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The COVID-19 pandemic affected many aspects of human mobility and resulted in unprecedented changes in population dynamics, including lifestyle and mobility. Recognizing the effects of the pandemic is crucial to understand changes and mitigate negative impacts. Spatial data on human activity, including mobile phone data, has the potential to provide movement patterns and identify regularly visited locations. Moreover, crowdsourced geospatial information can explain and characterize the regularly visited locations. The analysis of both mobility and routine locations in the same study has seldom been carried out using mobile phone data and linked to the effects of the pandemic. Therefore, in this article we study human mobility patterns within Portugal, using mobile phone and crowdsourced data to compare the population’s mobility and routine locations after the pandemic’s peak. We use clustering algorithms to identify citizens’ stops and routine locations, at an antenna level, during the following months after the pandemic’s first wave and the same period of the following year. Results based on two mobile phone datasets showed a significant difference in mobility in the two periods. Nevertheless, routine locations slightly differ.

There has been a recent push towards using opportunistic sensing data collected from sources like automatic vehicle location (AVL) systems, mobile phone networks, and global positioning system (GPS) tracking to construct origin-destination (O-D) matrices, which are an effective alternative to expensive and time-consuming traditional travel surveys. These data have numerous drawbacks: they may have inadequate detail about the journey, may lack spatial and temporal granularity, or may be limited due to privacy regulations. Taxi trajectory data is an opportunistic sensing data type that can be effectively used for O-D matrix construction because it addresses the issues that plague other data sources. This paper presents a new approach for using taxi trajectory data to construct a taxi O-D matrix that is dynamic in both space and time. The model's origin and destination zone sizes and locations are not fixed, allowing the dimensions to vary from one matrix to another. Comparisons between these spatiotemporal-varying O-D matrices cannot be made using a traditional method like matrix subtraction. Therefore, this paper introduces a new measure of similarity. Our proposed approaches are applied to the taxi trajectory data collected from Lisbon, Portugal as a case study. The results reveal the periods in which taxi travel demand is the highest and lowest, as well as the periods in which the highest and lowest regular taxi travel demand patterns take shape. This information about taxi travel demand patterns is essential for informed taxi service operations management.

Digital location traces are a relevant source of insights into how citizens experience their cities. Previous works using call detail records (CDRs) tend to focus on modeling the spatial and temporal patterns of human mobility, not paying much attention to the semantics of places, thus failing to model and enhance the understanding of the motivations behind people’s mobility. In this paper, we applied a methodology for identifying individual users’ routine locations and propose an approach for attaching semantic meaning to these locations. Specifically, we used circular sectors that correspond to cellular antennas’ signal areas. In those areas, we found that all contained points of interest (POIs), extracted their most important attributes (opening hours, check-ins, category) and incorporated them into the classification. We conducted experiments with real-world data from Coimbra, Portugal, and the initial experimental results demonstrate the effectiveness of the proposed methodology to infer activities in the user’s routine areas.

Abstract A longitudinal mobile phone data that include both location and communication logs is analyzed to infer social influence in terms of ego-network effect in the commute mode choice. The results show that person’s strong ties are more important to determine if driving is the person’s transport mode choice, whereas weak ties are more important to determine if public transit is the person’s choice. It is also evident from the results that social ties that are geographically closer are more influential for the commute mode choice than the ones who are farther away. For public transit, access distance is also one of the influential factors. The portion of transit users decreases as the access distance becomes larger. Moreover, social network is shown to influence the commute mode choice, as the likelihood of choosing a particular mode choice rises with the portion of social ties choosing that specific mode.

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitable features for classifying land use. As ground truth data, we used CORINE Land Cover (CLC), which is a solid and reliable dataset available for the whole European territory. It was used an artificial neural network (ANN) in different scenarios and our results reveal values of more than 90% for the accuracy and F-score in one experiment performed. Our analysis suggests that POI data have promising potential to characterize geographic spaces. The work described here aims to provide an alternative to the current methodologies for land use and land cover (LULC) classification, which are usually time-consuming and depend on expensive data types.

O texto trata da questão das alteridades corporais, refletindo sobre a condição de corpos que não se enquadram no padrão socialmente imposto, como o de travestis e transexuais. Para tanto, são utilizados o conto “Dia dos namorados”, de Rubem Fonseca, e as teorias de Judith Butler.