:recycle: Add national trends and contextual insights to EV analysis

Incorporated two new datasets to provide broader national context: vehicle offerings and registration trends by fuel type. Enhanced visualizations and storytelling, highlighting national light-duty vehicle patterns and their impact on EV adoption. Expanded user insights with clear comparative trends and key takeaways.

app.py

@@ -11,30 +11,87 @@ st.markdown("### Authors: Tony An, Nabeel Bashir, Devansh Kumar, Jiajun Li")
 data_path = 'data/Electric_Vehicle_Population_Data.csv'
 ev_data = pd.read_csv(data_path)
 
+# Contextual Data 1 (National Trends: Vehicle Offerings)
+contextual_data_1 = {
+    "Fuel Type": [
+        "Ethanol (E85)", "CNG (Dedicated and Bi-Fuel)", "Diesel", "Electricity*",
+        "Hybrid Electric", "Propane (Dedicated and Bi-Fuel)", "Hydrogen", "Methanol (M85)"
+    ],
+    "1991": [0, 0, 17, 0, 0, 0, 0, 2],
+    "1992": [1, 2, 14, 0, 0, 0, 0, 2],
+    "1993": [1, 2, 5, 0, 0, 0, 0, 4],
+    "1994": [1, 2, 12, 0, 0, 0, 0, 2],
+    "1995": [0, 10, 13, 1, 0, 0, 0, 2],
+    "1996": [1, 10, 12, 0, 0, 0, 0, 1],
+    "1997": [1, 9, 11, 3, 0, 3, 0, 1],
+    "1998": [2, 12, 11, 8, 0, 3, 0, 0],
+    "1999": [6, 16, 7, 16, 0, 5, 0, 0],
+    "2000": [8, 15, 3, 12, 2, 2, 0, 0],
+    "2001": [11, 16, 3, 10, 2, 5, 0, 0],
+    "2002": [16, 18, 4, 6, 3, 5, 0, 0],
+    "2003": [22, 16, 4, 5, 3, 1, 0, 0],
+    "2004": [19, 16, 7, 1, 3, 1, 0, 0],
+    "2005": [24, 5, 8, 0, 8, 0, 0, 0],
+    "2006": [22, 5, 6, 0, 8, 0, 0, 0],
+    "2007": [31, 1, 7, 0, 11, 0, 0, 0],
+    "2008": [31, 1, 6, 1, 16, 1, 0, 0],
+    "2009": [36, 1, 12, 1, 19, 1, 0, 0],
+    "2010": [34, 1, 14, 1, 20, 0, 0, 0],
+    "2011": [72, 1, 16, 2, 29, 0, 0, 0],
+    "2012": [62, 6, 17, 6, 31, 1, 1, 0],
+    "2013": [84, 11, 22, 15, 38, 6, 1, 0],
+    "2014": [90, 19, 35, 16, 43, 14, 2, 0],
+    "2015": [84, 17, 39, 27, 46, 10, 3, 0],
+    "2016": [66, 12, 29, 29, 31, 5, 3, 0],
+    "2017": [45, 9, 21, 51, 44, 8, 2, 0],
+    "2018": [53, 9, 38, 57, 43, 7, 2, 0],
+    "2019": [40, 7, 30, 72, 64, 7, 4, 0],
+    "2020": [25, 10, 20, 83, 81, 8, 4, 0],
+    "2021": [14, 4, 30, 95, 127, 4, 5, 0],
+    "2022": [17, 0, 29, 132, 149, 0, 5, 0],
+    "2023": [10, 0, 22, 116, 127, 0, 5, 0],
+    "2024": [5, 0, 20, 149, 143, 0, 5, 0],
+}
+contextual_df_1 = pd.DataFrame(contextual_data_1)
+
+# Contextual Data 2 (National Trends: Vehicle Registration Changes)
+contextual_data_2 = {
+    "Fuel Type": ["Gasoline", "Flex Fuel", "Diesel", "Hybrid Electric", "Electric",
+                  "Biodiesel", "PHEV", "Compressed Natural Gas", "Hydrogen",
+                  "Propane", "Bi-Fuel", "Other", "Unspecified"],
+    "2016-2017": [1.1, 10.0, 4.7, 7.3, 25.9, None, 26.3, -8.8, 59.6, 5.3, None, -0.3, -6.9],
+    "2017-2018": [0.7, 6.4, 2.0, 4.9, 33.7, None, 25.3, -8.9, 34.1, -58.3, None, -3.9, -4.5],
+    "2018-2019": [1.0, 4.5, 3.2, 6.0, 26.9, None, 14.0, -8.7, 62.0, -20.0, None, -4.7, -2.4],
+    "2019-2020": [0.3, 1.6, 3.5, 6.3, 23.1, None, 9.7, -7.5, 9.4, 9.1, None, -5.8, -4.7],
+    "2020-2021": [3.6, -32.1, -24.6, 12.8, 29.9, None, 21.5, -1037.8, -18.2, 99.9, None, None, None],
+    "2021-2022": [0.3, -1.4, 0.6, 12.2, 40.3, 13.1, 25.3, -135.0, 20.5, -7.2, None, None, None],
+    "2022-2023": [-1.5, -10.0, -1.7, 8.7, 21.5, 1.4, 11.5, -16.1, 8.4, -8.9, -43.3, -10.5, None]
+}
+contextual_df_2 = pd.DataFrame(contextual_data_2)
+
+
 # Data Preparation
 ev_data[['Longitude', 'Latitude']] = ev_data['Vehicle Location'].str.extract(r'POINT \((-?\d+\.\d+) (-?\d+\.\d+)\)')
 ev_data['Latitude'] = pd.to_numeric(ev_data['Latitude'], errors='coerce')
 ev_data['Longitude'] = pd.to_numeric(ev_data['Longitude'], errors='coerce')
 
-# Write-up
+# Introduction Story
 st.markdown("""
-Electric vehicles (EVs) represent a transformative shift in the transportation sector, combining technological innovation with environmental responsibility.
-
-Through this interactive article, we aim to uncover key trends in EV adoption, including the growing number of electric vehicles across counties in Washington State, the release patterns of EV models over the years, and the geographic distribution of EVs. By examining these trends, we can provide insights into the factors driving EV adoption and their implications for infrastructure and policy.
+Electric vehicles (EVs) are no longer a niche technology; they represent the future of transportation. From bustling urban hubs to quiet suburban roads, the adoption of EVs is reshaping mobility and environmental policy. This interactive story highlights **Washington State's EV trends**, places them within a **national context**, and explores the remarkable transformation of vehicle technology and registration trends.
 
-### Why Focus on Washington State?
-Washington State has emerged as a leader in promoting clean energy initiatives and adopting electric vehicles. With a robust network of charging stations and incentives for EV buyers, the state provides a rich dataset to analyze the adoption trends of electric vehicles. By focusing on a single state, we can gain deeper insights into localized patterns and challenges, which can be used to inform similar efforts in other regions.
-
-### What You'll Discover
-- The distribution and characteristics of EVs across counties in Washington State.
-- The timeline of EV model releases.
-- Insights into infrastructure, including potential charging station locations.
+Through maps, charts, and insights, we uncover:
+- The **geographic distribution** of EVs in Washington State.
+- The rapid growth of EV models over time.
+- National trends in vehicle offerings and registration changes.
+""")
 
-This analysis focuses on Washington State to provide a localized perspective and highlight trends at a manageable scale.
+# Visualization 1: Geographic Distribution
+st.subheader("Where Are Electric Vehicles Adopted the Most?")
+st.markdown("""
+Washington State's **King County** is at the forefront of EV adoption, with over 107,000 registered EVs. Snohomish and Pierce Counties follow, showing that urban infrastructure and policies are key drivers of EV growth.
 """)
 
 # Central Interactive Visualization
-st.subheader("Explore EV Distribution by County")
 county_counts = ev_data['County'].value_counts().reset_index()
 county_counts.columns = ['County', 'Count']
 
@@ -50,40 +107,8 @@ county_counts['Color'] = county_counts['County'].apply(
     lambda x: 'blue' if x == selected_county else 'green'
 )
 
-# Contextual Visualization 0: EV Bar Counts by County
-fig_county = px.bar(
-    county_counts,
-    x='County',
-    y='Count',
-    title=f"Electric Vehicle Counts by {selected_county if selected_county != 'All' else 'County'}",
-    labels={"Count": "Number of EVs", "County": "County"},
-    color='County',
-    color_discrete_map={
-        selected_county: 'blue',
-        'Other': 'green'
-    },
-    height=500
-)
-st.plotly_chart(fig_county, use_container_width=True)
-
-# Contextual Visualization 1: Vehicle Model Trends
-ev_data['Model Year'] = pd.to_numeric(ev_data['Model Year'], errors='coerce')
-model_year_counts = ev_data_filtered['Model Year'].value_counts().reset_index()
-model_year_counts.columns = ['Year', 'Count']
-model_year_counts = model_year_counts.sort_values('Year')
-
-fig_year = px.line(
-    model_year_counts,
-    x='Year',
-    y='Count',
-    title="Trend of EV Models Released Over Time",
-    labels={"Year": "Model Year", "Count": "Number of Models"},
-    markers=True
-)
-st.plotly_chart(fig_year, use_container_width=True)
-
-# Contextual Visualization 2: Map of EV Locations
-st.subheader("Geographic Distribution of EVs")
+# Visualization 1: Map of EV Locations
+# st.subheader("Geographic Distribution of EVs")
 filtered_data = ev_data_filtered.dropna(subset=['Latitude', 'Longitude'])
 st.pydeck_chart(pdk.Deck(
     initial_view_state=pdk.ViewState(
@@ -107,25 +132,77 @@ st.pydeck_chart(pdk.Deck(
     }
 ))
 
+# Visualization 2: Vehicle Model Trends
+ev_data['Model Year'] = pd.to_numeric(ev_data['Model Year'], errors='coerce')
+model_year_counts = ev_data_filtered['Model Year'].value_counts().reset_index()
+model_year_counts.columns = ['Year', 'Count']
+model_year_counts = model_year_counts.sort_values('Year')
+
+# Visualization 2: Trend of EV Models
+st.subheader("How Have EV Models Evolved Over Time?")
 st.markdown("""
-### Analysis and Key Takeaways
+The post-2010 era witnessed an explosion in EV models. This surge aligns with major technological advancements, government incentives, and growing consumer awareness.
+""")
+fig_year = px.line(
+    model_year_counts,
+    x='Year',
+    y='Count',
+    title="Trend of EV Models Released Over Time",
+    labels={"Year": "Model Year", "Count": "Number of Models"},
+    markers=True
+)
+st.plotly_chart(fig_year, use_container_width=True)
+
+# Visualization 3: National Trends in Vehicle Offerings
+st.subheader("National Light-Duty Vehicle Trends")
+st.markdown("""
+The national landscape highlights a **shift to clean energy**. Electric and hybrid vehicles have seen exponential growth, while older fuels like **diesel** and **CNG** stagnate or decline.
+""")
 
-1. **County-Level Distribution**: King County stands out as the hub for electric vehicles in Washington State, accounting for the vast majority of EVs. This concentration aligns with the county's urban population density and robust infrastructure supporting EVs.
+contextual_melted_1 = pd.melt(contextual_df_1, id_vars=['Fuel Type'], var_name='Year', value_name='Models Offered')
+fig_contextual_1 = px.bar(
+    contextual_melted_1,
+    x='Year',
+    y='Models Offered',
+    color='Fuel Type',
+    title='National Light-Duty Vehicle Offerings by Fuel Type',
+    labels={"Year": "Year", "Models Offered": "Number of Models Offered"},
+    barmode='stack'
+)
+st.plotly_chart(fig_contextual_1, use_container_width=True)
 
-2. **Model Release Trends**: The timeline of EV model releases shows a significant surge in recent years, reflecting advancements in EV technology and increasing consumer demand. The spike in 2020-2021 likely corresponds to policy incentives and a growing focus on reducing carbon emissions.
+# Visualization 4: Registration Trends
+st.subheader("Are EV Registrations Outpacing Traditional Vehicles?")
+st.markdown("""
+Recent years show **double-digit growth** in electric vehicle registrations, outpacing traditional fuels. Hybrid and electric vehicles dominate registration increases, reflecting shifting consumer preferences.
+""")
 
-3. **Geographic Insights**: The geographic visualization highlights key clusters of EV adoption, primarily around metropolitan areas. These clusters underline the importance of accessible charging stations and supportive local policies in driving EV adoption.
+contextual_melted_2 = pd.melt(contextual_df_2, id_vars=['Fuel Type'], var_name='Year', value_name='Change (%)')
+fig_contextual_2 = px.line(
+    contextual_melted_2,
+    x='Year',
+    y='Change (%)',
+    color='Fuel Type',
+    title='Change in U.S. Light-Duty Vehicle Registration Counts',
+    labels={"Year": "Year", "Change (%)": "Percentage Change"}
+)
+st.plotly_chart(fig_contextual_2, use_container_width=True)
 
-### Moving Forward
-As EV adoption continues to grow, insights from localized data like this can inform infrastructure planning and policy-making. Expanding charging networks, incentivizing adoption in underserved areas, and addressing technological barriers are crucial steps for achieving widespread EV integration.
+# Comparative Insights
+st.subheader("Key Insights")
+st.markdown("""
+- **King County** dominates EV adoption in Washington State.
+- **Post-2010 growth** in EV models highlights technological and policy-driven momentum.
+- Nationally, **electric vehicles lead registration growth**, while gasoline and diesel struggle to maintain their foothold.
+- Hybrid vehicles serve as a bridge, reflecting steady growth alongside BEVs.
 
+Electric vehicles are no longer a future promise—they are here, driving a revolution in transportation and sustainability.
 """)
 
-# Citations and Dataset Links
+# Citations
 st.markdown("""
 ### Sources
 1. Dataset: [Electric Vehicle Population Data](https://catalog.data.gov/dataset/electric-vehicle-population-data)
-2. Additional Visualization Inspiration: [US Department of Energy EV Statistics](https://afdc.energy.gov/data)
-
-All visualizations and data preparation were created by the authors, datasets found online.
+2. Contextual Data: [U.S. Light-Duty Vehicle Offerings](https://afdc.energy.gov/data/10303)
+3. Contextual Data: [U.S. Vehicle Registration Trends](https://afdc.energy.gov/data/10881)
 """)