ONESTONE BIRD MAP

Leaflet.js Firebase Chart.js Cloudinary Wikipedia API Google Geocoding Real-time Data GIS
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Project Overview

The ONE STONE BIRD MAP was developed to shed light on the underreported issue of avian mortality in urban areas, particularly from window collisions and outdoor cats. By geographically visualizing these incidents, the project transforms abstract statistics into a tangible representation of their impact, making the issue more accessible and compelling to the general public. This approach fosters awareness, informs conservation efforts, and highlights the need for action to mitigate human-driven threats to bird populations in the DMV region.

Architecture

Firebase Backend
Leaflet.js Maps
Chart.js Analytics

Core Features

Real-time Updates
Data Clustering
Interactive Filtering

Analytics

Species Tracking
Seasonal Patterns
Location Analysis

Data Analysis Interface

Interactive Map View

Dynamic geospatial visualization of 5,461 documented incidents with smart clustering system:

Cluster Types

  • Yellow: Minimal incident areas
  • Orange: Growing concern zones
  • Red: High-frequency locations

Interactive Features

  • Dynamic cluster expansion on zoom
  • Consolidated location markers
  • Hover previews for quick info
  • Click for detailed incident view

Incident Details

  • Date and time stamps
  • Fatality cause classification
  • Data source and verification
  • Precise location data
  • Incident photographs

Filter Controls

  • Date range selection
  • Incident type filtering
  • Species-specific views
  • Location-based filtering

Data Export

  • CSV format download
  • GeoJSON format export
  • Filter-aware exports
  • Full dataset access

Analytics Dashboard

Real-time data visualization platform featuring:

Filter Controls

  • Date range selection
  • State-level filtering
  • Species categorization
  • Incident type classification

Temporal Analysis

  • Monthly incident distribution
  • Seasonal patterns
  • Year-over-year trends (2014-present)
  • Day-of-week frequency

Dynamic Visualizations

  • Species-cause correlations
  • High-impact buildings
  • Critical area analysis
  • Building height distribution

Data Intelligence

  • Cached data management
  • Smart building recognition
  • Wikipedia species integration
  • Location type classification

Data Statistics

Currently tracking 5,461 incidents with advanced categorization:

Export Capabilities

  • Full dataset access
  • Filter-aware exports
  • Map view exports
  • Multiple format support

Cause Categories

  • Structural Collisions
  • Cat Predation
  • Wild Predation
  • Other Incidents

System Architecture

Frontend Layer

  • Core: JavaScript ES6+, HTML5, CSS3
  • Mapping: Leaflet.js with custom clustering
  • Analytics: Chart.js for data visualization
  • Performance: Multi-layer caching system

Backend Services

  • Database: Firebase Realtime Database
  • Storage: Cloudinary image management
  • Security: Data validation & access control
  • Cache: Performance optimization layer

Data Processing

  • Geospatial: Coordinate validation & processing
  • Analysis: Building recognition algorithms
  • Integration: Multiple API endpoints
  • Export: GeoJSON & CSV capabilities

Data Processing Layer

  • Clustering: Custom marker clustering with dynamic expansion
  • Caching: Client-side data caching for performance
  • Fuzzy Matching: Intelligent cause categorization system
  • State Management: Filter state preservation and recovery

Smart Data Management

  • Multi-layer Caching: Separate caching for building data and bird incidents
  • Fuzzy Matching: Intelligent cause categorization with pattern recognition
  • State Detection: Automatic geofencing for MD, VA, and DC regions
  • Building Recognition: Smart address and building name mapping system

Integration Features

  • Species Info: Real-time Wikipedia data integration
  • Image Storage: Cloudinary integration for incident photos
  • Location Services: Google Geocoding for address validation
  • Data Export: Context-aware CSV and GeoJSON exports

Performance Features

  • Progressive Loading: Lazy-loaded charts and visualizations
  • State Management: Filter state preservation and recovery
  • Error Handling: Graceful degradation with fallbacks
  • Mobile Optimization: Adaptive clustering and UI components

Data Flow

1

Data Collection

User submissions through interactive form with location detection and image upload capabilities

2

Processing & Storage

Real-time validation, normalization, and storage in Firebase with Cloudinary integration

3

Analysis & Visualization

Dynamic data processing with custom clustering and comprehensive analytics dashboard

Core Features

Interactive Mapping System

  • Custom-built marker clustering for efficient data visualization
  • Real-time geospatial data processing
  • Dynamic heatmap generation
  • Location-aware reporting system

Data Analysis Dashboard

  • Multi-dimensional data visualization
  • Species-specific trend analysis
  • Building impact correlation studies
  • Seasonal pattern recognition

Education Platform

  • Bird safety guidelines
  • Emergency response protocols
  • Building recommendations
  • Conservation resources

Technical Deep Dive

Intelligent Caching System

  • Dual-layer caching with separate expiration for building and incident data
  • Optimized localStorage management with timestamp validation
  • Selective data persistence based on view requirements
  • Automatic cache invalidation for stale data

Fuzzy Match Algorithm

  • Smart cause categorization with pattern recognition
  • Automated incident classification system
  • Multi-keyword matching for accurate categorization
  • Fallback categorization for edge cases

Progressive Loading

  • IntersectionObserver for lazy-loaded charts
  • Optimized marker clustering for large datasets
  • Debounced filter operations for performance
  • State preservation during view updates

Smart Building Recognition

  • Coordinate-based building identification
  • Geofencing for DMV region classification
  • Fallback naming system for unknown locations
  • Building height estimation algorithms

Technical Challenges & Solutions

Performance Optimization

Large dataset causing performance issues on mobile devices

Solution Implemented:

  • Hybrid caching system and data aggregation
  • Reduced load times by 60%
  • Optimized memory usage for mobile devices
  • Implemented progressive loading

Geospatial Accuracy

Inconsistent location data affecting analysis accuracy

Solution Implemented:

  • Implemented geofencing
  • Created building recognition algorithm
  • Built data cleaning pipeline

Cross-Platform Compatibility

Inconsistent user experience across devices

Solution Implemented:

  • Developed adaptive UI components
  • Created mobile-specific controls
  • Optimized touch interactions

Credits

This project would not be possible without the dedicated efforts of various organizations and individuals. Special thanks to City Wildlife, Inc. for providing invaluable data on avian mortality and rehabilitation efforts. Their collaboration has been instrumental in shaping the project's scope and impact.

We extend our gratitude to Dr. Daniel Hanley at George Mason University (GMU) for his guidance and expertise in avian research. His insights have significantly contributed to the project's scientific foundation. Additionally, Dr. Rebecca Forkner's support and expertise have been invaluable throughout the development process.

The project utilizes research-grade data from iNaturalist, whose contributions to biodiversity data collection are deeply appreciated. Efforts are ongoing to engage with rehabilitation organizations like City Wildlife for data sharing and to further enhance the project's scope and impact.

Data for this project is sourced from iNaturalist, a joint initiative by the California Academy of Sciences and the National Geographic Society. The platform's commitment to open data and community science has been instrumental in making urban wildlife research more accessible to everyone.