3,868 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
Data-Driven Allocation of Preventive Care With Application to Diabetes Mellitus Type II
Problem Definition. Increasing costs of healthcare highlight the importance
of effective disease prevention. However, decision models for allocating
preventive care are lacking.
Methodology/Results. In this paper, we develop a data-driven decision model
for determining a cost-effective allocation of preventive treatments to
patients at risk. Specifically, we combine counterfactual inference, machine
learning, and optimization techniques to build a scalable decision model that
can exploit high-dimensional medical data, such as the data found in modern
electronic health records. Our decision model is evaluated based on electronic
health records from 89,191 prediabetic patients. We compare the allocation of
preventive treatments (metformin) prescribed by our data-driven decision model
with that of current practice. We find that if our approach is applied to the
U.S. population, it can yield annual savings of $1.1 billion. Finally, we
analyze the cost-effectiveness under varying budget levels.
Managerial Implications. Our work supports decision-making in health
management, with the goal of achieving effective disease prevention at lower
costs. Importantly, our decision model is generic and can thus be used for
effective allocation of preventive care for other preventable diseases.Comment: Accepted by Manufacturing & Service Operations Managemen
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