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Use CaseRiskGovernment & Public Sector / NGO

Predict hazardous locations and establishments based on open source data

Location and establishments hazardous to consumers and individuals might include restaurants, illegal businesses, areas prone to crime and violence and others. Hazards may be medical, environmental, human-caused or other natural phenomena.

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Risk reduction
Risk reduction - Spot issue (eg copyright infringement)
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Risk reduction
Risk reduction - Reduce environmental physical risk
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Risk reduction
Risk reduction - Predictive diagnosis
Description

Predict hazardous locations and establishments based on open source data

Linked Case Studies
Case Study
Berkeley Lab
Berkeley Lab to use deep learning to analyse alternate data sources for more accurate predictions of air quality
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Case Study
University of Rochester
University of Rochester researchers have developed an app that monitors social media to identify health risks posed by restaurants using natural language processing and machine learning
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Case Study
Harvard University
Researchers detect restaurants with elevated risk of foodborne illness in real time using machine learning
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