Update src/streamlit_app.py

src/streamlit_app.py

@@ -0,0 +1,460 @@
+import streamlit as st
+import pandas as pd
+import altair as alt
+import json
+import urllib.request
+
+st.set_page_config(page_title="Crimes in Chicago 2026", page_icon="?", layout="wide")
+
+st.title("Crimes in Chicago - 2026")
+st.markdown("**Authors: Xinyi Chen, Zhongyin Wang** - Group 6")
+st.markdown("---")
+
+st.markdown(
+    """
+    ## What Is This About?
+
+    Every day, hundreds of crime incidents are reported across Chicago's 77 community areas.
+    But where do they happen? At what time? And does poverty play a role?
+
+    This interactive article walks you through 2026 Chicago crime data drawn directly from
+    the [Chicago Data Portal](https://data.cityofchicago.org/) to help you explore the
+    geography, timing, and social context of crime in one of America's largest cities.
+
+    The dataset records every reported crime incident in 2026, including the exact location,
+    date and time, crime type, and the police district that handled it. Each row is one
+    reported incident. We also include community-level socioeconomic data to examine the
+    relationship between poverty and crime rates across Chicago's neighborhoods.
+    """
+)
+
+# ---------------------------------------------------------------------------
+# Data loading
+# ---------------------------------------------------------------------------
+
+@st.cache_data(show_spinner="Loading Chicago crime data (full dataset)...")
+def load_crime_data():
+    """Fetch ALL 2026 records in batches. Bar/line/heatmap use the full df;
+    only geo rendering on maps uses sampling."""
+    all_chunks = []
+    limit  = 50000
+    offset = 0
+    while True:
+        url = (
+            "https://data.cityofchicago.org/resource/ijzp-q8t2.json"
+            "?$where=year=2026"
+            f"&$limit={limit}"
+            f"&$offset={offset}"
+            "&$order=date%20DESC"
+        )
+        try:
+            chunk = pd.read_json(url)
+        except Exception as e:
+            st.error(f"Failed to load crime data at offset {offset}: {e}")
+            break
+        if chunk.empty:
+            break
+        all_chunks.append(chunk)
+        if len(chunk) < limit:
+            break
+        offset += limit
+
+    if not all_chunks:
+        return pd.DataFrame()
+
+    df = pd.concat(all_chunks, ignore_index=True)
+
+    df["date"] = pd.to_datetime(df["date"], errors="coerce")
+    for col in ["latitude", "longitude"]:
+        df[col] = pd.to_numeric(df.get(col, pd.Series(dtype=float)), errors="coerce")
+    df = df.dropna(subset=["date"])
+
+    df["Date_Only"] = df["date"].dt.floor("d")
+    df["Hour"]      = df["date"].dt.hour
+    df["weekday"]   = df["date"].dt.day_name().str[:3]
+
+    df["Primary Type"] = df["primary_type"].str.upper() if "primary_type" in df.columns else "UNKNOWN"
+
+    if "district" in df.columns:
+        df["District_Str"] = (
+            pd.to_numeric(df["district"], errors="coerce")
+            .fillna(-1).astype(int).astype(str)
+        )
+        df["District"] = df["District_Str"]
+    else:
+        df["District_Str"] = df["District"] = "-1"
+
+    if "community_area" not in df.columns:
+        df["community_area"] = None
+
+    def get_period(h):
+        if   6  < h <= 12: return "Morning (6am-12pm)"
+        elif 12 < h <= 18: return "Afternoon (12pm-6pm)"
+        elif 18 < h <= 24: return "Evening (6pm-12am)"
+        else:              return "Late Night (12am-6am)"
+
+    df["Period"] = df["Hour"].apply(get_period)
+    return df
+
+
+@st.cache_data(show_spinner="Loading socioeconomic data...")
+def load_socio():
+    try:
+        df = pd.read_json("https://data.cityofchicago.org/resource/kn9c-c2s2.json")
+        df = df.dropna(subset=["ca"])
+        df["ca"] = df["ca"].astype(float).astype(int).astype(str)
+        df["poverty_rate"] = pd.to_numeric(df["percent_households_below_poverty"], errors="coerce")
+        return df
+    except Exception as e:
+        st.warning(f"Could not load socioeconomic data: {e}")
+        return pd.DataFrame(columns=["ca", "community_area_name", "poverty_rate"])
+
+
+@st.cache_data(show_spinner="Loading boundaries...")
+def load_geojson(url):
+    try:
+        with urllib.request.urlopen(url) as r:
+            return json.loads(r.read())
+    except Exception as e:
+        st.warning(f"Could not load GeoJSON: {e}")
+        return {"features": []}
+
+
+district_geojson  = load_geojson("https://data.cityofchicago.org/resource/24zt-jpfn.geojson")
+community_geojson = load_geojson("https://data.cityofchicago.org/resource/igwz-8jzy.geojson")
+
+df       = load_crime_data()
+df_socio = load_socio()
+districts   = alt.Data(values=district_geojson["features"])
+communities = alt.Data(values=community_geojson["features"])
+
+if df.empty:
+    st.error("Crime data could not be loaded.")
+    st.stop()
+
+df_geo = df.dropna(subset=["latitude", "longitude"]).copy()
+st.info(f"Loaded **{len(df):,}** crime records for 2026 ({len(df_geo):,} with coordinates).")
+
+# ---------------------------------------------------------------------------
+# SECTION 1 — Linked dashboard
+# ---------------------------------------------------------------------------
+st.markdown("---")
+st.header("Interactive Crime Dashboard")
+st.markdown(
+    """
+    This dashboard lets you explore Chicago crime data across three linked views.
+    **Drag a box on the map** to select a geographic area, or **click a district boundary**
+    to highlight it — both actions filter the bar chart on the right and the timeline below.
+    You can also **click a crime category** in the bar chart to drill into its temporal trend.
+
+    The bottom line chart breaks daily incident counts into four time-of-day periods
+    (plus a grey total line). The bar chart and line chart use the **full dataset** with
+    no sampling; only the map points are sampled to keep the browser responsive.
+    """
+)
+
+brush      = alt.selection_interval(name="brush")
+click_type = alt.selection_point(fields=["Primary Type"], name="click_type")
+click_dist = alt.selection_point(fields=["District_Str"], name="click_dist")
+
+MAP_SAMPLE   = 5000
+df_map_sample = df_geo.sample(min(MAP_SAMPLE, len(df_geo)), random_state=42)
+
+background = (
+    alt.Chart(districts)
+    .mark_geoshape(stroke="black", strokeWidth=0.6)
+    .transform_calculate(District_Str="datum.properties.dist_num")
+    .encode(
+        color=alt.condition(click_dist, alt.value("white"), alt.value("grey")),
+        opacity=alt.condition(click_dist, alt.value(0.5), alt.value(0.8)),
+        tooltip=[alt.Tooltip("properties.dist_num:N", title="District")],
+    )
+    .add_params(click_dist)
+)
+
+geo_points = (
+    alt.Chart(df_map_sample)
+    .mark_circle(size=5)
+    .encode(
+        longitude="longitude:Q",
+        latitude="latitude:Q",
+        color=alt.condition(
+            click_dist,
+            alt.Color("District:N", scale=alt.Scale(scheme="tableau10"),
+                      legend=alt.Legend(title="District", orient="right")),
+            alt.value("#e0dbd6"),
+        ),
+        opacity=alt.condition(click_dist, alt.value(0.6), alt.value(0.05)),
+        tooltip=[
+            alt.Tooltip("Primary Type:N", title="Crime Type"),
+            alt.Tooltip("District:N",     title="District"),
+            alt.Tooltip("date:T",         title="Date"),
+        ],
+    )
+    .add_params(brush)
+)
+
+map_layer = (background + geo_points).project(type="mercator").properties(
+    width=420, height=450,
+    title=f"Chicago Crime Map (map shows {MAP_SAMPLE:,} sampled points for performance)",
+)
+
+# Bar chart - full df
+type_chart = (
+    alt.Chart(df)
+    .mark_bar()
+    .encode(
+        x=alt.X("count():Q", title="Number of Crimes"),
+        y=alt.Y("Primary Type:N", sort="-x", title="Crime Type"),
+        color=alt.condition(click_type, alt.value("steelblue"), alt.value("lightgray")),
+        tooltip=["Primary Type:N", "count():Q"],
+    )
+    .properties(width=300, height=450, title="Crime Types (full dataset)")
+    .add_params(click_type)
+    .transform_filter(brush)
+    .transform_filter(click_dist)
+)
+
+# Line chart - full df
+period_order = ["Morning (6am-12pm)", "Afternoon (12pm-6pm)",
+                "Evening (6pm-12am)", "Late Night (12am-6am)", "Total Daily"]
+period_range = ["#f4a261", "#e9c46a", "#e76f51", "#264653", "grey"]
+
+period_lines = (
+    alt.Chart(df)
+    .mark_line(point=False, strokeWidth=1.5)
+    .encode(
+        x=alt.X("Date_Only:T", title="Timeline"),
+        y=alt.Y("count:Q", title="Number of Incidents", scale=alt.Scale(zero=True)),
+        color=alt.Color(
+            "Period:N",
+            scale=alt.Scale(domain=period_order, range=period_range),
+            legend=alt.Legend(title="Time of Day", orient="right"),
+        ),
+        tooltip=[
+            alt.Tooltip("Date_Only:T", title="Date"),
+            alt.Tooltip("Period:N",    title="Period"),
+            alt.Tooltip("count:Q",     title="Incidents"),
+        ],
+    )
+    .transform_filter(brush)
+    .transform_filter(click_type)
+    .transform_filter(click_dist)
+    .transform_aggregate(count="count()", groupby=["Date_Only", "Period"])
+    .transform_impute(impute="count", key="Date_Only", groupby=["Period"], value=0)
+)
+
+total_line = (
+    alt.Chart(df)
+    .mark_line(opacity=0.5)
+    .encode(
+        x=alt.X("Date_Only:T"),
+        y=alt.Y("count():Q"),
+        color=alt.datum("Total Daily"),
+        tooltip=[
+            alt.Tooltip("Date_Only:T", title="Date"),
+            alt.Tooltip("count():Q",   title="Total Incidents"),
+        ],
+    )
+    .transform_filter(brush)
+    .transform_filter(click_type)
+    .transform_filter(click_dist)
+)
+
+line_chart = (total_line + period_lines).properties(
+    width=760, height=220,
+    title="Daily Crime Trend by Time of Day (full dataset)",
+).resolve_scale(color="shared")
+
+dashboard = ((map_layer | type_chart) & line_chart).resolve_scale(color="independent")
+st.altair_chart(dashboard, use_container_width=True)
+
+# ---------------------------------------------------------------------------
+# SECTION 2 — When do crimes happen? heatmap + dropdown
+# Pre-aggregate to 7x24 = 168 rows in Python before rendering,
+# so switching crime types is instant - no re-streaming of raw data to browser.
+# ---------------------------------------------------------------------------
+st.markdown("---")
+st.header("When Do Crimes Happen in Chicago?")
+st.markdown(
+    """
+    Different crimes follow different schedules. Use the **dropdown below** to filter
+    the heatmap by crime category, or leave it on *All* to see the overall pattern.
+    Each cell shows the total number of incidents at that day-of-week x hour-of-day
+    combination across the full dataset; darker red means more incidents.
+
+    Across nearly every category, Friday and Saturday evenings (6 pm to midnight)
+    stand out as the most active windows, while the early-morning hours (2 to 5 am)
+    are consistently quietest.
+    """
+)
+
+top_types_hm = df["Primary Type"].value_counts().head(10).index.tolist()
+selected_hm  = st.selectbox("Select Crime Type", options=["All"] + top_types_hm, index=0)
+
+# Key fix: groupby in Python first -> only 168 rows reach Altair -> instant render
+weekday_order = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
+hm_source = df if selected_hm == "All" else df[df["Primary Type"] == selected_hm]
+hm_agg = (
+    hm_source
+    .groupby(["weekday", "Hour"])
+    .size()
+    .reset_index(name="crime_count")
+)
+
+heatmap = (
+    alt.Chart(hm_agg)
+    .mark_rect()
+    .encode(
+        x=alt.X("weekday:N", sort=weekday_order, title="Day of Week"),
+        y=alt.Y("Hour:O", title="Hour of Day (0-23)", sort="ascending"),
+        color=alt.Color("crime_count:Q", scale=alt.Scale(scheme="reds"), title="Number of Crimes"),
+        tooltip=[
+            alt.Tooltip("weekday:N",     title="Day"),
+            alt.Tooltip("Hour:O",        title="Hour"),
+            alt.Tooltip("crime_count:Q", title="Total Crimes"),
+        ],
+    )
+    .properties(
+        width=700, height=380,
+        title=f"Crime Heatmap - {selected_hm} (full dataset, darker = more incidents)",
+    )
+)
+st.altair_chart(heatmap, use_container_width=True)
+
+# ---------------------------------------------------------------------------
+# SECTION 3 — Poverty vs. Crime
+# Left: choropleth + binned geo-heatmap (no sampling, full density visible)
+# Right: scatter with full crime counts per community area
+# ---------------------------------------------------------------------------
+st.markdown("---")
+st.header("Does Poverty Predict Crime?")
+st.markdown(
+    """
+    Socioeconomic inequality is one of the most studied predictors of crime at the
+    neighborhood level. The choropleth map on the left shades each of Chicago's 77
+    community areas by their poverty rate - darker orange means higher poverty -
+    with a binned crime density heatmap overlaid. The heatmap uses the full dataset
+    with no sampling: each cell's color reflects how many incidents fall in that
+    geographic bin, giving a clear picture of crime hotspots.
+
+    The scatter plot on the right makes the poverty-crime relationship explicit:
+    each dot is one community area, and the dashed line is a statistical trend.
+    There is a moderate positive correlation, though it is far from deterministic -
+    policy, policing patterns, and reporting rates all play a role.
+
+    **Socioeconomic data source:** [Census Data - Chicago Data Portal](https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-Socioeconomic-Indicators-in-C/kn9c-c2s2)
+    """
+)
+
+col3, col4 = st.columns(2)
+
+with col3:
+    if not df_socio.empty and community_geojson["features"]:
+        poverty_map = (
+            alt.Chart(communities)
+            .mark_geoshape(stroke="white", strokeWidth=0.4)
+            .transform_lookup(
+                lookup="properties.area_num_1",
+                from_=alt.LookupData(df_socio, "ca", ["poverty_rate", "community_area_name"]),
+            )
+            .encode(
+                color=alt.Color("poverty_rate:Q", scale=alt.Scale(scheme="orangered"),
+                                title="Poverty Rate (%)"),
+                tooltip=[
+                    alt.Tooltip("properties.community:N", title="Community"),
+                    alt.Tooltip("poverty_rate:Q", title="Poverty Rate (%)", format=".1f"),
+                ],
+            )
+            .project(type="mercator")
+            .properties(width=360, height=440, title="Chicago Poverty Rate by Community Area")
+        )
+
+        # Binned geo-heatmap: full dataset, no sampling needed
+        # maxbins=50 -> ~2500 cells max, renders fast and shows full density
+        crime_density = (
+            alt.Chart(df_geo)
+            .mark_rect(opacity=0.55)
+            .encode(
+                longitude=alt.X("longitude:Q", bin=alt.Bin(maxbins=50)),
+                latitude=alt.Y("latitude:Q",   bin=alt.Bin(maxbins=50)),
+                color=alt.Color(
+                    "count():Q",
+                    scale=alt.Scale(scheme="blues"),
+                    title="Incident Count",
+                    legend=alt.Legend(title="Incidents"),
+                ),
+            )
+        )
+
+        st.altair_chart(
+            (poverty_map + crime_density).resolve_scale(color="independent"),
+            use_container_width=True,
+        )
+    else:
+        st.info("Socioeconomic or boundary data unavailable.")
+
+with col4:
+    if not df_socio.empty and df["community_area"].notna().any():
+        # Full df for crime counts - no sampling
+        df_crime_count = (
+            df.dropna(subset=["community_area"])
+            .groupby("community_area").size()
+            .reset_index(name="crime_count")
+        )
+        df_crime_count["ca"] = (
+            df_crime_count["community_area"].astype(float).astype(int).astype(str)
+        )
+        df_scatter = pd.merge(
+            df_socio[["ca", "community_area_name", "poverty_rate"]],
+            df_crime_count[["ca", "crime_count"]],
+            on="ca", how="inner",
+        )
+        if len(df_scatter) > 5:
+            sc = (
+                alt.Chart(df_scatter)
+                .mark_circle(size=80, opacity=0.75)
+                .encode(
+                    x=alt.X("poverty_rate:Q", title="Poverty Rate (%)"),
+                    y=alt.Y("crime_count:Q",  title="Crime Count (2026)"),
+                    color=alt.Color("poverty_rate:Q", scale=alt.Scale(scheme="orangered"),
+                                    legend=None),
+                    tooltip=[
+                        alt.Tooltip("community_area_name:N", title="Community"),
+                        alt.Tooltip("poverty_rate:Q",        title="Poverty Rate (%)", format=".1f"),
+                        alt.Tooltip("crime_count:Q",         title="Crime Count"),
+                    ],
+                )
+            )
+            reg = sc.transform_regression("poverty_rate", "crime_count").mark_line(
+                color="gray", strokeDash=[4, 4], strokeWidth=1.5
+            )
+            st.altair_chart(
+                (sc + reg).properties(
+                    width=360, height=440,
+                    title="Higher Poverty -> More Crimes? (each dot = one community area)",
+                ),
+                use_container_width=True,
+            )
+        else:
+            st.info("Not enough community-level overlap to render scatter plot.")
+    else:
+        st.info("Community area data not available in this dataset sample.")
+
+# ---------------------------------------------------------------------------
+# Citations
+# ---------------------------------------------------------------------------
+st.markdown("---")
+st.header("Data Sources & Citations")
+st.markdown(
+    """
+    | Dataset | Source | Link |
+    |---|---|---|
+    | Chicago Crimes 2001-Present | City of Chicago Data Portal | [ijzp-q8t2](https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-Present/ijzp-q8t2) |
+    | Socioeconomic Indicators by Community | City of Chicago Data Portal | [kn9c-c2s2](https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-Socioeconomic-Indicators-in-C/kn9c-c2s2) |
+    | Police District Boundaries (GeoJSON) | City of Chicago Data Portal | [24zt-jpfn](https://data.cityofchicago.org/Public-Safety/Boundaries-Police-Districts-current-/24zt-jpfn) |
+    | Community Area Boundaries (GeoJSON) | City of Chicago Data Portal | [igwz-8jzy](https://data.cityofchicago.org/Facilities-Geographic-Boundaries/Boundaries-Community-Areas-current-/cauq-8yn6) |
+
+    All data accessed April 2026. Visualizations built with [Altair](https://altair-viz.github.io/) and [Streamlit](https://streamlit.io/).
+    """
+)