PPT-Burglary

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. Offensive Weapon Anything that is not commonly used except as a weapon Anything capable of being used and intended by the defendant to be used for offensive purposes even though it is also capable of being used for innocent purposes In company Being 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. FACULTY . OF. ENVIRONMENT. GeoCrimeData. Understanding Crime Context with Novel Geo-Spatial . Data. Nick Malleson. *. Mark . Birkin. *. Alex . Hirschfield. +. Andrew . Newton. +. *University of Leeds. by Larisa Bumbled Burglary By Larisa Hecate3366@frontiernet.net Sun beat down through the windshield, shinned off the dashboard and blinded Remi. She used one hand to block what the visor couldn't an Maggie Madey. AKA. “The Deadly . D. rifter”. or. “The Confession Killer”. “I hated my life, I hated everybody”. Background. Born to alcoholic parents. Lost one of his eyes at 10 when brother knifed him . Sexual Assault. Tristan D. Svare. Deputy District Attorney, San Bernardino County. Family Violence Unit - Elder Abuse Prosecution. (909) 387-6533. tsvare@sbcda.org. Elder Abuse IS A Problem…. Elder Sexual Assault IS A Problem…. 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. 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. December 2016. Today’s objectives. Share . Ulta’s. burglary data. What do the analytics tell us? . Where do we see correlation with risk factors?. Can we predict burglary risk?. What’s worked for Ulta in impacting burglary losses?. 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- .  . Breaking. . (by actual or constructive force; and need not actually cause damage) and . Entering. . (actual or constructive) an degree of entry is sufficient) of the. dwelling. . of another. 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.