How the natural environment in downtown neighborhood affects physical activity and sentiment: Using social media data and machine learning

Sun, Peijin; Lu, Wei; Jin, Lan

doi:10.1016/j.healthplace.2023.102968

Cited by 18 publications

(5 citation statements)

References 53 publications

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“…Based on this structure, the weights of 16 types of alternatives-namely, site size, paving material, sky view factor, sky range, internal pedestrian, external pedestrian, green-vision rate, greening rate, decoration and aesthetics, plant species and richness, number of seats, distance of seats, material and form of seats, number of fitness facilities, distance of fitness facilities, and type of fitness facilities-were calculated. Then, the contribution of each environmental constituent factor was evaluated and calculated independently, as presented in Equation (1).…”

Section: Ahp Analysismentioning

confidence: 99%

“…Open spaces within neighbourhoods exhibit a close connection to the daily life of citizens, serving as common venues for outdoor exercise, recreational activities, and social Land 2024, 13 interactions [1]. In contrast to public open spaces situated at a considerable distance from residences, those within a 500-m radius [2,3] offer enhanced accessibility, are more frequently utilised by residents, and hold particular significance for children, seniors [4,5], and individuals with functional limitations [6].…”

Section: Introductionmentioning

confidence: 99%

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Assessing Neighbourhood Preference: An Evaluation of Environmental Features within Small-Scale Open Spaces

Han,

Song,

Shi

et al. 2024

Land

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Well-designed urban public spaces often attract residents and play a critical role in improving people’s wellbeing. Many studies have examined the importance of one or a few environmental features in urban public spaces, such as the size of the space, greenery coverage, seating arrangements, recreational facilities, etc. However, there is a lack of systematic understanding regarding (1) which environmental features have a significant impact on the usage of urban public spaces and (2) how these features influence people’s environmental preferences. To answer these questions, this investigation adopts a two-fold analytical structure: (1) first, an expert inquiry was conducted to evaluate the environmental features, and the analytic hierarchy process (AHP) was applied to determine the weight of each influencing factor; then, (2) on-site measurements were conducted across 104 spaces, accompanied by structured interviews with users of the spaces, based on which a decision tree analysis was employed to elucidate the decision-making processes of residents regarding their outdoor activities. The main findings of this investigation are as follows: (1) the site size, internal pedestrian flow, sky view factor, green-vision rate, and seat–circumference ratio are primary indicators affecting outdoor space usage, which are used in the objective evaluation index; (2) advantage value intervals for the sky view factor, green-vision rate, and seat–circumference ratio variables were calculated, and these three factors were found to significantly outweigh site size and internal pedestrian flow in terms of their effect on spatial preference. The interaction between the green-vision rate and seat–circumference ratio can affect the environmental preferences of residents: spaces with more seats exhibit lower requirements for greenery, while spaces with fewer seats should prioritise trees and greenery. Based on this study, an index based on influencing factors is proposed, enabling a better understanding of the environmental features affecting the usage of space. This study also provides valuable insights for future neighbourhood design through investigating the environmental preferences of residents, as well as the importance of various spatial features and their associated advantage value intervals.

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Section: Ahp Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Neighbourhood Preference: An Evaluation of Environmental Features within Small-Scale Open Spaces

Han,

Song,

Shi

et al. 2024

Land

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“…The overall accuracy and kappa value are very good for the suggested 3D CNN approach-Proposed 3D CNN approach makes better use of urbanization than the commonly utilized pixel-based support vector machine classifier The best performance belongs to the EfficientDet framework [30] Studying the effect of the natural environment in the downtown neighborhood on physical activity and sentiment social media data and machine learning…”

Section: Novel 3d Cnnsmentioning

confidence: 99%

A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes

et al. 2023

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The visual qualities of the urban environment influence people’s perception and reaction to their surroundings; hence the visual quality of the urban environment affects people’s mental states and can have detrimental societal effects. Therefore, people’s perception and understanding of the urban environment are necessary. This study used a deep learning-based approach to address the relationship between effective spatial criteria and people’s visual perception, as well as spatial modeling and preparing a potential map of people’s visual perception in urban environments. Dependent data on people’s visual perception of Tehran, Iran, was gathered through a questionnaire that contained information about 663 people, 517 pleasant places, and 146 unpleasant places. The independent data consisted of distances to industrial areas, public transport stations, recreational attractions, primary streets, secondary streets, local passages, billboards, restaurants, shopping malls, dilapidated areas, cemeteries, religious places, traffic volume, population density, night light, air quality index (AQI), and normalized difference vegetation index (NDVI). The convolutional neural network (CNN) algorithm created the potential map. The potential visual perception map was evaluated using the receiver operating characteristic (ROC) curve and area under the curve (AUC), with the estimates of AUC of 0.877 and 0.823 for pleasant and unpleasant visuals, respectively. The maps obtained using the CNN algorithm showed that northern, northwest, central, eastern, and some southern areas of the city are potent in pleasant sight, and southeast, some central, and southern regions had unpleasant sight potential. The OneR method results demonstrated that distance to local passages, population density, and traffic volume is most important for pleasant and unpleasant sights.

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“…Understanding the dissemination effect of sports and health knowledge is crucial for various stakeholders, including policymakers, health practitioners, and educators, to optimize public health interventions, promote physical activity [3], and improve overall well-being. Traditional methods of evaluating the effectiveness of knowledge dissemination often fall short of capturing the nuanced dynamics of social media engagement and sentiment.…”

Section: Introductionmentioning

confidence: 99%

Evaluation Model of Sports and Health Knowledge Dissemination Effect Based on Social Media Data and Sentiment Analysis

Jianhua Zhang

2024

jes

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In the contemporary era, social media platforms serve as prominent channels for the dissemination of sports and health-related information. Understanding the effectiveness of such dissemination efforts is crucial for optimizing public health interventions and promoting well-being. This paper proposes an innovative evaluation model that harnesses social media data and sentiment analysis techniques to assess the dissemination effect of sports and health knowledge. By leveraging sentiment analysis, the proposed model delves deeper into the public's perceptions and attitudes towards sports and health-related content on social media platforms. Through the integration of advanced analytics, including machine learning algorithms, the model provides insights into the reach, engagement, and impact of information dissemination campaigns. The application of this evaluation model facilitates evidence-based decision-making for policymakers, health practitioners, and stakeholders involved in sports and health promotion initiatives. This paper outlines the theoretical framework, methodology, and practical applications of the proposed evaluation model, offering a comprehensive approach to evaluating the effectiveness of knowledge dissemination efforts in the digital age.

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How the natural environment in downtown neighborhood affects physical activity and sentiment: Using social media data and machine learning

Cited by 18 publications

References 53 publications

Assessing Neighbourhood Preference: An Evaluation of Environmental Features within Small-Scale Open Spaces

Assessing Neighbourhood Preference: An Evaluation of Environmental Features within Small-Scale Open Spaces

A Hybridization of Spatial Modeling and Deep Learning for People’s Visual Perception of Urban Landscapes

Evaluation Model of Sports and Health Knowledge Dissemination Effect Based on Social Media Data and Sentiment Analysis

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