Crime Record Analysis and Prediction Using Report Delays

Understanding when a crime is reported can just be important as knowing where and when it actually happened. In many cases, there is a delay between the time when a crime takes place and when it gets officially reported. These reporting delays can happen for many reasons - like fear, confusion, lack of communication facility or even lack of trust in the system. However, these gaps in time are often ignored in crime prediction studies. In this research, we look closely at these delays to understand how they affect the way crime data is analyzed and predicted. Using real-world crime data, we studied how long it usually takes for crimes to be reported and how this delay differs by crime type, area, and the people involved. We noticed that late reporting can lead to mistakes in understanding crime patterns, which may affect decisions made by law enforcement. To solve this, we developed machine learning and deep learning models and added report delay as an important feature to consider and trained them to predict the status of crime cases - whether they're open, closed, or pending. Our results show that including report delays improves model accuracy, especially for rare or unusual cases. It also helps in identifying specific areas like types of crimes that need quicker response and more attention from police. This study shows that crime prediction can be more fair, accurate, and useful when we don't just focus on the obvious details. By considering the hidden timelines behind each case, we can support better decision-making, smarter policing, and more trust in public safety systems.