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Ecological Informatics 80 (2024) 102465

Available online 11 January 2024

nc/4.0/).

Exploring the association between the built environment and positive

sentiments of tourists in traditional villages in Fuzhou, China

Zhengyan Chen , Honghui Yang , Yishan Lin , Jiahui Xie , Yuanqin Xie , Zheng Ding

College of Arts College of Landscape Architecture, Fujian Agriculture and Forestry University, Fuzhou 350100, Fujian Province, China

ARTICLE INFO

Keywords:

Traditional village

Tourist sentiments

Built environment

Natural language processing

Machine learning

ABSTRACT

Promoting positive emotional experiences for tourists is crucial for sustaining development in rural areas.

However, existing research has limited focus on the rural built environment, particularly in developing a

framework to evaluate environmental sentiment on a small to medium scale with detailed indicators. This study

addresses this gap by examining the impact of the rural built environment on tourists’ emotions. Natural Lan-

guage Processing (NLP) technologies are employed to analyze web text data and determine the average senti-

ment index for traditional villages in Fuzhou, China. Additionally, data on the built environment were acquired

through the HRnet segmentation model and Matlab. To assess the association between environmental indicators

and the sentiment index, we used eXtreme Gradient Boosting (XGBoost), the SHapley Additive exPlanation

(SHAP) model, and ArcMap software. The study demonstrated that (1) the spatial distribution of the average

sentiment index was signicant. Houfu Village (9.91), Qianhu Village (9.88), and Ximen Village (9.75) had the

highest scores, while Doukui Village (− 0.85), Jiji Village (0.2), and Qiaodong Village (0.55) had the lowest. (2)

The indicators that have the most signicant impact on sentiment are Openness, Greenness, and Color

Complexity, with a contribution value above 0.7—followed by Enclosure, Visual Entropy, and Ground Exposure,

with a contribution between 0.5 and 0.7. Furthermore, analyzing the interaction mechanism of the indicators

showed a non-linear relationship. The environmental characteristics associated with high emotional index scores

are openness in the range of 0.2 to 0.5, greenness in the range of 0.4 to 0.6, and color complexity in the range of

0.3 to 0.5. This study provides observations pertinent to the sustainable development of traditional village en-

vironments. The ndings contribute to an understanding of how these environmental elements might be effec-

tively designed to improve tourists’ sentiment in rural settings.

1. Introduction

In recent years, rural development in China has undergone signi-

cant changes, primarily due to national policies focusing on the National

Agrotourism Demonstration Site, Beautiful Countryside Construction,

and Rural Revitalization strategy, thereby elevating tourism to a critical

developmental factor in traditional villages (Dai et al., 2023; Wang

et al., 2023). Rural tourism has emerged as a sustainable livelihood

strategy (Su et al., 2019), playing a pivotal role in reshaping rural

economies, social structures, and spatial dynamics. It has become a vital

conduit for alleviating rural poverty and enhancing built environments.

Notably, both personal and environmental factors greatly affect tourists’

overall emotional experiences during their travels (Chen, 2022).

Furthermore, positive tourism experiences can bolster sustainable con-

sumption motivations in rural tourism (Chen et al., 2023a), thereby

inuencing the economic earnings and sustainability of the destinations.

Consequently, scholars are increasingly exploring the interplay between

tourism emotions and the rural built environment, recognizing its

signicance.

Emotions, serving as adaptive responses to events in daily life, reveal

the psychological changes experienced in response to stimuli (Ekman,

1992). Anderson and Smith (2001) establish a link between emotions

and space, suggesting that spatial emotions illustrate social relations.

This concept forms the basis of affective geography, which examines the

relationship between emotions and physical environments. Kim and

Fesenmaier (2015) have shown that varying environments signicantly

impact human emotions and subsequently affect the overall experience

of an activity. However, the quality of the built environment has a sig-

nicant impact on public depression and other sentiment symptoms,

according to the 2016 edition of the China Family Panel Studies (Liu

* Corresponding author.

E-mail address: 000Q151039@fafu.edu.cn (Z. Ding).

Contents lists available at ScienceDirect

Ecological Informatics

journal homepage: www.elsevier.com/locate/ecolinf

https://doi.org/10.1016/j.ecoinf.2024.102465

Received 24 November 2023; Received in revised form 5 January 2024; Accepted 6 January 2024

Ecological Informatics 80 (2024) 102465

et al., 2021). Therefore, promoting positive emotions among individuals

in the environment is crucial for achieving sustainable development in

planning and design (Rui, 2023). This initiative also supports the United

Nations World Tourism Organization’s (UNWTO) Sustainable Develop-

ment Goals for Sustainable Cities and Communities as well as Health and

Well-Being (United Nations, 2015).

Environmental impact assessment is one of the most critical com-

ponents in sustainability surveys (Nabavi-Pelesaraei et al., 2021), In the

early stages of researching the correlation between environment and

emotion, traditional methods like questionnaires, interviews, and eld

surveys had notable limitations. Recently, however, the rise of social

networking platforms has made social media data a signicant source

for understanding consumer travel choices (Ghermandi et al., 2023; Shin

et al., 2019). This data is easy to obtain and has a high potential for

utilization. For example, Wang et al. (2021) developed an analytical

framework to examine geographically themed sentiment using social

media data. Studies like those by Liu et al. (2019) and Yu et al. (2023)

have shown that analyzing tourists’ emotions can provide insights into

destination image scores and satisfaction. The wealth of User Generated

Content (UGC) available on social media platforms provides researchers

with a valuable resource for studying various aspects of human-

environment interactions. These resources facilitate research into the

dynamics of human-nature relationships (Chai-allah et al., 2023a),

monitoring environmental states (Hartmann et al., 2022), understand-

ing environmental perception (Madjar et al., 2023), and quantifying

landscape preferences (Chai-allah et al., 2023b). These ndings under-

score the value of social media data in evaluating crowd sentiment and

aspects of the built environment. However, prior research has predom-

inantly leveraged the substantial volume and ready accessibility of so-

cial media data for quantitative analyses of extensive urban settings.

This focus has often overshadowed the potential applicability of such

data in examining smaller and medium-scale areas. Moreover, there has

been a notable paucity of studies dedicated to investigating the built

environment within rural contexts. In addition, previous related studies

have mainly used broad area quantitative data, such as standardized

questionnaire (Nabavi-Pelesaraei and Damgaard, 2023)， Street Map

View Imag (SVI), Normalized Difference Vegetation Index (NDVI), land

use, and POI (Point of Interest) data for analysis (Chen et al., 2023e; Ma,

2023; Richards and Lavorel, 2022). Moreover, there is a requirement to

explore the spatial correlation between User-generated content and

quantitative data, such as Points of Interest (POIs), road networks, and

land use, reveal a gap in the current data framework for urban imagery,

public perception, and micro-scale spatial assessment research. This gap

emphasizes the need for a more rened and matched data framework in

these domains.

In response to the increasing demand for processing multi-source

data in built environment research, researchers have increasingly

adopted machine learning models. Notably, the open-source eXtreme

Gradient Boosting (XGBoost) model, rooted in GBDT, has gained

prominence for its robust explanatory and predictive capabilities

(Todorovic et al., 2023; T. Chen and Guestrin, 2016). Employed across

various elds, XGBoost has been particularly useful in addressing

complex problems (Chen et al., 2023c). In recent years, scholars in

various elds have begun employing the XGBoost model and integrating

multi-source data for built environment studies. Their research spans

topics like spatial vitality (Liu et al., 2023; Zhang et al., 2023a), spatial

heterogeneity (J. Zhang et al., 2023b), landscape impacts on ecological

vulnerability (Xu et al., 2022), and implications for human health

(Zhang et al., 2022c). This method is extensively utilized in large-scale

research, notably in urban and transportation planning sectors. How-

ever, the development of a standardized assessment indicator system for

this methodology is still in progress. More research indicators with high

emotional matching with tourists need to be added in the experiment.

The utilization of macroscopic data, including Points of Interest (POIs)

and remotely sensed imagery, is often constrained by the lack of detailed

information about the built environment (Cao et al., 2018; Huang et al.,

2022). This limitation is particularly challenging in traditional villages,

characterized by complex planning scenarios. To delve deeper into how

the environment inuences individual emotions, specic environmental

indicators must be integrated into the research framework. The combi-

nation of the XGBoost model and the SHAP interpretation method fa-

cilitates the understanding of interactions among various variables. It

explains the extent to which detailed environmental indicators

contribute to the model, enabling effective evaluation of small- to

medium-sized spaces. The integration of visual and textual features in

studies establishes a novel framework (Rogers, 2021), addressing the

gap observed in previous research between macro-environmental data

and human emotional responses. This innovative approach opens new

avenues for future research, suggesting a more holistic methodology

that can bridge the divide between large-scale environmental data and

nuanced emotional experiences in built environments.

Fuzhou, as one of the pioneering cities in urban agriculture, is

renowned for its rich cultural heritage and abundant traditional village

resources (Wang et al., 2022b). However, the city faces challenges of

urban-rural development disparities and environmental issues due to

rapid industrialization (Chen et al., 2023b). In response to these chal-

lenges and in alignment with China’s “14th Five-Year Plan” and the

Five-Year Action Plan for Rural Habitat Improvement and Enhancement

(2021–2025), Fuzhou City is undertaking signicant measures. These

measures aim to address environmental concerns in traditional villages,

fostering sustainable rural development (Hao et al., 2023; Wei et al.,

2021). This study examines the correlation between built environment

variables and tourists’ emotions in the countryside by using social media

data and machine learning methods. Specically, the study focuses on

57 traditional villages in Fuzhou City, China. The experimental steps are

as follows: (1) Quantitatively analyze the text data and pictures obtained

from Dianping, Ctrip, and Weibo platforms by cleaning and organizing

them. (2) Score social media data for villages and determine the average

sentiment index using NLP technology based on LSTM and CNN. (3)

Organize corresponding image datasets into text data, quantify them

using semantic segmentation based on Hrnet and image complexity

calculation based on Matlab, and visualize the average quantied data

of different villages using ArcMap software. (4) The dataset was divided

into a training set and a test set before constructing the XGBoost model.

(5) The SHAP method was utilized to examine how elements of the built

environment, as captured in photographs, inuence the sentiment

index. This research provides a scientic foundation for rening rural

spatial congurations in Fujian, enriching tourist experiences in tradi-

tional villages, and acting as a guideline for the sustainable development

of other traditional villages.

2. Data and methodology

2.1. Study area

Fuzhou, the capital of Fujian Province, China, holds a pivotal posi-

tion in politics, economy, and culture, being one of the rst coastal

development cities. This experiment’s research scope entails examining

traditional villages found throughout Fuzhou City, with a representation

of their distribution depicted in Fig. 1. The city, divided into six central

districts (Gulou, Taijiang, Cangshan, Jinan, Mawei, and Changle) as well

as seven counties (Fuqing, Minhou, Lianjiang, Luoyuan, Minqing,

Yongtai, and Pingtan). The climate in Fuzhou is a typical subtropical

monsoon climate characterized by warmth and humidity throughout the

year, abundant sunshine, and precipitation. Fuzhou faces challenges of

uneven spatial patterns and traditional village landscape resources due

to its complex topography and geomorphology. (Pengyi, 2011). Tradi-

tional villages in Fuzhou lack adaptation to evolving production and

living requirements, necessitating professional environmental planning

guidance to prevent environmental decay and loss of historical and

cultural heritage (Ou and Xiao, 2020). This study, therefore, chose

Fuzhou City as a test site to provide recommendations for enhancing the

Z. Chen et al.

Ecological Informatics 80 (2024) 102465

emotional environment of traditional villages.

2.2. Theoretical framework

The experimental design is illustrated in Fig. 2. Our study aims to

explore the correlation between constructed environments and emotions

in rural areas. To achieve this, we propose utilizing a machine learning

and NLP-based evaluation model with three main stages. Firstly, we

extract text and photo information from relevant social media data. The

sentiment index is selected as the dependent variable, while the inde-

pendent variable comprises built environment data calculated during

subsequent experiments. Secondly, the data is sorted and cleaned before

conducting photo data calculations through a semantic segmentation

model based on Hrnet, used for identifying landscape elements in the

photographs. The visual and color complexity of the images is computed

using Matlab. The text data is evaluated using the NLP model of LSTM

with CNN framework pre-trained using Ernie to score sentiment indices.

Finally, the independent variables derived are matched with the

response variables to create an XGBoost model. The SHAP method is

then employed to interpret the calculations. The ndings are visualized

and analyzed through the ArcMap software.

2.3. Data collection

The data regarding traditional villages presented in this study is

derived from the 2023 list published by the Ministry of Housing and

Urban-Rural Development and other pertinent departments in China,

encompassing a total of 57 traditional villages. The social media data

were collected from Weibo (https://weibo.com/), Dianping (https:

//www.dianping.com/), and Ctrip (https://you.ctrip.com/) through

web crawlers. We used specic keywords associated with the traditional

villages to obtain information. We retrieved time information from

comments with images posted sometime between September 2018 and

September 2023. We collected information such as posting ID, text in-

formation, corresponding image URL, and the number of retweets. After

gathering and arranging data from 57 villages, The data cleaning process

ensured the removal of irrelevant information, maintaining the accuracy

and objectivity of experimental results.

2.4. Selection of variables

Based on the perspective of the impact of the built environment on

human emotions in traditional villages, this study integrates previous

studies on the impact of the built environment on human emotions

(Asgarzadeh et al., 2014; Kang et al., 2020; Xu et al., 2023). The in-

dicators were selected by combining the characteristics of traditional

villages, the practical needs of the study, and the availability of data.

Greenness, Openness, and Aquatic rate were selected as natural in-

dicators, while Enclosure, Pedestrian proportion, and Transportation

were chosen as human indicators among the built environment in-

dicators. Additionally, to further examine the impact of roads on peo-

ple’s emotions, Ground exposure and Paving degree were included in

the measurement of human indicators. Previous studies have yet to fully

consider the potential inuence of landscape features on visual quality

(Qi et al., 2023). As a result, this study will investigate how measurable

visual stimuli in the environment affect human emotions. For this pur-

pose, visual sensory metrics, such as Entropy value and color

complexity, were combined (Stamps, 2004; Montero et al., 2020). The

relevant meaning of each indicator and the quantication method are

shown in Table 1.

The semantic segmentation model based on Hrnet quanties various

aspects of the built environment data, including Greenness, Openness,

Enclosure, Ground exposure, Paving degree, Aquatic rate, Pedestrian

proportion, and Transportation. The Entropy value and Color

complexity are computed using MATLAB, and the Sentiment index is

Fig. 1. Study area.

Fig. 2. Theoretical framework.

Z. Chen et al.

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