Added: APP.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import folium
+from folium.plugins import HeatMap
+from streamlit_folium import st_folium
+import plotly.express as px
+from datetime import datetime
+
+# Set page config
+st.set_page_config(page_title="Nuisance Complaints Dashboard", layout="wide")
+
+# Title and introduction
+st.title("Nuisance Complaints Analysis Dashboard")
+st.markdown("""
+**Team Members:** 
+* Lu Chang ([email protected])
+* Qiming Li ([email protected])
+* Ruchita Alate ([email protected])
+* Shreyas Kulkarni ([email protected])
+* Vishal Devulapalli ([email protected])
+""")
+st.write("This dashboard analyzes nuisance complaints data from the City of Urbana.")
+
+# Load and clean data
+@st.cache_data
+def load_and_clean_data():
+    try:
+        # Load data
+        data = pd.read_csv('Nuisance_Complaints.csv')
+        
+        # Drop rows with missing 'File Number'
+        data = data.dropna(subset=['File Number'])
+        
+        # Convert dates and handle date-related columns
+        data['Date Reported'] = pd.to_datetime(data['Date Reported'])
+        data['Date Notice Mailed or Given'] = pd.to_datetime(data['Date Notice Mailed or Given'])
+        data['File Close Date'] = pd.to_datetime(data['File Close Date'], errors='coerce')
+        
+        # Handle 'Date Notice Mailed or Given'
+        median_delay = (data['Date Notice Mailed or Given'] - data['Date Reported']).dt.days.median()
+        data.loc[data['Date Notice Mailed or Given'].isna(), 'Date Notice Mailed or Given'] = \
+            data.loc[data['Date Notice Mailed or Given'].isna(), 'Date Reported'] + pd.Timedelta(days=median_delay)
+        
+        # Handle 'Type of Complaint'
+        data['Type of Complaint'] = data['Type of Complaint'].fillna('Unknown')
+        
+        # Handle 'Disposition'
+        most_common_disposition = data.groupby('Type of Complaint')['Disposition'].agg(
+            lambda x: x.mode().iloc[0] if len(x.mode()) > 0 else 'Pending'
+        )
+        data['Disposition'] = data.apply(
+            lambda row: most_common_disposition[row['Type of Complaint']] 
+            if pd.isna(row['Disposition']) else row['Disposition'], 
+            axis=1
+        )
+        
+        # Calculate processing time for resolved cases
+        data['Processing Time'] = np.where(
+            data['File Close Date'].notna(),
+            (data['File Close Date'] - data['Date Reported']).dt.days,
+            np.nan
+        )
+        
+        # Handle 'Method Submitted'
+        data.loc[
+            (data['Submitted Online?']) & (data['Method Submitted'].isna()),
+            'Method Submitted'
+        ] = 'Online'
+        data['Method Submitted'] = data['Method Submitted'].fillna(data['Method Submitted'].mode()[0])
+        
+        # Drop rows with missing critical values
+        data = data.dropna(subset=['Submitted Online?', 'Mapped Location'])
+        
+        # Extract and clean location data
+        data['Latitude'] = data['Mapped Location'].str.extract(r'\(([^,]+),')[0].astype(float)
+        data['Longitude'] = data['Mapped Location'].str.extract(r', ([^,]+)\)')[0].astype(float)
+        
+        # Ensure Year Reported is integer
+        data['Year Reported'] = data['Year Reported'].astype(int)
+        
+        return data
+        
+    except Exception as e:
+        st.error(f"Error in data preprocessing: {str(e)}")
+        raise e
+
+# Load the data
+try:
+    data = load_and_clean_data()
+    st.success("Data successfully loaded and cleaned!")
+except Exception as e:
+    st.error(f"Error loading data: {str(e)}")
+    st.stop()
+
+# Create sidebar
+
+st.sidebar.header("Dashboard Controls")
+
+# Get unique years and convert to list for selectbox
+year_list = sorted(data['Year Reported'].unique().tolist())
+year_options = ['All Time'] + [int(year) for year in year_list]  # Convert years to integers
+
+selected_year = st.sidebar.selectbox(
+    "Select Year",
+    options=year_options,
+)
+# Add visualization type selector
+viz_type = st.sidebar.selectbox(
+    "Select Visualization",
+    ["Complaint Types", "Geographic Distribution", "Resolution Status",
+     "Submission Methods", "Complaints by Disposition"]
+)
+
+
+# Filter data based on selected year
+if selected_year == 'All Time':
+    filtered_data = data  # Use complete dataset when 'All Time' is selected
+else:
+    filtered_data = data[data['Year Reported'] == selected_year]
+
+# Update header text
+if selected_year == 'All Time':
+    st.header("Analysis for All Time")
+else:
+    st.header(f"Analysis for Year {selected_year}")
+# Main content
+
+# Create metrics
+# Create metrics
+# Create metrics
+# Create metrics
+# Create metrics
+col1, col2, col3 = st.columns(3)
+with col1:
+    st.metric("Total Complaints", len(filtered_data))
+with col2:
+    avg_time = filtered_data['Processing Time'].mean()
+    st.metric("Average Processing Time", f"{avg_time:.1f} days" if pd.notna(avg_time) else "N/A")
+with col3:
+    if not filtered_data.empty:
+        most_common = filtered_data['Type of Complaint'].value_counts().index[0]
+        st.metric("Most Common Type", most_common)
+    else:
+        st.metric("Most Common Type", "N/A")
+if viz_type == "Complaint Types":
+    # Interactive Pie Chart
+    st.subheader("Interactive Complaint Types Pie Chart")
+    complaint_counts = filtered_data['Type of Complaint'].value_counts().reset_index()
+    complaint_counts.columns = ['Complaint Type', 'Count']
+
+    fig = px.pie(
+        complaint_counts,
+        names='Complaint Type',
+        values='Count',
+        title=f'Complaint Types Distribution in {selected_year}',
+        hole=0.4  # Donut style
+    )
+    fig.update_traces(textinfo='percent+label')
+    st.plotly_chart(fig, use_container_width=True)
+
+elif viz_type == "Geographic Distribution":
+    # Clustered Heatmap
+    st.subheader("Clustered Heatmap of Complaints")
+    map_center = [filtered_data['Latitude'].mean(), filtered_data['Longitude'].mean()]
+    m = folium.Map(location=map_center, zoom_start=12)
+
+    heat_data = filtered_data[['Latitude', 'Longitude']].dropna().values.tolist()
+    HeatMap(heat_data).add_to(m)
+
+    st_data = st_folium(m, width=700, height=500)
+
+
+elif viz_type == "Resolution Status":
+    st.subheader("Complaint Resolution Status")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    resolution_counts = filtered_data['Disposition'].value_counts()
+    sns.barplot(x=resolution_counts.values, y=resolution_counts.index)
+    plt.title(f'Resolution Status Distribution in {selected_year}')
+    st.pyplot(fig)
+
+elif viz_type == "Submission Methods":
+    st.subheader("Submission Methods Analysis")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    submission_counts = filtered_data['Method Submitted'].value_counts()
+    sns.barplot(x=submission_counts.values, y=submission_counts.index)
+    plt.title(f'Submission Methods in {selected_year}')
+    st.pyplot(fig)
+
+
+elif viz_type == "Complaints by Disposition":
+    st.subheader("Complaints by Disposition")
+    disposition_counts = filtered_data['Disposition'].value_counts()
+    
+    if not disposition_counts.empty:
+        fig, ax = plt.subplots(figsize=(10, 6))
+        sns.barplot(x=disposition_counts.values, y=disposition_counts.index, palette="viridis", ax=ax)
+        ax.set_title(f'Complaints by Disposition in {selected_year}', fontsize=14)
+        ax.set_xlabel('Number of Complaints', fontsize=12)
+        ax.set_ylabel('Disposition', fontsize=12)
+        st.pyplot(fig)
+    else:
+        st.write("No data available for the selected year.")
+
+# Additional insights
+st.header("Key Insights")
+col1, col2 = st.columns(2)
+
+with col1:
+    st.subheader("Top 3 Complaint Types")
+    top_complaints = filtered_data['Type of Complaint'].value_counts().head(3)
+    st.write(top_complaints)
+
+with col2:
+    st.subheader("Resolution Efficiency")
+    resolution_rate = (filtered_data['Disposition'].value_counts() /
+                      len(filtered_data) * 100).round(2)
+    st.write(resolution_rate)
+
+# Footer
+st.markdown("---")
+st.markdown("Dataset provided by the City of Urbana Open Data Portal")

requirements.txt

@@ -0,0 +1,76 @@
+altair==5.5.0
+asttokens==3.0.0
+attrs==24.2.0
+blinker==1.9.0
+branca==0.8.0
+cachetools==5.5.0
+certifi==2024.8.30
+charset-normalizer==3.4.0
+click==8.1.7
+comm==0.2.2
+contourpy==1.3.1
+cycler==0.12.1
+debugpy==1.8.9
+decorator==5.1.1
+executing==2.1.0
+folium==0.18.0
+fonttools==4.55.1
+gitdb==4.0.11
+GitPython==3.1.43
+idna==3.10
+ipykernel==6.29.5
+ipython==8.30.0
+jedi==0.19.2
+Jinja2==3.1.4
+jsonschema==4.23.0
+jsonschema-specifications==2024.10.1
+jupyter_client==8.6.3
+jupyter_core==5.7.2
+kiwisolver==1.4.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+matplotlib==3.9.3
+matplotlib-inline==0.1.7
+mdurl==0.1.2
+narwhals==1.15.2
+nest-asyncio==1.6.0
+numpy==2.1.3
+packaging==24.2
+pandas==2.2.3
+parso==0.8.4
+pexpect==4.9.0
+pillow==11.0.0
+platformdirs==4.3.6
+plotly==5.24.1
+prompt_toolkit==3.0.48
+protobuf==5.29.1
+psutil==6.1.0
+ptyprocess==0.7.0
+pure_eval==0.2.3
+pyarrow==18.1.0
+pydeck==0.9.1
+Pygments==2.18.0
+pyparsing==3.2.0
+python-dateutil==2.9.0.post0
+pytz==2024.2
+pyzmq==26.2.0
+referencing==0.35.1
+requests==2.32.3
+rich==13.9.4
+rpds-py==0.22.3
+seaborn==0.13.2
+six==1.17.0
+smmap==5.0.1
+stack-data==0.6.3
+streamlit==1.40.2
+streamlit_folium==0.23.2
+tenacity==9.0.0
+toml==0.10.2
+tornado==6.4.2
+traitlets==5.14.3
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.2.3
+watchdog==6.0.0
+wcwidth==0.2.13
+xyzservices==2024.9.0