User-Specific Incident Likelihood Modeling From Geolocation Data
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Solution Overview
Problem
Existing systems lack an effective method to determine a user-specific likelihood of incident occurrence at a geographic location, considering various data sources and types of information, which are often scattered and not integrated for personalized risk assessment.
Innovation Solution
A system that analyzes geolocation, user demographic and behavioral information, historical incident data, and contextual information using Bayesian-type statistical analysis to provide a user-specific likelihood indicator of incident occurrence, presented on client computing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources are integrated for personalized risk assessment, then measurement precision of incident likelihood is improved, but device complexity increases
Solution Approach 1:
The system segments the complex risk assessment task into distinct functional components: a geolocation component that processes location data, a user component that handles demographic and behavioral information, an incident component that manages historical incident data, a contextual component that processes event information, and a determination component that performs Bayesian analysis. This segmentation allows each component to specialize in processing specific data types while maintaining overall system coherence.
Solution Approach 2:
The determination component serves as an intermediary that receives processed information from all other components and performs the Bayesian statistical analysis to synthesize the likelihood assessment. This intermediary structure manages the complexity by providing a centralized processing point that coordinates data flow between multiple sources and produces the final integrated assessment.
2Loss of information
If diverse data sources are integrated, then information completeness is improved, but loss of information increases due to data integration challenges
Solution Approach 1:
Each component is designed with universal interfaces that can handle multiple types of data sources. The geolocation component can process GPS coordinates, address data, and location descriptors; the user component can integrate demographic data, behavioral patterns, and social information from various sources; the incident component can access historical incident data, crime statistics, and safety records. This multi-functionality ensures comprehensive information integration while maintaining data integrity.
3Reliability
If user-specific analysis is performed, then reliability of safety assessment is improved, but difficulty of detecting and measuring increases
Solution Approach 1:
The system applies local quality by tailoring the risk assessment to each user's specific characteristics and context. The user component analyzes individual demographic factors, behavioral patterns, and social connections; the determination component performs Bayesian analysis that weights evidence based on user-specific parameters. This localized approach ensures high reliability for each user while managing measurement complexity through structured data collection and processing.
Data Source
AI summary
Determination of a user-specific likelihood of incident occurrence at a geographic location may be performed. User information, historical incident information, contextual information, and/or other information may be obtained. User information may include user demographic information, user behavior information, user social information, and/or other user related information. Historical incident information may include data relating to crime, mortality, injury, morbidity rates and may be obtained from local law enforcement, local Departments of Motor Vehicles, national security agency such as the Federal Bureau of Investigation, foreign security agency such as the Central Intelligence Agency, international criminal policy organization such as Interpol, national public health agency such as Center for Disease Control, international public health agency such as World Health Organization and/or other sources. Contextual information may include information about events that have previously occurred at or near user's current geographic location. Determination of a user-specific likelihood of incident occurrence may be performed by analyzing collected sets of user data, historical incident data, and contextual information obtained from various sources to create a single incidence likelihood indicator.