cross-dataset-joins

What problem does it solve? CKAN's DataStore SQL cannot join across tables, so each query hits only one resource. This Skill provides the join keys, naming normalization rules, and ready-made recipes needed to combine Montréal's open datasets (trees, crime, permits, 311, fire, collisions) into unified analyses. ## Core Features & Use Cases - Join Key Reference: Documents borough, coordinate, and date fields across all major datasets, including inconsistencies like Ahuntsic - Cartierville vs Ahuntsic-Cartierville and crime data using PDQ districts instead of boroughs. - Four Join Recipes: Borough comparison, Haversine-based proximity joins, temporal correlation, and multi-dataset safety profiles, each with working Python code against the CKAN API. - Borough Normalization: A reusable normalize_borough function with alias mapping to reconcile naming differences across datasets. - Use Case: To answer "Which borough has the most trees per 311 request?", query each dataset with GROUP BY borough, normalize the borough names, and merge the results client-side. ## Quick Start Ask the agent to compare tree counts and 311 request volumes by borough using the cross-dataset join recipes.