The geographic disparity of agglomeration economies: Evidence from industrial activities in China's emerging greater bay area

Industrial agglomeration is a concentration phenomenon of economic activities in cities. In recent years, the geographic and functional structures of industries are constantly changing due to global industrialization and regional urbanization. Thus far, a scarcity of research has investigated spatial-functional organizations of sectoral industries in urbanized megaregions. By using points of interest (POIs) data collected in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), this paper seeks to portray the spatial extent of industrial agglomerations and to label their functional characteristics. A kernel density function is first applied to measure the spatial extent of industrial agglomerations. Next, we explore the industrial functions by implementing a semantic-based information retrieval to label the functional characteristics of industrial agglomerations via words that are tokenized from POI registered names. The empirical results suggest that the concentrations of industrial activities are strongly heterogeneous across different economic sectors, revealing that agglomerations across the GBA can provide a variety types of industrial products and services. Concerning manufacturing industries, the present analysis further confirms the existence of both specialization and diversification agglomerations along with far distinct spatial characteristics. This research supplements empirical evidence and provides novel insights into the geographical and functional organizations of economic activities regarding one of the largest urban megaregions in the world. The implications that are related to megaregional economic collaboration and development are discussed.

Characterizing the spatial-functional network of regional industrial agglomerations: A data-driven case study in China's greater bay area

Heatwave events (HWs) have become more frequent and intense due to climate change and urbanization, posing risks to human health, yet the influence of rapid temperature fluctuation on human adaptation during these events remains insufficiently explored. This study identified HWs and estimated population exposure across three major urban agglomerations in eastern China based on the Excess Heat Factor (EHF), which accounts for the superposed effect of extreme heat and human adaptability in response to rapid temperature fluctuations. From 1961 to 2022, the Beijing–Tianjin–Hebei (BTH) region and Guangdong–Hong Kong–Macao Greater Bay Area (GBA) suffered from moderate HWs with higher frequency and shorter duration, while HWs in the Yangtze River Delta (YRD) region were characterized by lower frequency and longer duration. Compared to EHF, the conventional approach that uses single temperature criteria to identify HWs tends to underestimate their intensity without accounting for the effects of sudden temperature rises on human adaptability. Based on the downscaled ensemble of 23 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), HWs and population exposure are expected to increase across the three urban agglomerations in the near-term (2025‒2035) and mid-term (2055‒2065) future, with GBA experiencing the greatest rise in HW days. However, YRD will have the highest population exposure due to its large population. During the projected explosive growth of severe/extreme HW days, low and intermediate GHG emission scenarios (SSP1-2.6, SSP2-4.5) could potentially avoid 29%/45%, 28%/42% and 44%/96% of the increase in population exposure to these events across the BTH, YRD, and GBA, respectively, in the mid-term future, compared to high GHG emission scenarios (SSP3-7.0, SSP5-8.5). Further analysis reveals that the expected increase in HWs in GBA and BTH is attributable to the combined effect of intensified temperature variability and warming, while the changes in HWs in YRD are primarily driven by rising temperatures. The results emphasize the urgent need to develop resilience to HWs in a changing climate.

Comparing hillside urbanizations of Beijing-Tianjin-Hebei, Yangtze River Delta and Guangdong–Hong Kong–Macau greater Bay area urban agglomerations in China

Industrial agglomeration, as a typical aspect of industrial structures, significantly influences policy development, economic growth, and regional employment. Due to the collection limitations of gross domestic product (GDP) data, the traditional assessment of industrial agglomeration usually focused on a specific field or region. To better measure industrial agglomeration, we need a new proxy to estimate GDP data for different industries. Currently, nighttime light (NTL) remote sensing data are widely used to estimate GDP at diverse scales. However, since the light intensity from each industry is mixed, NTL data are being adopted less to estimate different industries’ GDP. To address this, we selected an optimized model from the Gaussian process regression model and random forest model to combine Suomi National Polar-Orbiting Partnership—Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data and points-of-interest (POI) data, and successfully estimated the GDP of eight major industries in China for 2018 with an accuracy (R2) higher than 0.80. By employing the location quotient to measure industrial agglomeration, we found that a dominated industry had an obvious spatial heterogeneity. The central and eastern regions showed a developmental focus on industry and retail as local strengths. Conversely, many western cities emphasized construction and transportation. First-tier cities prioritized high-value industries like finance and estate, while cities rich in tourism resources aimed to enhance their lodging and catering industries. Generally, our proposed method can effectively measure the detailed industry agglomeration and can enhance future urban economic planning.

Mapping urban functional zones (UFZs) is of paramount importance in urban planning and construction. However, existing studies have predominantly focused on individual cities, which restricts their applicability to large-scale UFZ mapping. Although points of interest (POI) are commonly used to classify UFZs from remote sensing imageries, their unordered and uneven distributions hinder robust POI feature extrations. Inspired by the fact that LiDAR point clouds share these two properties, we proposed a novel POI-based method of large-scale UFZ mapping where POIs are treated as point clouds. A multimodal deep learning-based framework was adopted to extract deep features from high-spatial-resolution remote sensing images and POIs, in which a classic point cloud neural network was employed for POI feature extraction. Simultaneously, to fuse the heterogeneous features and mitigate two issues associated with POIs, the overlook of POI spatial relationships and bias of POIs among UFZs, a multi-head self-attention (MSA) layer with prompts was introduced, followed by a classification module for UFZ prediction. The proposed method was validated in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA), China, achieving an overall accuracy of 84.90% and a kappa coefficient of 82.36%. Furthermore, ablation experiments confirmed the effectiveness of the MSA layer and prompts.

Urban agglomeration is an advanced spatial form of integrating cities, resulting from the global urbanization of recent decades. Understanding spatiotemporal patterns and evolution is of great importance for improving urban agglomeration management. This study used continuous time-series NTL data from 2000 to 2018 combined with land-use images to investigate the spatiotemporal patterns of urbanization in the three most developed urban agglomerations in China over the past two decades: the Beijing–Tianjin–Hebei urban agglomeration (BTH), the Yangtze River Delta urban agglomeration (YRD), and the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The NTL intensity indexes, dynamic thresholds, extracted urban areas, and landscape metrics were synthetically used to facilitate the analysis. This study found that the urbanization process in the study areas could be divided into three stages: rapid urbanization in core cities from 2000 to 2010, a fluctuating urbanization process in both core cities and surrounding cities from 2010 to 2015, and stable urbanization, mainly in surrounding cities with a medium size after 2015. Meanwhile, the urbanization level of GBA was higher than that of YRD and BTH. However, with the acceleration of urban development in YRD, the gap in the urbanization level between GBA and YRD narrowed significantly in the third stage. In addition, this study confirmed that the scattered, medium-sized cities in YRD and GBA were more developed than those in BTH. This study showed that continuous NTL data could be effectively applied to monitor the urbanization patterns of urban agglomerations.

Purpose - The purpose of this paper is to study the impact of FDI and industrial agglomeration in China on Korea-China trade. In addition, this paper analyzes the interactional relationship between exports, FDI, and manufacturing agglomeration in China. Design/Methodology/Approach - This paper conducts an empirical analysis using panel data on exports from China’s 17 provinces to South Korea from 2010-2018. This paper establishes a research model based on the gravity model and the moderating effect model. The research model was analyzed using the two-way fixed effects of controlled provinces and years. This paper uses panel data of 31 provinces in China from 2008-2018 to establish a simultaneous equations model (SEM) for the analysis of the relationship between exports, FDI, and manufacturing agglomeration. Findings - The empirical research shows that FDI is positive for exports to Korea, and the interaction between FDI and manufacturing agglomeration is positive, indicating that manufacturing agglomeration plays a positive moderating effect in the relationship between FDI and exports to Korea. In China, exports and manufacturing agglomeration can promote FDI, exports and FDI can promote manufacturing agglomeration, and FDI can promote exports, but manufacturing agglomeration can not directly promote exports. Research Implications - The research results show that FDI and industrial agglomeration in China have a significant impact on Korea-China trade. Based on the results, this study provides a new perspective on trade policy and economic policy formulation for the two governments.

Analysis of the spatial patterns and dynamics of tourism services and facilities is crucial for tourism and land use planning. However, most studies in the spatial analysis of tourism rely on the city- or regional-level data; limited research has used POI (point of interest) data to accurately uncover the spatial distribution of tourism, especially its interactive and coupling relationship with the local economy and population. Based on POI data, this paper, therefore, investigates the spatial patterns and driving forces of tourism services distribution and how tourism space is coupled with the local economy and population in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA) of China. The results show the following: (1) Different categories of tourism services (catering, shopping, scenic spots, leisure, and sports) exhibit diverse spatial patterns and agglomerations, but they tend to align with the variables of economic level and population in a grid of 1 km2. (2) The spatial econometric models further reveal that population density, transportation, and hospitality facilities are positively correlated with the spatial distribution of tourism services, but GDP in a grid of 1 km2 shows a weak negative correlation with the POI of tourism services, which may be attributed to the incoordination between GDP and tourism in some areas. (3) The analysis of coupling degree further identifies the areas where tourism services have good interaction/coupling with the local GDP and population density, such that these areas can be viewed as hotspots suitable for tourism promotion. This paper thus offers meaningful policy implications by calling for an optimization of the coupling of tourism services with local social–economic factors in the GBA.

Nowadays, our daily life constantly creates and needs to utilize tremendous amounts of datasets. Fortunately, the technologies of the internet, both in software and hardware, have the capability to transmit, store, and operate big data. With China being the most populous country in the world, developing the big data industry is, therefore, seen as an urgent task. As generating industrial agglomeration is important for forming a mature industry, this study aims to characterize the phenomenon of big data industrial agglomeration in China, and to identify the factors for developing the big data industry using spatial analysis approaches and GIS technology from a geographer’s perspective. The problems and strengths of these representative cities are discussed, from which the solutions and the possible directions for the future are also provided. The findings argued that China is still at the primary stage of the development in the big data industry. Only several cities had the presence of a strong agglomeration, but the intercity space spillover was weak. However, comparing the changes in industry distribution, the trend of agglomeration have appeared, and the benefits of industrial agglomeration have also worked. The principal factors of the big data industry and its agglomeration include the support of government and the outstanding higher education agglomeration. In addition, it was also noted that each city has its own characteristics and potentials to attract more big data enterprises, talent, and investment.

Towards SDG 11: Large-scale geographic and demographic characterisation of informal settlements fusing remote sensing, POI, and open geo-data

Globalization and Industry Agglomeration in China

Long-term trend of surface wind speed in the Guangdong-Hong Kong-Macau Greater Bay Area during 1980–2020: Spatiotemporal variation and urbanization effect

Pathways to a more efficient and cleaner energy system in Guangdong-Hong Kong-Macao Greater Bay Area: A system-based simulation during 2015-2035

Accurately extracting semantic features of urban functional zones is crucial for understanding urban functional zone types and urban functional spatial structures. Points of interest provide comprehensive information for extracting the semantic features of urban functional zones. Many researchers have used topic models of natural language processing to extract the semantic features of urban functional zones from points of interest, but topic models cannot consider the spatial features of points of interest, which leads to the extracted semantic features of urban functional zones being incomplete. To consider the spatial features of points of interest when extracting semantic features of urban functional zones, this paper improves the Latent Dirichlet Allocation topic model and proposes a spatial semantic feature extraction method for urban functional zones based on points of interest. In the proposed method, an assumption (that points of interest belonging to the same semantic feature are spatially correlated) is introduced into the generation process of urban functional zones, and then, Gibbs sampling is combined to carry out the parameter inference process. We apply the proposed method to a simulated dataset and the point of interest dataset for Chaoyang District, Beijing, and compare the semantic features extracted by the proposed method with those extracted by the Latent Dirichlet Allocation. The results show that the proposed method sufficiently considers the spatial features of points of interest and has a higher capability of extracting the semantic features of urban functional zones than the Latent Dirichlet Allocation.