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Megaregion has emerged as a global urban form, typically based on the polycentric strategy to enhance regional development. How to measure megaregional spatial structure and discriminate different roles of cities has become increasingly important to enrich the knowledge of the formation of a megaregion. Meanwhile, various indices have been used to identify vital nodes in the field of complex network. Which indices, however, are suitable for megaregion analysis remain unsolved. To address this requirement, this study first reviewed the typical indices for identifying vital nodes in the complex network theory, and pointed out that in a weighted city network scenario, weighted degree centrality, hub & authority score, and S-core decomposition (which represent network centrality, connectivity, and structures, respectively) are suitable for analyzing megaregional spatial structures. Then, we explored the city hierarchies and spatial structure in Guangdong Province, China, using the three indices. The hierarchical structure of the weighted city network in Guangdong Province had been identified using S-core decomposition. From the perspective of polycentric structure, Guangzhou and Shenzhen have the strongest node degrees and strength of mobility flows, while the Guangzhou-Dongguan-Shenzhen corridor has been identified via the hub & authority score which is designed to evaluate the connectivity in a weighted network. Moreover, we conducted a comparison analysis of three indices. The findings of this study not only enrich the understanding of city hierarchies and the structure of a megaregion, but also highlight that although various indices are available, they should be applied selectively in accordance with the study context. [ABSTRACT FROM AUTHOR]

The Huff model is designed to estimate the probability of shopping centre patronage based on a shopping centre's attractiveness and the cost of a customer's travel. In this paper, we attempt to discover some general shopping trends by calibrating the Huff model in Shenzhen, China, and New York, USA, using taxi trajectory GPS data and sharing bikes GPS data. Geographical and Temporal Weighted Regression (GTWR) is used to fit the model, and calibration results are compared with Ordinary Least Squares (OLS) regression, Geographical Weighted Regression (GWR), and Temporal Weighted Regression (TWR). Results show that GTWR gives the highest performance due to significant geographical and temporal variation in the Huff model parameters of attractiveness and travel cost. To explain the geographical variation, we use residential sales' and rental prices in Shenzhen and New York as a proxy for customers' wealth in each region. Pearson product-moment correlation results show a medium relationship between localised sales' and rental prices and the Huff model parameter of attractiveness: that is, customer wealth explains geographic sensitivity to shopping area attractiveness. To explain temporal variation, we use census data in both Shenzhen and New York to provide job profile distributions for each region as a proxy to estimate customers' spare leisure time. Regression results demonstrate that there is a significant linear relationship between the length of spare time and the parameter of shopping area attractiveness. In particular, we demonstrate that wealthy customers with less spare time are more sensitive to a shopping centre's attractiveness. We also discover customers' sensitivities to travel distance are related to their travel mode. In particular, people riding bikes to shopping areas care much more about trip distance compared with people who take taxi. Finally, results show a divergence in behaviours between customers in New York and Shenzhen at weekends. While customers in New York prefer to shop more locally at weekends, customers in Shenzhen care less about trip distance. We provide the GTWR calibration of the Huff model as our theoretical contribution. GTWR extends the Huff model to two dimensions (time and space), so as to analyse the differences of residents' travel behaviours in different time and locations. We also provide the discoveries of factors affecting urban travel behaviours (wealth and employment) as practical contributions that may help optimise urban transportation design. In particular, the sensitivity of residents to the attraction of shopping areas has a significant positive linear relationship with the housing price and a significant negative linear relationship with the residents' length of spare time. [ABSTRACT FROM AUTHOR]

Understanding population distribution has excellent applications for planning and provision of municipal services. This study aims to explore the space-time structure of population distribution with area-level mobile phone data. We discuss a kind of Bayesian hierarchical models, fitted by Markov chain Monte Carlo simulation, that combines the overall spatial pattern and temporal trends as well as the departures from these stable components. We carry out an empirical study in Shenzhen, China, using the area-level mobile phone users in 24 hours. The results indicate that the estimation of the overall spatial pattern is not deteriorated when using a sophisticated spatio-temporal model. The temporal trend exhibits a reasonable fluctuation during the study period. Then we apply two rules to detect areas showing unstable trends of population fluctuation based on the posterior probabilities of the space-time interactions. We also include the population statistics and indices for mixed-use to explore the spatial pattern of population fluctuation. Our findings confirm that the Bayesian spatio-temporal model can enhance the understanding of the space-time variability of population distribution using mobile phone data. Further research should examine the spatial nonstationary effects of explanatory factors on mobile phone-based population fluctuation. [ABSTRACT FROM AUTHOR]

Global positioning system (GPS) data generated from taxi trips is a valuable source of information that offers an insight into travel behaviours of urban populations with high spatio-temporal resolution. However, in its raw form, GPS taxi data does not offer information on the purpose (or intended activity) of travel. In this context, to enhance the utility of taxi GPS data sets, we propose a two-layer framework to identify the related activities of each taxi trip automatically and estimate the return trips and successive activities after the trip, by using geographic point-of-interest (POI) data and a combination of spatio-temporal clustering, Bayesian inference and Monte Carlo simulation. Two million taxi trips in New York, the United States of America, and ten million taxi trips in Shenzhen, China, are used as inputs for the two-layer framework. To validate each layer of the framework, we collect 6,003 trip diaries in New York and 712 questionnaire surveys in Shenzhen. The results show that the first layer of the framework performs better than comparable methods published in the literature, while the second layer has high accuracy when inferring return trips. [ABSTRACT FROM AUTHOR]

Public transit, especially urban rail systems, plays a vital role in shaping commuting patterns. Compared with census data and survey data, large-scale and real-time big data can track the impacts of urban policy implementations at finer spatial and temporal scales. Therefore, this study proposed a multi-level analytical framework using transit smartcard data to examine urban commuting dynamics in response to rail transit upgrades. The study area was Shenzhen, one of the most highly urbanized and densely populated cities in China, which provides the opportunity to examine the effects of rail transit upgrades on commuting patterns in a rapidly developing urban context. Changes in commuting patterns were examined at three levels: city, region, and individual. At the city level, we considered the average commuting time, commuting speed, and commuting distance across the whole city. At the region level, we analyzed changes in the job accessibility of residential zones. Finally, this study evaluated the potential effects of rail transit upgrades on the jobs-housing relationship at the individual level. Difference-in-difference models were used for causal inference between rail transit upgrades and commuting patterns. In the very short term, the opening of new rail transit lines resulted in no significant changes in overall commuting patterns across the whole city; however, two effects of rail transit upgrades on commuting patterns were identified. First, rail transit upgrades enhanced regional connectivity between residential zones and employment centers, thus improving job accessibility. Second, rail transit improvement increased the commuting distances of individuals and contributed to the separation of workplaces and residences. This study provides meaningful insights into the effects of rail transit upgrades on commuting patterns. [ABSTRACT FROM AUTHOR]

The process of gentrification changes the composition and character of urban neighbourhoods in cities worldwide. Amenities such as art galleries, designer boutiques, fine dining, and specialty cafés interact with most gentrification processes and could act as indicators for measuring gentrification. Previous literature has explored the role of amenities in gentrification, and some have found distinctive amenity landscapes in different spatial contexts. However, there is a lack of a more generalized approach for identifying gentrification-related amenities across different regions. This study proposed a machine learning-based framework to identify localized gentrification amenities. Specifically, amenities were represented by Points of Interest (POIs) and matched to the North American Industry Classification System (NAICS), an industry classification system commonly used in amenity-related studies. Bridging POI categories and the NAICS hierarchy enables a dialog between big data and conventional statistical data. Then, given typical gentrification neighborhoods in an area, featured amenities can be identified via a supervised gradient boosting method. The framework was applied to Shenzhen, a major Chinese city. Results showed that Shenzhen has a distinct amenity landscape in its gentrified neighborhoods; for example, bubble tea beverage shops were recognized as a dominant amenity, as opposed to the cafés in many Western cities, as well as financial institutions, digital electronics, and car-related amenities. The proposed machine learning-based framework not only provides a generalized approach to identifying gentrification-related amenities in different regions, but also enables dynamic and fine-grained tracking of gentrification on the basis of big data. • Using POIs as proxy for amenities opens a possibility for big data in gentrification studies. • A machine-learning framework for identifying localized amenities. • Identified distinctive amenity landscapes in the study area, which may contribute to the gentrification literature. [ABSTRACT FROM AUTHOR]

Residential-employment mixed use has often been proposed to promote jobs–housing balance and to increase employment self-containment of residents, thereby reducing commuting distance and motorized travel. However, the relationship between residential-employment mixed use and work trips has few consensuses. Whether residential-employment mixed use is correlated with employment self-containment of residents remains to be explored. In this study, the relationships between residential-employment mixed use and employment self-containment of residents are examined in the industrial and commercial areas in Shenzhen, China. The relationships between commercial–residential and industrial–residential mixes and employment self-containment of residents are examined using mobile phone data. Results indicate that residential-employment mixed use is positively correlated with employment self-containment of residents in industrial–residential mix areas in the suburbs, but is not correlated in commercial–residential mix areas in the central city. Residential-employment mixed use alone does not achieve high employment self-containment of residents, which also depends on other factors such as its economic sector, location, and housing prices. • Residential-employment mixed use is not a sufficient condition in commercial–residential mix areas. • Other factors can also influence the correlation between residential-employment mixed use and jobs-housing balance. • This study will enhance our understanding of residential-employment mixed use and jobs-housing balance. [ABSTRACT FROM AUTHOR]

The existing studies concerning the influence of weather on public transport have mainly focused on the impacts of average weather conditions on the aggregate ridership of public transit. Not much research has examined these impacts at disaggregate levels. This study aims to fill this gap by accounting for intra-day variations in weather as well as public transport ridership and investigating the effect of weather on the travel behavior of individual public transit users. We have collected smart card data for public transit and meteorological records from Shenzhen, China for the entire month of September 2014. The data allow us to establish association between the system-wide public transit ridership and weather condition on not only daily, but also hourly basis and for each metro station. In addition, with the detailed trip records of individual card holders, the travel pattern by public transit are constructed for card holders and this pattern is linked to the weather conditions he/she has experienced. Multivariate modeling approach is applied to analyze the influence of weather on public transit ridership and the travel behavior of regular transit users. Results show that some weather elements have more influence than others on public transportation. Metro stations located in urban areas are more vulnerable to outdoor weather in regard to ridership. Regular transit users are found to be rather resilient to changes in weather conditions. Findings contribute to a more in-depth understanding of the relationship between everyday weather and public transit travels and also provide valuable information for short-term scheduling in transit management. [ABSTRACT FROM AUTHOR]

• First study using mobile phone data to examine economic disparities in PM2.5 exposures in China. • People living in areas with higher residential property prices are more exposed to PM2.5 pollution. • Hourly PM2.5 concentrations were estimated using machine learning and remote sensing data. • Exposure disparity was examined at the short-term, mid-term and long-term scales. Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales. This work aims to examine the associations between individual- and neighbourhood-level economic statuses and individual exposure to PM2.5 across multi-temporal scales. The study population included 742,220 mobile phone users on a weekday in Shenzhen, China. A geo-informed backward propagation neural network model was developed to estimate hourly PM2.5 concentrations by the use of remote sensing and geospatial big data, which were then combined with individual trajectories to estimate individual total exposure during weekdays at multi-temporal scales. Coupling the estimated PM2.5 exposure with housing price, we examined the associations between individual- and neighbourhood-level economic statuses and individual exposure using linear regression and two-level hierarchical linear models. Furthermore, we performed five sensitivity analyses to test the robustness of the two-level effects. We found positive associations between individual- and neighbourhood-level economic statuses and individual PM2.5 exposure at a daytime, daily, weekly, monthly, seasonal or annual scale. Findings on the effects of the two-level economic statuses were generally robust in the five sensitivity analyses. In particular, despite the insignificant effects observed in three of newly selected time periods in the sensitivity analysis, individual- and neighbourhood-level economic statuses were still positively associated with individual total exposure during each of other newly selected periods (including three other seasons). There are statistically positive associations of individual PM2.5 exposure with individual- and neighbourhood-level economic statuses. That is, people living in areas with higher residential property prices are more exposed to PM2.5 pollution. Findings emphasize the need for public health intervention and urban planning initiatives targeting socio-economic disparity in ambient air pollution exposure, thus alleviating health disparities across socioeconomic groups. [ABSTRACT FROM AUTHOR]