Mixture Model Time-Series Clustering for Crime Data Benchmarking

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Solution Overview

Problem

Current crime data analysis methods in law enforcement are limited to simple statistical summaries, failing to account for temporal dependencies and structured comparisons across spatial entities, which hampers the effectiveness of intelligence-led policing.

Innovation Solution

A method and system for mixture model-based time-series clustering and benchmarking of crime data across spatial entities, utilizing a processor and storage device to receive, process, and present crime clustering data, enabling accurate identification of similar regions and benchmarking of crime patterns through a user interface.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If simple statistical summaries are used for crime data analysis, then the analysis process is simple and fast, but the analysis precision and ability to identify temporal dependencies are insufficient

Engineering Contradiction:

Improveanalysis precisionVSAvoidanalysis complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent transforms crime data from simple statistical counts into time-series parameters with temporal dependencies, using mixture models to capture complex patterns. This changes the parameter representation from basic statistics to sophisticated temporal models, improving analysis precision while managing complexity through automated modeling.

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The patent replaces manual statistical analysis methods with automated mixture model-based clustering algorithms. This substitution of mechanical/manual processes with computational systems enables complex temporal analysis without proportionally increasing operational complexity, resolving the contradiction between precision and complexity.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Measurement precision

If mixture model-based time-series clustering is implemented, then crime pattern analysis precision is improved, but computational complexity and processing time increase

Engineering Contradiction:

Improvecrime pattern analysis precisionVSAvoidprocessing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent performs preliminary data preprocessing and feature extraction before applying mixture models. By preparing data in advance and organizing it into appropriate formats, the system reduces the computational burden during the actual clustering phase, thereby reducing processing time while maintaining analysis precision.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent segments the crime data analysis into distinct phases: data preprocessing, feature extraction, mixture model fitting, and result interpretation. This segmentation allows each phase to be optimized independently, reducing overall processing time while maintaining the precision benefits of comprehensive analysis.

Inventive Principle:

Principle #1Segmentation

3Adaptability or versatility

If structured comparison across spatial entities is performed, then benchmarking capability is improved, but data processing complexity increases

Engineering Contradiction:

Improvebenchmarking capabilityVSAvoiddata processing complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent creates a universal benchmarking framework that can compare crime patterns across different spatial entities using the same mixture model approach. This multi-functional system handles diverse crime types and geographic regions uniformly, improving benchmarking capability while managing complexity through standardized processing procedures.

Inventive Principle:

Principle #6Universality (Multi-functionality)

4Reliability

If temporal dependencies in crime patterns are analyzed, then predictive accuracy is improved, but computational requirements and complexity increase

Engineering Contradiction:

Improvepredictive accuracyVSAvoidcomputational complexity

Core Design Contradiction:

ReliabilityVSDevice complexity

Solution Approach 1:

The patent replaces complex manual temporal analysis with automated mixture model algorithms that naturally capture temporal dependencies. This computational substitution handles the complexity of temporal pattern recognition systematically, improving predictive accuracy while managing computational complexity through algorithmic efficiency.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS10922334B2Mixture model based time-series clustering of crime data across spatial entities

Publication Date: 2021.02.16 MODAXO ACQUISITION USA INC N K A MODAXO TRAFFIC MANAGEMENT USA INC

AI summary

A crime analysis system, method, and apparatus comprising at least one processor and a storage device communicatively coupled to the at least one processor, the storage device storing instructions which, when executed by the at least one processor, cause the processor to perform operations comprising receiving information provided by one or more data collection source, storing the information, wherein the stored information is formatted, processing the information to generate crime clustering data associated with at least one region and at least one crime, processing the crime clustering data associated with at least one region and at least one crime to generate benchmarking of the at least one region with at least one other region, and providing crime clustering data associated with at least one region and at least one crime, and benchmarking of the at least one region with at least one other region for presentation through a user interface.