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I revisited my original meteorite analysis project using NASA’s dataset, aiming to explore where meteorites are most likely to be found. To manage the dataset’s large scale, I used SQL for initial cleaning and transformation. This included removing null values from key columns and narrowing down entries to those from the U.S. within the last century (1912-2012). By refining the dataset, I was able to isolate data relevant to my question and set the stage for deeper analysis.
I revisited my original meteorite analysis project using NASA’s dataset, aiming to explore where meteorites are most likely to be found. To manage the dataset’s large scale, I used SQL for initial cleaning and transformation. This included removing null values from key columns and narrowing down entries to those from the U.S. within the last century (1912-2012). By refining the dataset, I was able to isolate data relevant to my question and set the stage for deeper analysis.
With the cleaned data, I created a Tableau dashboard to visualize key insights more clearly. The dashboard includes a map showing meteorite locations, where marker size represents mass, and various KPIs summarizing total meteorites and average mass. Interactive filters allow users to explore whether meteorites were found or fell and to select specific years for analysis. This setup helped reveal geographic and temporal patterns within the data.
The visualizations highlighted a hotspot in the central U.S. (especially Texas and surrounding areas), where meteorites are densely clustered. An interesting trend also emerged: heavier meteorites were more frequently recorded as “falling” rather than “found.” This suggests that larger meteorites are likely to have left recoverable remnants, pointing to the central U.S. as a prime location for future meteorite searches.
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