Yes Data

☔️ Series: Rainfall (No. 6) - Japan Japan's rainy season, tsuyu, brings heavy rainfall from June to mid-July. 🔧 Tools: Python (Rasterio, Rioxarray, Geopandas)

🐔 Series: Chicken Density (No. 3) - Indonesia Indonesia has a massive chicken population, supporting its huge poultry industry. 🔧 Tools: Python (Rasterio, Geopandas, Shapely)

🚗 Series: Roads (No. 6) - India India's 6.3 million km road network, the world's second largest, connects cities and villages, driving economic growth. 🔧 Tools: Python (Pandas, Geopandas, Matplotlib)

This map by @yesdata_ shows the population density across North America.

🗺️ Series: Population Density (No. 12) - North America North America's population density averages about 22 people per square kilometer, with significant regional variations. 🔧 Tools: Python (Rasterio, Geopandas, Shapely)

🚗 Series: Roads (No. 5) - Thailand Thailand's road network connects Bangkok to regional centers with major highways, while rural roads vary in quality. 🔧 Tools: Python (Pandas, Geopandas, Matplotlib)

🗺️ Series: Population Density (No. 11) - South America South America's population density is starkly uneven, clustering in cities while thinning out in rural and wilderness areas. 🔧 Tools: Python (Rasterio, Geopandas, Shapely)

🐮 Series: Cattle Density (No. 5) - India India has one of the highest cattle densities globally due to its large bovine population and limited agricultural land. 🔧 Tools: Python (Rasterio, Geopandas, Shapely)

🗺️ Series: UNESCO World Heritage Sites (No. 4) - North America North America hosts UNESCO World Heritage Sites like Yellowstone and Chichen Itza, valued for their natural and cultural significance. 🔧 Tools: Python (Geopandas, Shapely, Contextily)

👨‍🌾 Series: HDI (No. 6) - North America The Human Development Index assesses development via health, education, and living standards. The dataset contains anomalies. Values are estimates. 🔧 Tools: Python (Rasterio, Rioxarray, Shapely)

🧯 Series: Fires (No. 4) - Japan NASA uses its satellites to detect sources of heat on Earth. This map shows 2023 signals. It only shows 'type 0' heat sources (presumed vegetation fire) with confidence 'h' (high). 🔧 Tools: Python (Geopandas, Shapely, Contextily)

💧 Series: Rivers (No. 5) - Germany Germany's river system, including major rivers like the Rhine, Danube, and Elbe, plays a vital role in transportation, trade, and supporting diverse ecosystems across the country. 🔧 Tools: Python (Pandas, Geopandas, Shapely)

☔️ Series: Rainfall (No. 5) - Africa Africa's precipitation varies widely, with tropical regions like Central Africa receiving heavy rainfall, while vast deserts like the Sahara experience minimal precipitation. 🔧 Tools: Python (Rasterio, Rioxarray, Geopandas)

💧 Series: Rivers (No. 4) - Türkiye Türkiye's river system includes major rivers like the Euphrates, Tigris, and Kızılırmak, which flow through diverse landscapes, supporting agriculture, hydropower, and ecosystems. 🔧 Tools: Python (Pandas, Geopandas, Shapely)

🗺️ Series: UNESCO World Heritage Sites (No. 3) - D.R. Congo The D.R. Congo is home to five UNESCO World Heritage Sites, including Virunga National Park, known for its diverse ecosystems and endangered mountain gorillas. 🔧 Tools: Python (Geopandas, Shapely, Contextily)

💧 Series: Rivers (No. 3) - Nigeria Nigeria's Niger and Benue rivers form a vital confluence at Lokoja, driving ecosystems and culture. 🔧 Tools: Python (Pandas, Geopandas, Shapely)

👨‍🌾 Series: HDI (No. 5) - Brazil The Human Development Index assesses development via health, education, and living standards. The dataset contains anomalies. Values are estimates. 🔧 Tools: Python (Rasterio, Rioxarray, Shapely)

🚗 Series: Roads (No. 4) - Czechia Czechia's road network is extensive and well-maintained, with a radial structure centered around Prague and ongoing modernization efforts. 🔧 Tools: Python (Pandas, Geopandas, Matplotlib)

Holy cow! South Asia’s cattle density really is highest in India. HT @yesdata_ (give them a follow)

Wonderfully detailed map shows the Human Development Index across Europe. Concentration of development in Central Europe generally and within the big cities within each individual country. HT @yesdata_