curry_compare_inflow_outflow_vol

ELAN practice activity 3

ELAN practice activity 2

First ELAN practice activity

Comparison of two data wrangling envs.: R Tidyverse vs. Py Pandas

curry_WSE_outflow_compare_1

This technical report evaluates the feasibility and effectiveness of density-based spatial clustering methods, specifically DBSCAN, for objectively delineating real estate market areas in St. Tammany Parish, Louisiana. According to the International Association of Assessing Officers (IAAO), a market area is the geographic region from which demand originates and where competing properties are located. The primary research question is: How effectively can DBSCAN identify localized real estate market areas to enhance assessor workflows? Using real estate transaction data from 2024: including sale price, geographic coordinates, property characteristics, and transaction dates, this study demonstrates clustering applications exclusively using R. The effectiveness of DBSCAN will be evaluated primarily for spatial coherence and alignment with known market behaviors through qualitative assessment and descriptive statistics. This report outlines a practical framework for integrating density-based clustering techniques into assessor workflows.