| Author: | Yu, Zidong |
| Title: | The agglomeration economies in the megaregional context : a spatial and functional perspective using geospatial analytics |
| Advisors: | Liu, Xintao (LSGI) Xu, Yang (LSGI) |
| Degree: | Ph.D. |
| Year: | 2024 |
| Subject: | Urban economics Regional economics Space in economics Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Land Surveying and Geo-Informatics |
| Pages: | x, 98 pages : color illustrations |
| Language: | English |
| Abstract: | The population dynamics in cities have varied significantly in recent years. A large number of cities have experienced a significant increase in population due to urban migration and natural growth. Robust industrial agglomerations exhibit accelerated growth and increased benefits for economic activities and firms. The concept of urban agglomeration economies describing the clustering phenomenon within and cross cities related to economic prosperity, has gained attention in geography studies. As urbanization expands to densely populated megacity regions with abundant services and resources, there is a growing need for comprehensive research emphasizing agglomeration economies in megaregional contexts. The current reliance on economic census and surveys in literature has posed different challenges to capturing and evaluating economic activities, such as costly and undercounting. Therefore, a novel strategy using geospatial analytics should be introduced to bridge the gap between these concepts and depict agglomeration economies in megacity regions. This thesis presents an innovative data-driven strategy for studying industrial agglomeration economies within megaregions, aiming to achieve four step-by-step objectives: (1) examining urban agglomeration and its correlation with urban environments; (2) exploring the spatial and functional organization of agglomerations within megaregion; (3) analyzing the geographic disparities in agglomeration economies; and (4) delineating the spatial-functional network of regional industrial agglomerations. The first objective involves a thorough reassessment of prevalent official census data, establishing connections between urban environments and socio-demographics within cities through initial geospatial analytical methodologies. Following this, the second objective shifts the research focus towards leveraging advanced geospatial data sources and analytics to investigate the spatial and functional patterns, encompassing organization, disparity, and network, manifested by agglomeration economies within a megaregional context. The methodology integrates advanced geospatial analytics, including geospatial data science, machine learning, and network analysis, to quantify spatial and functional patterns of agglomeration economies. In the second case study, spatial extent is measured using kernel density functions on points of interest (POIs) data, while natural language processing (NLP) is employed for semantic-based information retrieval to label functional characteristics. In the third case study, thematic topics related to the local industrial sector are identified through topic modeling, and industrial agglomerations are clustered based on topic importance, illustrating spatial and functional variations. The final case study employs network analysis to describe the megaregional agglomeration network, utilizing bipartite network projection and community detection to reveal groups of closely connected agglomerations by their industrial functions. This thesis furnishes crucial empirical evidence, presenting alternative viewpoints on the geographic and functional intricacies of industrial agglomeration economies within megaregions. Through multiple case studies framed within a geospatial lens, the research provides robust support, showcasing the potent capabilities of utilizing geospatial data sources and analytics. The findings contribute significantly to the understanding of economic geography, regional studies, and urban studies, offering valuable insights and addressing fundamental questions in these domains. |
| Rights: | All rights reserved |
| Access: | open access |
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