Spatiotemporal Dynamic and Influencing Factors of Urban Innovation Space

A Case Study of Guangzhou, China

Bibliographic Data

ID19489236
AuthorsMeihong Ke (Fujian University of Technology, corresponding author), Huiran Xie (Fujian University of Technology), Xu Chen (0000-0002-8164-3088, Fujian University of Technology), Bin Cheng (0000-0001-8295-2467, Fujian University of Technology)
Year2026
Volume10
Issue5
Pages231
Publication date2026-04-28
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueUrban Science (JOURNAL)
Journal identifiersISSN: 2413-8851 • E-ISSN: 2413-8851
PublisherMDPI AG (PUBLISHER • IT)
DOI10.3390/urbansci10050231
OpenAlexW7158967056
LanguageEN
References cited32

Urban innovation spaces are crucial to stimulate innovative thinking and facilitate the integration of science, technology, and humanities. On the one hand, existing research on urban innovation spaces focuses on spatial patterns, associated networks, and spillover effects. They are limited to the macro scale and lack of innovation subject perspective. On the other hand, few studies have explored factors influencing the distribution by examining the needs of innovative talent. This study aimed to identify the evolution mechanism of urban innovation spaces. In total, 36,519 high-tech enterprises in Guangzhou from 2008 to 2023 were selected to represent urban innovation spaces. Spatial analysis methods and statistical methods were employed to investigate the spatiotemporal dynamic characteristics. Furthermore, employing multiscale geographically weighted regression, the study identifies multiple factors influencing the development of innovation spaces from the dual perspectives of the innovation environment and services. The results indicated that characterized by a southeast-northwest orientation, the urban innovation spaces of Guangzhou have displayed an apparent point–axis–face structural evolution, expanding from the central district into sparsely distributed in the suburbs. The factors influencing the distribution of urban innovation spaces, ranked by their degree of impact, were as follows: vehicle carrying, research institutions, public park, living convenience, university resources, business hotel, industrial structure height, and metro station. These findings facilitated the understanding of urban innovation space development and grasped the influencing factors and their functioning mechanisms. They provided references for innovation space planning amidst urban stock development

China · Innovation Management · Spillover effect · Urban planning · Urban spatial structure · Cultural Industries and Urban Development · Facilities and Workplace Management · Regional Economics and Spatial Analysis

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