The document proposes using predictive risk modeling and evidence-based indicators to identify at-risk youth and prevent violence, harm, and crime in schools and communities. It outlines collecting data from various sources to populate a predictive risk model, which would analyze patterns and alert authorities to potential issues. The goal is to understand and address underlying factors to strengthen opportunities, repair social cohesion, and advert negative occurrences through timely intervention.

1. RISK AVOIDANCE AND EVIDENCE BASED MODELING TO PREDICT RISK and PREVENT OCCURRENCE of Youth, Violence, Harm and Crime in SCHOOL AND COMMUNITY Office of Juvenile Justice and Delinquency Prevention

2. Points Mission Risks - Where & What Predictive Risk Predictive Indicators Examining Data Measuring Results Next Steps

3. Project - Predictive Risk / Prevention of Youth Crime Our mission is to provide an evidence based secured predictive youth crime risk modeling system and supportive web resources to “connect the dots” among reporting entities, see trouble brewing, gain resolve and alert before it explodes. Attain the benefits of national uniform predictive youth crime risk analysis using evidence data from community, campus, state and federal data banks to create information predictors and “beacons” that assess data, issue alerts of conditions favorable to youth violence, encourage timely intervention, diminished or eliminate occurrence, improve school, campus, and community safety for our youth. The goal is accomplished by enabling the school community: students, parents, teachers, administrators, local, state and federal law enforcement personnel, counselors, and others to understand and contribute to a growing, live, evidence based secured real-time predictive risk model.

4. Where’s the Risk? SAFE SCHOOLS IN THE CONTEXT OF SCHOOL IMPROVEMENT Effective Strategies for Creating Safer Schools and Communities Schools … “Viewed individually, such problems are challenging; together they can be overwhelming. Given that many problems experienced by students arise from the same underlying causes, it makes sense not to consider each one separately. Indeed, various policy and practice analyses indicate that it is untenable to do so. If schools are to be good and safe places, the agenda for school safety must be combined with other efforts to address the variety of factors that interfere with a school accomplishing its mission. All such efforts must be embedded in the larger agenda for school improvement.” (2007 DOJ National Conference)

5. Predictive Risk Modeling Using predictive software engineering and current DOJ crime modeling technology and accepting EVIDENCE data from schools, colleges, communities, state and federal law enforcement, and other resources. An example model includes: EI - Established Indicators - Community / School / State / National Sanctioned Resources PDC - Probability of Danger or Crime PLI - Predicted Level of Impact FVE - Frequency of Violence Exposure NPR - Number of Persons at Risk RI - Risk Index PRI - Predictive Risk Index = average of 15 observations LPR - Long Term PRI - Charted Stats over Timelines for Analysis and Sharing ARL - Alert Location

6. Adopting Predictive Risk Challenge: Can the past observations - converted to evidence data be used to predict future patterns and events and reduce school / college crimes? Yes. Model: Using the Defined Evidence Data, National Institute of Justice MAPS Program and the Crimemap Group on Google  and Infoflows predictive risk tool set. Result: Set out for national use a reliable predictive risk model to identify patterns and unusual occurrences, and enable timely alerts and peaceful solutions.

7. Four steps of the Predictive Risk process to help solve predictive problems. 1. Identify and define standard risk indicators and variables. Collect evidence data from current local assessments and other regional, state, national and global sources. 2. Populate the Infoflows Predictive RiskShell Predictor and Predictive RiskShell Classifier with the neural nets - and indicator data so that the interrelationships between the indicators and process variables are in the model. 3. Use the model with our Predictive RiskHunter to find those specially qualified indicator combinations (among the vast number possible) that produce the assessments and patterns and provide evidence of predictive behavior. 4. Implement an on-going process for updating the Predictor and Classifier shells. Products presented are formulated by Infoflows Risk Services using Ward System’s Group Advanced Neural Software. NeuroShell  is a registered trademark of Ward System’s Group. Inc.

8. Measuring and Implementing Predictive / Preventative Responses for School, Community Cohesion A key element of our study is to explore how schools, colleges, and communities [working with local, regional, state and national law enforcement*] could use risk and geographical indicators and evidence to identify educational institutions and areas experiencing significant or disproportionate criminality, fear, disorder, or tensions to predict occurrences - repair cohesion - advert harm and crime. *Accept input from: Rigel ｨ , CrimeStat , and Dragnet and other similar crime mapping systems.

10. Measuring and Implementing Predictive / Preventative Responses for School, College & Community Cohesion Addressing what Social Disorganization and Collective Efficacy have in relation to predicting youth behavior in and around educational centers to strengthen opportunities for avoiding violence, harm and crime.

11. Sample: Examining Data

12. Sample: Examining Data

14. Thank you. To Manage Risks for Safer Schools and Advert Juvenile Crime Contact: (We Need Your Help.) Neil B. Jackson, CISA Director, Risk Management Services Infoflows Corporation 1117 Jefferson Drive West Forest, VA 24551 434-426-2733 [email_address]