Compatibility mix degree index: A novel measure to characterize urban land use mix pattern

Zhuo, Yuefei; Zheng, Hongyu; Wu, Cifang; Zhang, Xu; Li, Guan; Yu, Zhenning

doi:10.1016/j.compenvurbsys.2019.01.005

Cited by 33 publications

(39 citation statements)

References 36 publications

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“…Varying terms such as 'mixed land use', 'mixed use development' and 'land use mix' have similar connotations; thus, we do not intend to provide a rigorous distinction for them. Here, the term land use mix (hereafter abbreviated as LUM) is used throughout the paper because it frequently appears in land use quantitative studies (Gehrke & Clifton, 2015;Hajna et al, 2014;Song et al, 2013;Tian et al, 2017;Zhuo et al, 2019). LUM appears to be a relatively straightforward concept as it suggests space usage that combines more than one land use.…”

Section: Definitions Of Land Use MIXmentioning

confidence: 99%

“…Therefore, LUM measures are multidimensional and can be summed on the basis of three basic concepts: quantity, distance, and attribute. The attribute of a piece of developed land refers to a specific land use type or function (Zhuo et al, 2019). As LUM produces spatial interaction or externalities requiring a comparison of attributes, then determining whether these functional relationships are positive or negative (Taleai et al, 2007).…”

Section: Dimensions Of Measuring Lummentioning

confidence: 99%

“…In view of better capturing land use mix patterns that closely correspond to theoretically depicted sustainable goals, this study aims to propose a systematic quantitative framework and a robust measure called the land use mix degree index (LMDI) for sustainable land use mix. The LMDI accounting for two concepts of quantity and distance, as well as the 'attribute' concept that is a specific land use type or function (Zhuo et al, 2019). In this manner, 'physical compatibility' (i.e.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Measuring and characterizing land use mix patterns of China’s megacities: A case study of Shanghai

et al. 2021

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Section: Definitions Of Land Use MIXmentioning

confidence: 99%

Section: Dimensions Of Measuring Lummentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring and characterizing land use mix patterns of China’s megacities: A case study of Shanghai

et al. 2021

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“…In general, the measurement of mixed land use is currently based on two dimensions: distance and quantity [34]. Some commonly used indicators are percentage and proportion, as well as balance, entropy, Herfindahl-Hirschman, Atkinson, clustering, dissimilarity, and Gini indexes [35]. This study uses entropy index and type number index to indicate the mixed land use degree in the block.…”

Section: Indicators Of Mixed Land Usementioning

confidence: 99%

Mixed Land Use Evaluation and Its Impact on Housing Prices in Beijing Based on Multi-Source Big Data

Yang

Wang

et al. 2021

Land

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The tense relationship between the supply and demand of land resources and the past spatial expansion of urban development in Beijing have brought many urban problems. Mixed land use is considered to be able to solve these urban problems as well as promote sustainable urban development. In this context, this study uses multi-source big data such as POI, OpenStreetMap and web crawler data to construct current land-use data of the area within the sixth ring road of Beijing, and then uses the entropy index and type number index to analyze the spatial distribution and aggregation characteristics of the mixed land-use level. Finally, a multi-scale geographically weighted regression is applied to explore the impact of the block and life circle scale mixed land use on housing prices. The results show that: (1) the accuracy of land use data obtained by using multi-source big data is high, and the consistency with the real land use situation is as high as 82.67%. (2) the mixed land use level in the study area is higher in the urban center and lower in the periphery of the city. However, it does not show the spatial distribution characteristics gradually decreasing with the increase of the distance from the urban center but shows that the area from the third to the fifth ring road is the highest. (3) the impact of block scale and life circle scale mixed land use on housing price is different. The type number index has a negative effect on the housing price in block scale mixed land use, while the entropy index has a positive effect on the housing price in life circle scale mixed land use. Based on the existing “bottom-up” individual-dominant development mode, the government of Beijing should issue relevant policies and documents to give “top-down” control and guidance in the future, so as to promote the maximization of the benefits of mixed land use. Furthermore, in the practice of mixed land use in Beijing, land use types should be reduced at the block scale and the area of different land use types should be balanced at the life circle scale.

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“…In light of gravity-based accessibility measures [38,39] coupled with the land-use mix index [40,41], we developed a new index to capture urban vitality by using bicycle-sharing data. Rather than merely consider the bicycle-share ridership, two important aspects of spatial interaction, namely, spatial distance and spatial diversity, are both incorporated into our new urban vitality index.…”

Section: Urban Vitality Indexmentioning

confidence: 99%

Investigating the Spatiotemporal Dynamics of Urban Vitality Using Bicycle-Sharing Data

2020

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In recent decades, the availability of diverse location-based service (LBS) data has largely stimulated the research in individual human mobility. However, less attention has been paid on the intra-city movement of cyclists coupled with their spatiotemporal dynamics. To fill the knowledge gap, drawing on bicycle-sharing data over one week in Shanghai, China, this study investigates the dynamics of bicycle-sharing users at two spatial scales (i.e., city level and subdistrict level) and explores the intra-city spatial interactions by those cyclists. At the city level, by applying the analysis of variance (ANOVA) test and the Wilcoxon signed-rank test, this study examines the temporal variation of cyclists across a seven-day period. At the subdistrict level, we develop a new index to capture the urban vitality using bicycle-sharing data with the consideration of trip flow allied with spatial weights. In terms of the computed urban vitality over the course of a day, 98 subdistricts are partitioned into 7 groups by using K-means clustering. In addition, spatial autocorrelation and hot spot analysis are also applied to examine the spatial features of urban vitality at different periods. Our results reveal that urban vitality has an obvious character of the spatial cluster and this cluster feature varies markedly over the course of a day. By shedding new lights on intra-city movement, we argue our results are important in informing urban planners on how to better allocate public facilities and increase bicycle usage as a way to progress towards more sustainable urban areas.

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Compatibility mix degree index: A novel measure to characterize urban land use mix pattern

Cited by 33 publications

References 36 publications

Measuring and characterizing land use mix patterns of China’s megacities: A case study of Shanghai

Measuring and characterizing land use mix patterns of China’s megacities: A case study of Shanghai

Mixed Land Use Evaluation and Its Impact on Housing Prices in Beijing Based on Multi-Source Big Data

Investigating the Spatiotemporal Dynamics of Urban Vitality Using Bicycle-Sharing Data

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