Quantitative Representation of Distribution and Mixture of Urban Land Use Through Spatial Autocorrelation and Information Entropy

Abstract

The distribution and mixture of urban land use in Japanese cities was quantitatively represented by using two indexes of texture analyses, spatial autocorrelation and information entropy. The spatial autocorrelation was calculated as Moran's I, and the entropy acquired by considering not only the composition of the areas of each land use class, but also the relation of adjacent cells. Land use data in urban areas except green areas were obtained from a digital land use map, and green areas by processing remote sensing data using an Aster GDS. These data were compiled by GIS, and the two indexes of each land use were calculated in each class. It was concluded that both indexes indicate different states of land use, and their relation was different in each land use class. In some it was sufficient to consider only the entropy, but in other cases, it was not sufficient. Using these indexes together gave a more accurate portrait than one alone for grasping the distribution and mixture of urban land use.

Behind the Land Use Mix: Measuring the Functional Compatibility in Urban and Sub-Urban Areas of China

“…Although LUM is identified with two key characteristics, namely diverse categories of land use and functional compatibility between various land uses, they are understood differently in academic literature. Until now, current methodological approaches focus largely on the former factor and form quite a few identifying methods including Shannon entropy [29,36], dissimilarity index [37], Simpson index [38,39], and many other indices. Song et al [5] present a comprehensive review on these indices and regard the concepts of "mix" and "diverse land uses" with a high degree of similarity.…”

Karma Arazi Kullanımı ve Arazi Kullanım Çeşitliliğinin Ölçülmesi

“…Yaya hacmi ve karma arazi kullanım arasındaki ilişkinin incelendiği bu çalışmada, %20 ticaret, %80 konut kullanımlarının yer aldığı bölge ile %20 konut, %80 ticaret kullanımlarının yer aldığı bölgede, aynı endeks değerlerine sahip olsalar da aynı derecede yaya hacmi çekme durumuna sahip olmadıkları belirtilmiştir (Im & Choi, 2019). Bir başka çalışmada mekânsal otokorelasyon ve entropi endeksi bir arada kullanılmakta, çeşitliliğin yanı sıra farklı arazi kullanımların nasıl dağıldığının da ölçülmesinin etkili bir yöntem olduğu aktarılmıştır (Kyakuno, 2008). Genel olarak çalışmalar incelendiğinde karma arazi kullanımın ölçülmesi önemli bir adım olarak karşımıza çıkmaktadır.…”