Intensive Variable and Its Application

Zheng, Xunhua; Xue, Chunlu; Yuan, Zhiyuan

doi:10.1007/978-3-642-54873-4

Cited by 2 publications

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“…As a result, the relationship between urban land use and sustainable development has received increasing attention from scholars and research organisations [7], [8]. Moreover, scientific studies of land use decision-making and management are conducted from various perspectives to make the planning of land use more efficient, thus decreasing the demand for new urban area expansion and protecting the environment [9]. The two most important perspectives are represented by studies on urban morphology and of the sustainable land use assessment system.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Intensive Redevelopment of Urban Central Blocks Using Data Envelopment Analysis and Deep Learning: A Case Study of Nanjing, China

Leng

Jie

2019

IEEE Access

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Intensification of morphology of blocks, which are the basic units of urban form and land management, is the key to sustainable urban development. Taking 206 blocks of Nanjing's central city area as an example, an evaluation system featuring six output-oriented indicators and five input-oriented indicators was constructed for data envelopment analysis to measure the blocks' intensity level. Relatively intensive blocks were selected as training data, and the output-oriented indicators of non-intensive blocks were predicted by a deep learning model, completing the procedure from evaluation of intensity to optimisation of morphological indicators. Finally, using the difference calculation and Arctool of weighted overlay in ArcGIS, a reference for the optimisation design of urban block morphology was created.

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Section: Introductionmentioning

confidence: 99%

Investigating the Intensive Redevelopment of Urban Central Blocks Using Data Envelopment Analysis and Deep Learning: A Case Study of Nanjing, China

Leng

Jie

2019

IEEE Access

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Long-term evaluation on urban intensive land use in five fast-growing cities of northern China with GEE support

Shang

Zheng

Han

et al. 2021

Sci Rep

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Intensive land use (ILU) is a multi-objective optimization process that aims to simultaneously improve the economic, social, and ecological benefits, as well as the carrying capacity of the land, without increasing additional land, and evaluation of the ILU over long time series has a guiding significance for rational land use. To tackle inefficient extraction of information, subjective selection of dominant factor, and lack of prediction in previous evaluation studies, this paper proposes a novel framework for evaluation and analysis of ILU by, first, using Google Earth Engine (GEE) to extract cities’ built-up land information, second, by constructing an index system that links economic, social and ecological aspects to evaluate the ILU degree, third, by applying Geodetector to identify the dominant factor on the ILU, finally, by using the S-curve to predict the degree. Based on the case study data from northern China’s five fast-growing cities (i.e., Beijing, Tianjin, Shijiazhuang, Jinan, Zhengzhou), the findings show that the ILU degree for all cities has increased over the past 30 years, with the highest growth rate between 2000 and 2010. Beijing had the highest degree in 2018, followed by Tianjin, Zhengzhou, Jinan, and Shijiazhuang. In terms of the time dimension, the dominant factor for all cities shifted from the output-value proportion of secondary and tertiary industries in the early stage to the economic density in the late stage. In terms of the space dimension, the dominant factor varied from cities. It is worth noting that economic density was the dominant factor in the two high-level ILU cities, Beijing and Tianjin, indicating that economic strength is the main driver of the ILU. Moreover, cities with high-level ILU at the current stage will grow slowly in the ILU degree from 2020 to 2035, while Zhengzhou and Jinan, whose ILU has been in the midstream recently, will grow the most among the cities.

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Intensive Variable and Its Application

Cited by 2 publications

References 0 publications

Investigating the Intensive Redevelopment of Urban Central Blocks Using Data Envelopment Analysis and Deep Learning: A Case Study of Nanjing, China

Investigating the Intensive Redevelopment of Urban Central Blocks Using Data Envelopment Analysis and Deep Learning: A Case Study of Nanjing, China

Long-term evaluation on urban intensive land use in five fast-growing cities of northern China with GEE support

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