Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen

Yang, Jun; Cao, Jason; Zhou, Yuzhen

doi:10.1016/j.tra.2020.11.014

Cited by 84 publications

(69 citation statements)

References 43 publications

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“…), and then displays the relative population distinguished by colors, where red represents high density, and blue represents low density (Figure 2). Recent studies have verified that BHM data could be used as a reasonable proxy for measuring the dynamics of human activities in different areas [29,43]. In this study, therefore, the BHM data at hourly intervals from 6:00 to 22:00 across the Nanjing city were collected on a weekday (October 14th in 2020, Wednesday) and a weekend (October 17th in 2020, Saturday) (Figure 2).…”

Section: Bhm Datamentioning

confidence: 99%

“…The first research stream applies various crowdsourced data to assess the spatiotemporal characteristics of urban vitality. The mobile phone data, social media data, GPS tracking data, as well as Baidu heat map (BHM) data serve as the most dominant proxies of urban vitality by reason that these data provide detailed information regarding people's behavioral characteristics [25][26][27][28][29]. The second research stream delves into the relationship between urban vitality and its determinants.…”

Section: Introductionmentioning

confidence: 99%

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How Did Built Environment Affect Urban Vitality in Urban Waterfronts? A Case Study in Nanjing Reach of Yangtze River

Fan

Jin

Luo

et al. 2021

IJGI

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The potential of urban waterfronts as vibrant urban spaces has become a focus of urban studies in recent years. However, few studies have examined the relationships between urban vitality and built environment characteristics in urban waterfronts. This study takes advantage of emerging urban big data and adopts hourly Baidu heat map (BHM) data as a proxy for portraying urban vitality along the Yangtze River in Nanjing. The impact of built environment on urban vitality in urban waterfronts is revealed with the ordinary least squares (OLS) and geographically weighted regression (GWR) models. The results show that (1) the distribution of urban vitality in urban waterfronts shows similar agglomeration characteristics on weekdays and weekends, and the identified vibrant cores tend to be the important city and town centers; (2) the building density has the strongest positive associations with urban vitality in urban waterfronts, while the normalized difference vegetation index (NDVI) is negative; (3) the effects of the built environment on urban vitality in urban waterfronts have significant spatial variations. Our findings can provide meaningful guidance and implications for vitality-oriented urban waterfronts planning and redevelopment.

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Section: Bhm Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

How Did Built Environment Affect Urban Vitality in Urban Waterfronts? A Case Study in Nanjing Reach of Yangtze River

Fan

Jin

Luo

et al. 2021

IJGI

View full text Add to dashboard Cite

show abstract

“…Recently, many scholars have attempted to disentangle the non-linear associations between the built environment and travel behaviors. The most used methods are the gradient boosting decision trees model (GBDT) and random forest model [ 26 , 27 , 28 , 29 , 30 ]. Compared with traditional linear models, the non-linear methods offer higher prediction precision and perform better in interpreting complex relationships.…”

Section: Introductionmentioning

confidence: 99%

Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model

Wang

Zhao

Liu

et al. 2021

IJERPH

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Global aging has raised increasing concerns on the health and well-being of older adults. Public transport is a viable option to improve the mobility and quality of life among older adults. However, policies that promote the public transport use among older adults are rare. This study utilizes the eXtreme Gradient Boosting (XGBoost) decision tree to explore the non-linear associations of the built and social environment with bus use among older adults in China. The bus use of older adults was obtained from the Zhongshan Household Travel Survey (ZHTS) in 2012. Results show that non-linear relationships exist among all built environment and social environment characteristics. Within certain thresholds, the percentage of green space land use, land use mixture, bus-stop density, and dwelling unit density are positively related to bus use among older adults. Likewise, one social environment variable, the proportion of older adults in a neighborhood, is the key social environment variable. Furthermore, the dwelling unit density and proportion of older adults appear to have an inverse U-shaped relationship. Additionally, age, ownership of motorcycles, and distance from home to the nearest bus stop also show non-linearity. The findings presented in this paper facilitate effective planning interventions to promote bus use among older adults.

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“…GBDT has some applications in the field of urban studies. For example, Yang, Cao, and Zhou (2021) used GBDT to investigate the non-linear relationship between subway accessibility and urban vitality in Shenzhen, China. Moreover, we compare the modeling outcomes of GBDT and traditional hedonic pricing models and confirm that GBDT has a more substantial predictive power than its hedonic pricing counterpart.…”

Section: Introductionmentioning

confidence: 99%

Machine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices

et al. 2021

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The adoption of bus rapid transit (BRT) systems has gained worldwide popularity over the past several decades. China is no exception as it has long been aiming at promoting public transportation. Prior studies have provided extensive evidence that BRT has substantial effects on house prices with traditional econometric techniques, such as hedonic pricing models. However, few of those investigations have discussed the non-linear relationship between BRT and house prices. Using the Xiamen data, this study employs a machine learning technique, namely the gradient boosting decision tree (GBDT), to scrutinize the non-linear relationship between BRT and house prices. This study documents a positive association between accessibility to BRT stations and house prices and a negative association between proximity to the BRT corridor and house prices. Moreover, it suggests a non-linear relationship between BRT and house prices and indicates that GBDT has more substantial predictive power than hedonic pricing models.

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Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen

Cited by 84 publications

References 43 publications

How Did Built Environment Affect Urban Vitality in Urban Waterfronts? A Case Study in Nanjing Reach of Yangtze River

How Did Built Environment Affect Urban Vitality in Urban Waterfronts? A Case Study in Nanjing Reach of Yangtze River

Non-Linear Effects of the Built Environment and Social Environment on Bus Use among Older Adults in China: An Application of the XGBoost Model

Machine learning for inference: using gradient boosting decision tree to assess non-linear effects of bus rapid transit on house prices

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