Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China
Scientific site selection for urban parks is an important way to increase urban resilience and safeguard people’s well-being. Aiming at the lack of systematic consideration in the traditional park siting research, this study utilizes geographically weighted regression to explore the various characte...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-04-01
|
| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2220-9964/14/5/184 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849327293299687424 |
|---|---|
| author | Haihong Li Li He |
| author_facet | Haihong Li Li He |
| author_sort | Haihong Li |
| collection | DOAJ |
| description | Scientific site selection for urban parks is an important way to increase urban resilience and safeguard people’s well-being. Aiming at the lack of systematic consideration in the traditional park siting research, this study utilizes geographically weighted regression to explore the various characteristic factors affecting the spatial distribution of parks, and based on this, combines the random forest model and the interpretable model to accurately assess the potential of parks on urban land in Shenzhen and provide the basis for site selection. The study indicates that: ① Shenzhen’s parks exhibit complex differentiation characteristics in terms of natural landscape elements and the intensity of economic activities; ② The geographically weighted random forest (GWRF) model has better learning and generalization capabilities compared to the random forest (RF) model, and the average accuracy of the GWRF model is improved by 0.04 compared to the traditional RF model; ③ The park’s development potential is divided according to the results of the GWRF model, with 52.01% denoted as the potential incubation zone, 21.15% the potential accumulation zone, 8.25% the potential growth zone, and 18.59% the potential core zone; ④ Through interpretability analysis, it is identified that vegetation coverage, the density of tourist attractions or points of interest (POI), slope, elevation, and nighttime light intensity are the most significant factors affecting park development potential, while the distance to roads and the distance to bodies of water are the least influential factors. The research systematically explores a quantitative evaluation framework for the development potential of Shenzhen’s parks, opening new theoretical pathways and practical paradigms for the sustainable development planning of Shenzhen under the “Park City” concept. |
| format | Article |
| id | doaj-art-085cfd534bcd416b9cbd3af5c89c8d9c |
| institution | Kabale University |
| issn | 2220-9964 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj-art-085cfd534bcd416b9cbd3af5c89c8d9c2025-08-20T03:47:54ZengMDPI AGISPRS International Journal of Geo-Information2220-99642025-04-0114518410.3390/ijgi14050184Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, ChinaHaihong Li0Li He1School of Urban Construction, Yangtze University, Jingzhou 434023, ChinaSchool of Urban Construction, Yangtze University, Jingzhou 434023, ChinaScientific site selection for urban parks is an important way to increase urban resilience and safeguard people’s well-being. Aiming at the lack of systematic consideration in the traditional park siting research, this study utilizes geographically weighted regression to explore the various characteristic factors affecting the spatial distribution of parks, and based on this, combines the random forest model and the interpretable model to accurately assess the potential of parks on urban land in Shenzhen and provide the basis for site selection. The study indicates that: ① Shenzhen’s parks exhibit complex differentiation characteristics in terms of natural landscape elements and the intensity of economic activities; ② The geographically weighted random forest (GWRF) model has better learning and generalization capabilities compared to the random forest (RF) model, and the average accuracy of the GWRF model is improved by 0.04 compared to the traditional RF model; ③ The park’s development potential is divided according to the results of the GWRF model, with 52.01% denoted as the potential incubation zone, 21.15% the potential accumulation zone, 8.25% the potential growth zone, and 18.59% the potential core zone; ④ Through interpretability analysis, it is identified that vegetation coverage, the density of tourist attractions or points of interest (POI), slope, elevation, and nighttime light intensity are the most significant factors affecting park development potential, while the distance to roads and the distance to bodies of water are the least influential factors. The research systematically explores a quantitative evaluation framework for the development potential of Shenzhen’s parks, opening new theoretical pathways and practical paradigms for the sustainable development planning of Shenzhen under the “Park City” concept.https://www.mdpi.com/2220-9964/14/5/184spatial differentiationmulti-source datainterpretable machine learninggeographically weighted random forestpotential measurement |
| spellingShingle | Haihong Li Li He Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China ISPRS International Journal of Geo-Information spatial differentiation multi-source data interpretable machine learning geographically weighted random forest potential measurement |
| title | Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China |
| title_full | Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China |
| title_fullStr | Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China |
| title_full_unstemmed | Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China |
| title_short | Park Development, Potential Measurement, and Site Selection Study Based on Interpretable Machine Learning—A Case Study of Shenzhen City, China |
| title_sort | park development potential measurement and site selection study based on interpretable machine learning a case study of shenzhen city china |
| topic | spatial differentiation multi-source data interpretable machine learning geographically weighted random forest potential measurement |
| url | https://www.mdpi.com/2220-9964/14/5/184 |
| work_keys_str_mv | AT haihongli parkdevelopmentpotentialmeasurementandsiteselectionstudybasedoninterpretablemachinelearningacasestudyofshenzhencitychina AT lihe parkdevelopmentpotentialmeasurementandsiteselectionstudybasedoninterpretablemachinelearningacasestudyofshenzhencitychina |