Group A: Dan Diecker, Uzair Bhatti, Puji Bandi, Latoya Lewis.

Slides:



Advertisements
Similar presentations
Indianapolis, Indiana Offender Notification Meetings.
Advertisements

2014 Annual Policing Plan – Q1 Results Edmonton Police Service Presented to the Edmonton Police Commission May 22,
6th International Conference on Evidence-Based Policing
2014 Annual Policing Plan – Q4 Results Edmonton Police Service Presented to the Edmonton Police Commission Feb 19,
A Michigan Nonprofit Association Affiliate DETROIT PARCEL SURVEY: APRIL 2014.
Community Crime Prevention CCTV in Victoria A Guide to Developing CCTV in Victoria Presenter:Simon Walker Title:Senior Policy Officer Date:13 November.
CAS: Crime Anticipation System Predictive Policing in Amsterdam.
IS6833 – Sauter – Spring 2012 Kuo-Luen (Alan) Chang Curt Rozycki Kristopher Turner.
Intelligence Mapping - Data Warehouse
Presentation for the Management Study of the Code Enforcement Process City of Little Rock, Arkansas August 3, 2006.
Insider for Oracle The Art Of Performance Tuning.
In Search of Insights and Better Analytics Prepared for: St. Louis Metropolitan Police Department 1 Nikolay Filipets / Ankit Patel / Melis Yilmaz / Divya.
HOMICIDE PREDICTIONS St. Louis 2013 Group A: Derek Clardy Maria Kattia Del Rio Tomita Seungyoon Kim Tamara Pokol.
2009 Strategic Highway Safety Plan Peer Exchange – SCOHTS Annual Meeting Kenneth L. Morckel National Outreach Rep. - NHTSA.
Initiative # 14 Data Driven Practices A/Lt Bryan Grenon A/Sgt. Christi Robbin Position and Title Start Here.
HIGH POINT, N. C. One City’s Success in Reducing Gun Violence.
Saint Louis Police Department BI Implementation Proposal Ben Shailesh Spencer Will.
1 This project was supported by Grant No DG-BX-K021 awarded by the Bureau of Justice Assistance. The Bureau of Justice Assistance is a component.
Ryan Buenemann Thomas Mcgeehon Birute Simkeviciute Thomas Starr Tam Tran Decision Support Systems for Business Intelligence 2011 HOMICIDE DETECTION.
Homicide Predictions : Saint Louis City IS6833 February 28, 2010 Group B: Chris Gaynor Robert Jones Kevin Prinke Poernomo Wikandjojo.
Crime in St. Louis City in 2011 David Adams John Fields Leticia Garcia Shawn Kainady Chris Schaefer Travis Tatum.
12 Business Analytics.
Business Intelligence in Crime Ji Zhang Lucas Matecki Lamont Davis Jenny Brunnert.
Partners Bureau of Justice Assistance, Office of Justice Programs, U.S. Department of Justice National Sheriffs’ Association.
IS6833 Homicide Prediction 2011 Michelle Bergesch Jeff Stahlhuth.
Predicting Homicides in St. Louis City for 2013 Chad Iseman - Geoff Hickman - Jon Perkins - Yanhui Long - Mustafa Khalili.
GARDINER POLICE DEPARTMENT SERVING SINCE 1849 SERVING SINCE 1849 Chief James M. Toman.
Dr. Rado Kotorov Technical Director Strategic Product Mgt. BI Applications For Crime Intelligence : Data Mining & Predictive Modeling.
American Community Survey Presented at the Meeting of the National Neighborhood Indicators Partnership Susan Schechter May
Community Demonstration Project Baltimore Police Department June 27, 2000 Regional Crime Analysis GIS (RCAGIS) Developed by the United States Department.
Business Intelligence Is Critical In The New Government Economy
Uzair Bhatti Dan Diecker Puji Bandi Latoya Lewis IS 6833 ANALYTICS ASSIGNMENT PREDICTING HOMICIDE RATE IN ST. LOUIS CITY FOR 2013.
A Strategic Approach to Violence Reduction: An Overview for Project SAFE Cabarrus Jim Frabutt, Ph.D. Center for Youth, Family, and Community Partnerships.
Development of a Methodology to Evaluate Waste and Recycling Rates Debra L. Kantner Bryan Staley, PhD, PE.
Emerging Trends in Data Dissemination – A Trinidad & Tobago Perspective By Mohanee Sinanan-Mitchell.
DEVELOPMENT OF GOVERNANCE, JUSTICE AND SECURITY STATISTICS IN KENYA ROBERT C. B. BULUMA KENYA NATIONAL BUREAU OF STATISTICS First Meeting of Governance,
Business Intelligence
PREDICTING THE 2013 SAINT LOUIS CITY HOMICIDE RATE SPENCER SCHNEIDENBACH SHAILESH LANJEWAR XUN ZHOU BEN HOLTMAN.
Public Safety Overview Daryl K. Roberts Chief of Police Hartford Police Department August 1, 2009.
Anet Badali Faculty Mentor James W. Meeker, Ph.D, J.D Criminology, Law and Society University of California, Irvine A SPATIAL ANALYSIS OF DOMESTIC VIOLENCE:
 Explain the importance of a personal budget  Describe how budgeting helps businesses manage their finances  Display understanding of how forecasting.
Data Collection Data to collect Data Warehouse Structure of the data network Platform Collaboration with Social network and other technologies Data Presentation.
American Community Survey (ACS) Program Review Webinar March 6, 2012.
Lothian Road Dedicated Bicycle Patrol A Safer City. A Safer Neighborhood. A Safer Street.
ST. LOUIS HOMICIDES SHAWN RAKERS, RENITA REED, ABIGAIL RUSNICA, JOSEPH SCHMOELE, DAVID WILHELM.
Developing Survey Handbooks as Educational Tools for Data Users Presented at the European Conference on Quality in Official Statistics May 2010 Deborah.
Security Program: S.E.A.L. Oak Forest Homeowners Association.
Spatial and Temporal Analysis: San Antonio Burglaries Neo Geo Spatial Detectives.
Building Data and Document-Driven Decision Support Systems How do managers access and use large databases of historical and external facts?
Chapter 7: Business Intelligence and Decision Support Copyright © 2013 Pearson Education, Inc. publishing as Prentice Hall Chapter
Jess Thornton Unit 9. Unit 9 Chores DiscussionQuiz Unit 9 Project (Look under unit 9 for direction) Seminar.
WebFOCUS Magnify: Search Based Applications Dr. Rado Kotorov Technical Director of Strategic Product Management.
Community Needs Analysis Proposal for Leavenworth, Kansas By Ricky Sirois, Emporia State University.
Introductory Criminal Analysis Thomas E. Baker PRENTICE HALL ©2005 Pearson Education, Inc. Introductory Criminal Analysis: Crime Prevention and Intervention.
Crime Analysis and Mapping Jonathan Lewin. Impacting Crime Considerations in Developing a Crime Mapping Application.
Mapping for the Next Millennium How CrimeRisk™ scores are formed.
Chapter 6 The Police: Role and Function. Police Organization  Most municipal police departments are independent agencies within the executive branch.
IS 6833 ANALYTICS ASSIGNMENT Ying Chen, Sri Murali, John Powell, Scott Weber.
The Need for Data Analysis 2 Managers track daily transactions to evaluate how the business is performing Strategies should be developed to meet organizational.
 FOCUS ON PEFORMANCE  Six Divisions and Offices  Over 60 employees  Internal and External customers ADMINISTRATION.
College Heights State College neighborhood plan SWOT analysis meetings pm State College Planning Commission Church of Christ.
Chief Constables Performance Report January 2015.
Progress Report: Mayor’s 2005 Crime Suppression Initiative Presented by: Mark W. Herder, Chief of Police City Council Meeting of November 29, 2005.
CCPD Board Meeting April 27, 2010 Overview and Consideration of Proposed CCPD Goals & Mission Statement.
Bexar County’s Smart Analytics for Effective Decision Support Catherine Maras Bexar County Chief Information Officer Mike Lozito Bexar County Judicial.
Police Organization and Management
The Police: Organization, Role, and Function
Tactical Crime Analysis
CJA/475 FORECASTING AND STRATEGIC PLANNING The Latest Version // uopcourse.com
CJA/475 CJA/ 475 cja/475 cja/ 475 FORECASTING AND STRATEGIC PLANNING The Latest Version // uopstudy.com
Presentation transcript:

Group A: Dan Diecker, Uzair Bhatti, Puji Bandi, Latoya Lewis

Objective The motivation behind this project is to come up with effective data management to help curb violent crime in the city of St. Louis. Demonstrating Data sources used Data maintained Use case examples for Management, Police Department, and Patrol Officers Finally overview of the BI system’s added value Scope of Project

Users: St. Louis Police Department Captains and Managers Lieutenants and Detectives Patrol Officers Value provided: We hope that this project will support the decision making process of the St. Louis police department on their mission to curb homicides in St. Louis city. Scope of Project

Examples of the support for decision making include: How to strategically place their man power Where to deploy patrol, mounted, or bike officers Patrolling frequencies (differs according to the area) Determine trends in criminal activity; likelihood of time and location Increased use of social media data to locate crime hot spots Track unfolding events in real-time; be informed of large, disruptive crowds or planned gatherings that could turn violent Scope of Project

U.S. Census Data American Community Survey Decennial Census Twitter & Social Media Historical Crime Data FBI Crime Statistics STLPD Data Data variables being maintained Mean Household income Education level Home Vacancy rates rented / owned Unemployment rate Data aggregated by district to neighborhood Data Sources

Historical Crime Data Data is entered as calls come in Tracks location, crime, and time Can flag similar crimes, e.g., vehicle or weapon used Census Data Loaded as it is released to build a demographic model of the neighborhood Twitter & Social Media Monitored Real-time for Keywords Mines text for location, can display potential trouble spots before any 911 calls are made Maintaining Data

System Structure System can be hosted externally or maintained in- house Based on STLPD requirements, hardware can be cloud-sourced or maintained in-house

Decision-makers can view historical trends in crime for all of St. Louis High-level summary Can view time-specific events; festivals, parades, etc. Can drill-down and view current resources assigned to specific neighborhoods Use Case: Management Petty Larceny Complaints – Week Ending 04/27/2013

Summary of a small geographic area – useful in determining local hot spots and trends Past crime trends Can view crimes that are similar Can see where patrol officers are assigned Use Case: Neighborhood Office Departure from Mean: Complaints a Year Ago +10% 0% -10% Displaying: Homicides Filter Home

Dashboard in Patrol Car Dashboard can display recent crimes in the area, along with a description of the suspect Can be informed of events from Social Media – can stay close to parties or gatherings before anything gets out of hand Use Case: Patrol Officer Pine Lawn 2 2 Critical Alerts Church function at Stratford Ave and Jennings Station Rd – Possible Gang Activity Potential repeat offender in area: 8 copper thefts in past week Map Details Related Map Details Related

Supports near real-time analysis of crime Not real-time; not all data can be maintained in real-time due to difficulty in collecting the information Predictive capabilities are near-future Can analyze weeks to a few months into the future, not years On-going maintenance and support will include a cost Support and IT Personnel Training for Users Investment in hardware (outsourced or internal) System Constraints

Overview As BI consultants to the City of St. Louis Police Department we conclude our presentation by discussing the following. In an effort to better facilitate the Police Department’s decision making processes, we proposed a BI system using available and easily-maintained data The BI dashboard developed by Group A has provides support to different levels of decision-makers in the STLPD The BI system provides increased responsiveness to crime trends, assists in optimizing the deployment of limited departmental resources, and supports analysis of different policing strategies Wrap-up