A Comparative Study on Preference Factors of Urban Parks by Cultural Area Based on Big Data Analysis
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Determining the usage and preferred activities of urban residents in urban parks is crucial for effective park planning, design, management, and operation. This study aimed to conduct a comparative analysis of urban park users' preferences and satisfaction factors during the pandemic in eight cultural spheres worldwide. We analysed 75,283 reviews on Google Maps for 49 urban parks in 49 countries across five continents to identify general preference factors in terms of economic power, urban park area, and cultural sphere. Furthermore, we investigated variations in preference factors by a cultural sphere and their reflection in each cultural sphere's urban park. We pre-processed the data and used topic modelling with Latent Dirichlet Allocation to classify satisfaction factors into natural/ecological, cultural/ facility, physical/active, and socialising aspects. The results showed an even distribution of preference factors worldwide, with differences in significant preference ratios of satisfaction factors, urban park usage patterns, and satisfaction factors by race and country across cultural spheres. This study contributes to urban park planning and design by demonstrating the possibility of analysing global spaces beyond spatial limitations using non-face-to-face surveys based on big data when face-to-face surveys are challenging.
Abstract
Determining the usage and preferred activities of urban residents in urban parks is crucial for effective park planning, design, management, and operation. This study aimed to conduct a comparative analysis of urban park users' preferences and satisfaction factors during the pandemic in eight cultural spheres worldwide. We analysed 75,283 reviews on Google Maps for 49 urban parks in 49 countries across five continents to identify general preference factors in terms of economic power, urban park area, and cultural sphere. Furthermore, we investigated variations in preference factors by a cultural sphere and their reflection in each cultural sphere's urban park. We pre-processed the data and used topic modelling with Latent Dirichlet Allocation to classify satisfaction factors into natural/ecological, cultural/ facility, physical/active, and socialising aspects. The results showed an even distribution of preference factors worldwide, with differences in significant preference ratios of satisfaction factors, urban park usage patterns, and satisfaction factors by race and country across cultural spheres. This study contributes to urban park planning and design by demonstrating the possibility of analysing global spaces beyond spatial limitations using non-face-to-face surveys based on big data when face-to-face surveys are challenging.
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