Advanced Techniques in Crime Analysis Battleground

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• 06 Oct, 2023
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• 8 Mins Read

Advanced Techniques in Crime Analysis Battleground

The battle between law enforcement and criminal activities has evolved into a landscape of innovative strategies. Over time, as crimes have grown to advancement, crime analysis has also transcended the traditional boundaries to deal with new-day crimes like cybercrime.

In today’s time, to deal with such sophisticated crimes, we need to follow advanced techniques in the crime analysis battleground. These advanced techniques to deal with cybercrime can help cybercrime investigators decipher the patterns, predict the possibilities, and understand the flow of the crime scenarios. Let us move further on the topic.

Big Data and Predictive Policing

Big Data and Predictive Policing is one of the advanced approaches that can efficiently assist in law enforcement activities to deal with crimes, by making use of advanced technologies and extensive datasets. We will be diving deeper into this topic.

Big Data

The term, ‘Big Data’, itself refers that it is a diverse collection of data, which is huge in volume, yet growing exponentially with time. We can imagine it as a collection of records, which may be in a structured or unstructured format. For example, big data on weather conditions in various, which can assist GPS services to provide safer routes to drivers.

Predictive Policing

Predictive Policing is the new approach practiced by law enforcement as a way to forecast criminal activities. This method uses computer systems to analyze Big Data, as we discussed earlier, which is usually about historical crimes and criminal patterns, to decide the possible future scenario for criminal activities to take place. 

Easily speaking, predictive policing is for crime-related predictions, just like the way we predict the possibility of rain whenever we see dark clouds outside.

Geographic Information Systems (GIS)

The Geographic Information Systems (GIS) plays an important role in crime analysis. Following are some points defining how it is crucial to law enforcement for crime analysis battleground:

1. Geographic Information Systems (GIS) uses geography and computer-generated maps as an interface to integrate, access and visualize a large amount of location-based information.
2. This process assists law enforcement in understanding the patterns, trends, relationships, and behavior related to criminal activity.
3. Along with this understanding, GIS can be used to get critical information about locations during emergencies, enhancing assistance for tactical planning and response.

The GIS creates maps to visually represent the crime data and components like the crime hotspots, density, and trends over time along with other patterns, which can help investigators to have a clear visualization of criminal activism. This process is called Mapping and visualization, which also includes the process of Spatial data analysis.

The Spatial Data Analysis helps to identify patterns and trends of crime data in a way more advanced than the traditional tabular formats. This involves techniques to identify areas with high crime rates(hotspots), the co-relation between them, and understanding the proximity of those locations like schools, parks, etc. which can help law enforcement with relevant planning and response for the scenario.

Machine Learning and Artificial Intelligence

This is one of the most trendy topics in technology, which is now being used in almost every field. Therefore, Machine Learning and Artificial Intelligence are also playing a significant role in crime analysis. Following are some points on how AI and ML are assisting in crime analysis:

1. Due to the ability to process large datasets, AI and ML make it able to efficiently integrate and analyze mass data of crime reports, arrest records, evidence, social media, etc.
2. Machine Learning algorithms can help in recognizing patterns, and trends in crime data. For example, cases of pickpocketing usually happen in crowded areas, hence based on datasets, ML algorithms can understand the pattern that pickpocketing crimes are mostly in crowded places like railway stations, temples, etc.
3. Based on the above patterns, and historical data, AI and ML can predict the possible future criminal activities. As discussed earlier, predictive policing is implemented with the concept of AI and ML.
4. Artificial Intelligence can help with facial recognition to identify suspects and people of interest, this technology can assist in quicker identification of suspects through public means like traffic cameras.

Social Network Analysis

Social Network has been a personal part of everyone’s life today. In the news, most of the time we have encountered cases where social network is a medium for criminal activities like having terror links, harassment, etc. Social media analysis can assist law enforcement for the following purposes:

1. Understanding criminal networks on social media.
2. Exploring relationships of individuals in network with criminal activism.
3. Monitoring groups and communities to analyze intents, operations, etc., and performing analysis of communication patterns. 

In many cases, investigators have also used social network analysis not only to investigate criminals but also to investigate victims to understand their backgrounds and find proof related to the offense.

Cybercrime Analysis

Cybercrimes are one of the most happening crimes in today’s world. From single individuals to massive organizations and celebrities, cybercrime has created a ruckus for everyone to a global extent. In such cases, cybercrime analysis is a necessity to deal with and solve criminal activism in cyberspace. Cybercrimes, as digital threats, can be of various types like financial frauds, social media hacking, phishing, cyberbullying, impersonation, etc. Such cases can be investigated and dealt with using the following approaches:

1. Tracing Digital Footprints: This includes the trails left online by cybercriminals during their activities. This may consist of network logs, server records, device metadata emails, chat conversations, etc.
2. Identifying the attackers: The information gathered by components like IP, device metadata, and logs may lead to the attackers’ identity disclosure. This helps in identifying the culprits.
3. Collaborating with cybersecurity experts: Collaborating with various minds can always be helpful to follow multiple approaches in solving a cybercrime. Collaborative teamwork with multiple cybersecurity experts during a cybercrime analysis can enhance the investigation.

Data Visualization

Data Visualization as the term itself says, involves graphical and interactive representation of crime data in an easily understandable and insightful form. In the case of criminology, data visualization is utilized for:

1. Incident Mapping: Visualizing crime hotspots with a geographic perspective, helping analysts to understand origin, target locations, and impactable areas.
2. Sentiment Analysis: Sentiments in criminal activism and discussions on digital platforms can be represented graphically using Sentiment score distributions, trend graphs, and word clouds.
3. Regression Analysis: Using scatter plots, bubble charts, etc. relationships between crime variables like crime rates, and demographic factors can be visualized to understand trends.

Ethical Considerations

This is one of the most crucial factors of crime analysis. Ethical concerns associated with advanced crime analysis techniques are important to care about as most of those techniques and tools are complex and can be misused as well. 

Hence, we need to take care of the following concerns about ethicality in crime analysis:

1. Privacy and Data Protection: Crime analysis procedures may include sensitive yet private information about an individual to assist in the investigation. Protection and maintenance of such information are crucial for the protection of an individual’s identity.
2. Bias and Fairness: Acknowledging and addressing required biases in data gathering, and analysis is important. Following fairness and non-discriminative approaches in crime analysis can improve legal integrity.
3. Responsible use of Technology: The advanced techniques and tools used during crime analysis are for lawful and ethical purposes meant for crime analysis only. Using those for personal and biased causes could infringe upon an individual’s right.
4. Accountability: The crime analysts must be accountable for providing clear information about data collection methods, following legal methods and purpose for crime analysis, as well as providing clear explanations of how analysis is conducted and results are used.

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Conclusion

Through the overall article, we can understand the importance, complexity, and necessity of advanced techniques in the crime analysis battleground. Also, we have learned about the analysis of new day techniques and how Artificial Intelligence and Machine Learning can assist in crime analysis.

We can come to the conclusion that, despite technology as a background may not sound like a strong necessity for law enforcement but it has turned into a need due to modern-day advanced crimes. The methods we follow to solve technical problems can assist in solving and understanding criminal cases may those be cybercrimes or any real-world offense.

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