Determination of Motivating Factors of Urban Forest Visitors through Latent Dirichlet Allocation Topic Modeling.

Doo-San Kim,Kwang-Hi Park,Byeong-Cheol Lee

doi:10.3390/ijerph18189649

Abstract

Despite the unique characteristics of urban forests, the motivating factors of urban forest visitors have not been clearly differentiated from other types of the forest resource. This study aims to identify the motivating factors of urban forest visitors, using latent Dirichlet allocation (LDA) topic modeling based on social big data. A total of 57,449 cases of social text data from social blogs containing the keyword “urban forest” were collected from Naver and Daum, the major search engines in South Korea. Then, 17,229 cases were excluded using morpheme analysis and stop word elimination; 40,110 cases were analyzed to identify the motivating factors of urban forest visitors through LDA topic modeling. Seven motivating factors—“Cafe-related Walk”, “Healing Trip”, “Daily Leisure”, “Family Trip”, “Wonderful View”, “Clean Space”, and “Exhibition and Photography”—were extracted; each contained five keywords. This study elucidates the role of forests as a place for healing, leisure, and daily exercise. The results suggest that efforts should be made toward developing various programs regarding the basic functionality of urban forests as a natural resource and a unique place to support a diversity of leisure and cultural activities.

Highlights

According to the UN DESA [1], 55% of the global population resides in urban areas.Individuals are increasingly migrating from rural to urban areas, and 68% of the population is predicted to reside in urban areas by 2050
This study aimed to identify the factors leading to the visit to urban forests through a novel method based on social big data that allows a large sample size, including the visitors’ experience of different urban forest types and the representability of the temporal range
A total of 57,449 cases of data from 2019 to 2020 were collected from the blogs related to urban forest visitors; after data cleaning, the final 40,110 cases of data were used to extract the motivating factors of urban forest visitors through text mining and latent Dirichlet allocation (LDA)

Summary

Introduction

According to the UN DESA [1], 55% of the global population resides in urban areas. Individuals are increasingly migrating from rural to urban areas, and 68% of the population is predicted to reside in urban areas by 2050. The report pointed out the importance of an integrated policy regarding the different problems of housing, transportation, education, and healthcare in line with gradual urbanization. In the U.S, the importance of “urban forest” was recognized, and in 1978, the “Cooperative Forestry Assistance (CFA) Act” was enacted for legislating various support and forestry management programs [2]. There has not been a firmly agreed definition of urban forests, it is commonly agreed that urban forests differ from forests outside urban areas in terms of their existence in dense areas of human settlements such as large cities and towns [3]. This study defines urban forests as trees, forests, green space, and associated ecosystem and cultural components growing in and around cities as well as communities where people live, work, and play

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