22,314 research outputs found
Mining large-scale human mobility data for long-term crime prediction
Traditional crime prediction models based on census data are limited, as they
fail to capture the complexity and dynamics of human activity. With the rise of
ubiquitous computing, there is the opportunity to improve such models with data
that make for better proxies of human presence in cities. In this paper, we
leverage large human mobility data to craft an extensive set of features for
crime prediction, as informed by theories in criminology and urban studies. We
employ averaging and boosting ensemble techniques from machine learning, to
investigate their power in predicting yearly counts for different types of
crimes occurring in New York City at census tract level. Our study shows that
spatial and spatio-temporal features derived from Foursquare venues and
checkins, subway rides, and taxi rides, improve the baseline models relying on
census and POI data. The proposed models achieve absolute R^2 metrics of up to
65% (on a geographical out-of-sample test set) and up to 89% (on a temporal
out-of-sample test set). This proves that, next to the residential population
of an area, the ambient population there is strongly predictive of the area's
crime levels. We deep-dive into the main crime categories, and find that the
predictive gain of the human dynamics features varies across crime types: such
features bring the biggest boost in case of grand larcenies, whereas assaults
are already well predicted by the census features. Furthermore, we identify and
discuss top predictive features for the main crime categories. These results
offer valuable insights for those responsible for urban policy or law
enforcement
Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors
In this paper we investigate the problem of localizing a mobile device based
on readings from its embedded sensors utilizing machine learning methodologies.
We consider a real-world environment, collect a large dataset of 3110
datapoints, and examine the performance of a substantial number of machine
learning algorithms in localizing a mobile device. We have found algorithms
that give a mean error as accurate as 0.76 meters, outperforming other indoor
localization systems reported in the literature. We also propose a hybrid
instance-based approach that results in a speed increase by a factor of ten
with no loss of accuracy in a live deployment over standard instance-based
methods, allowing for fast and accurate localization. Further, we determine how
smaller datasets collected with less density affect accuracy of localization,
important for use in real-world environments. Finally, we demonstrate that
these approaches are appropriate for real-world deployment by evaluating their
performance in an online, in-motion experiment.Comment: 6 pages, 4 figure
Prediction of big five personality traits from mobile application usage
Abstract. Smartphones evolved being an integral part of our daily lives and in recent days. Studies show that smartphone usage is correlated to user personality traits. This critical ecosystem is dependent on several variables such as geographic location, demographic traits, ethnic impact or cultural influence and so on. While significant number of demographic, environmental and medical analysis is done based on smartphone usage, there are inadequate amount of study carried out to analyse human personality. All of these information provide pivotal insights for improving user experience, creating recommendations, identifying marketing strategies and for a general overall usage improvement. This study is done with application usage data collected over 6 months from 739 Android smartphone users along with a 50-item Big Five Personality Trait questionnaire. The analysis focuses on the fact that, category-level aggregated application usage is enough for predicting Big Five personality traits achieving 9β14% error which is 86β91% accuracy on average. This study concludes that user personality generates a fundamental impact on smartphone application and application category usage. This work reflects the possible personality-driven research in future and depicts the significance and involvement of application categories in achieving proper accuracy in general traits, while pursuing for personality study
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