<![CDATA[Global climate monitoring is crucial for understanding variations in temperature and humidity, which directly influence ecosystems, human health, and socio-economic activities. This study presents a Geographic Information System (GIS)-based analysis and visualization of global temperature and humidity patterns using historical hourly weather data from 2012 to 2017. The dataset, obtained from open-access sources, was processed and analyzed in Google Colab using Python libraries such as pandas, geopandas, folium, and plotly. Data preprocessing involved merging city-level observations, cleaning missing values, and calculating mean temperature and humidity per location. The resulting dataset was then visualized through an interactive global map and a scatter plot to identify spatial relationships between the two climatic variables.To quantify these spatial relationships, a statistical correlation analysis was conducted, revealing a weak negative relationship between temperature and humidity (r = -0.25) across global regions.The findings reveal that regions near the equator exhibit consistently high temperatures and humidity, while higher-latitude cities show lower temperatures and more variable moisture levels. This GIS-based approach demonstrates the potential of open meteorological data for climate pattern recognition and supports reproducible workflows for environmental analysis. The results highlight the importance of integrating data science tools with GIS for accessible and scalable global climate visualization.]]>
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