Modeling pedestrian emotion in high-density cities using visual exposure and machine learning: Tracking real-time physiology and psychology in Hong Kong

Xiang, Luyao; Cai, Meng; Ren, Chao; Ng, Edward

doi:10.1016/j.buildenv.2021.108273

Cited by 48 publications

(23 citation statements)

References 42 publications

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“…Thus, residents have more positive sentiments in areas with higher land values. Areas with low land values tend to lower residents' positive sentiments because of poor living conditions [40]. From a sociological point of view, residents in high land value areas are dominated by social elites and people with high income and higher education.…”

Section: Review On the Relationship Between Land Value Andmentioning

confidence: 99%

Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China

Gai

Fan

Shen

et al. 2022

Wireless Communications and Mobile Computing

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Differences in urban land values affect residents' living experiences and may contribute to sentiment inequality. Due to the popularity of smart mobile devices and social media platforms, online tweets with location information can be used as objective information to reflect sentiment differences of urban residents in different locations, overcoming the limitations of previous studies with small sample sizes or a lack of spatial information. Sentiment quantification based on deep learning enables the identification of spatial patterns of urban residents' sentiments. It also provides a new approach for analyzing data from big data platforms using an intelligent computing platform. This paper quantitatively analyzes the sentiment contained in social media tweets using a deep learning sentiment analysis algorithm to reveal inequalities between urban residents' sentiments and land values. The Baidu Intelligent Cloud sentiment analysis platform is used to identify 460,000 Weibo tweets in Xiamen, China, in 2020. We quantitatively analyze the positive and negative sentiments of residents and create a spatial distribution map. The concentration curve indicates sentiment inequality and the impact of high land values on residents' sentiments. The positive sentiment concentration index (CI) and correlation analysis show that the CI value is 0.07, and significant sentiment inequality exists due to the high land value. The use of social media tweet data to analyze sentiment inequality provides a reference for future interdisciplinary research in psychology, urban planning, geography, and sociology. The proposed approach of analyzing social media data using an intelligent computing platform provides new insights into multiplatform data interaction in the context of the Internet of Everything.

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Section: Review On the Relationship Between Land Value Andmentioning

confidence: 99%

Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China

Gai

Fan

Shen

et al. 2022

Wireless Communications and Mobile Computing

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“…To alleviate the potential problem, we included neighborhood socioeconomic variables in regression analysis to reduce possible errors due to sampling biases. In general, though, how well such microblog-based data actually represent the general population and people's sentiments is an urgent and important research topic as bigdata and machine learning are becoming a primary approach to the study of urban emotions/sentiments [ 25 , 68 , 69 ]. Toward this end, a first step is to obtain metadata on the users of social media platforms and microbloggers, such as their socioeconomic and demographic characteristics.…”

Section: Discussionmentioning

confidence: 99%

“…Conditions of people's living environment might be correlated with COVID-19's sentimental and psychological impacts [ 23 ]. For instance, a number of studies have shown that urban greenspace has a positive effect on the sentiments of residents as it provides opportunities for residents to get close to nature, have a rest, take physical exercises, and relax their body and mind [ 3 , 17 , 24 , 25 ]. Does urban greenspace positively affect residents' sentiments or psychological wellbeing during extraordinarily trying times such as the COVID-19 pandemic?…”

Section: Introductionmentioning

confidence: 99%

Urban greenspace helps ameliorate people's negative sentiments during the COVID-19 pandemic: The case of Beijing

Guo

Tu²,

Huang³

et al. 2022

Building and Environment

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“…Machine learning helps detect users' needs and measure their satisfaction [19]. The ability to understand the interaction between human emotion and the built environment is possible in urban areas thanks to ML, especially in China [20].…”

Section: Machine Learning and Human Psychology "Internet Of Thoughts"mentioning

confidence: 99%

Influence of Internet of things on human psychology (internet of thoughts) for education, healthcare, and businesses

Mah

Skalna

Eric

et al. 2022

EAI Endorsed Trans Mob Com Appl

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INTRODUCTION: The study investigated how technological innovations and Embedded Systems, processes, and Communication tools influence human psychology. The IoTs have succeeded to connect humans with things, and it is now preoccupied with understanding how humans react to these things by influencing our actions, reactions, and behavior. OBJECTIVES: To identify items that IoTs use to influence human psychology in education, healthcare, and business and to find out how IoTs utilize AI, ML, Cloud computing, NLP, ICT tools, and big data technology to overpower human psychology. METHODS: The methods “Behavior-oriented drive and influential function method” and the qualitative-quantitative method were used. The qualitative-quantitative method makes use of 3 research questions while behavior-oriented drive and influential function focus on statistical data. RESULTS: The result indicates the least score of 12 out of 15 grades for all the measurements on a behavior-oriented drive and influential function and above 90% positive respondents for the qualitative-quantitative method. CONCLUSION: The study concluded that the high dependency of humans on IoTs services is the reason for the IoTs influence on our thoughts, actions, and behavior.

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Modeling pedestrian emotion in high-density cities using visual exposure and machine learning: Tracking real-time physiology and psychology in Hong Kong

Cited by 48 publications

References 42 publications

Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China

Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China

Urban greenspace helps ameliorate people's negative sentiments during the COVID-19 pandemic: The case of Beijing

Influence of Internet of things on human psychology (internet of thoughts) for education, healthcare, and businesses

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