Geographic Information Engineering and Geoenvironmental Sustainability
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 2655
Special Issue Editors
Interests: GIS; machine learning
Interests: geospatial data science; geosimulation; cybergis; health geography; spatial accessibility
Special Issue Information
Dear Colleagues,
One of the sustainability development goals (SDGs) of the United Nations (UN), SDG3 aims to “ensure healthy lives and promote well-being for all at all ages”. Some built environments, including green spaces, support active lifestyles and contribute to promoting people’s physical and mental health. The UN highlighted parks, green spaces and waterways as solutions to the effects of unsustainable urbanization on health and well-being. Cities are recognizing that the increasing stream of data and information can support rapid advances in human health and built environments. Data-driven approaches with statistical methods, machine learning or deep learning techniques, and geospatial analytics can help gain insights into the interaction between built environments and humans.
This Special Issue will include new conceptual tools and theoretical frameworks that work toward understanding the association between human health and built environments. In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Community health;
- Green and blue space and human health;
- Health inequalities;
- Urban planning for resilience and health;
- Land use/cover and human health;
- Environmental health;
- Built environments and physical activity;
- Urban environments and active travel modes;
- Sensor data and data analytics;
- Geospatial data analytics;
- Remote sensing and health.
We look forward to receiving your contributions.
Sincerely,
Dr. Kangjae Lee
Dr. Jeon-Young Kang
Guest Editors
Manuscript Submission Information
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Keywords
- statistical methods
- machine learning
- deep learning
- explainable artificial intelligence
- built environment
- green space
- physical health
- mental health
- environmental health
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