Evidence-Based and Intelligence-Led Policing Study Notes

Modern police work is changing to use new ideas and technology to be safer and more effective in communities.
Key approaches include:
  - Smart Policing: Using technology and working with experts from universities.
  - Evidence-Based Practices: Using proven methods that show real results.   - Predictive Analytics: Trying to guess where crime might happen next.
  - Intelligence-Led Strategies: Focusing on gathering and understanding information.
The main ideas behind these methods are based on:
  - Data-Driven Decisions: Making choices based on data instead of just feeling.
  - Community Engagement: Building trust and sharing information with people in the community.
  - Technology Integration: Using advanced tools in daily work.
  - Innovative Policing: Always looking for new ways to deal with changing crime patterns.

Smart Policing: This means police work that uses technology and partners with researchers to help lower crime rates.
History: The BJA started this idea in 2009.
Other Name: Sometimes called Strategies for Police Innovation.
Main Goal: To create good information that helps lower crime effectively.
Components of Smart Policing:
  - Evidence-Based Practices: Using strategies that have been proven to work.
  - Strategic Planning: Making police plans based on detailed analysis.
  - Data Collection: Gathering information to help with decisions.
  - Data Integration: Combining different sources of information for a full view.

Data-Driven Policing:
- Police collect and look at internal data to help decide how to use their resources.
Evidence-Based Policing:
- Uses outside research to guide broader police policies instead of just daily decisions.
Effectiveness of Approaches:
- Reactive Investigations: Looking into incidents after they happen to keep the community safer. - Rapid Response: Knowing that quick action during emergencies makes a big difference. - Random Patrol: Aiming to be visible in the community. - Hot Spot Patrol: Focusing on areas where crime happens frequently to lower those incidents.

Police policies are often based on four areas:
- Tradition: Long-lasting policies that don't easily change. - Authority: Decisions based on who is in charge or their orders. - Research: Using solid data and studies to guide policies. - Analysis: Decisions based on analyzing data for better efficiency.

Insights and Benefits:
  - Officer Awareness: Constant training helps officers use research methods effectively.
  - Research Value: Data insights improve police work day-to-day.
  - Long-term Strategy: Research supports better long-term police plans.   - External Help: Working with researchers and universities makes policing more effective.
Need for Research-Based Public Policy (Case Studies):
- Intensive Mobile Treatment (NYC): A program to help those with mental health issues; however, there was no proof that it helped clients effectively.
- Thrive NY: A mental health initiative where nearly $1 billion was unaccounted for, showing the risks of large policies without proper oversight and research.

CompStat Overview:
- Focuses on using data for police accountability and effectiveness. - Quote from Jack Maple at NYPD: "We will be relentless until New York is in fact the safest city in America."
Predictive Policing:
- Uses data to try to stop crime before it happens. - Focus Areas: Figuring out where and when crime might occur, who might be affected, and who might commit it. - Goal: Enables targeted police action in specific areas.

Main Types of Crime Analysis:
1. Geospatial Crime Analysis: Looks at where crimes happen. 2. Person-Based Analysis: Identifies specific people involved, either victims or offenders.
Geospatial Analysis Details:
- Uses maps and data systems to improve policing. - Helps allocate resources more effectively. - Involves trained civilians to handle data. - Important Note: Geospatial analysis helps develop information, but it isn't a strategy on its own.

ILP Components:
- Technological Integration: Using tech for improved strategies.
- Community Engagement: Building trust in local communities. - Person-Based Focus: Gathering information on specific individuals. - Data Analysis: Looking for crime patterns. - Place-Based Focus: Using geography to improve data analysis.
Limitations and Challenges of ILP:
- Issues with usability of information. - High costs for tech and resources. - Privacy concerns for citizens. - Difficulty in keeping accurate databases. - Negative Example: Concerns about the NYPD Gang Database.
Ethical Concerns:
- Issues of fairness in data systems. - The use of Predictive Policing software by LAPD. - Pasco County Sheriff's Office: Had a discontinued program aimed at identifying potential school shooters due to ethical concerns.