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python
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import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
@st.cache_data
def load_data(json_file):
df = pd.read_json(json_file)
df['CRASH_DATE'] = pd.to_datetime(df['CRASH_DATE'])
return df
@st.cache_data
def get_top_crash_locations(df, n=10):
location_counts = df.groupby(['LATITUDE', 'LONGITUDE']).size().reset_index(name='COUNT')
top_locations = location_counts.sort_values('COUNT', ascending=False).head(n)
return top_locations
def plot_top_crash_locations(df, top_locations):
fig, ax = plt.subplots(figsize=(10, 10))
ax.scatter(df['LONGITUDE'], df['LATITUDE'], s=1, color='lightgray', alpha=0.1)
for _, location in top_locations.iterrows():
radius = location['COUNT'] * 0.0001
circle = plt.Circle((location['LONGITUDE'], location['LATITUDE']), radius,
color='#FF0000', fill=False, linewidth=2)
ax.add_artist(circle)
ax.set_title('Top 10 Crash Locations in Chicago', size=16)
ax.set_xlabel('Longitude', size=12)
ax.set_ylabel('Latitude', size=12)
ax.set_xlim(df['LONGITUDE'].min(), df['LONGITUDE'].max())
ax.set_ylim(df['LATITUDE'].min(), df['LATITUDE'].max())
legend_element = [plt.Line2D([0], [0], marker='o', color='w', label='Crash Count',
markerfacecolor='#FF0000', markersize=10, markeredgecolor='#FF0000', markeredgewidth=2)]
ax.legend(handles=legend_element, loc='upper right')
plt.tight_layout()
return fig
def update_charts(df):
fig1, ax1 = plt.subplots(figsize=(15, 10))
streets = df['STREET_NAME'].value_counts().head(15)
streets.plot(kind='barh', ax=ax1)
ax1.set_xlabel('Collisions', size=20)
ax1.set_ylabel('Street', size=20)
ax1.set_title('Total Number of Collisions by Street', size=20)
ax1.tick_params(axis='both', which='major', labelsize=15)
for i, v in enumerate(streets):
ax1.text(v, i, str(v), va='center', fontsize=12)
fig2, ax2 = plt.subplots(figsize=(15, 10))
killed_df = df[df['INJURIES_FATAL'] > 0]
injured_df = df[(df['INJURIES_TOTAL'] > 0) & (df['INJURIES_FATAL'] == 0)]
nothing_df = df[(df['INJURIES_FATAL'] == 0) & (df['INJURIES_TOTAL'] == 0)]
ax2.scatter(nothing_df['LONGITUDE'], nothing_df['LATITUDE'], alpha=0.04, s=1, color='blue')
ax2.scatter(injured_df['LONGITUDE'], injured_df['LATITUDE'], alpha=0.1, s=1, color='yellow')
ax2.scatter(killed_df['LONGITUDE'], killed_df['LATITUDE'], color='red', s=5)
blue_patch = mpatches.Patch(label='car body damage', alpha=0.2, color='blue')
yellow_patch = mpatches.Patch(color='yellow', label='personal injury', alpha=0.5)
red_patch = mpatches.Patch(color='red', label='fatal accidents')
ax2.legend([blue_patch, yellow_patch, red_patch],
('car body damage', 'personal injury', 'fatal accidents'),
loc='upper left', prop={'size': 20})
ax2.set_title('Severity of Motor Vehicle Collisions in Chicago', size=20)
ax2.set_xlim((-87.92, -87.52))
ax2.set_ylim((41.64, 42.03))
ax2.set_xlabel('Longitude', size=20)
ax2.set_ylabel('Latitude', size=20)
fig3, ax3 = plt.subplots(figsize=(15, 10))
street_names = df['STREET_NAME'].value_counts().head(10).index.tolist()
ax3.scatter(df['LONGITUDE'], df['LATITUDE'], s=1, color='darkseagreen', alpha=0.1)
colors = ['red', 'blue', 'magenta', 'orange', 'yellow', 'purple', 'black', 'chartreuse', 'brown', 'darkgreen']
for i, street in enumerate(street_names):
street_df = df[df['STREET_NAME'] == street]
ax3.scatter(street_df['LONGITUDE'], street_df['LATITUDE'], s=2, color=colors[i], label=street, alpha=0.5)
ax3.legend(loc='upper left', prop={'size': 12})
ax3.set_title('Vehicle Collisions in Chicago - Top 10 Streets', size=20)
ax3.set_xlim((-87.92, -87.52))
ax3.set_ylim((41.64, 42.03))
ax3.set_xlabel('Longitude', size=20)
ax3.set_ylabel('Latitude', size=20)
fig4, ax4 = plt.subplots(figsize=(15, 10))
by_hour = df['CRASH_HOUR'].value_counts().sort_index()
colors = ['g', '0.75', 'y', 'k', 'b', 'r'] * 4
by_hour.plot(kind='barh', ax=ax4, color=colors[:len(by_hour)])
ax4.set_xlabel('Collisions', size=20)
ax4.set_ylabel('Hour of the Day', size=20)
ax4.set_title('Total Number of Collisions by Hour of the Day', size=20)
full_hours = range(24)
full_labels = [f"{h:02d}" for h in full_hours]
ax4.set_yticks(full_hours)
ax4.set_yticklabels(full_labels, fontsize=15)
ax4.set_ylim(-0.5, 23.5)
ax4.set_xlim(0, max(by_hour) * 1.1)
for i, v in enumerate(by_hour):
ax4.text(v, i, str(v), va='center', fontsize=12)
fig5, ax5 = plt.subplots(figsize=(15, 10))
by_day = df['CRASH_DAY_OF_WEEK'].value_counts().sort_index()
colors = ['g', '0.75', 'y', 'k', 'b', 'r', 'c']
by_day.plot(kind='barh', ax=ax5, color=colors[:len(by_day)])
ax5.set_xlabel('Collisions', size=20)
ax5.set_ylabel('Day of the Week', size=20)
ax5.set_title('Total Number of Collisions by Day of the Week', size=20)
full_days = range(1, 8)
day_labels = ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun']
ax5.set_yticks(full_days)
ax5.set_yticklabels(day_labels, fontsize=15)
ax5.set_ylim(0.5, 7.5)
ax5.set_xlim(0, max(by_day) * 1.1)
for i, v in enumerate(by_day):
ax5.text(v, i+1, str(v), va='center', fontsize=12)
return fig1, fig2, fig3, fig4, fig5
st.set_page_config(layout="wide")
# Load the data from JSON
json_file = 'chicago_crashes.json' # Update with the actual path
collisions_df = load_data(json_file)
# Sidebar for navigation
st.sidebar.title("Navigation")
analysis_type = st.sidebar.radio("Choose Analysis", ["Top 10 Crash Locations", "Monthly Trends"])
if analysis_type == "Top 10 Crash Locations":
st.title('Chicago Traffic Crashes Analysis - Top 10 Crash Locations')
# Get top 10 crash locations
top_locations = get_top_crash_locations(collisions_df)
# Display the top locations data
st.subheader("Top 10 Crash Locations Data")
st.dataframe(top_locations)
# Plot the map
fig = plot_top_crash_locations(collisions_df, top_locations)
col1, col2, col3 = st.columns([1, 3, 1])
with col2:
st.pyplot(fig)
st.info("The size of the circles represents the count of crashes at each location.")
elif analysis_type == "Monthly Trends":
st.title('Chicago Traffic Crashes Analysis - Monthly Trends')
# Update the charts with the data
fig1, fig2, fig3, fig4, fig5 = update_charts(collisions_df)
# Display the charts
with st.container():
col1, col2 = st.columns(2)
col1.pyplot(fig3)
col2.pyplot(fig2)
with st.container():
col3, col4 = st.columns(2)
col3.pyplot(fig1)
col4.pyplot(fig5)
st.pyplot(fig4)
# Display some overall statistics
st.sidebar.subheader("Overall Statistics")
st.sidebar.write(f"Total number of crashes: {len(collisions_df)}")
st.sidebar.write(f"Date range: {collisions_df['CRASH_DATE'].min().date()} to {collisions_df['CRASH_DATE'].max().date()}")Leave a Comment