Crime Forecasting via Geographical Clustering and Data Augmentation
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Solution Overview
Problem
Crime analysts face challenges in identifying and predicting patterns in crime occurrences due to the tedious manual examination of large datasets, especially in regions with sparse crime data, where existing methods lack accuracy and efficiency.
Innovation Solution
A crime forecasting system that receives time series datasets from multiple geographical regions, clusters regions with similar crime patterns, augments sparse data with neighboring regions' data, and calculates statistical features to forecast crime occurrences using machine learning techniques, such as Maharaj's distance and mixture models, to determine predictive rules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual examination of crime data is used, then crime patterns can be identified, but the process is tedious and time-consuming
Solution Approach 1:
The patent replaces manual mechanical examination of crime data with automated computational systems. Machine learning models and algorithms process crime datasets automatically, substituting human analysts' manual work with computer-based pattern recognition systems that operate faster and without fatigue.
Solution Approach 2:
The crime analysis system performs self-service by automatically identifying patterns without requiring continuous human intervention. The system autonomously processes crime data, generates insights, and updates predictions, reducing dependency on manual examination while maintaining analytical accuracy.
2Measurement precision
If data from multiple geographical regions is combined, then forecasting accuracy improves, but data complexity increases
Solution Approach 1:
The patent segments crime data by geographical regions before combining them. By organizing data into distinct regional segments with similar characteristics, the system manages complexity through structured segmentation rather than handling raw heterogeneous data, making the combination process more manageable and interpretable.
Solution Approach 2:
The system transforms raw crime data into standardized parameters and features that can be consistently compared across different geographical regions. By changing the data representation into uniform parameters, the system reduces complexity while preserving the information needed for accurate multi-regional forecasting.
3Quantity of substance
If clustering is used to group similar regions, then data augmentation is enabled, but computational requirements increase
Solution Approach 1:
The patent merges crime data from multiple clustered regions that share similar characteristics. By combining datasets from geographically and criminally similar regions, the system effectively augments the volume of available data for analysis, particularly benefiting regions with sparse crime data through data sharing across clusters.
Data Source
AI summary
Datasets relating time information to crime occurrences in the geographical regions are received. Time based crime patterns are extracted. Based on similarities among the crime patterns, the geographical regions are clustered. A selected time series dataset is augmented with a second time series dataset from the same cluster. Based on the augmented time series dataset, a new crime pattern is extracted. Based on the new crime pattern, a crime forecast is made for the selected geographical region.