The analysis of Urban Heat Islands (UHIs) utilized a structured methodology, starting with the definition of Metropolitan Statistical Areas (MSAs) based on census data. Within these rapidly growing urban areas, 500 random geospatial points were generated to capture temperature variations, acquiring monthly averages for July—historically the warmest month—from the PRISM climate database.
Raw climate data were first processed using a Python script for raw data cleanup, which organized the large datasets into a manageable format. This script utilized pandas, a powerful data manipulation library, allowing for systematic management and transformation of the data.
Subsequently, the tabular data were converted into point features using Python's arcpy module, as documented in the script for converting tables to point data. This conversion was critical, marking the transition from tables to a spatial dataset and setting the stage for geostatistical analysis.
Kriging analysis was then implemented through a Python script to create predictive temperature surfaces. This interpolation technique was tailored via the script to accommodate specific model parameters, enabling visualization and examination of the UHI effect over a continuous spatial field.
The focus on developing cities, susceptible to rapid UHI intensification, was a strategic choice. Monitoring the UHI effect as these cities expanded was vital for proactive urban planning and environmental management.
This methodology culminated in the development of an interactive web application. Combining HTML, CSS, and JavaScript, the application presents the data through dynamic maps and graphs, offering an engaging platform for users to investigate urban temperature trends and their implications for future city development.