Measuring the Non-Linear Relationship between Three-Dimensional Built Environment and Urban Vitality Based on a Random Forest Model

Abstract

Urban vitality is a major indicator used for evaluating the sustainability and attractiveness of an urban environment. Global experience indicates that urban vitality can be stimulated through a reasonable urban design. However, it remains incompletely understood in the literature which building-related indicators can substantially affect urban vitality in Asian countries. To give an insight into this question, our study took a step forward by focusing specifically on the influence of the three-dimensional built environment on urban vitality, based on which decision makers could enhance urban vitality from the perspective of vertical building design. A machine-learning-based framework was developed in this study. First, we utilized several building-related indicators to thoroughly measure the spatial characteristics of buildings at the township level. Second, the relationship between a three-dimensional built environment and urban vitality was revealed based on a combined use of the correlation method, scatter charts, and a random forest. In the random forest, both a benchmark and a new model were constructed to evaluate the importance of those building-related indicators. The results suggested that urban vitality was closely related to the three-dimensional built environment, which played an even more important role than common benchmark factors in stimulating urban vitality. The building coverage ratio, density of tall buildings, and floor area ratio were essential spatial drivers behind urban vitality. Therefore, urban designers and decision makers should not only take traditional factors into account but also carefully consider the potential influence of high-rise buildings and the outdoor thermal environment so that urban vitality can be enhanced. Our study’s results can offer practical recommendations for improving urban vitality from the perspective of vertical building design. The proposed framework can also be used for measuring the potential influence of the three-dimensional built environment in other areas.

Investigation of the Solubility of Elemental Sulfur (S) in Sulfur-Containing Natural Gas with Machine Learning Methods

“…Due to two random variables, RF is insensitive to noisy data and can avoid overfitting. The use of RF is widespread due to its good performance [ 32 ], and support vector machine (SVM) is commonly used, since it is suitable for small samples with poor information [ 33 ]. By assigning weights to the training errors in WLSSVM, the learning ability of the model is improved and noise in the training samples is effectively reduced.…”

Statistical Modeling of Traffic Flow in Commercial Clusters Based on a Street Network

“…A commercial cluster is an urban functional area that integrates commodity purchase, marketing display, entertainment experience, commercial offices, and regional logistics, rendering it the fifth generation of merchandising markets in China. Studies have shown that improving the number of visits to commercial stores, increasing the assortment of businesses, and balancing the homogeneity of commercial vitality can contribute to the effective operation of a commercial cluster [1][2][3]. The commercial vitality of a mature commercial cluster needs to be well-balanced, and store visits need to be evenly distributed among the commercial clusters [4,5].…”

“…The aim of this research was to explain the spatiotemporal variations in, and pattern of, traffic flow, to control the error rate, and to improve the accuracy of prediction results through empirical studies [13][14][15]. (3) Using the detection results of the traffic flow, including information on the traffic flow, vehicle travel speed, and urban spatial distribution, researchers aimed to study the problem of urban carbon emissions, urban traffic, and urban infrastructure services and facilities [16][17][18]. The abovementioned studies on traffic flow are limited in the following ways: (1) They require the completeness of the original data, making it difficult to obtain traffic information for a wide range of areas in locations with low data openness, resulting in prediction errors.…”

Evaluating Urban Vitality of Street Blocks Based on Multi-Source Geographic Big Data: A Case Study of Shenzhen

“…The term "urban vitality" was widely accepted to describe the quality, sustainability, attractiveness of spaces that attract people for different types of activities to produce diverse city life [1,2]. Higher urban vitality usually means stronger attraction to people, the ability to support demands of different people, and the potentiality of development [3,4]. Improving the urban vitality is significant to the urban development and human lives, and the evaluation of current situation is the basic work [2,5].…”

“…With the help of open data platform, web crawler, and network API, etc., open-access geographic big data can be easily acquired. Different from the standard raster data format of remote sensing data, the spatial quantization of geographic big data within cities still needs specific analysis for data types and research basic unit [4].…”