crime concentration index

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Interpretation • The two highest concentration neighborhoods have just over 900% more robberies than would be expected given the distribution of robberies in the city – (10.72-1=9.72 or 972% greater concentration) • 42.7% of the neighborhoods have a higher than expected concentration of robberies – 35 out of 82 neighborhoods
± 0 1 2 4 Miles Robbery Concentration CCI Value 0.000000 - 0.446111 0.446112 - 1.000000 1.000001 - 3.349550 3.349551 - 5.989783 5.989784 - 10.718559
Page 1 and 2: Using Crime Concentration Indices i
Page 3 and 4: The map is not the territory. -Alfr
Page 5 and 6: Our Fair City
Page 7: ± 0 1 2 4 Miles
Page 10 and 11: Crime Count Example LAX Inglewood-L
Page 12 and 13: Display as Graduated Colors ± Homi
Page 14 and 15: Selection: 184 Tracts Area in Sq Mi
Page 16 and 17: Illustration Dark & Bram, 2007
Page 18 and 19: Crime rates standardize crime count
Page 20 and 21: Crime rate Crime Rates Count of cri
Page 22 and 23: In ArcGIS, create a new Field • S
Page 25 and 26: Using the Field Calculator • This
Page 27 and 28: This is the LAX Tank Farm & support
Page 29 and 30: The Location Quotient • Location
Page 31 and 32: Unemployment Example LQ UE 12 local
Page 33 and 34: Crime Concentration Index • Both
Page 35 and 36: The original reference Brantingham,
Page 37 and 38: Robbery in Roanoke: Concentration b
Page 40: Use a spatial join to create a Coun
Page 44 and 45: Populate the new field…
Page 46 and 47: Add a new field for the CCI • We
Page 48 and 49: The Formula: CCI Robbery C C BG BG
Page 50 and 51: The Sorted CCIs
Page 54 and 55: ± 0 1 2 4 Miles Robbery Concentrat
Page 56 and 57: The Problem • The Commander of th
Page 58 and 59: The Process • First, develop a la
Page 60 and 61: The Field Calculator
Page 62: 1.663018 1.465418 1.297896 0.519309
Page 65 and 66: Neighborhood Organizations • Like
Page 67 and 68: Generating the Neighborhood CCI •
Page 69 and 70: The CCI Formula CCI ( Robbery AreaS
Page 71 and 72: The Results Only 10 out of the 41 a
Page 73 and 74: Drilling down for a micro-spatial a
Page 75 and 76: Getting started • Using the “Go
Page 77 and 78: Finish it up • Do a spatial join
Page 79 and 80: An application to a linear analysis
Page 81 and 82: PALM MELROSE FOREST PARK OLIVE LAFA
Page 83 and 84: The Results CCI (Robberies close to
Page 85 and 86: The Results • But, to be realisti
Page 87 and 88: The Alternative • Using a 755-foo

crime concentration index

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