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 (Now using local CSV for extreme speedup) # --------------------------------------------------------------------------- @st.cache_data(show_spinner="Loading local Chicago crime data...") def load_crime_data(): """Robust loading for Hugging Face Spaces (handles path issues).""" import os try: # 获取当前脚本所在目录 BASE_DIR = os.path.dirname(os.path.abspath(__file__)) file_path = os.path.join(BASE_DIR, "Crimes_-_2026_20260417.csv") # 读取 CSV df = pd.read_csv(file_path) # 标准化列名 df.columns = [c.lower().replace(" ", "_") for c in df.columns] except FileNotFoundError: st.error("❌ CSV file not found. Check filename and path.") return pd.DataFrame() except Exception as e: st.error(f"❌ Failed to read CSV: {e}") return pd.DataFrame() # 后处理 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. *(Note: If the map points look like a strict grid, it is because the Chicago Police Department anonymizes crime locations to the nearest block level, aligning perfectly with Chicago's grid street system!)* """ ) 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 (NOW LAG-FREE) # --------------------------------------------------------------------------- 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. *(This is now fully interactive in your browser, filtering happens instantly without lag!)* """ ) # 提取前10大罪案类型 top_types_hm = df["Primary Type"].value_counts().head(10).index.tolist() # 在 Python 预先计算所有 (Primary Type, weekday, Hour) 的聚合数量，减少传到前端的数据量 hm_agg = ( df.dropna(subset=["Primary Type"]) .groupby(["Primary Type", "weekday", "Hour"]) .size() .reset_index(name="crime_count") ) # 🔥 核心提速秘籍：创建一个 Altair 原生的下拉绑定，把过滤操作全推给前端浏览器做，不重启 Streamlit！ dropdown = alt.binding_select( options=[None] + top_types_hm, labels=["All"] + top_types_hm, name="Filter by Crime Type: " ) type_select = alt.selection_point(fields=["Primary Type"], bind=dropdown) weekday_order = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"] 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"), # 使用 sum(crime_count) 确保选 All 的时候数字正确累加 color=alt.Color("sum(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("sum(crime_count):Q", title="Total Crimes"), ], ) .add_params(type_select) # 绑定前端选择器 .transform_filter(type_select) # 让图表根据选择器过滤数据 .properties( width=700, height=380, title="Crime Heatmap (Instantly filterable)", ) ) st.altair_chart(heatmap, use_container_width=True) # --------------------------------------------------------------------------- # SECTION 3 — Poverty vs. Crime # --------------------------------------------------------------------------- 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") ) # --- FIX: 改成了更高精度的 round(3) 结合 mark_circle 来实现细腻的热力图外观 --- df_geo_binned = df_geo.copy() # round(3) 大约对应100米的网格，比原来的 1.1公里 (round 2) 精细很多 df_geo_binned['lat_bin'] = df_geo_binned['latitude'].round(3) df_geo_binned['lon_bin'] = df_geo_binned['longitude'].round(3) # 统计每个细微网格的案件数量 density_agg = df_geo_binned.groupby(['lat_bin', 'lon_bin']).size().reset_index(name='incident_count') # Binned geo-heatmap: 使用半透明的小圆点(mark_circle)模拟完美的热力云图 crime_density = ( alt.Chart(density_agg) .mark_circle(opacity=0.6, size=15) # 调小了size，换成了圆形 .encode( longitude="lon_bin:Q", latitude="lat_bin:Q", color=alt.Color( "incident_count:Q", scale=alt.Scale(scheme="blues"), title="Incident Count", legend=alt.Legend(title="Incidents"), ), tooltip=[ alt.Tooltip("incident_count:Q", title="Total 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(): 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/). """ )