Introduction

As data scientists working for a law firm that specializes in fighting parking and camera tickets, our task is to uncover hidden patterns in New York City violation data. Specifically, we’ll explore how payment amounts vary across different groups.

We’ll answer three key questions:

  1. Do certain issuing agencies issue higher payments?
  2. Do drivers from the tri-state area (NY, NJ, CT) pay more?
  3. Do certain counties tend to have higher payment amounts?

The dataset comes from the NYC Open Data Portal.

Load and Prepare the Data

# Download from NYC API
if (file.exists("camera_data.RData")) {
  load("camera_data.RData")
  message("Loaded local dataset: camera_data.RData")
} else {
  message("Downloading dataset from NYC Open Data...")
  endpoint <- "https://data.cityofnewyork.us/resource/nc67-uf89.json"
  resp <- GET(endpoint, query = list("$limit" = 99999, "$order" = "issue_date DESC"))
  camera <- fromJSON(content(resp, as = "text"), flatten = TRUE)
  save(camera, file = "camera_data.RData")
  message("Saved dataset locally as camera_data.RData")
}
## Loaded local dataset: camera_data.RData
# Confirm structure
glimpse(camera)
## Rows: 99,999
## Columns: 20
## $ plate                     <chr> "HPK2083", "FFZ7198", "BLANKPLATE", "BLANKPL…
## $ state                     <chr> "NY", "NY", "99", "99", "99", "99", "99", "9…
## $ license_type              <chr> "PAS", "PAS", "999", "999", "999", "999", "9…
## $ summons_number            <chr> "1420103131", "1405797526", "1405210989", "1…
## $ violation_time            <chr> "00:00A", "06:49A", NA, "00:00A", NA, "00:00…
## $ violation                 <chr> "INSP. STICKER-EXPIRED/MISSING", "OBSTRUCTIN…
## $ fine_amount               <chr> "65", "95", "45", "0", "0", "0", "115", "45"…
## $ penalty_amount            <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0",…
## $ interest_amount           <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0",…
## $ reduction_amount          <chr> "65", "95", "45", "0", "0", "0", "115", "45"…
## $ payment_amount            <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0",…
## $ amount_due                <chr> "0", "0", "0", "0", "0", "0", "0", "0", "0",…
## $ precinct                  <chr> "025", "000", "104", "000", "000", "000", "0…
## $ issuing_agency            <chr> "POLICE DEPARTMENT", "POLICE DEPARTMENT", "D…
## $ county                    <chr> NA, "Q", NA, "Q", NA, "Q", NA, "K", "NY", NA…
## $ violation_status          <chr> NA, "HEARING HELD-NOT GUILTY", NA, "HEARING …
## $ issue_date                <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ judgment_entry_date       <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ summons_image.url         <chr> "http://nycserv.nyc.gov/NYCServWeb/ShowImage…
## $ summons_image.description <chr> "View Summons", "View Summons", "View Summon…
# Convert numeric variables
camera <- camera %>%
  mutate(across(
    c("fine_amount","interest_amount","reduction_amount","payment_amount",
      "amount_due","penalty_amount"),
    ~as.numeric(.)
  ))

# Filter valid dates
camera <- camera %>%
  filter(str_detect(issue_date, "^\\d{4}-\\d{2}-\\d{2}T"))
camera$issue_date <- as.Date(camera$issue_date)

1. Issuing Agency and Payment Amount

Visualization

ggplot(camera, aes(x = issuing_agency, y = payment_amount)) +
  geom_boxplot(fill = "steelblue", color = "gray30") +
  coord_flip() +
  theme_minimal() +
  labs(title = "Payment Amount by Issuing Agency",
       x = "Issuing Agency", y = "Payment Amount ($)")

Descriptive Statistics

favstats(payment_amount ~ issuing_agency, data = camera) %>%
  arrange(desc(mean))
##                        issuing_agency    min     Q1 median       Q3    max
## 1            HEALTH DEPARTMENT POLICE 243.81 243.81 243.81 243.8100 243.81
## 2         SEA GATE ASSOCIATION POLICE 190.00 190.00 190.00 190.0000 190.00
## 3                     FIRE DEPARTMENT 180.00 180.00 180.00 180.0000 180.00
## 4  NYS OFFICE OF MENTAL HEALTH POLICE   0.00 180.00 180.00 190.0000 210.00
## 5                      PORT AUTHORITY   0.00 180.00 180.00 190.0000 242.76
## 6           ROOSEVELT ISLAND SECURITY   0.00 135.00 180.00 190.0000 246.68
## 7                    NYS PARKS POLICE   0.00   0.00 180.00 190.0000 242.58
## 8                   POLICE DEPARTMENT   0.00  65.00 180.00 190.0000 260.00
## 9                    PARKS DEPARTMENT   0.00  90.00 180.00 190.0000 245.28
## 10      TAXI AND LIMOUSINE COMMISSION 125.00 125.00 125.00 125.0000 125.00
## 11   HEALTH AND HOSPITAL CORP. POLICE   0.00   0.00 180.00 190.0000 245.64
## 12                           CON RAIL   0.00   0.00  95.00 228.8875 243.87
## 13       DEPARTMENT OF TRANSPORTATION   0.00  50.00  75.00 125.0000 690.04
## 14                            TRAFFIC   0.00  65.00 115.00 115.0000 245.79
## 15                  TRANSIT AUTHORITY   0.00   0.00  75.00 125.0000 190.00
## 16           DEPARTMENT OF SANITATION   0.00  48.75  65.00 115.0000 115.00
## 17               LONG ISLAND RAILROAD   0.00   0.00   0.00   0.0000   0.00
##         mean        sd     n missing
## 1  243.81000        NA     1       0
## 2  190.00000   0.00000     2       0
## 3  180.00000        NA     1       0
## 4  161.33333  65.99423    15       0
## 5  150.49319  80.53742    47       0
## 6  149.16083  90.57967    24       0
## 7  142.50970  90.27092    33       0
## 8  136.71574  82.82498   190       0
## 9  128.47736  78.92728   144       0
## 10 125.00000        NA     1       0
## 11 124.71373  98.60130    51       0
## 12 112.62000 124.87146     6       0
## 13  99.52822  82.88394 87273       0
## 14  94.59362  44.47453 12091       0
## 15  78.00000  82.05181     5       0
## 16  66.25000  45.48351    12       0
## 17   0.00000        NA     1       0

Inferential Statistics

anova_agency <- aov(payment_amount ~ issuing_agency, data = camera)
summary(anova_agency)
##                   Df    Sum Sq Mean Sq F value Pr(>F)    
## issuing_agency    16   1063435   66465   10.59 <2e-16 ***
## Residuals      99880 627060364    6278                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
supernova(anova_agency)
##  Analysis of Variance Table (Type III SS)
##  Model: payment_amount ~ issuing_agency
## 
##                                     SS    df        MS      F   PRE     p
##  ----- --------------- | ------------- ----- --------- ------ ----- -----
##  Model (error reduced) |   1063434.678    16 66464.667 10.587 .0017 .0000
##  Error (from model)    | 627060364.280 99880  6278.137                   
##  ----- --------------- | ------------- ----- --------- ------ ----- -----
##  Total (empty model)   | 628123798.957 99896  6287.777

Interpretation

If the F-value is large and p < .05, there are statistically significant differences in mean payment amounts between issuing agencies.
Though, the PRE (Proportion Reduction in Error) shows how much variance is explained. A small PRE (less than 0.05) means minimal real-world impact.
Any differences likely reflect agency-specific violation types rather than behavioral differences.

2. Tri-State Drivers (NY, NJ, CT) and Payment Amount

Visualization

ggplot(camera %>% filter(state %in% c("NY","NJ","CT")),
       aes(x = state, y = payment_amount)) +
  geom_boxplot(fill = "tan", color = "gray30") +
  theme_minimal() +
  labs(title = "Payment Amount by Driver State (Tri-State Area)",
       x = "Driver State", y = "Payment Amount ($)")

Descriptive Statistics

favstats(payment_amount ~ state, data = camera) %>%
  filter(state %in% c("NY","NJ","CT")) %>%
  arrange(desc(mean))
##   state min Q1 median  Q3    max     mean       sd     n missing
## 1    NJ   0 50     75 115 682.35 101.5746 89.97170  8654       0
## 2    NY   0 50     75 125 690.04 101.0978 80.92861 79528       0
## 3    CT   0 50     75 100 276.57  80.6627 46.07849  1457       0

Inferential Statistics

tri_state <- camera %>% filter(state %in% c("NY","NJ","CT"))
anova_state <- aov(payment_amount ~ state, data = tri_state)
summary(anova_state)
##                Df    Sum Sq Mean Sq F value Pr(>F)    
## state           2    603061  301530    45.5 <2e-16 ***
## Residuals   89636 593994009    6627                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
supernova(anova_state)
##  Analysis of Variance Table (Type III SS)
##  Model: payment_amount ~ state
## 
##                                     SS    df         MS      F   PRE     p
##  ----- --------------- | ------------- ----- ---------- ------ ----- -----
##  Model (error reduced) |    603060.721     2 301530.360 45.502 .0010 .0000
##  Error (from model)    | 593994008.724 89636   6626.735                   
##  ----- --------------- | ------------- ----- ---------- ------ ----- -----
##  Total (empty model)   | 594597069.446 89638   6633.315

Interpretation

A significant p-value (< .05) means payment amounts differ among NY, NJ, and CT drivers.
If out-of-state drivers (NJ or CT) pay more, this could show processing delays or additional penalties.
Even with statistical significance, small PRE values would suggest that differences are limited.
The firm might focus marketing on out-of-state drivers if they tend to pay higher amounts.

3. County and Payment Amount

Clean County Names

camera <- camera %>%
  mutate(county = case_when(
    county == "K" ~ "Kings County",
    county == "Q" ~ "Queens County",
    county == "BX" ~ "Bronx County",
    county == "NY" ~ "New York County",
    county == "R" ~ "Richmond County",
    TRUE ~ county
  ))

Visualization

ggplot(camera %>% filter(!is.na(county) & county != ""),
       aes(x = county, y = payment_amount)) +
  geom_boxplot(fill = "lightgreen", color = "gray30") +
  coord_flip() +
  theme_minimal() +
  labs(title = "Payment Amount by County",
       x = "County", y = "Payment Amount ($)")

Descriptive Statistics

favstats(payment_amount ~ county, data = camera) %>%
  arrange(desc(mean))
##             county min  Q1 median     Q3    max      mean        sd     n
## 1             RICH 180 180    180 180.00 180.00 180.00000        NA     1
## 2  Richmond County   0  65    180 180.00 245.79 139.67920  80.35405   863
## 3            Bronx 115 115    115 115.00 115.00 115.00000        NA     1
## 4              Qns 115 115    115 115.00 115.00 115.00000        NA     1
## 5               BK   0  50     75 100.00 690.04 113.54971 131.50278 14560
## 6    Queens County   0  65    115 125.00 244.46 102.35114  52.58054   983
## 7               MN   0  50     50 125.06 281.80 100.54274  73.46670 14518
## 8     Bronx County   0  65     75 160.00 245.64 100.32037  67.45720   243
## 9  New York County   0  65    115 115.00 260.00  92.95323  38.30536  8950
## 10    Kings County   0  65     65 115.00 243.81  86.09225  49.12610  1547
## 11              QN   0  50     50 100.00 283.03  82.35782  60.30923 16373
## 12              ST   0  50     50  75.00 250.00  69.66361  45.80596   485
## 13           Kings   0   0      0   0.00   0.00   0.00000        NA     1
##    missing
## 1        0
## 2        0
## 3        0
## 4        0
## 5        0
## 6        0
## 7        0
## 8        0
## 9        0
## 10       0
## 11       0
## 12       0
## 13       0

Inferential Statistics

county_clean <- camera %>% filter(!is.na(county) & county != "")
anova_county <- aov(payment_amount ~ county, data = county_clean)
summary(anova_county)
##                Df    Sum Sq Mean Sq F value Pr(>F)    
## county         12   9978556  831546   116.7 <2e-16 ***
## Residuals   58513 416929615    7125                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
supernova(anova_county)
##  Analysis of Variance Table (Type III SS)
##  Model: payment_amount ~ county
## 
##                                     SS    df         MS       F   PRE     p
##  ----- --------------- | ------------- ----- ---------- ------- ----- -----
##  Model (error reduced) |   9978556.010    12 831546.334 116.701 .0234 .0000
##  Error (from model)    | 416929614.778 58513   7125.419                    
##  ----- --------------- | ------------- ----- ---------- ------- ----- -----
##  Total (empty model)   | 426908170.788 58525   7294.458

Interpretation

A significant ANOVA indicates payment amounts vary by county.
This could reflect local enforcement intensity or differences in violation types.
If PRE is relatively larger here than in the previous analyses, county may be the most useful predictor for marketing strategy.

Final Summary

Across all analyses, issuing agency, driver state, and county show statistically significant differences in payment amounts, primarily because of the very large dataset.
However, only county likely represents meaningful differences related to enforcement or geographic patterns.
The law firm should prioritize county in its marketing strategy, focusing advertising and outreach in areas with higher average payment amounts.