Accession Number : ADA563653


Title :   Crime Trend Prediction Using Regression Models for Salinas, California


Descriptive Note : Master's thesis


Corporate Author : NAVAL POSTGRADUATE SCHOOL MONTEREY CA


Personal Author(s) : Shingleton, Jarrod S


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/a563653.pdf


Report Date : Jun 2012


Pagination or Media Count : 89


Abstract : Salinas, California has been battling an above average crime rate for over 30 years. This is due primarily to two rival gangs in Salinas: the Norte os and the Sure os. The city and the surrounding community have implemented many methods to mitigate the crime level, from community involvement to the inception of a gang task force. As of yet, none of the efforts have had long-lasting effects. In a 2009 thesis, Jason A. Clarke and Tracy L. Onufer postulated that various socio-economic variables are influential on the crime level in Salinas. They characterized crime as a summation of homicides, assaults and robberies reported. Their thesis determined that to lower overall violence levels, officials in Salinas should focus on: reducing the unemployment rate, the number of vacant housing units, and the high school dropout rate; and increasing the high school graduation rate and average daily attendance. A deeper examination of the data could lead not only to assumptions about how to lower crime rates, but also to a means of predicting future crime rates by using various methods of multiple value regression.


Descriptors :   *CALIFORNIA , *CRIMES , *REGRESSION ANALYSIS , COMMUNITIES , DAILY OCCURRENCE , ECONOMICS , GRADUATES , HIGH RATE , HOUSING(DWELLINGS) , MATHEMATICAL MODELS , PREDICTIONS , TASK FORCES , UNEMPLOYMENT , URBAN AREAS


Subject Categories : Economics and Cost Analysis
      Sociology and Law
      Numerical Mathematics


Distribution Statement : APPROVED FOR PUBLIC RELEASE