This report analyzes data from Florida counties to see which factors, whether be income, education, or urban envirnemnt, best predicts crime rates. The results show that the percentage of urban population is the strongest predictor of crime. Counties with larger urban populations tend to have higher crime rates, while those with higher income and education levels tend to have lower crime, in urban environments. The findings can help us lower crime rates throughout Florida, by pinpointing correlations, and possible causation of crime.

data <- read_excel("C:/Users/laura/Downloads/Florida County Crime Rates.xlsx")


data <- data %>%
  rename(
    County = County,
    Crime = C,
    Income = I,
    HighSchoolGrad = HS,
    UrbanPop = U
  )


data$County <- str_to_title(tolower(data$County))


head(data)
## # A tibble: 6 × 5
##   County   Crime Income HighSchoolGrad UrbanPop
##   <chr>    <dbl>  <dbl>          <dbl>    <dbl>
## 1 Alachua    104   22.1           82.7     73.2
## 2 Baker       20   25.8           64.1     21.5
## 3 Bay         64   24.7           74.7     85  
## 4 Bradford    50   24.6           65       23.2
## 5 Brevard     64   30.5           82.3     91.9
## 6 Broward     94   30.6           76.8     98.9
summary(data)
##     County              Crime           Income      HighSchoolGrad 
##  Length:67          Min.   :  0.0   Min.   :15.40   Min.   :54.50  
##  Class :character   1st Qu.: 35.5   1st Qu.:21.05   1st Qu.:62.45  
##  Mode  :character   Median : 52.0   Median :24.60   Median :69.00  
##                     Mean   : 52.4   Mean   :24.51   Mean   :69.49  
##                     3rd Qu.: 69.0   3rd Qu.:28.15   3rd Qu.:76.90  
##                     Max.   :128.0   Max.   :35.60   Max.   :84.90  
##     UrbanPop    
##  Min.   : 0.00  
##  1st Qu.:21.60  
##  Median :44.60  
##  Mean   :49.56  
##  3rd Qu.:83.55  
##  Max.   :99.60
psych::describe(data[, c("Crime", "Income", "HighSchoolGrad", "UrbanPop")])
##                vars  n  mean    sd median trimmed   mad  min   max range  skew
## Crime             1 67 52.40 28.19   52.0   51.60 25.20  0.0 128.0 128.0  0.32
## Income            2 67 24.51  4.68   24.6   24.33  5.34 15.4  35.6  20.2  0.34
## HighSchoolGrad    3 67 69.49  8.86   69.0   69.48 11.56 54.5  84.9  30.4 -0.02
## UrbanPop          4 67 49.56 33.97   44.6   49.75 42.40  0.0  99.6  99.6 -0.02
##                kurtosis   se
## Crime             -0.29 3.44
## Income            -0.70 0.57
## HighSchoolGrad    -1.30 1.08
## UrbanPop          -1.49 4.15
ggplot(data, aes(x = Income, y = Crime)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  theme_minimal()

ggplot(data, aes(x = Crime)) +
  geom_histogram(bins = 20, fill = "skyblue", color = "white") +
  theme_minimal()

The scatterplot shows a positive relationship (r = .43) between income and crime. As income increases, crime also tends to increase slightly, however, the findings are moderate and with wide variabiltiy between counties. The histogram shows that most Florida counties have moderate crime rates, with a few counties showing higher rates. This distribution shows that crime is consistent across most regions, with urban counties having higher levels of crime.

cor_matrix <- cor(data[, c("Crime", "Income", "HighSchoolGrad", "UrbanPop")])
cor_matrix
##                    Crime    Income HighSchoolGrad  UrbanPop
## Crime          1.0000000 0.4337503      0.4669119 0.6773678
## Income         0.4337503 1.0000000      0.7926215 0.7306983
## HighSchoolGrad 0.4669119 0.7926215      1.0000000 0.7907190
## UrbanPop       0.6773678 0.7306983      0.7907190 1.0000000
ggcorrplot(cor_matrix, lab = TRUE)

m1 <- lm(Crime ~ Income, data = data)
summary(m1)
## 
## Call:
## lm(formula = Crime ~ Income, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -42.452 -21.347  -3.102  17.580  69.357 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -11.6059    16.7863  -0.691 0.491782    
## Income        2.6115     0.6729   3.881 0.000246 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 25.6 on 65 degrees of freedom
## Multiple R-squared:  0.1881, Adjusted R-squared:  0.1756 
## F-statistic: 15.06 on 1 and 65 DF,  p-value: 0.0002456
m2 <- lm(Crime ~ Income + HighSchoolGrad + UrbanPop, data = data)
summary(m2)
## 
## Call:
## lm(formula = Crime ~ Income + HighSchoolGrad + UrbanPop, data = data)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -35.407 -15.080  -6.588  16.178  50.125 
## 
## Coefficients:
##                Estimate Std. Error t value Pr(>|t|)    
## (Intercept)     59.7147    28.5895   2.089   0.0408 *  
## Income          -0.3831     0.9405  -0.407   0.6852    
## HighSchoolGrad  -0.4673     0.5544  -0.843   0.4025    
## UrbanPop         0.6972     0.1291   5.399 1.08e-06 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 20.95 on 63 degrees of freedom
## Multiple R-squared:  0.4728, Adjusted R-squared:  0.4477 
## F-statistic: 18.83 on 3 and 63 DF,  p-value: 7.823e-09
AIC(m1, m2)
##    df      AIC
## m1  3 628.6045
## m2  5 603.6764

These regression models show us that crime increases with urban environments, and also with lower income and education(but not as prevalent than with urban environments.)
The R² ≈ .70, indicated in the multiple regression model explains that 70 % of the difference in crime across counties can be explained with the three reasons given (be urban population, education and income). The correlation matrix shows that crime is most strongly correlated with urban population (r = .68), followed by education (r = .47) and income (r = .43). All relationships are positive, meaning that as (lower)income, education, and urbanization increase, crime increases. However, even after accounting for income and education, urban population remains the strongest indicator for crime throughout Florida.

Memo to the Chief of the Florida Police Department:

Based on my analysis, the multiple regression model best predicts Florida’s county-level crime rates. This model shows that urbanization is the strongest and most significant predictor of crime, meaning that counties with larger urban populations tend to experience higher crime rates. To effectively reduce crime across the state, the Florida Police Department should focus attention and resources on highly urbanized areas, where the risk is greatest.

That said, it would be useful to take a closer look at what types of crimes are most common in these areas before deciding on next steps. If most of the crimes are things like theft, vandalism, or assault, then adding more officers and patrol coverage could help reduce incidents. But if a large portion of the crime comes from social or behavioral issues—like substance abuse, domestic violence, or mental health crises—then it’s just as important to invest in social services, including mental health professionals and social workers. Working together, law enforcement and community services can tackle both the symptoms and the deeper causes of crime. A balanced approach that combines policing with prevention and support will likely be the most effective way to reduce crime in Florida’s urban counties.