Abstract
Smart city infrastructure has a significant impact on improving the quality of humans life. However, a substantial increase in the urban population from the last few years poses challenges related to resource management, safety, and security. To ensure the safety and security in the smart city environment, this paper presents a novel approach by empowering the authorities to better visualize the threats, by identifying and predicting the highly-reported crime zones in the smart city. To this end, it first investigates the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to detect the hot-spots that have a higher risk of crime occurrence. Second, for crime prediction, Seasonal Auto-Regressive Integrated Moving Average (SARIMA) is exploited in each dense crime region to predict the number of crime incidents in the future with spatial and temporal information. The proposed HDBSCAN and SARIMA based crime prediction model is evaluated on ten years of crime data (2008-2017) for New York City (NYC). The accuracy of the model is measured by considering different time scenarios such as the year-wise, (i.e., for each year), and for the total considered duration of ten years using an 80:20 ratio. The 80% of data was used for training and 20% for testing. The proposed approach outperforms with an average Mean Absolute Error (MAE) of 11.47 as compared to the highest scoring DBSCAN based method with MAE 27.03.
Highlights
The smart city’s primary objective is to improve its citizens’ quality of life by efficient utilization of the city’s resources
EXTRACTING SPATIO-TEMPORAL CRIME PREDICTORS Given a specific dense crime region, the DiscoverCrimePredictor() method which is mentioned in Algorithm 1 discovers a forecasting model to predict the number of crimes that will happen in its specific area
One of the crucial challenges of smart city infrastructure is to provide a reliable and secure environment that is addressed by detecting crime hot-spots and predicting the number of crimes in those regions
Summary
The smart city’s primary objective is to improve its citizens’ quality of life by efficient utilization of the city’s resources. The fairly increasing population in cities is posing challenges such as resource planning, public safety, and an enormous amount of data generated from sensors, cameras, and tracking devices [4]. To overcome the crime spiking into different spots, strict yet effective policies need to be enforced For this purpose, predictive policing is one of the few ways based on statistical prediction techniques to analyze the likelihood of the number of crime occurrence in the near future [16]. 1) First, given a raw dataset, pre-processing techniques are applied to remove outliers so that crime hotspot detection and prediction approaches can be exploited efficiently. The rest of the paper is organized as follows: Section II discusses previously available literature on Spatio-temporal crime hotspot detection and prediction.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
