Examining the Relationship Between Urban Park Quality and Residents’ Health in South Korean Cities Using Public Data
Abstract
1. Introduction
2. Literature Review
3. Research Scope and Methodology
3.1. Research Scope
3.2. Analysis Method for the Korean ParkScore
3.2.1. Selection of Evaluation Indicators
- We evaluated acreage by calculating the average park size and the proportion of park area relative to the total urban area.
- We measured amenities as the density of park facilities, using the ratio of total park facilities to the total park area within each city. This approach was chosen over simply counting the number of physical activity facilities to provide a more standardized measure of amenity provisions relative to the available park space, reflecting the intensity of park use potential.
- We assessed investment based on the average annual municipal budget allocated to urban parks over the past three years.
- We defined accessibility as the percentage of the population living within 0.75 km (km) of an urban park. The 0.75 km radius was selected as a proxy for a 10 min walk, a commonly used standard in urban planning and public health research for assessing convenient access to local amenities. This specific distance was chosen based on its practical applicability to available national spatial data in South Korea.
- For equity, we departed from TPL’s race- and income-based approaches and instead used the Gini coefficient of park distribution within each city to reflect spatial fairness. The Gini coefficient, typically used to measure income inequality, was adapted here to quantify the equitable distribution of park resources across different areas within a city. A lower Gini coefficient indicates a more even distribution of park space. This approach was chosen because, while income disparities exist in South Korea, direct, publicly available, fine-grained income data linked to park access at the municipal level are limited. Furthermore, focusing on the spatial distribution of parks provides a robust measure of equity regardless of the specific demographic characteristics, aligning with the goal of creating a globally adaptable framework. By tailoring these indicators to the Korean context, the Korean ParkScore offers a more accurate and locally relevant tool for evaluating urban parks and informing public policy. Since TPL operates primarily in North America, using race and income levels as indicators of equity poses few limitations in that context. However, for a globally adaptable framework, particularly in countries with a predominantly single-race population, assessing equity based on the spatial distribution of parks is more appropriate. Therefore, this study adjusted the equity metric by using the Gini coefficient to assess whether park resources are concentrated in specific areas or evenly distributed across the urban landscape.
3.2.2. Data Collection
- Acreage data came from the National Urban Park Standard Dataset on the Public Data Portal.
- Amenities data were primarily from the same dataset and the Information Disclosure Portal. When data were incomplete, we contacted the relevant government agencies and local municipalities to fill in the gaps.
- Investment data were from budget reports issued by the urban park departments of local governments.
- Accessibility data came from the National Spatial Information Platform, using population data within a 0.75 km service radius of urban parks.
- Equity data were from regional statistics and administrative district datasets, allowing us to assess the equity of park resource distribution across urban areas.
3.3. Analysis Method for the Relationship Between ParkScore and Mental Health
4. Results
4.1. Results of Parkscore Evaluation
4.2. Relationship Between ParkScore, Mental Health, and Obesity Rates
5. Discussion and Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Category | Indicator |
---|---|
Accessibility | Percent of residents within a 10 min walk to the park |
Acreage | Median park size Parkland as % of city area |
Investment | Spending per resident (3 years) |
Amenities | Basketball hoops per 10,000 residents Dog parks per 100,000 residents Playgrounds per 10,000 residents Recreation and senior centers per 20,000 residents Restrooms per 10,000 residents Splashpads and spraygrounds per 100,000 residents |
Equity | % of people of color within 10 min walk to park % of low-income households Neighborhoods of color have a—distribution of park space than white neighborhoods Low-income neighborhoods have a—distribution of park space than high-income neighborhoods |
Province | City Name |
---|---|
Metropolitan Cities | Busan, Daegu, Daejeon, Gwangju, Incheon, Sejong, Seoul, Ulsan |
Gangwon-do | Donghae, Gangneung, Samcheok, Sokcho, Taebaek, Wonju, Chuncheon |
Gyeonggi-do | Ansan, Anseong, Anyang, Bucheon, Dongducheon, Gimpo, Goyang, Gwacheon, Gwangju, Gwangmyeong, Gunpo, Guri, Hanam, Hwaseong, Icheon, Namyangju, Osan, Paju, Pocheon, Pyeongtaek, Seongnam, Siheung, Suwon, Uijeongbu, Uiwang, Yangju, Yeoju, Yongin |
Gyeongsangnam-do | Changwon, Gimhae, Geoje, Jinju, Miryang, Sacheon, Yangsan, Tongyeong |
Gyeongsangbuk-do | Andong, Gimcheon, Gumi, Gyeonju, Gyeongsan, Mungyeong, Pohang, Sangju, Yeongcheon, Yeongju |
Jeollanam-do | Gwangyang, Mokpo, Naju, Suncheon, Yeosu |
Jollabuk-do | Gimje, Gunsan, Iksan, Jeonju, Jeongeup, Namwon |
Jeju Special Self-Governing Province | Jeju, Seogwipo |
Chungcheongnam-do | Asan, Boryeong, Cheonan, Dangjin, Gongju, Gyeryong, Nonsan, Seosan |
Chungcheongbuk-do | Cheongju, Chungju, Jecheon |
Disease | Pearson’s Correlation Coefficient | p-Value | |
---|---|---|---|
Mental Health Disorders | Insomnia | −0.3145 | 0.0034 |
Anxiety Disorders | −0.3514 | 0.0010 | |
Depression | −0.2923 | 0.0066 | |
Bipolar Disorder | −0.3255 | 0.0024 | |
Schizophrenia | −0.3444 | 0.0012 | |
Attention Deficit Hyperactivity Disorder (ADHD) | −0.2848 | 0.0082 | |
Obesity Rates | −0.2561 | 0.0180 |
ParkScore Dimension | Insomnia | Anxiety Disorders | Depression | Bipolar Disorder | Schizophrenia | ADHD | Obesity Rates |
---|---|---|---|---|---|---|---|
Access | −0.28 * | −0.32 ** | −0.25 * | −0.29 ** | −0.30 ** | −0.24 * | −0.20 |
Acreage | −0.15 | −0.18 | −0.12 | −0.16 | −0.17 | −0.10 | −0.08 |
Investment | −0.20 | −0.23 * | −0.18 | −0.21 * | −0.22 * | −0.17 | −0.15 |
Equity | −0.25 * | −0.28 * | −0.22 * | −0.26 * | −0.27 * | −0.21 * | −0.19 |
Amenities | −0.10 | −0.12 | −0.08 | −0.10 | −0.11 | −0.07 | −0.05 |
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Kwon, Y.; Park, K.; Kang, I.; Shin, C.; Lee, G.; Lee, S. Examining the Relationship Between Urban Park Quality and Residents’ Health in South Korean Cities Using Public Data. Land 2025, 14, 1191. https://doi.org/10.3390/land14061191
Kwon Y, Park K, Kang I, Shin C, Lee G, Lee S. Examining the Relationship Between Urban Park Quality and Residents’ Health in South Korean Cities Using Public Data. Land. 2025; 14(6):1191. https://doi.org/10.3390/land14061191
Chicago/Turabian StyleKwon, Yoonku, Kyeongjun Park, Ingu Kang, Changyeong Shin, Giyeol Lee, and Sanghoon Lee. 2025. "Examining the Relationship Between Urban Park Quality and Residents’ Health in South Korean Cities Using Public Data" Land 14, no. 6: 1191. https://doi.org/10.3390/land14061191
APA StyleKwon, Y., Park, K., Kang, I., Shin, C., Lee, G., & Lee, S. (2025). Examining the Relationship Between Urban Park Quality and Residents’ Health in South Korean Cities Using Public Data. Land, 14(6), 1191. https://doi.org/10.3390/land14061191