Investigate feasibility of automated analysis

Could we run the containers on some sample files and validate the output using analysis previously conducted by the CLAD team to ensure the geocodes performance and speed is within expectations.

Useful links:

• https://github.com/brian-cy-chang/CLAD_Geospatial/raw/refs/heads/main/output/OMOP_location_flagged_successful.csv
• https://www.dhs.gov/gmo/hifld
• https://uwnetid-my.sharepoint.com/:u:/g/personal/loganap_uw_edu/IQB6KKubfavMRqZo3AJbSrlQAedNZpWabukYj9jIW9I3fWo?e=S5x7j6
• https://github.com/jphuong/gc_strategy/tree/main
• https://uwnetid-my.sharepoint.com/:u:/g/personal/loganap_uw_edu/IQBSzIxgxp0DTJC1PyYZT_j6Ad2DH4xavjxeWEvbJYXzP3Y?e=EgPR1D
• https://github.com/uwrit/cladcloud/blob/main/test/address_test.csv

More links:

• https://github.com/brian-cy-chang/UW_Geospatial/tree/main
• https://github.com/jphuong/gc_strategy/blob/main/notional_data_exploration_full.qmd
• https://www.transportation.gov/sites/dot.gov/files/2023-07/NAD_Schema_202304.pdf

Using the OMOP file, we do simple average distance (geodesic) in meters on the true vs "predicted" points as a regression test. This is included in #17's main python script. Results to be tabulated and stored here when completed before closing this issue.