Regionalization Methods and Application in Analysis of Cancer Data - Liu, Lingbo; Wang, Fahui (Computational Methods and GIS Applications in Social Science - Lab Manual, 2024)
Rates of rare events (e.g., cancer, AIDS, homicide) are often subject to the small population (numbers) problem, where the denominators are small or vary a great deal. The problem can lead to unreliable rate estimates, sensitivity to missing data and other data errors, and data suppression in sparsely populated areas. One approach to mitigate the problem is regionalization (or spatial clustering) by grouping smaller areas into larger homogenous regions with comparable population sizes. A case study on mapping breast cancer rates in Louisiana is used to illustrate various regionalization methods such as SKATER, AZP, MaxP, SCHC and REDCAP methods. In addition, the Mixed-Level Regionalization (MLR) method is introduced to decompose areas of large population and merge areas of small population simultaneously to derive regions with comparable population size. A case study on mapping breast cancer rates in Louisiana is used to illustrate various regionalization methods such as SKATER, AZP, MaxP, SCHC and REDCAP methods. In addition, the Mixed-Level Regionalization (MLR) method is introduced to decompose areas of large population and merge areas of small population simultaneously to derive regions with comparable population size.