Who Will Save You Now?

Brief Presentation on Firearm Incident Risk Analysis

Introduction: General Results Overview

1. Purpose of Analysis:

   • This study examines the spatial dynamics of firearm violence across various regions in the U.S., analyzing how factors like population density, income, and proximity to churches and schools influence violence rates.

   • Key types of violence examined include mass shootings, homicides, suicides, and injuries.

2. Main Predictors:

   • Population Density: Areas with higher density tend to have elevated violence rates, likely due to urban pressures and social proximity.

   • Income Density: Surprisingly, areas with higher income density also showed a positive relationship with violence rates, particularly suicides, highlighting socio-economic stressors.

   • Area Per Person: Larger area per person, often found in rural settings, was negatively associated with violence rates, suggesting that lower density may act as a protective factor.

3. Churches and Schools:

   • Church Proximity: Found to have a weak, statistically significant negative effect on violence, suggesting localized dampening of violence near religious sites.

   • School Proximity: Showed a small but significant positive relationship, reflecting higher violence rates in urban areas where schools are more densely located.

4. Model Fit and Diagnostics:

   • The model explains a substantial portion of the variance (R² ~51%) but highlights geographic disparities and unmodeled factors influencing violence.

   • Diagnostics show heteroskedasticity and non-normality, indicating regional and cultural dynamics not fully captured.

Key Findings by Violence Type

1. Education Density:

   • One of the most consistent and significant mitigators of violence. Higher density of schools correlates with reduced homicide and suicide rates but is positively associated with urban-related violence due to population clustering.

2. Religious Density:

   • A mixed relationship, with slight protective effects against homicides but positive associations with injury and suicide rates, suggesting complex socio-cultural factors at play.

3. Spatial Patterns:

   • Hot spots in urban areas with high population and income density.

   • Cold spots in rural regions with lower density and larger area per person.

Conclusion: Church vs. School Proximity

1. General Safety Dynamics:

   • The analysis reveals collinearity between school and church density with other variables like population and income. These factors heavily influence violence rates, making it difficult to isolate their unique effects.

   • Near schools, the increased violence rate is tied to urban settings, while near churches, the slight decrease suggests localized community cohesion or deterrence.

2. Critical Assumptions:

   • Proximity alone does not define safety; broader socio-economic and cultural dynamics drive these patterns.

   • For schools, urban clustering might mask their protective societal role.

   • For churches, varying regional cultural attitudes toward religion could shape the observed dampening effect.

3. Takeaway:

   • While churches may show a slight advantage for localized violence reduction, the broader context indicates that population density and socio-economic inequalities are more significant determinants of safety.

   • This nuanced relationship emphasizes the need for deeper, targeted strategies in urban planning and community resource allocation.

Key Findings by Violence Type

1. Education Density:

   • One of the most consistent and significant mitigators of violence. Higher density of schools correlates with reduced homicide and suicide rates but is positively associated with urban-related violence due to population clustering.

2. Religious Density:

   • A mixed relationship, with slight protective effects against homicides but positive associations with injury and suicide rates, suggesting complex socio-cultural factors at play.

3. Spatial Patterns:

   • Hot spots in urban areas with high population and income density.

   • Cold spots in rural regions with lower density and larger area per person.

Conclusion: Church vs. School Proximity

1. General Safety Dynamics:

   • The analysis reveals collinearity between school and church density with other variables like population and income. These factors heavily influence violence rates, making it difficult to isolate their unique effects.

   • Near schools, the increased violence rate is tied to urban settings, while near churches, the slight decrease suggests localized community cohesion or deterrence.

2. Critical Assumptions:

   • Proximity alone does not define safety; broader socio-economic and cultural dynamics drive these patterns.

   • For schools, urban clustering might mask their protective societal role.

   • For churches, varying regional cultural attitudes toward religion could shape the observed dampening effect.

3. Takeaway:

   • While churches may show a slight advantage for localized violence reduction, the broader context indicates that population density and socio-economic inequalities are more significant determinants of safety.

   • This nuanced relationship emphasizes the need for deeper, targeted strategies in urban planning and community resource allocation.

This presentation reflects the complexity of firearm violence and underscores the interplay between spatial, social, and economic factors in shaping safety near schools and churches.

Summary of Getis Ord Gi* Overall Firearm Incident Risk Analysis

Key Takeaways:

1. Intercept:

• Coefficient: 0.002499

• Indicates the baseline weighted violence rate when all explanatory variables are at their average values. It is positive and statistically significant.

2. Key Predictors:

• Area Per Person (AREAPP_KDEZONALMEAN):

• Coefficient: -5.804769

• Strong negative relationship with overall violence rate, suggesting less densely populated areas have lower violence rates.

• Population Density (POPULATION_KDEZONALMEAN):

• Coefficient: 0.000015

• Positive and highly significant, indicating that higher population density increases the weighted violence rate.

• Income Density (INCOME_KDEZONALMEAN):

• Coefficient: 0.000153

• Strong positive relationship, suggesting that areas with higher income density have a slightly increased violence rate.

• ReligiousAssembly_point:

• Coefficient: -0.000000

• Weak but statistically significant negative effect, suggesting localized dampening of violence near religious points.

• EducationCenters_point:

• Coefficient: 0.000002

• Small but statistically significant positive effect, reflecting increased violence in denser urban educational zones.

3. Model Diagnostics:

• R-Squared: 0.511538

• Indicates that the model explains about 51% of the variance in the weighted violence rate. This is a solid fit given the complexity and variability of the data.

• Koenker Statistic: Highly significant, suggesting heteroskedasticity. Robust probabilities should be relied on.

• Jarque-Bera Statistic: Highly significant, indicating that residuals are not normally distributed. Additional geographic or unmodeled factors may exist.
  

  
  Summary of Results for the Income-Violence Model

  Map 3.1

  {

  Response Variable: KDE_MeanWeighted_Violence

  Explanatory Variable: Income_KDEzonalMEAN

  }

  Key Takeaways

  1. Intercept (0.000976):

  • Positive and statistically significant (p < 0.01).

  • Represents the baseline violence rate when income density is at its mean value.

  2. Income Density (INCOME_KDEZONALMEAN):

  • Coefficient (0.000204): Positive and highly significant (p < 0.01).

  • Indicates a strong positive relationship between income density and violence rates. This suggests that areas with higher income density tend to experience slightly elevated violence rates, possibly reflecting socio-economic inequalities or stressors.

  Model Diagnostics

  • R-Squared (0.459528):

  • Indicates that the model explains ~46% of the variance in violence rates using income density.

  • A reasonable fit for a single-variable regression.

  • Adjusted R-Squared (0.459356):

  • Adjusts for the number of predictors; consistent with the R-Squared.

  • Akaike's Information Criterion (AICc):

  • Value of -22,190.17 indicates model fit; lower values signify better fit.

  • Joint F-Statistic (2662.087469):

  • Highly significant (p < 0.01), indicating that income density as a predictor contributes significantly to the model.

  • Koenker (BP) Statistic (234.994106):

  • Significant (p < 0.01), indicating heteroskedasticity (non-constant variance). Use robust probabilities (Robust_Pr) for interpreting variable significance.

  • Jarque-Bera Statistic (45,676.423636):

  • Significant (p < 0.01), suggesting non-normality in residuals. Geographic or unmodeled factors may influence violence rates.

  Key Insights

  • Income Density's Influence:

  • Higher income areas exhibit slightly elevated violence rates, potentially reflecting complex socio-economic dynamics. While income may indicate affluence, it could also highlight disparities within those areas that contribute to violence.

  • Model Fit:

  • The model provides a meaningful explanation of the relationship between income and violence but leaves significant variance unexplained, likely due to other contributing factors like education, population density, or cultural dynamics.
    
    
    Getis Ord Gi* = Weighted Gun Violence Analysis

    Map 1.1

    • WHERE TARGET VARIABLE IS

      KDE_MeanWeighted_Violence = {

      (0.35 * !MassShooting_KDEzonalMEAN!) +

      (0.25 * !Homicide_KDEzonalMEAN!) +

      (0.25 * !Suicide_KDEzonalMEAN!) +

      (0.15 * !Injury_KDEzonalMEAN!)

      }

  • AND AND EXPLANATORY VARIABLES ARE

    AreaPP_KDEzonalMEAN

    Population_KDEzonalMEAN

    Income_KDEzonalMEAN

    ReligiousAssembly_point

    EducationCenters_point

So, where is safer? This study's Overall Analysis (Map 1.1 and OLS Regression Results) is based on county-level centroid data derived from KDE means for the variables in our study. The results reveal contrasting impacts of education and religious density on violence rates. Educational density generally correlates negatively with all violence types, significantly reducing suicide, homicide, and injury incidents, but remains a contributor to mass casualty events. Conversely, religious density shows mixed results, positively associated with injuries and negatively linked to suicide and homicide rates, while remaining insignificant in mass casualty incidents. These findings suggest that statistically, educational institutions broadly mitigate violence risks, while religious buildings exhibit more variable impacts depending on the violence type, highlighting their complex community role.

It should be noted that sub-chart analysis is driven by the result of individual Kernel Density Estimations prioritizing the precise locations of educational buildings, religious congregations, and mass firearm violence with increased scrutiny for generalized public safety, showing that proximity to religious congregations, on average, offers a safer environment compared to educational institutions. These factors are significant, but of weak correlation.