Incidents Location-based Graph: This graph is drawn to display the incidents that have occurred in various locations. The trend that is shown spans the years 2015 through 2019. The graph indicates that over the years, most incidents occurred at residence/home.
Scatter plot Analysis: Scatter plots are a method of displaying a connection; by charting the data points, a scattering of points on a graph is generated. In the below plot, we are comparing the Hate crime dataset and Mental Wellbeing Facilities dataset against each other to demonstrate the "cause-and-effect" relationship between them.
Findings :
Through visualization and the dashboard, insights like, Michigan had the highest number of gender-related hate crimes and Ohio had the highest number of disability-related hate crimes over the years are uncovered.
Through a scatter plot analysis, it is observed that Oklahoma proves to be the haven of hate crime it's relatively high crime density and a low number of Mental Well-being Facilities. This establishes our premise that a low number of Mental Well-being Facilities will result in higher hate crime density.
New York, Ohio, and California have lower hate crime density and a higher count of Mental Well-being Facilities while Pennsylvania, Illinois, and Oklahoma have higher crime densities thanks to their low count of Mental Well-being Facilities.Texas with its high medical wellbeing centers density has a very normal rate of crimes.This again adds gravitas to our initial finding that higher medical facilities reduce crimes.
Michigan and Ohio both have a relatively better count of Mental Well-being Facilities with respect to population. However, these specific motivations for hate crime seem to be unaffected by the presence of Mental Well-being Facilities. These states might require another solution for reducing this uniquely large number of motivation-specific hate crimes.
Thus, from our findings, we can conclude that states with a larger population and more urban environment require proportionate Mental Well-being Facilities to combat and reduce hate crime.
The Regression analysis does not indicate a linear relationship between population, Count of Mental well-being facilities, and the number of hate crimes. However, the multiple r-squared errors indicate a relationship factor of 71%. This means that 71% of the dependent, that is the count of mental well-being facilities is dependent on the independent variables in population and count of hate crimes. The low probability of the t factor does not allow us to establish a direct relationship which is true in the case of analyzing geographic data but this allows helps us to identify the urban-rural divide of hate crime occurrence.