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Large-scale assessment of window views is demanded for precise housing valuation and quantified evidence for improving the built environment, especially in high-rise, high-density cities. However, the absence of a semantic segmentation dataset of window views forbids an accurate pixel-level assessment. This paper presents a City Information Model (CIM)-generated Window View (CIM-WV) dataset comprising 2,000 annotated images collected in the high-rise, high-density urban areas of Hong Kong. The CIM-WV includes seven semantic labels, i.e., building, sky, vegetation, road, waterbody, vehicle, and terrain. Experimental results of training a well-known deep learning (DL) model, DeepLab V3+ , on CIM-WV, achieved a high performance (per-class Intersection over Union (IoU) ≥ 86.23%) on segmenting major landscape elements, i.e., building, sky, vegetation, and waterbody, and consistently outperformed the transfer learning on a popular real-world street view dataset, Cityscapes. The DeepLab V3+ model trained on CIM-WV was robust (mIoU ≥ 72.09%) in Hong Kong Island and Kowloon Peninsula, and enhanced the semantic segmentation accuracy of real-world and Google Earth CIM-generated window view images. The contribution of this paper is three-fold. CIM-WV is the first public CIM-generated photorealistic window view dataset with rich semantics. Secondly, comparative analysis shows a more accurate window view assessment using DL from CIM-WV than deep transfer learning from ground-level views. Last, for urban researchers and practitioners, our publicly accessible DL models trained on CIM-WV enable novel multi-source window view-based urban applications including precise real estate valuation, improvement of built environment, and window view-related urban analytics. [ABSTRACT FROM AUTHOR]

Rapid urbanisation has generated numerous environmental consequences, particularly regarding open space fragmentation. Open space fragmentation is the transformation of open space from a state of homogeneity, integration, and continuity to a state of heterogeneity, division, and incoherence. Nevertheless, one main obstacle to understanding this issue is how to address the spatial heterogeneity of the impact of urbanisation on open space fragmentation. Thus, this paper provides a comprehensive framework for the mechanistic associations between open space fragmentation and urbanisation in Hong Kong's built-up area. The results illustrate that both open space fragmentation index and urbanisation index values are noticeably higher in dense urban areas. Land urbanisation, represented by the percentage of construction land in total land, has the highest explanatory power for spatial differentiation in open space fragmentation, followed by social and population urbanisation factors. Furthermore, the relational interrelations of open space fragmentation drivers are the bivariate and nonlinear enhancement interactions. Social urbanisation and land urbanisation have the strongest bivariate enhancement interaction for the use fragmentation form and the largest nonlinear enhancement interaction for the internal fragmentation form. Based on initial urban planning, open space fragmentation is an adaptation outcome of population, land, and social urbanisation factors, and this self-organisation phenomenon has been further emphasised in the historical process. These insights significantly enrich our understanding of how urbanisation affects open space fragmentation and provide valuable guidance for better open space strategies. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of Guangdong-Hong Kong-Macao Greater Bay Area, the passenger flow between cities in the region has increased rapidly, and the requirements for transportation efficiency of intercity railways are getting higher and higher. Moving block and automatic turn-back technology is an important development direction of signal system to improve railway transportation efficiency, which can effectively shorten the tracking interval between trains and the turn-back interval of turn-back stations, thus improving the transportation capacity and efficiency of lines. At present, moving block technology and automatic turn-back have not been applied in intercity railway. According to the technical status of moving block and automatic turn-back, the feasibility of function optimization of existing intercity CTCS2+ATO train control system in Pearl River Delta is deeply analyzed in this paper, and the overall framework of realizing moving block and automatic turn-back in the intercity CTCS2+ATO train control system in Pearl River Delta is put forward. Finally, the working principle and realization of CCS block mode switching, driving permit calculation, automatic turn-back function optimization and turn-back process are introduced emphatically. The results show that it is feasible to realize moving block and automatic turn-back under the condition that the existing intercity CTCS2+ATO train control system in the Pearl River Delta is slightly changed, which is of great significance to improve the transportation efficiency of intercity railway lines in the Pearl River Delta. [ABSTRACT FROM AUTHOR]

Problem definition: A significant percentage of online consumers place consecutive orders within a short duration. To reduce the total order arrangement cost, an online retailer may consolidate consecutive orders from the same consumer. We investigate how long the retailer should hold the consumer's orders before sending them to a third-party logistics provider (3PL) for processing. In this order-holding problem, we optimize the holding time to balance the total order arrangement cost and the potential delay in delivery. Methodology/results: We model the order-holding problem as a Markov decision process. We show that the optimal order-holding decisions follow a threshold-type policy that is straightforward to implement: Hold any pending orders if the holding time is within a threshold or send them to the 3PL otherwise. Whenever the consumer places a new order, the holding time is reset, and the threshold is updated based on a cumulative set of the past consecutive orders in the consumer's shopping journey. Using a consumer's sequential decision model, we personalize the threshold by finding its closed-form expression in the consumer's order features. We determine the model's coefficients and evaluate the threshold-type policy using the data of the 2020 MSOM Data Driven Research Challenge. Extensive numerical experiments suggest that the personalized threshold-type policy outperforms two commonly used benchmarks by having fewer order arrangements or shorter holding times. Furthermore, personalizing the order-holding decisions is significantly more valuable for "enterprise" customers. Managerial implications: Our research suggests a higher threshold for consumers who are more likely to place consecutive orders within a short duration. The consumers' demographic information has a significant effect on the threshold. Specifically, the threshold is higher for "plus" consumers, female consumers, and consumers in the age group of 16–25 years. The threshold for tier 1 cities is lower than that for tier 2 to tier 4 cities but higher than that for tier 5 cities. History: This paper has been accepted as part of the 2020 MSOM Data Driven Challenge. Funding: This work was supported by the National Natural Science Foundation of China [Grants 71931009, 72201237, and 72231009], the Research Grants Council of Hong Kong [Grants 15501920 and 15501221], the Singapore Ministry of Education Academic Research Fund [Tier 1, Grant RG17/21; Tier 2, Grant MOE2019-T2-1-045], the Association of South-East Asian Nations Business Research Initiative Grant [Grant G17C20421], and the Neptune Orient Lines [Fellowship NOL21RP04]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2023.1201. [ABSTRACT FROM AUTHOR]

Urban morphology and spatial forms shape a city's character and user behavior, often embracing a particular type of people and unwelcoming other kinds of gender. City design policies and legislation have favored men since its beginning, affecting overall urban planning and design. While the world is delving into new techniques and methods to empower women, constraints in public space activities remain prevalent. This paper explores the scope of urban design strategies to catalyze women's social and physical inclusion in these spaces. It presents a systematic approach to identifying women's social roles during the use of public spaces and introduces the 'GenUrban: Shaping Cities for All Genders' framework to guide researchers and designers alike promote inclusive urban design and planning. The investigation, in the central area of Hong Kong, focuses on the cultural landscape created by the women-centric 'Foreign Domestic Helpers' community. A desktop literature review, direct observation of public spaces, and in-depth interviews with a focus group of women in the city's heart established and mapped the marginalized users' collective viewpoint and their appropriation of space. The framework can be used by professionals to transform urban spaces through a gender lens to improve the overall quality of public spaces and make them more inclusive and accessible. The paper generates gender-sensitive urban design principles that can be translated for planners to use for various user groups and contexts. When women become key drivers in activating public space, they are given back their right to the city. • Women's traveling patterns are frequent due to their caregiving responsibilities. • The unique cultural landscape of a city creates gender-centered activities. • Integrating gender-specific features ensures urban spaces cater to women's needs. • Reliable public transport is vital for women's mobility and engagement in city life. • A city catering to women tends to accommodate those they care for. [ABSTRACT FROM AUTHOR]

In recent years, promoting regional synergistic development has become the shared objective in city-regions during the new stages of development. This paper aims to uncover the dynamics of synergistic development in urban agglomerations by constructing an index measurement system based on four dimensions: industrial synergy, innovation synergy, infrastructure synergy, and institutional synergy. By comprehensively employing theories and methods such as "space of flow" big data analysis, network analysis, and interlocking network models, this study examines patterns and capacities of synergistic development in the Guangdong-Hong Kong-Macao Greater Bay Area. The main instances of industrial cooperation between cities are observed in Hong Kong-Shenzhen and Guangzhou-Shenzhen; capital flows primarily concentrate in Guangzhou-Shenzhen and Hong Kong-Shenzhen. Research paper and patent collaborations between cities are predominantly concentrated in Hong Kong-Shenzhen; joint laboratories are mainly established in Hong Kong-Macao and Hong Kong-Guangzhou; inter-city co-construction of higher education institutions is mainly focused on Guangzhou-Foshan and Shenzhen-Hong Kong. Infrastructure synergistic development performs best in Guangzhou-Shenzhen, Guangzhou-Foshan, and Shenzhen-Dongguan. Cross-border collaboration benefits from institutional advantages held by Hong Kong and Macao. Urban interactions with outstanding performance include Guangzhou-Shenzhen, Hong Kong-Shenzhen, Guangzhou-Foshan, Shenzhen-Dongguan. Additionally, increasing interaction trends can be observed between Guangzhou-Dongguan as well as Shenzhen-Zhuhai/Shenzen-Foshan partnerships. Understanding the typical characteristics and potential trends of synergetic development within the GBA holds significant practical value for establishing a first-class bay area along with a world-class urban agglomeration. • Concept and connotation of synergistic development in urban agglomeration area was examined. • Indicators and methods for evaluation of synergistic development in the GBA were conducted. • Patterns and capacities of synergistic development at urban scale in the GBA were explained. • Urban interaction with highest synergistic development level is Guangzhou-Shenzhen. [ABSTRACT FROM AUTHOR]

Many intervention strategies, such as patient isolation and contact tracking, had been implemented in different countries to slow down and control the spread because of the enormous threats and losses caused by COVID-19. Since the contact relationships of millions of people within cities change over time, it is difficult to accurately predict the dynamics of large-scale outbreaks and evaluate the impact of the contact tracking and isolation strategy based on individual contact history on epidemic transmission. Here, we propose a non-markov spreading model based on individual contact dynamic network, to simulate the dynamic contact processes in a city with millions of people. In this model, the historical contact population of each infected person can be backtracked and tracked. Our model can accurately describe the COVID-19 epidemic in Wuhan and Hong Kong. We assess the impact of four agent-based epidemic intervention strategies: travel control, contact tracking and isolation, vaccination, and regular nucleic acid testing to all residents on the epidemic evolution and economic losses. We find that for the original SARS-CoV-2 virus, a strict travel control strategy is effective in both suppressing the spread of COVID-19 and minimizing economic losses. For the Omicron variant (BA.2) with stronger infectious capacity, a relatively loose travel control and an appropriate combination of the other three strategies can effectively control the epidemic outbreak while minimize economic losses. This paper provides an efficient framework for assessing the combination of different agent-based strategies by large-scale simulations in the case of unknown historical contact information of large populations, and the studies on different combinations of control strategies can provide theoretical guidance for future prevention and control. • We propose a non-markov spreading model based on individual contact dynamic network. • Our model can accurately describe the COVID-19 epidemic in Wuhan and Hong Kong. • We assess the impact of four agent-based epidemic intervention strategies. • A strict travel control strategy may lead to relatively high economic losses. • Tracking strategy can control the epidemic outbreak while minimize economic losses. [ABSTRACT FROM AUTHOR]

Local governments face urgent challenges from sea-level rise (SLR), impacting coastal urban areas significantly. Despite a shared commitment to adapting to climate change and SLR-induced hazards, strategies for SLR adaptation vary significantly between cities, reflecting a spectrum of political, socio-economic, and cultural contexts. This paper examines potential SLR adaptation strategies in Shenzhen and Hong Kong, cities with different political and socio-economic frameworks, to understand the effectiveness of varied local adaptation pathways to meet adaptation objectives. Through an analysis of shoreline adaptation infrastructures, including natural shelters and hard defense structures, alongside historical coastal reclamation data from 1985 to 2020, we identify the most cost-effective adaptation pathway. Specifically, our cost-benefit analysis reveals that Shenzhen's "Hybrid management" pathway offers superior economic efficiency, increasing benefits by up to 63% for only a 7%–16% rise in protection costs. Conversely, Hong Kong's "Relative-risk Reduction" pathway shows less timely adaptation actions, indicating a need for more dynamic approaches. The findings highlight the pitfalls of overly rigid SLR adaptation strategies and the necessity for flexible, transition-capable adaptation pathways to better manage future uncertainties. Lessons learned from this study will enable refinement of local SLR adaptation pathway approaches and could significantly improve decision-making under uncertainty in other coastal cities based on investment perspective. • Integrate coastal structures and reclamation to explore SLR adaptation pathways. • Limited investments constrains the efficacy of nature-based adaptation strategies. • Prioritise adaptation options is critical to address uncertainty in SLR management. [ABSTRACT FROM AUTHOR]

Equity and inclusion continue to pose significant challenges for many cities. Disparities in access to resources and facilities among the residents of these areas are often influenced by factors such as geographical location and income level, among others. Therefore, creating a quantitative basis to detect spatial and socio-economic inequities in residents' access to vital infrastructures is crucial for advancing smart cities. This paper introduces a novel measure of Spatial Inequity that is based on 1) computing building-level scores for each building in terms of access to facilities, and 2) comparing the distribution of the scores of these facilities using the Gini coefficient. The proposed building-level scores will enable the identification of GIS hotspots at a high resolution. Then, the Gini-based measure will facilitate a comparison of the distribution of the scores, and determine if they are evenly distributed and equitably accessible. The Gini coefficient was highest (0.89) for waste management and elderly care, and lowest (0.56) for hospitals, reflecting varying levels of inequality. This study applies this Spatial Ineqity methodological framework to Hong Kong, a representative of Highly Dense Cities. The framework benchmarks urban infrastructure, identifies hotspots, and selects interventions for equitable urban development. • Novel approach identifies spatial inequities via building-level scores & Gini coefficient. • Precise hotspot identification enhances targeted interventions in dense urban areas. • Applied methodology in Hong Kong offers insights into urban development complexities. • Gini coefficient unveils varying degrees of inequality across essential services. • Geospatial analysis & quantitative measures support equitable urban resource allocation. [ABSTRACT FROM AUTHOR]

Estimating city–scale emissions using gridded inventories lacks direct, precise measurements, resulting in significant uncertainty. A Kalman filter integrates diverse, uncertain information sources to deliver a reliable, accurate estimate of the true system state. By leveraging multiple gridded inventories and a Kalman filter fusion method, we developed an optimal city–scale (3 km) FFCO2 emission product that incorporates quantified uncertainties and connects global–regional–city scales. Our findings reveal the following: (1) Kalman fusion post–reconstruction reduces estimate uncertainties for 2000–2014 and 2015–2021 to ±9.77% and ±11.39%, respectively, outperforming other inventories and improving accuracy to 73% compared to ODIAC and EDGAR (57%, 65%). (2) Long–term trends in the Greater Bay Area (GBA) show an upward trajectory, with a 2.8% rise during the global financial crisis and a −0.19% decline during the COVID-19 pandemic. Spatial analysis uncovers a "core–subcore–periphery" emission pattern. (3) The core city GZ consistently contributes the largest emissions, followed by DG as the second–largest emitter, and HK as the seventh–highest emitter. Factors influencing the center–shift of the pattern include the urban form of cities, population migration, GDP contribution, but not electricity consumption. The reconstructed method and product offer a reliable solution for the lack of directly observed emissions, enhancing decision–making accuracy for policymakers. [ABSTRACT FROM AUTHOR]

When considering the economic growth of the Greater Bay Area, digital transformation stands out as a crucial catalyst. Given its significance, it is imperative to delve into both the theoretical and empirical aspects of how digital transformation affects the region's position in the global value chain. Theoretical hypotheses are put forward regarding the impact and mechanisms of digital transformation on the global value chain position of the Greater Bay Area based on a global value chain perspective. A three-tier nested input–output table that incorporates the Greater Bay Area is constructed, and trade decomposition and global value chain position measurement models specific to the area are developed for analyzing its current state in terms of global value chain position. Empirical testing was conducted to examine how digital transformation impacts this position. Digital transformation will significantly enhance the position of the Greater Bay Area in the division of global value chains, mainly through enhancing technological innovation transformation capabilities and value added by exports from the Greater Bay Area. Digital transformation within the Greater Bay Area has had a more substantial positive impact than the digital transformation in other provinces in China and digital transformation from foreign sources. In terms of city heterogeneity, it is evident that the global value chain from Hong Kong and Macao has experienced a more significant impact from digital transformation compared to cities in the Pearl River Delta. Therefore, the Greater Bay Area should increase the use of both digital transformation and differentiated use of digital transformation. PRD cities should actively learn from Hong Kong and Macao's forms of digital construction and promote the Greater Bay Area's global value chain status. [ABSTRACT FROM AUTHOR]

In the context of the digital information era, the impact of "The Internet Plus," "Big Data," and other technologies on urban social development has been far beyond any preceding era, under the influence of information technology, urban agglomeration space exhibits a new layout. Based on the search engine data of eleven cities in the Guangdong-Hong Kong-Macao Greater Bay Area from 2012 to 2021, this research constructs the inter-city information network strength linkage matrix to examine the evolution characteristics of city network structure and its driving causes. The results reveal that (1) the overall information linkage strength exhibits a pattern of steadily growing the radiating effect from the leading cities of Guangdong, Shenzhen, and Hong Kong to the surrounding cities, and a closer and more balanced information linkage network is gradually built. (2) Guangzhou-Shenzhen-Hong Kong-Guangdong-Hong Kong-Macao Greater Bay Area information linkage absolute control advantage, four cities Foshan, Dongguan, Zhuhai, Macao regional hub position steadily highlighted. The entire information connection network of the urban agglomerations tends to be flat and polycentric at the same time. (3) The regional core-edge hierarchy is well established, with the four cities of Guangzhou, Dongguan, Shenzhen, and Hong Kong creating a northwest-southeast orientation. The core metropolis regions of Guangdong, Hong Kong, and Macao in the Greater Bay Area increasingly exert a radiation spreading effect to the northeast and southwest. (4) The urban economy, transportation distance, and information infrastructure have substantial effects on the information connection intensity network of urban clusters. [ABSTRACT FROM AUTHOR]

• A method based on Neural Network is proposed for provincial-level CO 2 emissions estimation. • An autoencoder is proposed to downscale the provincial-level CO 2 emissions to city-level. • A complete city-level CO 2 emissions inventory of GBA and surrounding cities is complied. • Spatial dependence and agglomeration characteristics of CO 2 emissions are analyzed. • Spatio-temporal heterogeneity of factors affecting CO 2 emissions is investigated. Significant attention has been given to the issue of CO 2 emissions worldwide, especially for China as the largest emitter. Cities, as the main carriers of China's economic development, account for up to 85 % of CO 2 emissions from energy use in the country. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) and surrounding cities play important leading roles in low-carbon development. However, compiling city-level CO 2 emissions is challenging due to the limited availability of data. In this paper, a new idea is proposed by using SSA-BP for estimating provincial-level CO 2 emissions and downscaling provincial-level CO 2 emissions to city-level CO 2 emissions by reconstructing missing values of city-level CO 2 emissions with an autoencoder. This study compiles a city-level CO 2 emissions inventory and conducts a spatio-temporal analysis of CO 2 emissions in the GBA and surrounding cities. The results show that CO 2 emissions exhibit a spatial distribution pattern with spatial dependence. Furthermore, according to the analysis of spatio-temporal heterogeneity, GDP influenced CO 2 emissions the most within the five influencing factors, and the influences of GDP, population, and energy intensity were mainly positive, while the influence of patents was mainly negative, and that of trade was both positive and negative. [ABSTRACT FROM AUTHOR]

There is considerable evidence that word reading in Chinese is predicted by morphological awareness. However, does learning to read contribute conversely to the later development of morphological awareness in Chinese? This two-year longitudinal study of Chinese children from Beijing and Hong Kong demonstrated that word reading predicted morphological awareness in Chinese. The findings from a latent growth curve analysis suggested that both the initial level and the growth rate of word reading predicted morphological awareness, with phonological awareness statistically controlled across the two Chinese-speaking societies. However, the association patterns were slightly different in the two cities. In Beijing, phonological awareness was found to partially mediate the prediction of reading growth in morphological awareness, but this mediation effect was not found in Hong Kong. Overall, these findings highlight the importance of early reading experience in the development of morphological awareness in Chinese children across different Chinese-speaking societies. [ABSTRACT FROM AUTHOR]

The aim of this study is to comprehensively evaluate the economic network patterns and their influencing factors of the Guangdong–Hong Kong–Macao Greater Bay Area (GHMGBA) in China, considering population scale, economic development level, and land-resource endowment. By employing a modified gravity model and a social-network method, we quantitatively analyzed urban agglomeration integrity indices, such as network density, edge–core structure, cohesive-subgroup index, and urban individual index (e.g., centrality degree) of this region, encompassing nine cities in Guangdong Province and two special administrative regions. The results revealed significant changes in the economic network patterns within the GHMGBA over time. Furthermore, the quadratic assignment procedure correlation analysis index was used to identify the various factors affecting the strength of the economic interaction. The findings demonstrated an annual increase in the strength of economic interaction between cities and regions within the GHMGBA over the past 20 years, along with the emergence of a polycentric economic development pattern. The results also suggest that the spatial location and level of economic development are key determinants influencing the strength of economic linkages in this area. This study supports the conclusion that deepening exchanges and cooperation among core cities, bolstering economic development in sub-core cities, and facilitating the construction of an integrated regional transportation network will expedite the process of economic integration. [ABSTRACT FROM AUTHOR]

This study investigated the spatiotemporal evolution of economic development in 23 cities across Guangdong Province, Hong Kong, and Macau from 1990 to 2022. The results indicated a general growth trend in overall economic development, with substantial fluctuations in absolute differences and a significant reduction in relative differences. The economic development appeared to be balanced both within and between cities. Spatially, the study revealed an increasing agglomeration effect, evident polarization in economic levels, and a distinct core–periphery structure, with central cities in the Greater Bay Area exerting a growing influence on their peripheries. Nevertheless, the peripheral cities, despite their economic strides, necessitate augmented policy interventions and capital influx to bridge regional chasms. The study's outcomes serve as a foundational reference for sharpening policy decisions, fostering regional amalgamation, and invigorating urban blueprinting and city-to-city synergies. This research provides an in-depth analysis of economic development trends across 23 cities in Guangdong, Hong Kong, and Macau over 32 years. For policymakers, urban planners, and business leaders, the findings present a clearer picture of the region's economic landscape. Notably, while central cities within the Greater Bay Area have become influential economic hubs, surrounding cities still face challenges. These insights suggest a need for more targeted policies and investments, especially in peripheral cities, to promote more uniform growth across the region. By understanding these dynamics, governments and businesses can make more informed decisions that foster city-to-city collaborations and develop better urban and regional strategies. [ABSTRACT FROM AUTHOR]

In old urban districts with tenement buildings, street markets address the working class's social and economic needs. They are pivotal in fostering supportive communities, in particular for low-income residents in sub-standard housing. However, their outdated overall environment impedes the delivery of adequate support to residents. Prior research on street markets has focused on those in the Americas and Europe, and has revealed the societal support values of these markets. However, studies on street markets in high-density cities like Hong Kong are lacking. This study introduces an evaluation framework to assess the impact of street markets on the development of supportive communities for Hong Kong's sub-divided apartment residents. Employing the Pei Ho Street market in Sham Shui Po as a case study, an importance-performance analysis highlights critical but underperforming factors including public facilities, activity venues, commerce, and employment. Based on these findings, the study proposes four improvement strategies for the renewal of street markets in high-density communities, which could apply to other cities with similar high-density urban environments. It concludes that street markets in Hong Kong display unique spatial characteristics shaped by low-income lifestyles in a high-density urban environment, enriching daily social life and promoting sustainable community development. [ABSTRACT FROM AUTHOR]

• Actual travel distance is a stable factor in the port-hinterland relationship. • Distance-decay effect follows the exponential model instead of a power function. • The 'islands' are commonly located in the border areas between cities. Ports are pivotal in the socioeconomic development of regions, countries, and cities as they serve as gateways for the transportation of goods between sea and land. Analyses of port hinterlands can enable port authorities and shippers to develop competitive strategies while assisting local authorities in identifying areas where inland transportation systems require improvement. In general, physical distance is a robust proxy for transportation costs. However, the role of distance in the context of port regionalization remains under debate, despite the spatial interaction model, with distance decay being commonly used to analyze the port–hinterland relationship. Doubt concerning the explanatory power of distance may act as an obstacle for port managers and local authorities in assessing the market share of ports, as well as in constructing planning agendas related to facility locations. This study examines the distance decay of port hinterlands using more than 0.18 billion points of heavy-duty truck GPS trajectory data from the Guangdong–Hong Kong–Macau Greater Bay Area. We develop an algorithm to compute heavy-duty truck flows and actual travel distance between the port and identified facility areas, and we apply the weighted least squares method to evaluate the explanatory power of six distance-decay models. Our findings reveal that the actual travel distance is a significant reduction factor in the port–hinterland relationship, while it follows the exponential model instead of a power function. This paper presents new evidence to reshape the role of distance in delimiting port hinterlands, providing port managers and local authorities with novel insights into the structure of port hinterlands in the context of port regionalization. [ABSTRACT FROM AUTHOR]

As the population and industries continue to gather in cities, such cities face acute shocks caused by various natural disasters and high pressure caused by human interference. The means of accurately assessing and improving the level of urban resilience (UR) to cope with disturbances and shocks, enhance regional resilience, and improve sustainable urban development has become a hot topic. In this study, we constructed an UR assessment framework using "social-economic-institutional-ecological-engineering" to explore the dynamic evolution of spatial and temporal patterns in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China, to reveal the main factors influencing the evolution of this spatial pattern and the strength and interaction mechanisms among such influencing factors and to dynamically simulate the spatial and temporal evolution characteristics in the next 16 years. The results show that from 2000 to 2019, the difference in UR level in the GBA is obvious. Hong Kong and Macao are obviously ahead of other cities, and the comprehensive resilience level of Guangzhou and Shenzhen cities in the mainland is developing rapidly. The difference in per capita consumption capacity, industrial structure optimization, foreign trade vitality, infrastructure support and other factors are the main causes of the spatial differentiation of UR in the GBA. In the future, the UR level of the GBA urban agglomeration will show an overall upwards trend. The three development poles of Guangzhou-Foshan, Shenzhen-Hong Kong, and Zhuhai-Macao have obvious advantages in spatial distribution. The results of the study can enrich regional UR research, and also provide theoretical references to facilitate the high-quality development of the area. [Display omitted] • We constructed a "social-economic-institutional-ecological-engineering" urban resilience assessment framework. • The paper developed an evaluation system for the urban resilience to reveal its spatial and temporal evolution patterns. • The main factors influencing the evolution of the spatial patterns of urban resilience is investigated. • Our results provide a basis for the development of comprehensive management framework for urban resilient. [ABSTRACT FROM AUTHOR]

In recent times, a new wave of scientific and technological advancements has significantly reshaped the global economic structure. This shift has redefined the role of regional innovation, particularly in its contribution to developing the Guangdong–Hong Kong–Macao Greater Bay area (GBA) into a renowned center for science, technology, and innovation. This study constructs a comprehensive evaluation system for the Regional Innovation Ecosystem (RIE). By applying the coupling coordination degree model and social network analysis, we have extensively analyzed the spatial structure and network attributes of the coupled and coordinated innovation ecosystem in the GBA from 2010 to 2019. Our findings reveal several key developments: (1) There has been a noticeable rightward shift in the kernel density curve, indicating an ongoing optimization of the overall coupling coordination level. Notably, the center of gravity for coupling coordination has progressively moved southeast. This shift has led to a reduction in the elliptical area each year, while the trend surface consistently shows a convex orientation toward the center. The most significant development is observed along the 'Guangdong–Shenzhen–Hong Kong–Macao Science and Technology Innovation Corridor', where the level of coupling coordination has become increasingly pronounced. (2) The spatial linkages within the GBA have been strengthening. There are significant spatial transaction costs in the regional innovation ecological network. In the context of the 2019 US-China trade war, the cities of Jiangmen and Zhaoqing experienced a notable decrease in connectivity with other cities, raising concerns about their potential marginalization. (3) Guangzhou, Shenzhen, and Hong Kong have emerged as core nodes within the network. The network exhibits a distinctive "core–edge" spatial structure, characterized by both robustness and vulnerability in various aspects. [ABSTRACT FROM AUTHOR]