Abstract
Crime detection is one of the most important research applications in machine learning. Identifying and reducing crime rates is crucial to developing a healthy society. Big Data techniques are applied to collect and analyse data: determine the required features and prime attributes that cause the emergence of crime hotspots. The traditional crime detection and machine learning‐based algorithms lack the ability to generate key prime attributes from the crime dataset, hence most often fail to predict crime patterns successfully. This paper is aimed at extracting the prime attributes such as time zones, crime probability, and crime hotspots and performing vulnerability analysis to increase the accuracy of the subject machine learning algorithm. We implemented our proposed methodology using two standard datasets. Results show that the proposed feature generation method increased the performance of machine learning models. The highest accuracy of 97.5% was obtained when the proposed methodology was applied to the Naïve Bayes algorithm while analysing the San Francisco dataset.
Highlights
In the last few decades, there has been an exceptional growth in urban population which has led to the demand for a secured, hospitable, and sustainable society
We have considered 5 as the value for k and the kNN used in this model produces 86.61% accuracy
This paper concentrates on feature generation methods such as time zone classification, crime probability calculation, analysis of crime hotspots, and vulnerability analysis
Summary
In the last few decades, there has been an exceptional growth in urban population which has led to the demand for a secured, hospitable, and sustainable society. With the ever-expanding growth of city, engulfing suburbs and rural spaces, the management of urbanization remains a major challenge for administrative authorities. Cities are getting overpopulated, compelling governments to undertake smart city initiatives that would help achieve better management of infrastructure and overcome the major challenges of security, sustainability, and development. Smart city initiatives have gained immense momentum with promises to enhance quality of life, it does have its own challenging aspects as well. One of the major challenges in smart city life is public safety. Various studies have been conducted to help understand crime patterns and its relationship to the social economic development of particular regions, the human characteristics, their level of education, and family bonding [1]
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