Crime Aware Navigation

Abstract Analysis of crime is a methodological approach to the identification and assessment of criminal patterns and trends. In several respects, it costs our community profoundly. We have to go to many places regularly for our daily purposes, and many times in our everyday lives we face numerous safety problems such as hijack, kidnapping, and harassment. In general, we see that when we need to go any whereat first, we search for Google Maps; Google Maps shows one, two, or more ways to get to the destination, but we always choose the shortcut route, but we do understand the path situation correctly. Is it secure or not that’s why we face many unpleasant circumstances; in this job, we use different clustering approaches of data mining to analyze the crime rate of Bangladesh and we also use the K-means algorithm to train our dataset. For our job, we are using main and secondary data. By analyzing the data, we find out for many places the prediction rate of different crimes and use the algorithm to determine the prediction rate of the path. Finally, to find out our safe route, we use the forecast rate. This job will assist individuals to become aware of the crime area and discover their secure way to the destination. Crime Numerous safety problems route safety issues raises In India, the crime rate is increasing each day. In the current situation, recent technological influence, effects of social media, and modern approaches help offenders to achieve their crimes. Both analysis and prediction of crime are systematized methods that classify and examine crime patterns. There exist various clustering algorithms for crime analysis and pattern prediction, but they do not reveal all the requirements. Among these, the K means algorithm provides a better way of predicting the results. The proposed research work mainly focused on predicting the region with higher crime rates and age groups with more or less criminal tendencies. We propose an optimized K means algorithm to lower the time complexity and improve efficiency. Crime analysis is a systematic approach for identifying and assessing patterns and trends in criminal activities, which profoundly impact communities. In our daily routines, we often face safety issues like hijacking, kidnapping, and harassment. While navigating urban environments, people frequently rely on tools like Google Maps, which offer various route options, but they may not always account for the safety of the chosen path. This research introduces a crime rate prediction system using data mining techniques and the K-means clustering algorithm to assess crime rates and predict safer routes. By analyzing primary and secondary data, we can forecast the likelihood of crimes in specific areas, helping individuals avoid high-risk zones and make informed decisions. The system trains the dataset using K-means, providing predictions about crime-prone areas and allowing users to choose the safest routes based on crime forecasts.