Real Time Big Data Analytics For Public Safety In Smart City

Abstract

Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has led to the continuous generation of a large amount of data. Smart city projects have been implemented in various parts of the world where public data analysis helps provide a better quality of life. Recently, big data analytics has played an essential role in many data-driven applications. Big data technologies are moving towards knowledge discovery from the raw data in real time. Real-time data analytics is essential in industries such as finance, healthcare and e-commerce, where decisions need to be made quickly to stay competitive. Multiple data sources also enable organizations to gain a more complete picture of their business, customers and operations. However, processing and analyzing data from multiple sources in real-time present significant challenges including data integration, data quality and scalability. To overcome these challenges, organizations must have a well-designed architecture, the right tools and technologies, skilled data analysts and engineers. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve more accurate data-driven solutions. Public safety is one of the major concerns in any smart city project in which real-time analytics is useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generate emergency alerts for making appropriate decisions to provide security to the people and the safety of the city’s infrastructure. In this research, we propose a real-time big data analytics framework using multiple data sources from a smart city to find better data-driven solutions for public safety. Public safety is one of the major concerns in any smart city project where real-time analytics is much useful to provide security to the people and safety to city infrastructure. Analytics using multiple data sources for a specific data-driven solution helps to find more data patterns, increasing the accuracy ofanalytics results. Data preprocessing is challenging in data analytics when data is ingested continuously in real time into the analytics system. The proposed system helps preprocess the real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from various sources are ingested in real time as input data and is also flexible to use any additional data source of interest. The experimental work was carried out with the proposed framework using multiple data sources to find real time crime-related insights that help the smart city’s public safety solutions. The experimental outcome using the proposed data blending mechanism shows a significant increase in the number of identified useful data patterns as number of data sources increase. A real time emergency alert system to help the public safety solution is implemented using a machine learning based classification model with the proposed framework. Early detection and crime prevention are critical challenges to provide better safety within the city. Predictive policing has been around for a long time to monitor crimes based on past crime records by identifying the crime hotspots. Previous works on crime prediction have used historical data from specific sources to build the prediction model. In this work, real time data from multiple sources and historical data are used to improve the prediction model’s performance.