Statistics primer

Complex phenomena like crime emerge due to a variety of factors and circumstances, rather than from a single cause. Multiple linear regression is a helpful tool for researching such complexities. A regression model is a statistical tool that helps researchers understand how different factors relate to an outcome. Think of it as a way of asking, “If we hold everything else constant, what happens to crime when this one factor changes?” 

This analysis compares Utah census block groups using a composite crime index, utilizing data from 2024. Each factor analyzed in the regression model gets a coefficient, which expresses the size and direction of its relationship with crime: a positive number means crime tends to increase as that factor increases, while a negative number means crime tends to decrease as that factor increases. 

The results from this analysis are correlational, rather than causal; therefore, causal claims about the relationship between a given variable and crime rates cannot be made based on this study. In addition, the strongest effect sizes in this study are fairly small, and there is enormous variability in the data, even under conditions where the strongest effect sizes would be expected.

Rule #1:

Population growth in Utah’s cities and towns doesn’t necessarily lead to more crime; in fact, population growth is negatively associated with crime when accounting for other factors . 

  • The data indicate a negative relationship between crime and population size, with an effect size of .33 percent.  For example, a block group with 2,200 residents is expected to have a 3.3 percent lower crime rate compared to a similar block group with 2,000 residents.

The data also indicate a very weak, negative correlation between population density and crime. A 10% increase in population density correlates to a 0.4% decrease in crime rate.

How to interpret these findings

  • This analysis compares crime rates across census block groups of different sizes.  

  • This analysis does not test how an increase in population density within a block group will affect crime rates over time.

National literature indicates that shifts in overall population composition have a greater effect on crime than population growth does.  Specifically, younger, economically disadvantaged populations tend to see higher crime rates, while areas with higher economic stability tend to see lower crime.

Rule #2:

The percent of renters in an area has the strongest positive association with total crime when other factors are held constant. However, it is important to note that the effect size is small. 

  • The data indicates a positive correlation between percent of renters and total crime. A 10% increase in the share of renters in a block group is correlated with a 3.3% increase in total crime. 

Even for this relationship, the strongest positive correlation identified in the data, there is high variability. For instance, crime counts become more variable (and higher on average), when a block-group enters the top 20% of renter-dominated block groups in Utah in which 13.8% or more of the population is renting. Even in this group there is enormous variation including many concrete examples of high-renter low-crime areas.

how to interpret these findings

These results describe differences between block groups at a point in time, not what happens within a block group as the share of renters grows. 

  • This analysis establishes that the average block group with 30% renters has 3.3% higher crime rates than an average block group with 20% renters, all else held equal.  

  • The analysis does not indicate that 10% increase in the share of renters in a block group (e.g., the construction of a large rental apartment building) will increase crime rates.

Takeaways for planning and zoning

While there are statistically significant relationships between certain growth and development-related factors and crime rates, their implications for land use policy are obscured by variability and small effect sizes. Utah communities should consider these results within the context of other community goals.

What cities can do

Cities hoping to influence crime can focus on how spaces are designed, maintained, and used, and on strengthening social capital—the everyday relationships and informal norms that help communities self-regulate. Designing places that make it easier for residents to know one another, be present in public space, and participate in community life can help strengthen social capital and deter crime. 

The Utah Foundation’s Social Capital Index highlights that social capital — the networks, trust, and connections among people that benefit individuals and communities — is a foundational component of thriving places. Utah consistently ranks high in measures such as community life and civic participation, though several of these indicators (including neighborhood participation and time spent with friends and neighbors) have declined in recent years.

Utah communities can contribute to social capital by fostering civic engagement and social trust. Community design may also play a role in shaping social capital by supporting community life. Emerging evidence supports the idea that shared spaces (for example, parks, plazas, and pedestrian oriented streets) can serve as social infrastructure if well designed. Promoting elements of Crime Prevention Through Environmental Design, such as visibility and natural surveillance, lighting, clearly defined spaces, and upkeep, is a well established approach to deterring crime. 

Housing policy can also influence social capital. Fostering welcoming communities with a variety of housing options has been shown to support social mobility by increasing the likelihood that lower income kids and families will build relationships with higher income families.

 

Click here to see the full statistical analysis of crime in Utah.