Violent Crime vs. Property Crime and Explanatory Variables in the Four Corner States
Dublin Core
Title
Violent Crime vs. Property Crime and Explanatory Variables in the Four Corner States
Subject
Violent Crime and Property Crime OLS Regression Analysis and Descriptive Maps
Description
This collection compares violent crime and it's explanatory variables with property crime and the same variables in the 4 corner states. Property crime and violent crime have different variables with strong correlations for each type of crime.
Creator
Brad Wangsgard
Date
Data from 2018
Contributor
Brad Wangsgard
Language
English
Type
PDF
Coverage
UT, AZ, CO, NM
Collection Items
Percent of People Below Poverty Level by County Subdivision, 2018, 4 Corner States
This map shows high poverty rates in the areas with high numbers of Native Americans, especially on reservations, such in the 4 corners area and certain parts of AZ and NM. It also shows generally higher poverty levels in urban areas and areas close…
Number of Property Crimes per 10,000 people in 2018 in UT, AZ, CO, NM
This map shows higher property crime in most urban areas, and seemingly random higher rates in rural areas. It only includes county subdivisions that had 2018 crime data available. Most reservations and areas with very low or spread-out populations…
Number of Violent Crimes per 10,000 people in 2018 in UT, AZ, CO, NM
This map shows higher violent crime in most urban areas, and seemingly random higher rates in rural areas. It somewhat corresponds with the property crime distribution but varies in certain areas. It only includes county subdivisions that had 2018…
OLS Regression Analysis for Property Crime in 2018 in AZ, CO, UT, NM by County Subdivision
This statistical analysis of the map shows a strong correlation between percent male between 18-25 and property crime, with a higher percent male leading to more property crime (p=.0001). Residual colors on the map show where the true data values are…
OLS Regression Analysis for Violent Crime in 2018 in AZ, CO, UT, NM by County Subdivision
Residual colors on the map show where the true data values are either higher or lower than the predicted values from the regression, Lighter coloring (like yellow and light orange) shows where predicted values were smaller than true data, and darker…