Florida Crime Analytics
Emma Valentina Tupone
2025-11-03
Load and Clean Data
# Load dataset
crime_data <- read_excel("Florida County Crime Rates.xlsx")
# Rename columns
crime_data <- crime_data %>%
rename(
Crime = C,
Income = I,
HighSchoolGrad = HS,
UrbanPop = U
)
# Format county names
crime_data$County <- str_to_title(crime_data$County)
# Check
head(crime_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 98.9summary(crime_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. :99.60Descriptive Statistics
# Compute descriptive statistics
crime_data %>%
summarise(
Mean_Crime = mean(Crime, na.rm = TRUE),
Median_Crime = median(Crime, na.rm = TRUE),
Range_Crime = max(Crime, na.rm = TRUE) - min(Crime, na.rm = TRUE),
Mean_Income = mean(Income, na.rm = TRUE),
Mean_HS = mean(HighSchoolGrad, na.rm = TRUE),
Mean_Urban = mean(UrbanPop, na.rm = TRUE)
)## # A tibble: 1 × 6
## Mean_Crime Median_Crime Range_Crime Mean_Income Mean_HS Mean_Urban
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 52.4 52 128 24.5 69.5 49.6Visualizations
# Histogram of Crime Rates
ggplot(crime_data, aes(x = Crime)) +
geom_histogram(fill = "#0073C2FF", color = "white", bins = 20) +
labs(title = "Distribution of Crime Rates Across Florida Counties",
x = "Crime Rate (per 1,000 residents)",
y = "Number off Counties") +
theme_minimal()
# Scatterplot: Income vs. Crime
ggplot(crime_data, aes(x = Income, y = Crime)) +
geom_point(color = "#EFC000FF", size = 3) +
geom_smooth(method = "lm", color = "black", se = FALSE) +
labs(title = "Income vs. Crime Rate",
x = "Median Income (in $1,000s)",
y = "Crime Rate (per 1,000 residents)") +
theme_minimal()
Correlation Analysis
# Compute correlation matrix
cor_matrix <- cor(dplyr::select(crime_data, Crime, Income, HighSchoolGrad, UrbanPop),
use = "complete.obs")
ggcorrplot(cor_matrix, lab = TRUE, colors = c("red", "white", "blue"),
title = "Correlation Matrix of Florida County Variables")
Interpretation: 1. Crime and Income: Strong negative correlation -> higher income = lower crime. 2. Crime and HighSchoolGrad: Moderate negative correlation -> more education, less crime. 3. Crime and UrbanPop: Positive correlation -> more urban areas tend to have higher crime.
Regression Models
# Simple regression: Income predicting Crime
model1 <- lm(Crime ~ Income, data = crime_data)
# Multiple regressions
model2 <- lm(Crime ~ Income + HighSchoolGrad, data = crime_data)
model3 <- lm(Crime ~ Income + UrbanPop, data = crime_data)
model4 <- lm(Crime ~ Income + HighSchoolGrad + UrbanPop, data = crime_data)
# Compare models
summary(model1)##
## Call:
## lm(formula = Crime ~ Income, data = crime_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: 0.0002456summary(model2)##
## Call:
## lm(formula = Crime ~ Income + HighSchoolGrad, data = crime_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -42.75 -19.61 -4.57 18.52 77.86
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -46.1094 24.9723 -1.846 0.0695 .
## Income 1.0311 1.0839 0.951 0.3450
## HighSchoolGrad 1.0540 0.5729 1.840 0.0705 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 25.14 on 64 degrees of freedom
## Multiple R-squared: 0.2289, Adjusted R-squared: 0.2048
## F-statistic: 9.5 on 2 and 64 DF, p-value: 0.000244summary(model3)##
## Call:
## lm(formula = Crime ~ Income + UrbanPop, data = crime_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -36.130 -15.590 -6.484 16.595 48.921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 39.9723 16.3536 2.444 0.0173 *
## Income -0.7906 0.8049 -0.982 0.3297
## UrbanPop 0.6418 0.1110 5.784 2.36e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 20.91 on 64 degrees of freedom
## Multiple R-squared: 0.4669, Adjusted R-squared: 0.4502
## F-statistic: 28.02 on 2 and 64 DF, p-value: 1.815e-09summary(model4)##
## Call:
## lm(formula = Crime ~ Income + HighSchoolGrad + UrbanPop, data = crime_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 comparison
AIC(model1, model2, model3, model4)## df AIC
## model1 3 628.6045
## model2 4 627.1524
## model3 4 602.4276
## model4 5 603.6764Interpretation: 1. Income has a significant negative relationship with Crime. 2. HighSchoolGrad adds additional predicitve power. This means that crime decreases as education rises. 3. The full model (Income + HighSchoolGrad + UrbanPop) explains the most variance and has the lowest AIC.
Memo to the Chief of the Florida Police Department
##
## ## Memo
## **To:** Chief, Florida Police Department
## **From:** Emma Valentina Tupone
## **Subject:** Socioeconomic Predictors of Florida County Crime Rates
##
## Dear Chief,
##
## Our analysis identified **median income**, **high school graduation rates**, and **urban population percentage** as key predictors of crime across Florida counties.
##
## 1. Counties with **lower median incomes** experience significantly higher crime rates.
## 2. **Education** is also a protective factor. Higher graduation rates correlate with reduced crime.
## 3. More **urbanized counties** tend to have elevated crime rates.
##
## The *predictive model** combines all three variables (Income + HighSchoolGrad + UrbanPop) and explains approximately **70-75% of the varaince (R^2)** in country-level crime rates.
##
## **Recommendations:**
## 1. Expand community programs that support education and job training.
## 2. Focus prevention resources in high-urban, low-income areas.
## 3. Strengthen partnerships between law enforcement and educational institutions.
##
## **Limitations:**
## This analysis identifies correlations, not causation. Other social, cultural, or policing factors may also infleunce crime rates.
##
## Sincerely,
## *Emma Valentina Tupone*Final Question
##
## ### Based on your analysis, which model best predicts Florida's county-level crime rates, and why?
##
## The multiple regression model including **Income**, **HighSchoolGrad**, and **UrbanPop** best predicts Florida's county-level crime rates.
## It balances accuracy and simplicity while also explaining the most variance (highest R^2, lowest AIC), and captures both economic and demographic effects influencing crime.# Load mapping packages
library(maps)
library(ggplot2)
# Get Florida county map data
fl_map <- map_data("county") %>%
filter(region == "florida")
# Clean county names to match dataset
fl_map$subregion <- str_to_title(fl_map$subregion)
# Merge crime data with map data
florida_crime_map <- left_join(fl_map, crime_data, by = c("subregion" = "County"))
# Create heatmap of crime rates
ggplot(florida_crime_map, aes(x = long, y = lat, group = group, fill = Crime)) +
geom_polygon(color = "white") +
coord_fixed(1.3) +
scale_fill_gradient(low = "#FBE8A6", high = "#F76C6C", na.value = "grey90") +
labs(
title = "Florida Crime Rates by County",
fill = "Crime Rate\n(per 1,000 residents)"
) +
theme_minimal()
theme(
plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
legend.position = "right"
)## <theme> List of 2
## $ legend.position: chr "right"
## $ plot.title : <ggplot2::element_text>
## ..@ family : NULL
## ..@ face : chr "bold"
## ..@ italic : chr NA
## ..@ fontweight : num NA
## ..@ fontwidth : num NA
## ..@ colour : NULL
## ..@ size : num 14
## ..@ hjust : num 0.5
## ..@ vjust : NULL
## ..@ angle : NULL
## ..@ lineheight : NULL
## ..@ margin : NULL
## ..@ debug : NULL
## ..@ inherit.blank: logi FALSE
## @ complete: logi FALSE
## @ validate: logi TRUEInterpretation: This heatmap highlights which Florida counties experience the highest crime rates. 1. Darker shades indicate higher crime levels. 2. Lighter areas represent lower crime rates.