Zobrazeno 1 - 10
of 333
pro vyhledávání: '"Martínez, Vanessa"'
Autor:
Wu, Jiahui, Frias-Martinez, Vanessa
Deep learning architectures enhanced with human mobility data have been shown to improve the accuracy of short-term crime prediction models trained with historical crime data. However, human mobility data may be scarce in some regions, negatively imp
Externí odkaz:
http://arxiv.org/abs/2406.06645
Autor:
Wu, Jiahui, Frias-Martinez, Vanessa
Deep learning crime predictive tools use past crime data and additional behavioral datasets to forecast future crimes. Nevertheless, these tools have been shown to suffer from unfair predictions across minority racial and ethnic groups. Current appro
Externí odkaz:
http://arxiv.org/abs/2406.04382
Social media platforms like Twitter (now X) have been pivotal in information dissemination and public engagement, especially during COVID-19. A key goal for public health experts was to encourage prosocial behavior that could impact local outcomes su
Externí odkaz:
http://arxiv.org/abs/2405.17710
The COVID-19 Forecast Hub, a repository of COVID-19 forecasts from over 50 independent research groups, is used by the Centers for Disease Control and Prevention (CDC) for their official COVID-19 communications. As such, the Forecast Hub is a critica
Externí odkaz:
http://arxiv.org/abs/2405.14891
Early in the pandemic, counties and states implemented a variety of non-pharmacological interventions (NPIs) focused on mobility, such as national lockdowns or work-from-home strategies, as it became clear that restricting movement was essential to c
Externí odkaz:
http://arxiv.org/abs/2405.11121
One of the central difficulties of addressing the COVID-19 pandemic has been accurately measuring and predicting the spread of infections. In particular, official COVID-19 case counts in the United States are under counts of actual caseloads due to t
Externí odkaz:
http://arxiv.org/abs/2405.10355
COVID-19 forecasting models have been used to inform decision making around resource allocation and intervention decisions e.g., hospital beds or stay-at-home orders. State of the art deep learning models often use multimodal data such as mobility or
Externí odkaz:
http://arxiv.org/abs/2405.09483
Human mobility data has been extensively used in covid-19 case prediction models. Nevertheless, related work has questioned whether mobility data really helps that much. We present a systematic analysis across mobility datasets and prediction lookahe
Externí odkaz:
http://arxiv.org/abs/2407.10304
Publikováno v:
Journal of Cycling and Micromobility Research (2024)
The results show that pedelecs are generally associated with longer trip distances, shorter trip times, higher speeds, and lower rates of uphill elevation change. The origin-destination analysis considering the business, mixed use, residential, and o
Externí odkaz:
http://arxiv.org/abs/2404.18075
In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacol
Externí odkaz:
http://arxiv.org/abs/2210.03901