audit-contracts

You can detect spending acceleration and lobbying correlation by running Python scripts that retrieve and cross-reference USASpending and lobbying data across a cohort of 3-10 government contractors to identify systemic anomalies.

Revolving door detection identifies former government officials who have transitioned to contractor roles by cross-referencing personnel data, helping uncover potential conflicts of interest within a cohort of government contractors.

Yes, this Skill retrieves and analyzes USASpending, FEC, and HigherGov data for cohorts of 3-10 contractors, using Python scripts to detect spending acceleration, lobbying correlations, and revolving door patterns.

Yes, the analysis requires a Python environment with sqlite3, requests, json, pandas, and numpy dependencies to retrieve data and perform network analysis on procurement and lobbying datasets.

This analysis is limited to cohorts of 3-10 contractors and requires access to USASpending, lobbying, HigherGov, and FEC data sources to effectively detect spending acceleration and network anomalies.

The best way to map partnership networks is using Python scripts with pandas and numpy to retrieve procurement data, cross-reference lobbying expenditures, and detect revolving door patterns across a contractor cohort.