Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,72 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import pandas as pd
+import altair as alt
+
+st.set_page_config(layout="wide", page_title="BFRO Sightings Analysis")
+
+st.title("BFRO Sightings Dashboard")
+st.markdown("### Exploration of Bigfoot Reports using Streamlit and Altair")
+st.write("This dashboard visualizes reports from the BFRO database, focusing on geographic distribution and seasonal patterns.")
+
+@st.cache_data
+def load_data():
+    url = "https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/bfro_reports_fall2022.csv"
+    df = pd.read_csv(url)
+    df_clean = df.dropna(subset=['season', 'state', 'latitude', 'longitude'])
+    return df_clean
+
+df_clean = load_data()
+
+alt.data_transformers.disable_max_rows()
+
+
+season_dropdown = alt.binding_select(options=df_clean['season'].unique().tolist(), name="Select Season: ")
+season_select = alt.selection_point(fields=['season'], bind=season_dropdown)
+
+brush = alt.selection_interval()
+
+map_chart = alt.Chart(df_clean).mark_rect().encode(
+    x=alt.X('longitude:Q', bin=alt.Bin(maxbins=60), title='Longitude'),
+    y=alt.Y('latitude:Q', bin=alt.Bin(maxbins=40), title='Latitude'),
+    color=alt.Color('count()', scale=alt.Scale(scheme='inferno'), title='Report Density'),
+    tooltip=['count()']
+).add_params(
+    brush, season_select  
+).transform_filter(
+    season_select 
+).properties(
+    width=400,
+    height=400,
+    title='1. Geographic Density (Drag to Select)'
+)
+
+heatmap = alt.Chart(df_clean).mark_bar().encode(
+    x=alt.X('count()', title='Total Reports'),
+    y=alt.Y('state:N', title='State', sort='-x'),
+    color=alt.Color('season:N', title='Season'), 
+    tooltip=['state', 'season', 'count()']
+).transform_filter(
+    brush  
+).properties(
+    width=300,
+    height=400,
+    title='2. Reports by State (Filtered by Map)'
+)
+
+dashboard = map_chart | heatmap
+
+st.altair_chart(dashboard, use_container_width=True)
+
+
+st.divider()
+st.header("Analysis & Write-up")
+
+st.subheader("Visualization 1: Geographic Density Map")
+st.markdown("""
+This visualization highlights the spatial distribution of Bigfoot reports across the United States. I chose a **rectangular binning** approach (`mark_rect`) rather than plotting individual points to better handle the data density and avoid overplotting in high-activity areas like the Pacific Northwest. The **'inferno' color scheme** was selected to provide high contrast, where lighter colors immediately draw the viewer's eye to areas of high report density. This plot is interactive; it includes a dropdown to filter by season, allowing users to analyze whether sighting locations shift during different times of the year, and it acts as a filter for the second chart. **If I had more time**, I would overlay these bins onto a geographic base map (using `mark_geoshape`) to provide better context regarding state borders and physical geography.
+""")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.subheader("Visualization 2: Reports by State")
+st.markdown("""
+This bar chart highlights the frequency of reports aggregated by state. I used a **bar mark** as it effectively compares magnitudes across categories (states). The states are **sorted in descending order** of report counts to instantly reveal the most active locations without requiring the user to scan the entire axis. The color encoding represents the 'season', providing a secondary dimension of information consistent with the map. This visualization is linked to the map via a brush filter; dragging a selection box on the map dynamically updates this bar chart to show the state breakdown for only the selected region. **If I had more time**, I would normalize the data by state population or land area to provide a per-capita or per-square-mile perspective, which might reveal different hotspots than raw counts alone.
+""")