Predictive Policing using Machine Learning (With Examples)

Trying to predict the patterns of crime and criminal behavior is very effective but challenging to execute in the traditional framework. A few years ago, Crime analysts were expected to go through a large volume of data to understand the underlying patterns and use that information to prevent crimes in the future.

A report by the RAND Corporation found that predictive policing can effectively reduce crime rates. With technologies like machine learning and artificial intelligence, predictive policing has got a new lease on life. As crime patterns can be automatically identified using this technology, police departments across the world can get to stopping them rather than sifting through a database to identify patterns. The mathematically principled approach is useful in discovering patterns quickly and helping to catch offenders.

What is Predictive Policing?

Predictive policing is a method of law enforcement that uses data analysis and machine learning algorithms to identify areas and times where crimes are most likely to occur. This approach is based on the idea that patterns and trends can be identified in crime data, allowing law enforcement to anticipate and prevent criminal activity before it happens.

The use of technology in the area of policing helps to scale the efforts quickly and ensures that the authorities can provide a better experience to the citizens. 

Role of Machine Learning in Predictive Policing

Machine learning plays a crucial role in predictive policing by enabling law enforcement to analyze large amounts of data quickly and accurately. Machine learning algorithms can identify patterns and trends in crime data that may not be immediately apparent to human analysts. By analyzing these patterns, machine learning algorithms can make predictions about where and when crimes are most likely to occur, enabling law enforcement to take preventive action.

A report by the University of Chicago found that the machine learning algorithm model developed by the University was able to predict crimes one week before with 90% accuracy. The model also suggested bias in police response as crimes in wealthy areas led to more arrests while arrests in poor areas dropped. The report further added that the advanced in machine learning and artificial intelligence had generated interest from several governments to use these technologies for predictive policing.

How to Use Machine Learning in Predictive Policing?

Machine learning can be used effectively in a variety of areas in predictive policing. Here is how one can use machine learning in predictive policing: 

Data collection

In predictive policing using machine learning, data collection is a crucial step. Data can be collected from a variety of sources, including crime reports, arrest records, and social media. The more data means, the more accurate predictions. However, it is important to ensure that the data is collected ethically and in compliance with privacy laws. At the same time, it is important to understand that the data set needs to be large enough for it to start giving out accurate predictions.

Data preprocessing

Before the data can be used to train a machine learning model, it must be preprocessed. This involves cleaning the data, filling in missing values, and transforming the data to make it suitable for analysis. Data preprocessing is important to ensure that the data is accurate and consistent, which can improve the accuracy of the model.

Feature selection

Feature selection involves selecting the most relevant features, or variables, to include in the model. This step is important because including irrelevant or redundant features can reduce the accuracy of the model. Feature selection can be made manually, or it can be automated using machine learning algorithms. 

Training the model

Once the data has been preprocessed and the features have been selected, the machine learning model can be trained. The model is trained using labeled data, where the correct outputs are known. During training, the model adjusts its parameters to minimize the error between its predicted outputs and the actual outputs. The accuracy of the model depends on the quality and quantity of the data used for training. 

Model evaluation

After the model has been trained, it must be evaluated to determine its accuracy and effectiveness. The model is evaluated on a separate set of data to test its ability to make accurate predictions on new and unseen data. Model evaluation is important to ensure that the model is not overfitting to the training data, which can reduce its effectiveness in making accurate predictions. Studies have shown that predictive policing using machine learning can be effective in reducing crime rates.

Advantages of Predictive Policing

There are several advantages of using predictive policing using machine learning. Here are the key advantages:

Enhanced accuracy

One of the main advantages of predictive policing using machine learning is the enhanced accuracy of crime prediction. Machine learning algorithms can analyze large amounts of data to identify patterns and make accurate predictions about where and when crimes are likely to occur. This can help law enforcement agencies to allocate their resources more effectively, resulting in a reduction in crime rates. By closing in on the geographical location of the expected crime sites, the police can focus more effectively on controlling its occurrence. 

Cost-effective

Another advantage of predictive policing using machine learning is that it can be cost-effective. By using machine learning algorithms to analyze data, law enforcement agencies can identify areas and times where crimes are most likely to occur. This can help to reduce the overall cost of policing by reducing the need for patrol officers to be present in all areas at all times. While this may seem not aligned with the mission of reducing crime rates, it is important to consider that police departments across the world are dealing with increasing crime rates and, as a result, increasing costs. 

Efficient use of resources

Predictive policing can also help law enforcement agencies to use their resources more efficiently. By identifying areas and times where crimes are most likely to occur, agencies can allocate their resources to those places, which can result in more effective crime prevention. With effective utilization of the available resources, it becomes easier to work on the set targets.

Crime prevention 

The ultimate goal of predictive policing using machine learning is to prevent crimes from occurring in the first place. By identifying areas and times where crimes are most likely to occur, law enforcement agencies can take proactive steps to prevent crimes before they happen. This can result in a safer community for all residents. It is particularly helpful in ensuring that the identified patterns can be put forward in the form of actionable insights with which the crime rate can be reduced.

Challenges of Predictive Policing

While predictive policing using machine learning has several advantages, it also faces several challenges that must be addressed to ensure its effective and ethical use. The key challenges are as follows:

Data bias

One of the main challenges of predictive policing using machine learning is the issue of data bias. Machine learning algorithms are only as good as the data they are trained on. If the data used to train the algorithms is biased or discriminatory, the resulting predictions will also be biased or discriminatory. Therefore, it is important to ensure that there is no data bias in the mix, as it can lead to questioning the entire exercise.

For example, if the historical data used to train a predictive policing algorithm disproportionately targets people of color, the algorithm will be more likely to target people of color in the future, perpetuating the same bias.

Privacy concerns

Another challenge of predictive policing using machine learning is privacy concerns. Predictive policing systems rely on large amounts of data, including sensitive information about individuals. This raises concerns about how this data is collected, stored, and used and whether it violates individuals' privacy rights. In today's digital age, it is important that data is handled and processed with care. Therefore, the machine learning model must consider the privacy concerns of the dataset.

Lack of transparency

Predictive policing algorithms can be complex and difficult to understand, which raises concerns about transparency. Law enforcement agencies must be transparent about how they are using predictive policing and how the algorithms work to avoid creating confusion or distrust in the community. It is critical to resolving this challenge before scaling up the machine learning model.

Ethical considerations

Several ethical considerations must be addressed when implementing predictive policing. For example, law enforcement agencies must ensure that the use of these systems does not violate individuals' civil rights or perpetuate systemic biases. It is important to consider the potential negative consequences of using these systems and to work to mitigate them. With responsible implementation, predictive policing can create safer communities and help prevent crime while protecting individuals' rights and promoting fairness and justice.

Real-world examples of Predictive Policing using Machine Learning

Predictive policing using machine learning has been implemented by several law enforcement agencies around the world. Let's check the three real-world examples of predictive policing in the United States.

New York City Police Department

The New York City Police Department (NYPD) has implemented a predictive policing system called the Domain Awareness System (DAS). The system uses machine learning algorithms to analyze data from a variety of sources, including surveillance cameras, license plate readers, and social media, to identify potential threats and predict crime patterns. However, the system has faced criticism for privacy concerns and lack of transparency. 

Los Angeles Police Department

The Los Angeles Police Department (LAPD) implemented a predictive policing system called PredPol in 2011. The system used machine learning algorithms to analyze data from past crimes to identify potential hotspots and predict where crimes are likely to occur in the future. However, the program came to an end amid reports on how it led to the over-policing of black and brown communities.

Chicago Police Department

The Chicago Police Department implemented a predictive policing system called Strategic Subject List (SSL). The system used machine learning algorithms to analyze data from past crimes and identify individuals who are most likely to commit violent crimes in the future. However, the system has faced criticism for issues of data bias and lack of transparency and was decommissioned in 2020.

For any predictive policing model to work effectively, the data the model is fed must be accurate. The most common criticism of such models and solutions is that they are biased against a section of a population. It is important to understand the solution processes and the available data, and if the data has some level of bias, it will be reflected in the analysis of the solution. As a result, the solution must have accurate data about the situation on the ground.

Conclusion

Predictive policing using machine learning has the potential to revolutionize the way law enforcement agencies prevent and fight crime. By analyzing large amounts of data and identifying patterns and trends, machine learning algorithms can help law enforcement agencies allocate their resources more effectively and reduce crime rates.

One must consider the ethical implications of predictive policing using machine learning and work to address the challenges it presents. By doing so, they can ensure that predictive policing using machine learning is used responsibly and effectively to create safer communities.

Predictive policing using machine learning has the potential to make communities safer and reduce crime rates. However, one must address the challenges it presents and implement these systems responsibly and ethically. Our AI experts prioritize the ethics involved in building NLG systems which are trained on custom data sets, which helps reduce bias in the processed data

Visit our resources to read more such informational articles.