app.py

import streamlit as st
import pandas as pd
import altair as alt

# Title and Introduction
st.title("Building Inventory Analysis")
st.markdown("""
This application provides insights into the building inventory dataset through two visualizations:
1. **Building Count by County**: Displays the number of buildings in each county.
2. **Year-wise Construction of Buildings**: Shows the count of buildings constructed each year.
Each visualization is accompanied by a brief explanation, including design choices and potential improvements.
""")

# Load Dataset
@st.cache_data
def load_data():
    url = "https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/building_inventory.csv"
    return pd.read_csv(url)

data = load_data()

# Data Preprocessing
# Ensure 'Year Constructed' is numeric
data['Year Constructed'] = pd.to_numeric(data['Year Constructed'], errors='coerce')

# Visualization 1: Building Count by County
st.header("1. Building Count by County")
county_count = data['County'].value_counts().reset_index()
county_count.columns = ['County', 'Building Count']

county_chart = alt.Chart(county_count).mark_bar(color='teal').encode(
    alt.X('Building Count:Q', title='Number of Buildings'),
    alt.Y('County:N', sort='-x', title='County')
).properties(
    width=700,
    height=400,
    title='Number of Buildings per County'
)

st.altair_chart(county_chart, use_container_width=True)

st.markdown("""
**Explanation**:
This bar chart illustrates the number of buildings in each county, highlighting areas with higher concentrations of government buildings.
- **Design Choices**:
  - A horizontal bar chart is used to accommodate long county names and facilitate easy comparison.
  - Counties are sorted in descending order based on building count to emphasize those with the most buildings.
  - The teal color provides a calm and professional appearance.
- **Potential Improvements**:
  - Create grouped bar charts to compare building counts by county across different years.
  - Add a map visualization to provide spatial context to the data.
""")

# Visualization 2: Year-wise Construction of Buildings
st.header("2. Year-wise Construction of Buildings")
# Filter out rows where 'Year Constructed' is 0 or NaN
data_filtered = data[(data['Year Constructed'] > 0) & (~data['Year Constructed'].isna())]

# Group by 'Year Constructed' and count the number of buildings
yearly_construction = data_filtered['Year Constructed'].value_counts().reset_index()
yearly_construction.columns = ['Year Constructed', 'Building Count']
yearly_construction = yearly_construction.sort_values('Year Constructed')

year_chart = alt.Chart(yearly_construction).mark_line(point=True, color='orange').encode(
    alt.X('Year Constructed:Q', title='Year Constructed'),
    alt.Y('Building Count:Q', title='Number of Buildings')
).properties(
    width=700,
    height=400,
    title='Number of Buildings Constructed Over Time'
)

st.altair_chart(year_chart, use_container_width=True)

st.markdown("""
**Explanation**:
This line chart displays the number of buildings constructed each year, revealing trends and periods of increased construction activity.
- **Design Choices**:
  - A line chart effectively shows changes over time and highlights trends.
  - Data points are marked to emphasize individual years.
  - The orange color draws attention to the trend line.
- **Potential Improvements**:
  - Overlay a trendline to highlight long-term construction patterns while reducing the effect of year-to-year fluctuations.
  - Allow users to filter the data by building type or agency to explore specific trends.
""")

# Footer
st.markdown("""
---
**Data Source**: [Building Inventory Dataset](https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/building_inventory.csv)  
**Author**: Maanas Agrawal
""")