Using kernel density function as an urban analysis tool: Investigating the association between nightlight exposure and the incidence of breast cancer in Haifa, Israel

The kernel density (KD) function estimates the intensity of events across a surface by calculating the overall number of cases situated within a given search radius from a target point. To form a continuous surface from individual observations, the KD technique does not require the presence of a parameter's value in a given location (e.g., the incidence rate of a disease). This feature of KD smoothing is especially beneficial for empirical studies in which individual observations are represented by geographic coordinates only and have no other attributes, required by more commonly used smoothing techniques, such as spline and kriging. In the present study, we illustrate the use of KD technique for a study of association between the geographical distributions of breast cancer cases and exposure to artificial illumination during nighttime (light-at-night or LAN) in the city of Haifa, Israel.