Research on Economic Vitality Based on Combination Weighting Method and Grey Relational Analysis

Wang, Xin; Li, Yang; Gao, Hao; Mu, Yicheng

doi:10.1109/yac51587.2020.9337688

Cited by 1 publication

(1 citation statement)

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“…Residents' daily activities, such as consumption and movement, contribute to the demand for regional construction land, which can significantly shape the spatial structure of cities [27]. Economic vitality is the primary indicator of urban dynamism, reflecting the present and future economic development, and is an important driver of urban development and expansion [28], [29]. These two factors are often characterized with dynamic features, and most traditional static data with outdated information and poor spatial resolution cannot represent, such as statistical and survey data [30].…”

Section: Introductionmentioning

confidence: 99%

Integrating Multisource Geographic Big Data to Delineate Urban Growth Boundary: A Case Study of Changsha

Gao,

Xia,

Zhuang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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An urban growth boundary (UGB) is an important policy tool used to control urban sprawl, which can effectively balance the urban construction needs, residents' quality of life, and urban ecological protection. Current studies of UGB delineation and its indicators have paid little attention to human factors such as human activities and economic vitality, and weights for evaluation indicators have been determined highly subjectively. In response to these problems, this study integrated multi-source geographic big data to construct a total of 30 natural, human, and ecological evaluation indicators. The GIS technology and machine learning (ML) approach were combined to determine indicator weights with an officially manually drawn 2035 UGB as the reference, aiming to reduce the subjectivity. The suitability score was then calculated from indicator grading 4-1 and related weights, and high suitability (>2.15) regions were eventually delineated as the UGB. Results showed a delineated UGB of 1,528.06 km 2 with an overall accuracy of over 93% and high consistence with reference data for 2035 in the Chinese city of Changsha. The geographic big data totally contributed more than 33.72%, which mainly characterized role of human elements, and a 5-6 percentage point reduction in accuracy was found without these data. Compared with existing studies, our delineated UGB had higher accuracy and closer spatial pattern to the reference data, verifying the effectiveness and reasonability of ML-based weight setting approach and index system with geographic big data. The proposed method can provide scientific and accurate framework for UGB delineation, which can promote the territorial spatial planning and sustainable urban development.

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