A statistical measure of the relationship between two variables — how much one tends to change when the other changes.
Correlation ranges from -1 to +1. A correlation of +1 means the two variables move perfectly together (when one increases, the other increases by a proportional amount). A correlation of -1 means they move perfectly in opposite directions. A correlation of 0 means no linear relationship.
A critical principle: correlation does not imply causation. Two variables can be correlated without one causing the other. Both might be caused by a third variable, or the correlation might be coincidental.
Example: An analysis of open data finds a strong positive correlation between neighbourhood income levels and park access scores. Higher-income neighbourhoods tend to have better park access. This correlation is interesting and worth investigating, but it does not prove that income causes park access — both might be influenced by historical planning decisions, land values, or other factors.