DOI: 10.57760/sciencedb.31914
title: A Dataset on the Association between Green Space Exposure and Health Status of Older Adults in Central Hangzhou, China (2025)
title: A Dataset on the Association between Green Space Exposure and Health Status of Older Adults in Central Hangzhou, China (2025)

description: Dataset AbstractThis dataset stems from a cross-sectional study investigating the association between urban green space environments and the health status of older adults. Data were collected between March and May 2025 across 33 typical residential communities in central Hangzhou, China.The dataset comprises two core components:Survey Data: Obtained through face-to-face, interview-based questionnaires with 1,339 older adults aged 60 and above who had lived in their community for at least two years. The survey covers socio-demographic characteristics, perceived green space quality, green space usage patterns (frequency, duration, intensity), self-rated physical health, and self-rated mental health assessed via the 15-item Geriatric Depression Scale (GDS-15).Objective Green Space Metrics: Derived from Gaofen-6 (GF-6) satellite imagery and OpenStreetMap road network data using GIS techniques. Multiple green space indicators were calculated for a 1000m network-based service area around each residential community. These include park count, green space area, green coverage rate, Normalized Difference Vegetation Index (NDVI), and landscape pattern metrics (fragmentation, connectivity, shape complexity) computed using Fragstats.This dataset integrates detailed subjective survey responses with objective geospatial measurements, providing a high-quality, multi-dimensional resource for research in urban planning, public health, gerontology, and geography on the impact of the built environment, particularly urban green spaces, on the health and well-being of older adults.Detailed Dataset Description1. MethodsStudy Design and Sampling: A cross-sectional study design was employed. Thirty-three residential communities in central Hangzhou were purposively selected as study sites. Respondents were screened using a strict filter questionnaire (age ≥ 60, residence duration ≥ 2 years), and the sample was balanced for socio-demographic attributes like age and gender. A total of 1,339 valid questionnaires were obtained.Data Collection Period: Pre-survey: March 2025; Formal Survey: March to May 2025.Survey Methodology: All questionnaires were administered in an interview format. Trained investigators asked questions orally and recorded the responses to ensure full comprehension and data quality, considering the older age of the respondents.Sources of Objective Green Space Data:Remote Sensing Data: Gaofen-6 (GF-6) satellite imagery (March 2025), with a 2-meter pan-sharpened spatial resolution. Imagery was pre-processed (including radiometric calibration, atmospheric correction, orthorectification, and image fusion) and urban green spaces were extracted using an object-based classification method.Road Network Data: Sourced from the open-source OpenStreetMap (OSM) platform.Park Data: Vector data of Hangzhou's park boundaries, also collected from OpenStreetMap.2. Data Contents and StructureThe dataset primarily consists of two data files:A. Main Data File (Survey_Data.csv) - 1,339 rows × ~40 columnsEach row represents the complete questionnaire response from one older adult. Variables are organized into the following modules:Screening Section: Confirmation of age and residence duration.A. Socio-Demographic Information:A01: Age (in years or categorized)A02: GenderA03: Education LevelA04: Health Insurance CoverageA05: Pension Insurance CoverageA06: Marital StatusB. Perceived Green Space Quality:B0101-B0106: Satisfaction with green space accessibility, safety, quality of activity spaces, facility quality, management, and overall experience (5-point Likert scale from 1="Very Dissatisfied" to 5="Very Satisfied").C. Green Space Usage Patterns:C0101: Days of green space use for leisure in the past week.C0201, C0202...C0501, C0502: For vigorous, moderate, light physical activities, and sedentary behavior in green spaces: days per week and typical daily duration. These can be used to calculate total physical activity levels (e.g., MET-minutes/week).D. Health Status:D0101: Self-rated physical health (5-point scale from 1="Very Healthy" to 5="Very Unhealthy").D0201-D0215: Scores for the 15 items of the Geriatric Depression Scale (GDS-15). The total score indicates the level of depressive symptoms (higher score = more symptoms).Linking ID: A Community_ID variable for merging with the objective green space data file.B. Objective Green Space Data File (GreenSpace_Data.csv) - 33 rows × ~8 columnsEach row represents the green space characteristics for one residential community, calculated within its 1000m pedestrian network service area.Community_ID: Residential community identifier (links to the main survey file).Park_Count: Number of parks.Green_Space_Area: Area of green space (hectares).Green_Coverage_Rate: Percentage of area covered by green space.Fragmentation_PD: Patch Density (quantifying landscape fragmentation).Connectivity_COHESION: Patch Cohesion Index (quantifying landscape connectivity).Shape_Complexity_SHAPE_AM: Area-Weighted Mean Shape Index (quantifying patch shape complexity).NDVI_Mean: Mean Normalized Difference Vegetation Index (quantifying vegetation greenness).3. Data Processing and Quality ControlSurvey Quality Control: The process involved a pre-survey, questionnaire revision, interviewer training, interview-based administration, and rigorous validation (e.g., exclusion of incomplete questionnaires) to ensure data reliability and validity.Remote Sensing Data Processing: Professional processing was conducted using ENVI 5.6 and ArcGIS platforms. Manual visual interpretation was incorporated to correct the automated classification, ensuring high accuracy in green space extraction.Accessibility Analysis: Service Area Analysis based on the actual road network was used, which more accurately reflects the real walking accessibility for older adults compared to traditional straight-line buffer analysis.Data Anonymization: All personally identifiable information (e.g., name, exact address) has been removed. Only a community identifier for analysis is retained.Potential Applications and Reuse ValueThis dataset has significant value for interdisciplinary research, including:Urban Planning and Public Health: Quantifying how different types and configurations of green spaces impact the physical and mental health of older adults, providing evidence for creating "healthy cities" and "age-friendly communities".Environmental Psychology and Sociology: Exploring the relationships between older adults' subjective perceptions of green space (satisfaction), their objective usage patterns, and health outcomes.Geography and Landscape Ecology: Linking micro-scale individual behaviors with macro-scale landscape pattern metrics to deepen the understanding of person-environment interactions.Epidemiology: Serving as cross-sectional baseline data for future longitudinal studies or for building health impact models.Methodological Research: Serving as a case study to compare network-based versus Euclidean distance-based accessibility measures in health research.AcknowledgementsWe extend our sincere gratitude to all the older adults who participated in this study for their time and valuable insights. We acknowledge the providers of the Gaofen-6 satellite imagery and the contributions of the OpenStreetMap community. We also thank all members of the research team for their diligent efforts in data collection, processing, and analysis.
description: Dataset AbstractThis dataset stems from a cross-sectional study investigating the association between urban green space environments and the health status of older adults. Data were collected between March and May 2025 across 33 typical residential communities in central Hangzhou, China.The dataset comprises two core components:Survey Data: Obtained through face-to-face, interview-based questionnaires with 1,339 older adults aged 60 and above who had lived in their community for at least two years. The survey covers socio-demographic characteristics, perceived green space quality, green space usage patterns (frequency, duration, intensity), self-rated physical health, and self-rated mental health assessed via the 15-item Geriatric Depression Scale (GDS-15).Objective Green Space Metrics: Derived from Gaofen-6 (GF-6) satellite imagery and OpenStreetMap road network data using GIS techniques. Multiple green space indicators were calculated for a 1000m network-based service area around each residential community. These include park count, green space area, green coverage rate, Normalized Difference Vegetation Index (NDVI), and landscape pattern metrics (fragmentation, connectivity, shape complexity) computed using Fragstats.This dataset integrates detailed subjective survey responses with objective geospatial measurements, providing a high-quality, multi-dimensional resource for research in urban planning, public health, gerontology, and geography on the impact of the built environment, particularly urban green spaces, on the health and well-being of older adults.Detailed Dataset Description1. MethodsStudy Design and Sampling: A cross-sectional study design was employed. Thirty-three residential communities in central Hangzhou were purposively selected as study sites. Respondents were screened using a strict filter questionnaire (age ≥ 60, residence duration ≥ 2 years), and the sample was balanced for socio-demographic attributes like age and gender. A total of 1,339 valid questionnaires were obtained.Data Collection Period: Pre-survey: March 2025; Formal Survey: March to May 2025.Survey Methodology: All questionnaires were administered in an interview format. Trained investigators asked questions orally and recorded the responses to ensure full comprehension and data quality, considering the older age of the respondents.Sources of Objective Green Space Data:Remote Sensing Data: Gaofen-6 (GF-6) satellite imagery (March 2025), with a 2-meter pan-sharpened spatial resolution. Imagery was pre-processed (including radiometric calibration, atmospheric correction, orthorectification, and image fusion) and urban green spaces were extracted using an object-based classification method.Road Network Data: Sourced from the open-source OpenStreetMap (OSM) platform.Park Data: Vector data of Hangzhou's park boundaries, also collected from OpenStreetMap.2. Data Contents and StructureThe dataset primarily consists of two data files:A. Main Data File (Survey_Data.csv) - 1,339 rows × ~40 columnsEach row represents the complete questionnaire response from one older adult. Variables are organized into the following modules:Screening Section: Confirmation of age and residence duration.A. Socio-Demographic Information:A01: Age (in years or categorized)A02: GenderA03: Education LevelA04: Health Insurance CoverageA05: Pension Insurance CoverageA06: Marital StatusB. Perceived Green Space Quality:B0101-B0106: Satisfaction with green space accessibility, safety, quality of activity spaces, facility quality, management, and overall experience (5-point Likert scale from 1="Very Dissatisfied" to 5="Very Satisfied").C. Green Space Usage Patterns:C0101: Days of green space use for leisure in the past week.C0201, C0202...C0501, C0502: For vigorous, moderate, light physical activities, and sedentary behavior in green spaces: days per week and typical daily duration. These can be used to calculate total physical activity levels (e.g., MET-minutes/week).D. Health Status:D0101: Self-rated physical health (5-point scale from 1="Very Healthy" to 5="Very Unhealthy").D0201-D0215: Scores for the 15 items of the Geriatric Depression Scale (GDS-15). The total score indicates the level of depressive symptoms (higher score = more symptoms).Linking ID: A Community_ID variable for merging with the objective green space data file.B. Objective Green Space Data File (GreenSpace_Data.csv) - 33 rows × ~8 columnsEach row represents the green space characteristics for one residential community, calculated within its 1000m pedestrian network service area.Community_ID: Residential community identifier (links to the main survey file).Park_Count: Number of parks.Green_Space_Area: Area of green space (hectares).Green_Coverage_Rate: Percentage of area covered by green space.Fragmentation_PD: Patch Density (quantifying landscape fragmentation).Connectivity_COHESION: Patch Cohesion Index (quantifying landscape connectivity).Shape_Complexity_SHAPE_AM: Area-Weighted Mean Shape Index (quantifying patch shape complexity).NDVI_Mean: Mean Normalized Difference Vegetation Index (quantifying vegetation greenness).3. Data Processing and Quality ControlSurvey Quality Control: The process involved a pre-survey, questionnaire revision, interviewer training, interview-based administration, and rigorous validation (e.g., exclusion of incomplete questionnaires) to ensure data reliability and validity.Remote Sensing Data Processing: Professional processing was conducted using ENVI 5.6 and ArcGIS platforms. Manual visual interpretation was incorporated to correct the automated classification, ensuring high accuracy in green space extraction.Accessibility Analysis: Service Area Analysis based on the actual road network was used, which more accurately reflects the real walking accessibility for older adults compared to traditional straight-line buffer analysis.Data Anonymization: All personally identifiable information (e.g., name, exact address) has been removed. Only a community identifier for analysis is retained.Potential Applications and Reuse ValueThis dataset has significant value for interdisciplinary research, including:Urban Planning and Public Health: Quantifying how different types and configurations of green spaces impact the physical and mental health of older adults, providing evidence for creating "healthy cities" and "age-friendly communities".Environmental Psychology and Sociology: Exploring the relationships between older adults' subjective perceptions of green space (satisfaction), their objective usage patterns, and health outcomes.Geography and Landscape Ecology: Linking micro-scale individual behaviors with macro-scale landscape pattern metrics to deepen the understanding of person-environment interactions.Epidemiology: Serving as cross-sectional baseline data for future longitudinal studies or for building health impact models.Methodological Research: Serving as a case study to compare network-based versus Euclidean distance-based accessibility measures in health research.AcknowledgementsWe extend our sincere gratitude to all the older adults who participated in this study for their time and valuable insights. We acknowledge the providers of the Gaofen-6 satellite imagery and the contributions of the OpenStreetMap community. We also thank all members of the research team for their diligent efforts in data collection, processing, and analysis.