Evidence-Based & Intelligence-Led Policing Study Notes

Modern law enforcement has shifted toward a reliance on information and embracing modern approaches to protect communities.
The core strategies of this evolution include: - Smart Policing: Initiatives focused on reducing crime through technological advancement and academic collaboration. - Evidence-Based Practices: Utilizing research findings to guide outcomes and policy. - Predictive Analytics: Using data to forecast criminal activity. - Intelligence-Led Strategies: Leveraging intelligence gathering to inform tactical and strategic decisions.
These approaches necessitate: - Strategic reliance on information to drive data-driven decisions. - Deep community engagement to build trust and gather localized intelligence. - Integration of technology and innovative policing methods to modernize the force.

Definition: Smart Policing refers to any initiative where police agencies aim to reduce crime by incorporating technology and forming partnerships with academics.
The primary goal of Smart Policing is the development of information specifically tailored to reduce crime.
Origin: The initiative was formalized in $2009$ by the Bureau of Justice Assistance (BJA).
It is often referred to under the umbrella of Strategies for Police Innovation.
BJA’s Smart Policing Initiative Components: - Implementing evidence-based practices: Prioritizing methods proven to result in better outcomes. - Strategic Planning: Crafting operational strategies based on insights derived from data analysis. - Data Collection: Systematically gathering data to facilitate effective decision-making. - Data Integration: Merging various data sources to achieve comprehensive operational insights. - Research Findings: Directly utilizing academic and internal research to inform policing strategies.

Data-Driven Policing: - Focuses on the collection and analysis of departmental data by police leaders. - Aimed at making informed decisions regarding day-to-day operations.
Evidence-Based Policing: - Focuses on the application of external and internal research findings. - Used to inform long-term policies and strategic planning.
Effectiveness Approaches: - Reactive Investigations: Thoroughly investigating incidents after they occur to enhance community safety. - Rapid Response: Ensuring quick intervention during emergencies to significantly improve situational outcomes. - Random Patrol: Maintaining visibility and a community presence to deter crime. - Hot Spot Patrol: Targeting specific areas prone to high crime volumes to effectively reduce incidents.

Police policy decisions are often rooted in several distinct bases: - Tradition: Long-standing policies that frequently resist change or innovations. - Authority: Policies that rely on the hierarchy and established command structures. - Research: Policies utilizing objective data, analysis, and evidence-based findings. - Analysis: Enhancing efficiency through data-driven decisions.
Research-Driven Insights: - Officer Awareness: Continuous training is necessary to enhance knowledge of various research methods. - Research Relevance: Insights derived from data improve daily police operations. - Policy Decisions: Academic and empirical research informs the creation of law enforcement strategies. - External Agencies: Collaborating with professional researchers boosts the overall effectiveness of a police department.

Intensive Mobile Treatment (IMT): - Designed to assist individuals with mental health issues to reduce their reliance on police intervention. - Audit Findings: An audit of the New York City (NYC) program found that the city "cannot determine whether the program is actually helping clients make progress with their treatment."
Thrive NY: - A mental health services initiative in New York City. - Controversy: Approximately $1$ billion dollars ( $1,000,000,000$ ) associated with the program was reported as unaccounted for.

CompStat (Computer Statistics) is a data-driven policing model that enhances accountability and effectiveness.
Jack Maple (NYPD) Quote: "We will be relentless until New York is in fact the safest city in America."
The model emphasizes the importance of data analysis and the "Relentless Pursuit" of crime reduction.
It is used as a tool for administrative and operational accountability within the New York Police Department (NYPD).

Predictive Policing: The use of data to attempt to predict and prevent crime.
Key focus areas of prediction: - Where crimes will occur. - When crimes will occur. - Against whom crimes will be committed. - Who will commit the crimes.
This allows for highly focused police actions and incorporates elements of: - Data-driven policing. - CompStat. - Intelligence-led policing.
Methods of Crime Analysis: - Geospatial Crime Analysis: A location-focused method utilizing maps and data management. - Uses dispatch systems ( $911$ ). - Often involves the use of specially trained civilians. - Limitation: It is a method for developing and analyzing information, but it is not a crime reduction strategy by itself. Its full potential is considered yet to be realized. - Person-Based Analysis: A focus on the identification of specific individuals suspected of criminal activity.

Intelligence-Led Policing components: - Technological Integration: Leveraging tech tools to improve strategies. - Community Engagement: Building trust with residents to facilitate intelligence flow. - Person-Based: Focusing on specific individuals for gathering intelligence. - Data Analysis: Identifying crime patterns. - Place-Based: Utilizing geography to enhance analysis.
Ethical Concerns in Predictive and Intelligence-Led Policing: - Bias or Unfairness: Algorithms or data collection may target specific demographics unfairly. - LAPD and Predpol: Previous implementations have faced scrutiny over methodology. - Pasco County Sheriff's Office: Utilized a "Model of school shooter" to identify potential threats; this program was discontinued in $2023$ .
Limitations of ILP: - Challenges include the usability of the information collected. - High resource costs for personnel and technology. - Significant privacy concerns for the public. - The complexities involved in the creation and maintenance of databases. - Example: Controversies surrounding the NYPD Gang Database regarding accuracy and entry criteria.