📊 Data Analysis Overview
This panel summarizes the results of Exploratory Data Analysis.
🌱 CleanSight NYC
New York is an incredibly beautiful city, but both residents and visitors agree that cleanliness remains one of its most visible challenges. Analysis of DSNY data for 2024 shows that approximately 8,147 tons of garbage are collected daily from residential buildings.
Despite the scale of DSNY operations, street cleanliness issues persist. To better understand why, this project analyzes sanitation-related 311 complaints alongside waste tonnage, pedestrian traffic, and sanitation infrastructure.
📂 Data Sources
- NYC Population by Community Districts — Population estimates by district
- DSNY Monthly Tonnage Data — Monthly waste collection records
- DSNY Litter Basket Locations — Public trash can locations
- NYC 311 Trash Reports — Sanitation-related complaints
- Bi-Annual Pedestrian Counts — Foot traffic indicators
- OpenStreetMap (OSM) — Census tracts & neighborhood boundaries
1. Analysis Dashboard
The Analysis section provides exploratory data analysis and summary insights before users interact with the map. It highlights waste patterns, complaint behavior, and dominant sanitation issues across New York City.
🚮 DSNY Tonnage Analysis
- Annual DSNY waste tonnage by borough
- Waste volume comparisons across boroughs and districts
- Tonnage trends over time
🗑️ 311 Sanitation Complaints
- Total complaints and complaints by category
- Temporal trends and seasonality
- Complaint intensity by borough
- Identification of high-complaint areas
📈 Analysis of Top Cleanliness Issues
Identifies the most common sanitation issues faced by each borough and examines how pedestrian traffic influences litter complaints.
2. Interactive Map
The Map section serves as the project’s primary research tool, using multi-layered geospatial visualizations to explore sanitation conditions across neighborhoods and census tracts.
DSNY Monthly Waste Data Layer
- Monthly and annual waste totals
- Relative waste volume differences
- High-waste neighborhood identification
311 Sanitation Complaints Layer
- Complaint density by census tract
- Filters by complaint type
- Time coverage: 2010–2025
Top Sanitation Issues (“Common Issues”)
- Dominant issue per borough
- Winner-take-all classification
- Color-coded issue categories
DSNY Trash Can Layer
Displays the spatial distribution of DSNY-operated trash cans across New York City.
🔬 Methodology
CleanSight NYC integrates multiple public datasets to investigate the extent and drivers of New York City’s sanitation challenges. Over 1.7 million 311 sanitation-related complaints (2010–2025) were analyzed and categorized into major issue types, including trash collection, litter baskets, and street sweeping.
Complaints were aggregated at the census tract level for spatial analysis. Monthly DSNY tonnage data was analyzed both in absolute terms and normalized by population. Pedestrian counts were incorporated as a proxy for human activity, and DSNY trash can locations were overlaid to assess sanitation infrastructure adequacy.
🔍 Key Findings
- Household Waste Volume: Queens, Staten Island, and Brooklyn show higher per-capita waste tonnage than Manhattan, likely due to housing structure and storage capacity differences.
- 311 Complaint Patterns: Litter complaints dominate the data, while street cleaning issues are likely underreported.
- Structural Change: Litter basket complaints increased by approximately 30% after mid-2020 and did not return to pre-pandemic levels.
- Operational Shifts: Complaint spikes align with known service disruptions, including labor shortages and policy changes.
💡 Our Solutions
- Make sanitation data visible and accessible to residents
- Encourage increased reporting through the 311 system
- Promote awareness of sanitation reporting tools
- Support data-driven resource allocation by DSNY
- Encourage waste reduction through community education
👥 About Us
CleanSight NYC is a data-driven student project developed as part of a data visualization course. The project aims to engage communities, inform policy decisions, and improve neighborhood cleanliness through transparent, accessible analytics.
Meet the Team →