geodatasim 0.3.3

Geographic + Socioeconomic + Climate intelligence with ML, auto-update, and visualization - World's most comprehensive city data platform

🌍 GeoDataSim v0.3.0 - Intelligence Boost

World's most comprehensive city data platform with ML, auto-update, and visualization

Geographic + Socioeconomic + Climate intelligence library with ML clustering, auto-update engine, and interactive visualization. All data from free public APIs (World Bank, REST Countries, Open-Meteo).

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🚀 What's NEW in v0.3.0 - Intelligence Boost

🤖 ML-Powered Intelligence

• City Clustering (KMeans, DBSCAN, Agglomerative)
• 10x Faster Similarity (numba JIT optimization)
• Advanced Feature Engineering (sklearn integration)

📊 Interactive Visualization

• Plotly Charts (scatter, heatmap, radar, bar, geo)
• Export to HTML (interactive, shareable)
• Quick visualization APIs

🔄 Auto-Update Engine

• Monthly data refresh from World Bank API
• 30-day cache (avoids unnecessary API calls)
• Update history tracking
• No API key required (100% free sources)

✅ Production-Ready Features

• Pydantic validation (type-safe data models)
• Progress bars (tqdm integration)
• Enhanced geopy distance calculations
• Comprehensive error handling

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📦 Installation

pip install geodatasim

Requirements: Python 3.10+

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⚡ Quick Start

Basic Usage

from geodatasim import City

# Create city with automatic data loading
istanbul = City("Istanbul")

print(f"Population: {istanbul.population:,}")
print(f"GDP per capita: ${istanbul.gdp_per_capita:,.2f}")
print(f"Climate: {istanbul.climate_zone} ({istanbul.avg_temperature}°C)")
print(f"HDI: {istanbul.hdi}")

# Find similar cities
similar = istanbul.find_similar(n=5)
for city in similar:
    print(f"  - {city.name}, {city.country}")

🆕 ML Clustering (v0.3.0)

from geodatasim.ml import CityClustering, cluster_cities
from geodatasim.analysis import BatchAnalyzer

# Get data
analyzer = BatchAnalyzer(["Istanbul", "Paris", "Tokyo", "New York"])
df = analyzer.to_dataframe()

# Cluster cities
clustering = CityClustering(n_clusters=3, method='kmeans')
clustering.fit(df)

print(f"Silhouette score: {clustering.silhouette_score_:.3f}")
summary = clustering.get_cluster_summary(df)
print(summary)

🆕 Interactive Visualization (v0.3.0)

from geodatasim.viz import CityVisualizer

viz = CityVisualizer()

# Scatter plot
fig = viz.scatter(df, x='population', y='gdp_per_capita',
                  color='region', size='population')
fig.show()  # Interactive in browser
fig.write_html("cities.html")

# Correlation heatmap
viz.heatmap(df, columns=['population', 'gdp', 'hdi']).show()

# Radar chart comparison
viz.radar(df, metrics=['population', 'gdp', 'hdi'],
          cities=['Istanbul', 'Paris', 'Tokyo']).show()

🆕 Auto-Update Engine (v0.3.0)

from geodatasim import City
from geodatasim.core.updater import UpdateEngine, update_city

engine = UpdateEngine()

# Method 1: Update City object (easiest)
istanbul = City("Istanbul")
updated_data = engine.update(istanbul)
if 'population' in updated_data:
    print(f"Population: {updated_data['population']:,}")
if 'gdp_per_capita' in updated_data:
    print(f"GDP: ${updated_data['gdp_per_capita']:,.2f}")

# Method 2: Update specific fields
pop_result = engine.update_population(istanbul)
if pop_result:
    population, metadata = pop_result
    print(f"Updated population: {population:,}")

gdp_result = engine.update_gdp(istanbul)
if gdp_result:
    gdp, metadata = gdp_result
    print(f"Updated GDP: ${gdp:,.2f}")

# Method 3: Update with city data dictionary
city_data = {
    'name': 'Istanbul',
    'country_code': 'TUR',
    'latitude': 41.0082,
    'longitude': 28.9784
}
updated = engine.update_city_all(city_data)

# Method 4: Convenience function
from geodatasim.core.updater import update_city
updated = update_city(istanbul)

# Check if update needed (30-day interval)
needs_update = engine.should_update('Istanbul', 'population')
if needs_update:
    print("Update available!")

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📊 Features

v0.3.0 - Intelligence Boost 🆕

• 🤖 ML Clustering (KMeans, DBSCAN, Agglomerative)
• ⚡ 10x Faster Similarity (numba optimization)
• 📊 Interactive Visualization (plotly)
• 🔄 Auto-Update Engine (monthly refresh)
• ✅ Pydantic Validation
• 📈 Progress Bars (tqdm)

v0.2.0 - Data Science Tools

• ✅ Batch Analysis
• ✅ Rankings & Filtering
• ✅ Export (CSV, Excel, JSON, Markdown)
• ✅ pandas Integration
• ✅ Statistical Analysis

v0.1.0 - Core Features

• ✅ 46 cities from 36 countries
• ✅ 20+ data fields per city
• ✅ World Bank API integration
• ✅ Smart caching (90-day TTL)
• ✅ City similarity algorithm
• ✅ Distance calculations

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📈 Data Sources

All from free, public domain sources:

Source	Data	API Key Required
World Bank	GDP, Population, HDI	❌ No
REST Countries	Country metadata	❌ No
Open-Meteo	Climate data	❌ No

✅ Safe for commercial use - All sources are public domain

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🎯 Use Cases

Data Science & ML

from geodatasim.ml import CityClustering
clustering = CityClustering(n_clusters=5)
clustering.fit(cities_df)

Urban Planning

istanbul = City("Istanbul")
similar = istanbul.find_similar(min_population=5_000_000)

Business Intelligence

from geodatasim.analysis import CityRankings
rankings = CityRankings()
wealthy_cities = rankings.filter_cities(min_gdp=40000)

Interactive Dashboards

from geodatasim.viz import CityVisualizer
viz = CityVisualizer()
viz.scatter(df, 'population', 'gdp').show()

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📖 Examples

# Test basic features
python test_v0_3_0.py

# Run comprehensive examples
python examples/v0_3_0_intelligence_boost.py

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🛣️ Roadmap

v0.4.0 - Performance (Polars, UMAP, PyArrow) v0.5.0 - Geo Intelligence (geopandas, folium) v1.0.0 - Complete Platform (100+ cities, predictions)

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📄 License

MIT License

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📬 Contact

PyPI: pypi.org/project/geodatasim GitHub: github.com/teyfikoz/GeoDataSim

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GeoDataSim v0.3.0 🚀 ML · Visualization · Auto-Update · Intelligence