PPT-Burglary Analysis

December 2016 Todays objectives Share Ultas burglary data What do the analytics tell us Where do we see correlation with risk factors Can we predict burglary risk Whats worked for Ulta in impacting burglary losses. 213 Section Introduction: Burglary is the unlawful entry into a private structure for the purposes of committing a crime therein. Note that the second crime need not take place for the requirements Professor Shane D . Johnson. UCL Department of Security and Crime . Science. shane.johnson@ucl.ac.uk. Predicting future patterns. Questions we might ask. How many burglaries are . expected in the next few days?. Increasing the Likelihood of detection in Completed, Attempted and In-Progress Burglaries . Colin Paine & Barak Ariel. All residential burglaries in Thames Valley from 1. st. March 2010 to 31. st. Unit 4,. . Introduction to Artificial . Intelligence, . Stanford . online course. Made by: Maor Levy, . Temple University 2012. 1. Inference . in Bayes . Nets. What is the probability of getting a strong . FACULTY . OF. ENVIRONMENT. GeoCrimeData. Understanding Crime Context with Novel Geo-Spatial . Data. Nick Malleson. *. Mark . Birkin. *. Alex . Hirschfield. +. Andrew . Newton. +. *University of Leeds. Austin Public Safety Commission. March 7, 2011. Introduction. Local neighborhood associations in . Austin have expressed concerns about . an increase in residential burglary. While not “newsworthy,” burglaries . California Criminal Law Concepts . Chapter 14. 1. Mike Reid. LAHC. Elements. Corpus . Delicti. - _____. Does not have to be _______. May be a legal entry as into a store open for business. ____ of person does not have to physically enter structure. A general scenario:. Query . variables:. . X. Evidence . (. observed. ) . variables and their values: . E. = . e. . Unobserved . variables: . Y. . Inference problem. : answer questions about the query variables given the evidence variables. Police Department. Clear . Lake Division. . Captain . J. C. Stevens. . Law Enforcement Sensitive/Official Use Only. Clear Lake Division. 2015. 2016. Change. Murder. 7. 7. 0% . Sexual Assault (RAPE). A general scenario:. Query . variables:. . X. Evidence . (. observed. ) . variables and their values: . E. = . e. . Unobserved . variables: . Y. . Inference problem. : answer questions about the query variables given the evidence variables. picture game . Burglary picture game . What are the main features of 91a and 91b?. game . This task is to test your knowledge of the relevant names and legal principles of cases in Burglary. . Using your mini whiteboards, write the name and the principles of the following case.. 12/1/14-11/30/15. Captain Paul Broxterman. Data Prepared by CRMA Autumn Morris. A Typical Day in District 4. Total number of officers in district 4- . 142. Number of officers assigned to patrol duties- . Stuff from Chapters . 11-14. Thursday: Drugs . Explaining the Violence Drop. ▪ “Usual Suspects”. ▪ Police tactics. ▪ Increase in prison populations . ▪ Economic conditions. ▪ Changes in demographics. A general scenario:. Query . variables:. . X. Evidence . (. observed. ) . variables and their values: . E. = . e. . Unobserved . variables: . Y. . Inference problem. : answer questions about the query variables given the evidence variables.