From Measuring Land Use Mix to Measuring Land Use Pattern -- New Methods for Assessing Land Use

Abstract

Land use mix is one of the central concepts in the urban planning field, though its measure has been found to have many fallacies. In this study, we propose multiple alternative methods to the Conventional Shannon Entropy land use mix index, which is generally employed to measure land use diversity. The study attempted to measure land use mix from two new perspectives and test the performance of the new measures with economic, transportation, and social outcomes. We first offered an improved single measure, entropy-based land use mix index weighted by adding surrounding land use attributes and the regional land use context to mitigate the modifiable area unit problem (MAUP) and equal composition issue. Second, we designed a multi-measure method to describe land use patterns via clustering. We found that the new single measure method is more effective than the existing Conventional Shannon Entropy index in accurately delivering the vision of land use mix that Jane Jacobs originally proposed. We also found the multi-measure clustering method performed much better in the regression model on various effect variables, compared with both conventional and other new index. Upon further review, we found that land use pattern complexity's relationship with various outcomes makes a single definition measure very difficult to represent true land use fully. Therefore, we recommend that future research measure whether or not land use is mixed to then assess land use pattern.