Exploring the rich-club characteristic in internal migration: Evidence from Chinese Chunyun migration

Zhu, Ruoxin; Wang, Yujing; Lin, Diao; Jendryke, Michael; Xie, Mowen; Guo, Jianzhong; Meng, Liqiu

doi:10.1016/j.cities.2021.103198

Cited by 21 publications

(9 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Western scholars frequently use Twitter data to examine the temporal and spatial distribution characteristics of population flow [23]. Chinese scholars have frequently used data from Sina Weibo [29,30], Baidu migration data [31], and Tencent population migration data [32,33] in recent years. For example, Li et al [9] used Baidu migration data to examine the characteristics of population migration during the Spring Festival in China.…”

Section: Related Workmentioning

confidence: 99%

Comparison of Intercity Travel Network Structure during Daily Time and Holiday in China

2021

View full text Add to dashboard Cite

Intercity travel by residents promotes the regathering and dissemination of social and economic factors. Based on big data from Tencent’s location-based service, 346 cities above the prefecture level in China were chosen as study objects, with 2018 as the study time node. To construct the intercity residents’ travel network, complex network analysis and GIS spatial analysis methods were used. Furthermore, when analyzing the structural characteristics and spatial differences of Chinese residents’ intercity travel from different time perspectives (the whole year, daily, Spring Festival travel rush, and special holidays), Gephi network analysis tools and ArcGIS spatial analysis software were used. The following are the major findings: daily and the whole year intercity travel by Chinese residents, as well as intercity travel during special holidays and the Spring Festival, all exhibit the “diamond” structure, with Beijing, Shanghai, Guangzhou-Shenzhen, and Chengdu-Chongqing at the core. The distribution of lines in and around the “diamond” is large and concentrated from the perspective of the hierarchical nature of the residents’ intercity travel network. Significant increases in high-intensity population flow lines within the “diamond” can be seen during Spring Festival travel and holidays. The number of cities involved in the inflow line is significantly greater than that involved in the outflow line, as demonstrated by the number of residents in the first point of travel, indicating that there is a difference between the central cities flowing into and out of the network. The first flow of the central city is the most visible during the Spring Festival travel period. Most cities in the resident intercity travel network have relatively low degrees of centrality, closeness centrality, and betweenness centrality, and the number of cities with large values of the three is small, and they are concentrated in the apex and interior of the “diamond” structure.

show abstract

Section: Related Workmentioning

confidence: 99%

Comparison of Intercity Travel Network Structure during Daily Time and Holiday in China

2021

View full text Add to dashboard Cite

show abstract

“…The total population of the GBA fluctuates significantly with numerous population migrations during the year . Gaode Map and Baidu Map, two of the most popular map and navigation applications in China, provide migration big data that are generated by cell phone users using the map applications.…”

Section: Methodsmentioning

confidence: 99%

“…The total population of the GBA fluctuates significantly with numerous population migrations during the year. 53 Gaode Map and Baidu Map, two of the most popular map and navigation applications in China, provide migration big data that are generated by cell phone users using the map applications. Due to the widespread use of cell phones in China, the map applications capture the temporal variation of daily and seasonal population migration.…”

Section: Meteorological Datamentioning

confidence: 99%

Predicting Regional Wastewater Treatment Plant Discharges Using Machine Learning and Population Migration Big Data

Tian

Jing

et al. 2023

ACS EST Water

View full text Add to dashboard Cite

Quantifying the temporal variation of wastewater treatment plant (WWTP) discharges is essential for water pollution control and environment protection in metropolitan areas. This study develops an ensemble machine learning (ML) model to predict discharges from WWTPs and to quantify the contribution of extraneous water (mixed precipitation and infiltrated groundwater) by leveraging the power of ML and population migration big data. The approach is applied to predict the discharges at 265 WWTPs in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) in China. The major conclusions are as follows. First, the ensemble ML model provides an efficient and reliable way to predict WWTP discharges using data easily accessible to the public. The predicted treated sewage amount increased from 20.4 × 106 m3/day in 2015 to 24.5 × 106 m3/day in 2020. Second, the predictors, including daily precipitation, average precipitation of past proceeding days, daily temperature, and population migration, play different roles in predicting different city’s discharges. Finally, mixed precipitation and infiltrated groundwater account for, on average, 1.6 and 10.3% of total discharges from WWTPs in the GBA. This study represents the first attempt to bring population migration big data into data-driven environmental engineering modeling and can be easily extended to predict other environmental variables of concern.

show abstract

“…Metro lines are the driving force for development through the combination of traffic points and traffic axes to help planners realize regional development projects (Lu, 1986;Zhu et al, 2021). Therefore, by comparing the results in different metro lines, we can observe the development status and degree of equilibrium for different drive axes, which can assist in setting the departure frequency and carriage capacity for different lines.…”

Section: Metro Linesmentioning

confidence: 99%

Node-place model extended by system support: Evaluation and classification of metro station areas in Tianfu new area of Chengdu

Shen²,

Liang³

et al. 2022

Front. Environ. Sci.

View full text Add to dashboard Cite

The construction of new urban areas has become one of the important ways for urban regeneration throughout the process of polycentric urban development. New urban area construction following transit-oriented development (TOD) conception advocates development around the public transportation stations. To gain more benefits around the stations, it is necessary to conduct an ex-ante evaluation of TOD projects in the new urban area. The Node-place (NP) model is a commonly used method for TOD evaluation and classification, which essentially designs an analytical framework for assessing station areas in both transport (node) and land use (place) aspects. The objective of our study is twofold. First, based on the original NP model, we propose the node-place-system support (NPS) model by introducing a novel evaluation dimension—system support—which quantitatively describes the relationship between local stations and the overall urban system. Second, taking advantage of multi-sourced data and Geographical Information System (GIS) techniques, we employ the proposed NPS model to evaluate and classify the metro stations in the Tianfu New Area of Chengdu, China. The results show that most stations present a balanced relationship between transport and land use performances. However, for a fraction of these balanced stations, we observe a mismatch between the system support and NP performances. Accordingly, we identify the system-mismatched stations and provide targeted improvement strategies for urban design.

show abstract

Exploring the rich-club characteristic in internal migration: Evidence from Chinese Chunyun migration

Cited by 21 publications

References 51 publications

Comparison of Intercity Travel Network Structure during Daily Time and Holiday in China

Comparison of Intercity Travel Network Structure during Daily Time and Holiday in China

Predicting Regional Wastewater Treatment Plant Discharges Using Machine Learning and Population Migration Big Data

Node-place model extended by system support: Evaluation and classification of metro station areas in Tianfu new area of Chengdu

Contact Info

Product

Resources

About