Taecharungroj, Viriya and Millington, Steve ORCID: https://orcid.org/0000-0001-5143-3074 (2023) Amenity mix of innovation districts. Journal of Place Management and Development, 16 (1). pp. 125-143. ISSN 1753-8335
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Abstract
Purpose – The purpose of this paper is to compare and contrast amenity mixes of innovation districts worldwide and to identify the types of amenities in the districts. Design/methodology/approach – Urban amenity data were collected from Google Maps as 31,236 points of interest (POIs) in 24 innovation districts. The data were compared and categorised based on the density and diversity of amenity mixes using correspondence analysis. An overall amenity space of the 24 innovation districts was created using correlation and social network analyses. Findings – This study found that innovation districts have broad ranges of diversity and density. Five groups were identified by correspondence analysis, including retail, foodie, balanced, emerging and healthcare districts. The amenity space of innovation districts created using correlation and social network analyses categorised amenities into four broad types – necessary, anchor, vibrant and contextual amenities – based on eigenvector centrality scores and density. Practical implications – To improve their innovation districts, managers are encouraged to create a foundation of innovations with anchor amenities, to ensure the “live-work-play” environment with necessary amenities, to foster buzz and vibes with vibrant amenities and to strengthen the identity and authenticity with contextual amenities. Originality/value – Despite some understanding, no previous research has thoroughly analysed the amenities available in innovation districts. This study is the first to comprehensively explore the amenities in innovation districts using data from Google Maps. Place managers can use the method introduced in this research to analyse innovation districts and other kinds of places.
Impact and Reach
Statistics
Additional statistics for this dataset are available via IRStats2.