Crime rates are at an all-time high worldwide. It has become the need of the hour to take the aid of technological advances to lower crime rates. Crime forecasting is the latest technology that can be used to forecast future crimes, most vulnerable locations and decide prevention efforts.
Through this technology, data scientists develop algorithms that are ultimately aimed at reducing the crime rates based on the data collected from police records.
Free Step-by-step Guide To Become A Data Scientist
Subscribe and get this detailed guide absolutely FREE
What Is Crime Forecasting?
Yes, human behavior has a huge scope for unpredictability but there’s a certain structure that can be investigated even in the most chaotic scenarios. Using repetitive statistics, data collected from hot spots of crime can help analyze the intricacies of crime prevention.
Simply put, crime forecasting or predictive policing is based on large amounts of data collected from previous crimes. It uses algorithms and other methods to help police officers handle and share observations so that better early warning systems can be created to ensure more safety for people.
Why Do We Need To Have Crime Forecasting?
The main purpose of crime forecasting is to help prevent recurring crimes in an area by tracking the patterns of crimes as well as the most common types of crime in an area. Since the data will be stored in cloud and warehouses, high volumes can be kept protected from any form of cyber threats. This also paves way for the opportunity to access integral information from the smallest villages to the largest cities for comparative analysis of crime rates.
How Does Crime Forecasting Benefit The Community?
Based on the insights derived from crime forecasting, municipal bodies and citizens can carry out community efforts that are aimed at preventing crimes from occurring in the first place. Early warning systems and constant vigilance through police patrolling can be employed in hot spots of crime. This will ensure a better sense of safety within communities.
How Does Crime Forecasting Aid Police Forces?
The greatest advantage with crime forecasting technology is that law enforcement agencies will be able to make prompt responses and within a short response time. PredPol, a predictive policing system developed by P Jeffrey Brantingham, a professor of anthropology at University of California, Los Angeles, is now being used by several police departments in the US and UK. It has reportedly helped limit incidences of criminal activities such as burglary and vehicle thefts.
Consistency in record keeping, analyzing what type of force is needed, the number of encounters and fatalities can be collected using crime forecasting.
Moreover, with the number of insights gained from data, police officers can also understand in what areas they would need more training or a change in policies for effective crime prevention.
What Are Challenges Encountered With Crime Forecasting?
The most tedious part of crime forecasting is that it’s a hi-tech innovation which is still in its nascent stages. At present, it has been used in the UK, USA, China and some parts of Europe. However, the results have not been foolproof!
Algorithms are prone to inaccuracies too. If the crime-related data is inconsistent, crime patterns cannot be predicted correctly. This eventually leads to poor crime forecasting.
Experts are currently of the view that predictive policing is not a go-to crime prevention method yet. It has to be employed with a real-time human intervention that begins with crime prevention at a community level itself.
Since data science and artificial intelligence are already being used in defense, it’s only a matter of time before they will become a crucial part of law enforcement in general. As of now, crime forecasting has a lot of dependencies. If perfected, it can become one of the most useful tools for the safety of mankind at large. If this article motivated you to learn more about data science, you should definitely check out the Data Science Masters course from Acadgild. Happy Learning!