1,721,066 research outputs found
Defining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data
No full textThe urban impact of AI: modelling feedback loops in location-based recommender systems
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Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences
Full text linkNowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors
scikit-mobility: A Python Library for the Analysis, Generation, and Risk Assessment of Mobility Data
Full text linkThe last decade has witnessed the emergence of massive mobility datasets, such as tracks generated by GPS devices, call detail records, and geo-tagged posts from social media platforms. These datasets have fostered a vast scientific production on various applications of mobility analysis, ranging from computational epidemiology to urban planning and transportation engineering. A strand of literature addresses data cleaning issues related to raw spatiotemporal trajectories, while the second line of research focuses on discovering the statistical "laws" that govern human movements. A significant effort has also been put on designing algorithms to generate synthetic trajectories able to reproduce, realistically, the laws of human mobility. Last but not least, a line of research addresses the crucial problem of privacy, proposing techniques to perform the re-identification of individuals in a database. A view on state-of-the-art cannot avoid noticing that there is no statistical software that can support scientists and practitioners with all the aspects mentioned above of mobility data analysis. In this paper, we propose scikit-mobility, a Python library that has the ambition of providing an environment to reproduce existing research, analyze mobility data, and simulate human mobility habits. scikit-mobility is efficient and easy to use as it extends pandas, a popular Python library for data analysis. Moreover, scikit-mobility provides the user with many functionalities, from visualizing trajectories to generating synthetic data, from analyzing statistical patterns to assessing the privacy risk related to the analysis of mobility datasets
A complexity science perspective on human mobility
No full textFueled by big data collected by a wide range of high-throughput tools and technologies, a new wave of data-driven, interdisciplinary science has rapidly proliferated during the past decade, impacting a wide array of disciplines, from physics and computer science to cell biology and economics. In particular, the ICTs are inundating us with huge amounts of information about human activities, offering access to observing and measuring human behavior at an unprecedented level of detail. These large-scale data sets, offering objective description of human activity patterns, have started to reshape, and are expected to fundamentally alter, our discussions on quantifying and understanding human behavior. An impressive shift has been witnessed in statistical physics and complex system theory since the beginning of the new millennium, when the possibility of analyzing large data sets of human activities and social interactions boosted a renewed interest in the study of human mobility on one side, and of social networks on the other side. The understanding of how objects move, and humans in particular, is a longstanding challenge in the natural sciences, since the seminal observations by Robert Brown in the nineteenth century, but it has attracted particular interest in recent years, due to the data availability and to the relevance of the topic in various domains, from urban planning and virus spreading to emergency response
A survey on deep learning for human mobility
No full textThe study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above, and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility
Mobility constraints in segregation models
Full text linkSince the development of the original Schelling model of urban segregation,
several enhancements have been proposed, but none have considered the impact of
mobility constraints on model dynamics. Recent studies have shown that human
mobility follows specific patterns, such as a preference for short distances
and dense locations. This paper proposes a segregation model incorporating
mobility constraints to make agents select their location based on distance and
location relevance. Our findings indicate that the mobility-constrained model
produces lower segregation levels but takes longer to converge than the
original Schelling model. We identified a few persistently unhappy agents from
the minority group who cause this prolonged convergence time and lower
segregation level as they move around the grid centre. Our study presents a
more realistic representation of how agents move in urban areas and provides a
novel and insightful approach to analyzing the impact of mobility constraints
on segregation models. We highlight the significance of incorporating mobility
constraints when policymakers design interventions to address urban
segregation
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