HSI’s Pioneering Use of Data Analytics in Investigations
Contents
HSI’s Pioneering Use of Data Analytics in Investigations
Data analytics is changing the game for investigations and forensic science. With new technologies like hyperspectral imaging (HSI), investigators can gather way more data from a crime scene or piece of evidence than ever before. But with great data comes great responsibility! Investigators now need data science skills to make sense of all that data. HSI is a pioneering technique that lets you see way more than the naked eye. But you need the right analytics approach to turn those data into leads.
In this article, we’ll look at how HSI works, and the new data science techniques investigators are using to capitalize on all that rich data. We’ll also consider the implications as data analytics enters its forensic primetime. There are definitely pros and cons to this new frontier of crimefighting, and we’ll dig into the ethical issues too. Let’s dive in!
How HSI Works
So what makes hyperspectral imaging so special? In a nutshell, it lets you see a way broader spectrum of light. Human eyes can only see visible light – the rainbow colors from red to violet. But HSI systems can see invisible infrared and ultraviolet light too! So they can detect all sorts of clues the naked eye would miss.
Most cameras capture light in 3 bands – red, green and blue. But an HSI camera uses hundreds or thousands of bands across the electromagnetic spectrum! Each pixel in a hyperspectral image contains a full spectrum of data. So you can analyze that spectrum for all sorts of hidden patterns and details.
For example, you could identify trace evidence by its spectral signature. Each material reflects and absorbs different wavelengths in its own unique way. So your hyperspectral image can pinpoint the chemical composition of tiny particles or residues. Pretty cool right?
HSI scans objects or areas line-by-line. The data is captured as “data cubes” with two spatial dimensions (x and y) and one spectral dimension (wavelength). Sophisticated analytics software is needed to process and interpret those cubes. Let’s look at some of the techniques investigators are pioneering:
Pioneering Analytics Techniques
False Color Imaging
One simple but powerful technique is false color imaging. The software assigns different colors to different wavelengths in the spectrum. This reveals patterns the naked eye would miss. Differences in composition and texture suddenly pop out!
For example, you could see traces of blood, saliva or semen that would be invisible under normal lighting. Or subtle variations in fabrics that point to tampering. False color HSI could be a game changer for sexual assault investigations.
Spectral Unmixing
This technique identifies the unique spectral signature of each material in a scan. It mathematically separates and enhances the contribution of each one. This reveals the composition and spatial distribution of traces and residues.
It’s like untangling mixed fingerprints! With spectral unmixing, even tiny particles of gunshot residue hidden against a complex background can be spotted. This will transform how investigators reconstruct crime scenes.
Anomaly Detection
AI algorithms can now search hyperspectral data cubes for anomalies. Finding distinctive objects or patterns that don’t match the background. It’s like facial recognition for crime scenes!
For example, a blood stain pattern that doesn’t match the rest of the room decor. Or a buried object in a field glowing with an unexpected spectral signature. The AI can highlight any such anomalies for investigators to focus on.
Dimensionality Reduction
Hyperspectral data cubes generate a mind-boggling amount of data. Dimensionality reduction simplifies all that complexity using algorithms like principal component analysis.
It transforms the data into a lower dimensional space that preserves the key spectral differences. This makes the data much easier to explore interactively. Investigators can zero in on the most salient spectral signatures for further analysis.
The Pros and Cons of HSI Analytics
These techniques clearly have huge potential to improve forensic investigations. But there are also downsides to consider with any new technology. What are the pros and cons of using HSI and analytics in policing?
Pros:
- Finds clues invisible to the naked eye
- Non-destructive testing of evidence
- Faster and cheaper than lab testing
- Makes crime scene reconstruction more accurate
- AI assists with search to find anomalies
- Creates objective evidence trail for court
Cons:
- Expensive equipment and training required
- Potential for bias in algorithm design
- AI black boxes hard to explain in court
- Mass surveillance implications
- Data security and privacy concerns
Many of the cons focus on the risks of new technology being misused or mishandled. But with careful governance, HSI analytics could make policing more just.
The Future of Crimefighting
Hyperspectral imaging heralds a new data-driven era for forensic investigations. But it’s just the tip of the iceberg when it comes to applying data science to law enforcement. Here are some other frontiers to watch:
Predictive Policing
AI systems that forecast crime hotspots and recommend optimal patrol routes. This is controversial due to bias and privacy risks. But if done ethically, predictive policing could help focus resources where needed most.
Video Analytics
Automated analysis of CCTV footage using computer vision AI. To detect suspicious behavior, track suspects, and reconstruct events. This will generate a massive amount of surveillance data – requiring safeguards against misuse.
Biometric Identification
Next-gen techniques like gait analysis, voice recognition, and even odor detection! This expands the forensic toolkit, but also increases the power imbalance between police and public. Strict regulation of biometric data is essential to prevent abuse.
Data mining platforms that aggregate and analyze public social media posts to aid investigations. This could help reconstruct crime timelines, but also raises major ethical concerns around privacy and consent.
The common thread is law enforcement having vastly more data at their fingertips. This supercharges their capabilities, but also requires great responsibility. While data analytics offers many benefits, the risks of misuse, bias and overreach cannot be ignored. Oversight and accountability will be critical.
The Bottom Line
Hyperspectral imaging represents an exciting new frontier for forensic science. The ability to extract clues and patterns from rich spectral data will transform crime scene investigation. But it’s just the first ripple of a rising data science tide that will fundamentally reshape policing. We have to keep ethics, privacy and justice central as these powerful technologies advance. Data-driven law enforcement is coming – let’s get it right!
References
[2] Data Science Education – Concord Consortium
[3] Leading your research team in data analysis: 7 tips for investigators
[4] Forensic Science: The Future of Criminal Justice