CRASH_DATA_GUIDE.md

Crash Data Analysis Guide

Complete guide to analyzing crash data with metadata, geospatial, and vehicle/traveler dimensions.

Quick Start

from crash_analyzer import load_switrs_data

# Load SWITRS CSV data
analyzer = load_switrs_data('accidents.csv', 'vehicles.csv', 'parties.csv')

# Generate full report
report = analyzer.generate_summary_report()

Data Source: SWITRS

Reference: https://tims.berkeley.edu/help/crashdata/switrs-geocoding.php

SWITRS (Statewide Integrated Traffic Records System) provides comprehensive California highway crash data with three main record types:

1. Accidents Table (Core Records)

Primary crash incident information.

Field	Type	Description	Example
case_id	int	Unique crash identifier	123456
accident_date	date	Date of crash	2024-01-15
accident_time	time	Time of crash (24-hr)	14:30
latitude	float	Geocoded latitude	37.7749
longitude	float	Geocoded longitude	-122.4194
county	string	County name	"San Francisco"
district	string	CHP district	"SF"
severity	string	Crash severity	F/I/P (Fatal/Injury/PropertyDamage)
collision_type	string	Type of collision	"Rear End", "Broadside", etc.
weather_1	string	Primary weather	"Clear", "Rain", "Fog"
weather_2	string	Secondary weather	Optional
surface_condition	string	Road surface	"Dry", "Wet", "Snow"
traffic_control_device	string	Traffic control status	"Functioning", "Not Functioning"
fatality_count	int	Number of fatalities	0-5
injury_count	int	Number of injuries	0-10

2. Vehicles Table (Vehicle Details)

Vehicle information for each crash.

Field	Type	Description
case_id	int	Link to accidents table
vehicle_id	int	Unique vehicle in crash
vehicle_type	string	Car, Truck, Motorcycle, SUV, Van, etc.
vehicle_year	int	Vehicle model year
vehicle_make	string	Manufacturer (Toyota, Ford, etc.)
vehicle_model	string	Model name
vehicle_color	string	Color
damage_extent	string	Severe, Moderate, Minor
direction_of_travel	string	N, S, E, W, etc.

3. Parties Table (Occupants & Pedestrians)

Information about crash participants (drivers, passengers, pedestrians).

Field	Type	Description
case_id	int	Link to accidents table
party_number	int	Sequence in crash
party_type	string	Driver, Passenger, Pedestrian, Cyclist
age	int	Age in years
gender	string	M/F
sobriety	int	1=Sober, 2=Drinking, 3=Intoxicated, 4=Unknown
injury_level	int	0=None, 1=Possible, 2=Probable, 3=Severe, 4=Fatal
safety_equipment_used	string	Seatbelt, Helmet, etc.
ejection	string	Yes/No/Unknown
primary_collision_factor	string	Cause (Speeding, DUI, etc.)

---

Analysis Dimensions

1. METADATA ANALYSIS

Time-Based Patterns

# Identify dangerous times
temporal = analyzer.analyze_temporal_patterns()
# Returns: crashes by hour, day, month, year + deadliest times

What to Look For:

• Rush hour peaks (7-9am, 4-6pm)
• Weekend spikes
• Holiday effect (New Year, July 4th)
• Seasonal trends (winter weather, spring break)

Severity Analysis

severity = analyzer.analyze_severity()
# Returns: distribution of Fatal/Injury/Property Damage

What to Report:

• Fatality rate (total deaths / total crashes)
• Injury-to-crash ratio
• Crash trend over time (increasing/decreasing)

Collision Types

collisions = analyzer.analyze_collision_types()
# Returns: most common collision types and deadliest types

Key Metrics:

• Most common collision type
• Deadliest collision type (by fatality rate)
• Preventable vs. unpreventable patterns

---

2. GEOSPATIAL ANALYSIS

Geographic Summaries

geo = analyzer.get_geographic_summary()
# Returns: crashes by county/district with severity breakdown

Geographic Hotspot Detection

hotspots = analyzer.identify_hotspots()
# Returns: grid-based clustering of high-crash areas

Interpretation:

• Cluster size = grid cell crashes
• Coordinates = center of high-risk area
• Fatal count = number of fatal crashes in area
• Focus enforcement/infrastructure improvements on hotspots

Use Cases:

• Identify intersections needing traffic signal upgrades
• Plan emergency response deployments
• Target enforcement activities
• Prioritize road maintenance

Visualization Recommendations:

• Heatmap of crash density (Folium, Kepler.gl)
• Cluster markers on interactive map
• Severity overlay (color-coded pins)
• Temporal animation (crashes by month)

---

3. VEHICLE/TRAVELER ANALYSIS

Vehicle Risk Analysis

vehicles_analysis = analyzer.analyze_vehicle_types()
# Returns: crashes per vehicle type + fatality rates

Insights:

• Which vehicle types are overrepresented in crashes?
• Which have highest fatality rates?
• Older vs. newer vehicles
• Motorcycle vs. car comparison

Traveler Demographics

demographics = analyzer.analyze_demographics()
# Returns: age distribution, gender, sobriety, injury levels

Key Demographics:

• Age group risk (teenagers, elderly highest risk)
• Gender patterns
• Impairment prevalence
• Safety equipment usage (seatbelt, helmet)

Injury Patterns

injuries = analyzer.analyze_injury_patterns()
# Returns: injury severity by age group, party type

Focus Areas:

• Vulnerable road users (pedestrians, cyclists)
• Age-specific injury severity
• Occupant vs. pedestrian injury rates
• Effectiveness of safety equipment

---

Data Preparation

Step 1: Obtain SWITRS Data

Options:

• TIMS Website: https://tims.berkeley.edu (public access, some limitations)
• California Highway Patrol: Direct request
• Local Law Enforcement: May have county-specific data

Step 2: Data Format

• Expected format: CSV files (one file per table type)
• Delimiter: Comma (,)
• Character encoding: UTF-8
• Missing values: Blank or NULL

Step 3: Clean Data

import pandas as pd

accidents = pd.read_csv('accidents.csv')

# Remove duplicates
accidents = accidents.drop_duplicates(subset=['case_id'])

# Handle missing geolocation
accidents = accidents.dropna(subset=['latitude', 'longitude'])

# Validate date format
accidents['accident_date'] = pd.to_datetime(accidents['accident_date'], errors='coerce')

Step 4: Load into Analyzer

from crash_analyzer import CrashDataAnalyzer

analyzer = CrashDataAnalyzer(accidents, vehicles, parties)

---

Common Analyses

Find Most Dangerous Intersections

hotspots = analyzer.identify_hotspots(grid_size=0.005)  # ~500m cells
# Focus on grid cells with >10 crashes

Compare Age Groups

demographics = analyzer.analyze_demographics()
# Look for age groups with highest injury rates

Identify Impairment Patterns

# Filter parties where sobriety == 3 (intoxicated)
impaired_crashes = analyzer.parties[analyzer.parties['sobriety'] == 3]
# Analyze time of day, day of week, location

Seasonal Trends

temporal = analyzer.analyze_temporal_patterns()
# Compare summer vs. winter crash counts

Vehicle Type Safety

vehicles = analyzer.analyze_vehicle_types()
# Motorcycle fatality rate vs. car fatality rate

---

Visualization Examples

1. Time Series: Crashes by Hour

import matplotlib.pyplot as plt

hourly = analyzer.accidents.groupby('hour').size()
plt.bar(hourly.index, hourly.values)
plt.xlabel('Hour of Day')
plt.ylabel('Number of Crashes')
plt.title('Crashes by Hour of Day')

2. Geographic Hotmap (Folium)

import folium

m = folium.Map(location=[37.0, -120.0], zoom_start=6)

for hotspot in analyzer.identify_hotspots():
    folium.CircleMarker(
        location=[hotspot['center_lat'], hotspot['center_lon']],
        radius=5 + hotspot['crash_count'] / 10,
        color='red' if hotspot['fatal_count'] > 0 else 'orange',
        popup=f"{hotspot['crash_count']} crashes"
    ).add_to(m)

m.save('hotspots_map.html')

3. Vehicle Risk Comparison (Plotly)

import plotly.express as px

vehicle_risk = analyzer.analyze_vehicle_types()['vehicle_risk_analysis']
df = pd.DataFrame(vehicle_risk).T

px.bar(df, y='fatality_rate', title='Fatality Rate by Vehicle Type').show()

4. Age Demographics (Seaborn)

import seaborn as sns

sns.histplot(analyzer.parties, x='age', hue='injury_level', kde=True)
plt.title('Injury Severity by Age')

---

Key Metrics to Track

Metric	Formula	Interpretation
Fatality Rate	Fatal Crashes / Total Crashes	Higher = more dangerous
Injury Rate	Injury Crashes / Total Crashes	Focus on prevention
Fatality per Crash	Total Fatalities / Total Crashes	Severity measure
Peak Hour	Hour with most crashes	Traffic management
Hotspot Density	Crashes per sq km	Infrastructure need
Demographics Risk	Deaths / Population Group	Target safety programs

---

API Reference

CrashDataAnalyzer Methods

class CrashDataAnalyzer:
    # Metadata
    analyze_temporal_patterns() -> Dict
    analyze_severity() -> Dict
    analyze_collision_types() -> Dict
    
    # Geospatial
    get_geographic_summary() -> Dict
    identify_hotspots(lat_col, lon_col, grid_size) -> List[Dict]
    
    # Vehicle/Traveler
    analyze_vehicle_types() -> Dict
    analyze_demographics() -> Dict
    analyze_injury_patterns() -> Dict
    
    # Combined
    generate_summary_report() -> Dict

---

Tips for Analysis

✅ Do:

• Start with summary statistics before deep dives
• Validate data quality before conclusions
• Cross-reference multiple data sources
• Account for population denominators (risk per capita)
• Visualize before presenting findings

❌ Don't:

• Confuse correlation with causation
• Ignore data quality issues
• Over-interpret small sample sizes
• Make policy recommendations without context
• Ignore survivor bias in injury data

---

Resources

• TIMS: https://tims.berkeley.edu/
• SWITRS Documentation: https://tims.berkeley.edu/help/crashdata/switrs-geocoding.php
• Berkeley Safe Transportation Research & Education Center: https://safetrec.berkeley.edu
• CHP Statewide Integrated Traffic Records System: https://www.chp.ca.gov/