Investigating the Association between Environmental Characteristics and Psychological Well-Being

Project Title: Investigating the Association between Environmental Characteristics and Psychological Well-Being
Program:
CAREERS (323)

Project Image: https://support.access-ci.org/system/files/webform/project/229/Feng.Qi_.1.png
Tags:
ai (271), api (394), data-analysis (422), gis (275), image-processing (299), machine-learning (272), programming (5), programming-best-practices (49), python (69)

Status: Complete
Project Leader
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Project Leader:
Feng Qi

Email: fqi@kean.edu
Mobile Phone: {Empty}
Work Phone: {Empty}

Project Personnel
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Mentor(s):
Udi Zelzion (626)

Student-facilitator(s):
Yisha Zhang (1874)

Mentee(s):
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Project Information
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Project Description:
Characteristics of the environment affect our psychological and general well-being. Natural elements and green space in the environment, for example, promote stress reduction and lower the risk of stress-related diseases. While the environment’s psychological impacts on humans have been widely studied, many questions remain unanswered. For example, little is known about the roles that specific characteristics of the environment play in promoting health benefits and how such relationships are moderated by factors such as sociocultural backgrounds, individuals’ dispositions, and ways of relating to emotional experiences.

This study investigates the objective characteristics of environmental settings and subjective perceptions of such settings among individuals with different identities and stances toward their emotional experiences. We use Google Street View (GSV) images to represent a variety of everyday environments people encounter. The images will be used for immersive visualization in the Visualization and Immersive Studio for Education and Research (VISER) at Kean University.

Participants will be recruited to immerse in the virtual environment and answer questions about their perceptions of the environment and related psychological measures. The GSV images will be analyzed through image analysis to derive quantitative measures of the environmental characteristics using methods such as semantic segmentation. A machine learning approach such as deep learning will be adopted. Traditional statistical analysis and nonparametric methods will be used to explore the relationships between characteristics of the environment and aspects of psychological well-being. The findings offer practical implications for urban environmental design and can also be incorporated into ubiquitous computing technologies with mobile devices to provide real-time aids to people engaged in outdoor activities.

Project Information Subsection
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Project Deliverables:
1. GSV images representative of a variety of neighborhoods in Union and Essex Counties, NJ:
2. Compiled semantic segmentation code for analyzing GSV images
3. Feature sets derived from the selected GSV images to characterize the environments (percentage of green space, blue space, buildings, cars, etc.)
4. Findings on the relationships between environmental characteristics and psychological responses
5. Hypothesis on how specific environmental characteristics relate to psychological well-being and other moderating factors

Project Deliverables:
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Student Research Computing Facilitator Profile:
- Grad or undergrad
- Experienced with Python programming
- Familiarity with computer vision and machine learning
- Knowledge of Google Street View integrated IDE will be helpful but not required
- Interested in environmental and psychological research

Mentee Research Computing Profile:
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Student Facilitator Programming Skill Level: Practical applications
Mentee Programming Skill Level: {Empty}
Project Institution: Kean University
Project Address:
1000 Morris Ave
Union, New Jersey. 07083

Anchor Institution: CR-Rutgers
Preferred Start Date: 02/01/2022
Start as soon as possible.: No
Project Urgency: Already behind3Start date is flexible
Expected Project Duration (in months): 6
Launch Presentation: {Empty}
Launch Presentation Date: 07/20/2022
Wrap Presentation: {Empty}
Wrap Presentation Date: 01/18/2023
Project Milestones:
- Milestone Title: Launch
  Milestone Description: Give a brief launch presentation during the next CAREERS Monthly Meeting
- Milestone Title: Data acquisition
  Milestone Description: Research and identify or develop tool/code to sample and download GSV images from the GSV server
- Milestone Title: Representative image sets
  Milestone Description: Compile a set of GSV images representing the different types of neighborhood in Union and Essex Counties of NJ
- Milestone Title: ML image analysis
  Milestone Description: Research and compile semantic segmentation codes (machine learning-based methods) for image analysis
- Milestone Title: Derive environmental characteristics
  Milestone Description: Conduct image analysis to derive environmental characteristics
- Milestone Title: Relationship analysis
  Milestone Description: Explorative analysis of relationships between environmental features and psychological responses
- Milestone Title: Wrap-up
  Milestone Description: Give a brief wrap presentation during a CAREERS Monthly Meeting.

Github Contributions: {Empty}
Planned Portal Contributions (if any):
TBD

Planned Publications (if any):
One journal publication planned in the field of Environmental Psychology and/or Medical Geography
Conference proceedings also expected

What will the student learn?:
Polish programming skills and expand the specific technical skills of computer science to applied computing, problem solving, and computational thinking in a broader scope

What will the mentee learn?:
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What will the Cyberteam program learn from this project?:
Effort involved in recruiting and training junior-level research computing & data facilitators for this kind of project.
Better understand how to expand the specific technical skills of students to applied computing, problem solving, and computational thinking in a broader scope.

HPC resources needed to complete this project?:
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Notes:
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Final Report
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What is the impact on the development of the principal discipline(s) of the project?:
The project findings add to the literature in environmental psychology and provide new evidence for further epidemiological research about specific pathways and functional forms of relationships between nature and stress, anxiety, depression, health and wellbeing, and how these vary by context and population.

What is the impact on other disciplines?:
This interdisciplinary project uses tools/methods from modern computational science and experimental psychology. The findings from this study can provide insights for smart nature therapies as well as for urban landscape/environmental design that promotes mental and general health across diverse populations. The findings can be incorporated into ubiquitous computing technologies with mobile devices to provide real-time aids to people engaged in outdoor activities. Thus it has impact on urban planning and environmental design, as well as AI-assisted health care.

Is there an impact physical resources that form infrastructure?:
NA

Is there an impact on the development of human resources for research computing?:
NA

Is there an impact on institutional resources that form infrastructure?:
NA

Is there an impact on information resources that form infrastructure?:
NA

Is there an impact on technology transfer?:
NA

Is there an impact on society beyond science and technology?:
The findings can be used to evaluate existing landscape/environments in diverse urban neighborhoods for a comprehensive assessment of their abilities in promoting or detracting from residents’ psychological wellbeing upon collection of images of the cities/neighborhoods concerned. It thus has impact on environmental justice and provide recommendations for modifications of the physical and built environment in needed urban communities. 

Lessons Learned:
The computational support provided by the CAREERS program is absolutely valuable. But more computational resources (computing power and research students) are needed from the host institution (Kean University).

Overall results:
The project has had solid progress and deliverables (image segmentation program and results). Hypotheses were generated during the first stage of the study. More in-depth psychological experiment is on-going to test the hypotheses and develop models for practical use