdsourced geographical information. These results have implications for the design of applications based on CDR data like recommendation of places and routes, retail store placement, and estimation of transport effects from pollution alerts. [ABSTRACT FROM AUTHOR]

Pervasive data have become a key source of information for mobility and transportation analyses. However, as a secondary source, it has a different methodological origin than travel survey data, usually relying on unsupervised algorithms, and so it requires to be assessed as a dataset. This assessment is challenging, because, in general, there is not a benchmark dataset or a ground truth scenario available, as travel surveys only represent a partial view of the phenomenon and suffer from their own biases. For this critical task, which involves urban planners and data scientists, we study the design space of the visualization of cross-origin, multivariate flow datasets. For this purpose, we introduce the Modalflow system, which incorporates and adapts different visualization techniques in a notebook-like setting, presenting novel visual encodings and interactions for flows with modal partition into scatterplots, flow maps, origin-destination matrices, and ternary plots. Using this system, we extract general insights on visual analysis of pervasive and survey data for urban mobility and assess a mobile phone network dataset for one metropolitan area. [ABSTRACT FROM AUTHOR]

The rising availability of digital traces provides a fertile ground for data-driven solutions to problems in cities. However, even though a massive data set analyzed with data science methods may provide a powerful and cost-effective solution to a problem, its adoption by relevant stakeholders is not guaranteed due to adoption barriers such as lack of interpretability and interoperability. In this context, this paper proposes a methodology toward bridging two disciplines, data science and transportation, to identify, understand, and solve transportation planning problems with data-driven solutions that are suitable for adoption by urban planners and policy makers. The methodology is defined by four steps where people from both disciplines go from algorithm and model definition to the development of a potentially adoptable solution with evaluated outputs. We describe how this methodology was applied to define a model to infer commuting trips with mode of transportation from mobile phone data, and we report the lessons learned during the process. [ABSTRACT FROM AUTHOR]

People often base their mobility decisions on subjective aspects of travel experience, such as time perception, space usage, and safety. It is well recognized that different groups within a population will react differently to the same trip, however, current data collection methods might not consider the multi dimensional aspects of travel perception, which could lead to overlooking the needs of large population groups. In this paper, we propose to measure several aspects of the travel experience from the social media platform Twitter, with a focus on differences with respect to gender. We analyzed more than 400,000 tweets from 100,000 users about transportation from Santiago, Chile. Our main findings show that both genders express themselves differently, as women write about their emotions regarding travel (both, positive and negative feelings), that men express themselves using slang, making it difficult to interpret emotion. The strongest difference is related to harassment, not only on transportation, but also on the public space. Since these aspects are usually omitted from travel surveys, our work provides evidence on how Twitter allows the measurement of aspects of the transportation system in a city that have been studied in qualitative terms, complementing surveys with emotional and safety aspects that are as relevant as those traditionally measured. [ABSTRACT FROM AUTHOR]

Cities are growing at a fast rate, and transportation networks need to adapt accordingly. To design, plan, and manage transportation networks, domain experts need data that reflect how people move from one place to another, at what times, for what purpose, and in what mode(s) of transportation. However, traditional data collection methods are not cost-effective or timely. For instance, travel surveys are very expensive, collected every ten years, a period of time that does not cope with quick city changes, and using a relatively small sample of people. In this paper, we propose an algorithmic pipeline to infer the distribution of mode of transportation usage in a city, using mobile phone network data. Our pipeline is based on a Topic-Supervised Non-Negative Matrix Factorization model, using a Weak-Labeling strategy on user trajectories with data obtained from open datasets, such as GTFS and OpenStreetMap. As a case study, we show results for the city of Santiago, Chile, which has a sophisticated intermodal public transportation system. Importantly, our pipeline delivers coherent results that are explainable, with interpretable parameters at each step. Finally, we discuss the potential applications and implications of such a system in transportation and urban planning. [ABSTRACT FROM AUTHOR]

In Latin America, shopping malls seem to offer an open, safe and democratic version of the public space. However, it is often difficult to quantitatively measure whether they indeed foster, hinder, or are neutral with respect to social inclusion. In this work, we investigate if, and by how much, people from different social classes are attracted by the same malls. Using a dataset of mobile phone network records from 387,152 devices identified as customers of 16 malls in Santiago de Chile, we performed several analyses to study whether malls with higher social mixing attract more people. Our pipeline, which starts with the socio-economic characterization of mall visitors, includes the estimation of social mixing and diversity of malls, the application of the gravity model of mobility, and the definition of a co-visitation model. Results showed that people tend to choose a profile of malls more in line with their own socio-economic status and the distance from their home to the mall, and that higher mixing does positively contribute to the process of choosing a mall. We conclude that (a) there is social mixing in malls, and (b) that social mixing is a factor at the time of choosing which mall to go to. Thus, the potential for social mixing in malls could be capitalized by designing public policies regarding transportation and mobility to make some malls strong social inclusion hubs. [ABSTRACT FROM AUTHOR]