Spatial and temporal characteristics and factors influencing the network attention degree of urban parks based on multi-source data

doi:10.12302/j.issn.1000-2006.202308025

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (6): 183-192.doi: 10.12302/j.issn.1000-2006.202308025

Previous Articles Next Articles

Spatial and temporal characteristics and factors influencing the network attention degree of urban parks based on multi-source data

YE Yongxiang(), CUI Tingting, CHEN Xin, ZHU Liying^*()

College of Landscape Architecture and Arts, Fujian Agriculture and Forestry University, Fuzhou 350100, China

Received:2023-08-15 Revised:2024-01-04 Online:2024-11-30 Published:2024-12-10
Contact: ZHU Liying E-mail:2362540946@qq.com;fjndzly@126.com

Abstract

Abstract:

【Objective】By analyzing the change characteristics and factors influencing the network attention degree of an urban park network, we made proposals for urban park planning and management.【Method】Using the multi-source data, we assessed the network attention degree of an urban park network consisting of 131 urban parks in the main urban area of Fuzhou City. We identified the spatial and temporal characteristics of the urban park network and devised a six-group index system of service function distribution, urban location characteristics, surrounding spatial characteristics, park attributes, the quality of regional distribution, and land use nature attributes. The driving factors of the network attention degree for the urban parks and its spatial variability were determined using geographic probes and the multi-scale geographically weighted regression model.【Result】(1) The monthly differences in urban park network attention degree were significant, with obvious volatility, mutability and periodicity. (2) Spatially, there was a “one-core and two-slice” layout, and the results of a Moran’s I test showed that the network attention degree presented a positively correlated spatial distribution. (3) The road network density was the main influencing factor, and the park area was the secondary influencing factor, while the interaction of the two factors clearly promoted the network attention degree. (4) The sum of the spatial coefficients of variation of the indexes followed the order of: the quality of the regional distribution (G₅) > the peripheral spatial characteristics (G₃) > the characteristics of the city’s location (G₂) > the attributes of the parks themselves (G₄).【Conclusion】Enhancing the level of urban park network attention degree creates a focus on the creation of service quality in urban parks, improving the appearance of the landscape, increasing visitor carrying capacity and the surrounding road transport network, and facilitating tourists. Combined with the temporal changes in the characteristics of the urban park network, the study provides novel insights for the planning and management of urban parks.

Key words: urban park, network attention degree, spatio-temporal characteristics, multiscale geographically weighted regression(MGWR), Fuzhou City

CLC Number:

TU986

YE Yongxiang, CUI Tingting, CHEN Xin, ZHU Liying. Spatial and temporal characteristics and factors influencing the network attention degree of urban parks based on multi-source data[J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(6): 183-192.

Figures/Tables 9

Table 1

Urban park network attention related indicators"

类型type	变量variable	单位unit	备注note
服务功能分布(G₁) service function distribution(G₁)	POI密度(X₁)	个/hm²	距离公园500 m范围内POI(point of interest)密度
	生活服务POI密度(X₂)	个/hm²	距离公园500 m范围内生活服务POI密度
	教育医疗POI密度(X₃)	个/hm²	距离公园500 m范围内教育医疗POI密度
	商业设施POI密度(X₄)	个/hm²	距离公园500 m范围内商业设施POI密度
	休闲娱乐POI密度(X₅)	个/hm²	距离公园500 m范围内休闲娱乐POI密度
	住宅住宿POI密度(X₆)	个/hm²	距离公园500 m范围内住宅住宿POI密度
	公交站密度(X₇)	个/hm²	距离公园500 m范围内公交站密度
城市区位特征(G₂) characteristics of the city location(G₂)	与中心区距离(X₈)	m	公园与鼓楼区人民政府最短驱车距离
	平均出行时间成本(X₉)	min	“小o地图”计算获取
	周边房价(X₁₀)	元/m²	距离公园500 m范围内房价均值
	建筑平均高度(X₁₁)	m	距离公园500 m范围内建筑平均高度
周边空间特征(G₃) surrounding spatial features(G₃)	平均高程(X₁₂)	m	距离公园500 m范围内平均海拔
	平均坡度(X₁₃)	(°)	距离公园500 m范围内平坡坡度
	道路网密度(X₁₄)	km/km²	离公园500 m范围内路网密度
公园自身属性(G₄) park own attributes(G₄)	公园面积(X₁₅)	hm²	公园地理边界范围面积
	水体面积(X₁₆)	hm²	计算改进归一化水体指数
	道路密度(X₁₇)	km/km²	公园内部路网长度/公园面积
区域分布质量(G₅) regional distribution quality(G₅)	建筑密度(X₁₈)	%	距离公园500 m范围内建筑密度
	停车场数量(X₁₉)	个	通过高德地图获取
用地性质属性(G₆) attributes of the nature of the site(G₆)	周边公共服务用地比例(X₂₀)	%	距离公园500 m范围内周边公共服务用地比例
	周边居住用地比例(X₂₁)	%	距离公园500 m范围内周边居住用地比例

Table 1

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Table 2

Table 3

Table 4

Model index comparison"

模型指标 model indicators	OLS	GWR	MGWR
AICc	1 982	1 971	1 965
R²	0.450	0.538	0.560
$R 调整 2$	0.419	0.478	0.501

Table 4

Fig. 5

References 37

[1]	刘源, 张凯云, 王浩. 城市公园绿地整体性发展分析[J]. 南京林业大学学报(自然科学版), 2013, 37(6):101-106.
	LIU Y, ZHANG K Y, WANG H. Research on overall development of the urban public park[J]. J Nanjing For Univ (Nat Sci Ed), 2013, 37(6):101-106.DOI:10.3969/J.ISSN.1000-2006.2013.06.002.
[2]	GUO S H, YANG G G, PEI T, et al. Analysis of factors affecting urban park service area in Beijing:perspectives from multi-source geographic data[J]. Landsc Urban Plan, 2019, 181:103-117.DOI: 10.1016/j.landurbplan.2018.09.016.
[3]	邵旻煜, 苏晓毅. 城市公园分布与周边人口活动密度及交通便利性分析[J]. 西南林业大学学报(社会科学), 2019, 3(1):76-79.
	SHAO M Y, SU X Y. Study on urban park distribution, population density and traffic condition[J]. J Southwest For Univ (Soc Sci), 2019, 3(1):76-79.
[4]	李丹丹, 郑钰旦, 邵锋, 等. 城市口袋公园使用后评价(POE)研究[J]. 中国城市林业, 2018, 16(3):41-46.
	LI D D, ZHENG Y D, SHAO F, et al. Research on urban vest-pocket parks by using POE[J]. J Chin Urban For, 2018, 16(3):41-46. DOI:10.3969/j.issn.1672-4925.2018.03.009.
[5]	何仲禹, 李岳昊. 基于开放数据的上海城市公园使用活跃度时空特征研究[J]. 中国园林, 2020, 36(10):45-50.
	HE Z Y, LI Y H. The temporal-spatial characteristics of usage activeness of urban parks in Shanghai based on open data[J]. Chin Landsc Archit, 2020, 36(10):45-50. DOI:10.19775/j.cla.2020.10.0045.
[6]	达婷, 李琰, MATHIAS K G. 基于场所依恋的城市滨水公园社会连接研究[J]. 南京林业大学学报(自然科学版), 2023, 47(6):227-235.
	DA T, LI Y, MATHIAS K G. Research on social connectivity of urban waterfront park based on place attachment[J]. J Nanjing For Univ (Nat Sci Ed), 2023, 47(6):227-235. DOI:10.12302/j.issn.1000-2006.202212019.
[7]	梁改童, 高敏华, 白洋. 新疆5A级旅游景区网络关注度时空分布特征研究[J]. 西北师范大学学报(自然科学版), 2021, 57(2):118-126.
	LIANG G T, GAO M H, BAI Y. A study on the spatial and temporal distribution characteristics of the network attention degree of 5A tourist attractions in Xinjiang[J]. J Northwest Norm Univ (Nat Sci), 2021, 57(2):118-126. DOI:10.16783/j.cnki.nwnuz.2021.02.019.
[8]	杨利, 李冬杰, 都刘宁, 等. 国家公园类景区网络关注度时空变化特征研究:以泛长三角地区为例[J]. 资源开发与市场, 2017, 33(9):1142-1146, 1154.
	YANG L, LI D J, DU L N, et al. Study on temporal and spatial variation of network attention in national park scenic spots: a case study of Pan-Changjiang River Delta[J]. Resour Dev Mark, 2017, 33(9):1142-1146, 1154. DOI:10.3969/j.issn.1005-8141.2017.09.024.
[9]	李磊, 陶卓民, 赖志城, 等. 长征国家文化公园红色旅游资源网络关注度及其旅游流网络结构分析[J]. 自然资源学报, 2021, 36(7):1811-1824.
	LI L, TAO Z M, LAI Z C, et al. Analysis of the internet attention and tourism flow network structure of red tourism resources in Long March National Cultural Park[J]. J Nat Resour, 2021, 36(7):1811-1824. DOI:10.31497/zrzyxb.20210713.
[10]	徐凡, 尤玮, 周年兴, 等. 基于百度指数的网络空间关注时空分布研究:以长三角5A级景区为例[J]. 资源开发与市场, 2016, 32(4):489-493.
	XU F, YOU W, ZHOU N X, et al. Tourist attention based on Baidu Index of network: temporal and space distribution: taking 5A grade scenic spot in Changjiang River delta as an example[J]. Resour Dev Mark, 2016, 32(4):489-493. DOI:10.3969/j.issn.1005-8141.2016.04.021.
[11]	陶思宇, 周忠学. 基于旗舰物种的生态系统文化服务供需及流研究:以大熊猫国家公园为例[J]. 生态学杂志, 2022, 41(8):1643-1652.
	TAO S Y, ZHOU Z X. The supply, demand, and flow of ecosystem cultural services based on flagship species: case of the Giant Panda National Park, China[J]. Chin J Ecol, 2022, 41(8):1643-1652. DOI:10.13292/j.1000-4890.202206.009.
[12]	蔚海燕, 戴泽钒, 许鑫, 等. 上海迪士尼对上海旅游流网络的影响研究:基于驴妈妈游客数字足迹的视角[J]. 旅游学刊, 2018, 33(4):33-45.
	YU H Y, DAI Z F, XU X, et al. The impact of Shanghai Disneyland on Shanghai’s tourist flow network from the perspective of tourists’digital footprints on the Lyumama website[J]. Tour Tribune, 2018, 33(4):33-45. DOI:10.3969/j.issn.1002-5006.2018.04.009.
[13]	戴璐, 白彩全, 梁龙武. 中国红色旅游政策实施对网络关注度的空间溢出效应:基于语义分析与空间计量的实证[J]. 自然资源学报, 2021, 36(11):2778-2796.
	DAI L, BAI C Q, LIANG L W. Spatial spillover effect of the red tourism policy on public online attention in China: an empirical study based on semantic analysis and spatial econometric model[J]. J Nat Resour, 2021, 36(11):2778-2796. DOI:10.31497/zrzyxb.20211105.
[14]	GUO X J, YAO H L, CHEN X P, et al. A study on the online attention of emergency events of torrential rain in Shanxi and Henan[J]. Water, 2022, 14(14):2183.DOI: 10.3390/w14142183.
[15]	刘娟, 殷豪, 曹可心, 等. 深圳市不同类型公园服务评价及提升策略[J]. 深圳大学学报(理工版), 2021, 38(4):433-440.
	LIU J, YIN H, CAO K X, et al. Service evaluation and promotion strategies for different types of parks in Shenzhen[J]. J Shenzhen Univ (Sci Eng), 2021, 38(4):433-440. DOI:10.3724/SP.J.1249.2021.04433.
[16]	CLARK M, WILKINS E J, DAGAN D T, et al. Bringing forecasting into the future: using Google to predict visitation in U.S. national parks[J]. J Environ Manage, 2019, 243:88-94. DOI:10.1016/j.jenvman.2019.05.006.
[17]	DERGIADES T, MILAS C, PANAGIOTIDIS T. Tweets, Google trends, and sovereign spreads in the GIIPS[J]. Oxf Econ Pap, 2015, 67(2):406-432. DOI:10.1093/oep/gpu046.
[18]	杨利, 谢慧, 谢炳庚. 中国大陆31个省(市、自治区)湿地旅游网络关注度时空差异及其影响因素[J]. 湖南师范大学自然科学学报, 2022, 45(4):77-85.
	YANG L, XIE H, XIE B G. Spatio-temporal differences and influencing factors of wetland tourism network attention in 31 Provinces (Municipalities and Autonomous Regions) in China’s mainland[J]. J Nat Sci Hunan Norm Univ, 2022, 45(4):77-85. DOI:10.7612/i.issn.2096-5281.2022.04.009.
[19]	郑昭彦. 重大事件对举办地景区网络关注度的影响:以杭州G20峰会为例[J]. 地域研究与开发, 2019, 38(5):111-114.
	ZHENG Z Y. Influence of major events on network attention of the locations’ scenic spots: a case from G20 summit in Hangzhou City[J]. Areal Res Dev, 2019, 38(5):111-114. DOI:10.3969/j.issn.1003-2363.2019.05.021.
[20]	上官莎逸, 刘健, 余坤勇, 等. 福州城市公园绿地可达性影响因素[J]. 福建农林大学学报(自然科学版), 2018, 47(4):494-502.
	SHANG G S Y, LIU J, YU K Y, et al. Factors affecting accessibility of urban park green space in Fuzhou[J]. Agric For Univ, 2018, 47(4):494-502. DOI:10.13323/j.cnki.j.fafu(nat.sci.).2018.04.018.
[21]	关文川, 吴志峰, 吴卓, 等. 基于微信宜出行数据的城市人口活动时空探测及其影响因素分析[J]. 现代城市研究, 2021, 36(8):2-9.
	GUAN W C, WU Z F, WU Z, et al. Study on spatial-temporal detection and influencing factors of urban population activity based on Easygo data[J]. Mod Urban Res, 2021, 36(8):2-9. DOI:10.3969/j.issn.1009-6000.2021.08.001.
[22]	秦诗文, 杨俊宴, 冯雅茹, 等. 基于多源数据的城市公园时空活力与影响因素测度:以南京为例[J]. 中国园林, 2021, 37(1):68-73.
	QIN S W, YANG J Y, FENG Y R, et al. Spatiotemporal vitality and influencing factors of urban parks based on multi-source data: a case study of Nanjing[J]. Chin Landsc Archit, 2021, 37(1):68-73. DOI:10.19775/j.cla.2021.01.0068.
[23]	李方正, 钱蕾西, 臧凤岐, 等. 基于腾讯出行大数据的北京市郊野公园游憩使用及影响因素研究[J]. 风景园林, 2019, 26(4):77-82.
	LI F Z, QIAN L X, ZANG F Q, et al. Study on recreational use and influencing factors of country parks in Beijing based on Tencent travel big data[J]. Landsc Archit, 2019, 26(4):77-82. DOI:10.14085/j.fjyl.2019.04.0077.06.
[24]	钮心毅, 康宁. 上海郊野公园游客活动时空特征及其影响因素:基于手机信令数据的研究[J]. 中国园林, 2021, 37(8):39-43.
	NIU X Y, KANG N. Spatio-temporal characteristics and influencing factors of tourist activities in Shanghai country parks: a study based on mobile phone signaling data[J]. Chin Landsc Archit, 2021, 37(8):39-43. DOI:10.19775/j.cla.2021.08.0039.
[25]	潘雄锋, 刘清, 彭晓雪. 基于全局熵值法模型的我国区域创新能力动态评价与分析[J]. 运筹与管理, 2015, 24(4):155-162.
	PAN X F, LIU Q, PENG X X. Evaluation and analysis of regional innovation ability in China based on overall entropy method[J]. Oper Res Manag Sci, 2015, 24(4):155-162. DOI:10.3969/j.issn.1007-3221.2015.04.023.
[26]	陈赟, 沈浩, 王晓慧, 等. 基于Mann-Kendall趋势检验的城市能源碳达峰评估方法[J]. 上海交通大学学报, 2023, 57(7):928-938.
	CHEN Y, SHEN H, WANG X H, et al. Assessment method for urban energy carbon emission peak based on Mann-Kendall trend test[J]. J Shanghai Jiao Tong Univ, 2023, 57(7):928-938. DOI:10.16183/j.cnki.jsjtu.2021.524.
[27]	孔锋, 孙劭, 王一飞, 等. 近56 a来中国东部地区雨涝事件时空演变特征[J]. 长江流域资源与环境, 2018, 27(7):1554-1564.
	KONG F, SUN S, WANG Y F, et al. Temporal and spatial variation pattern of waterlogging events in eastern China in recent 56 years[J]. Resour Environ Yangtze Basin, 2018, 27(7):1554-1564. DOI:10.11870/cjlyzyyhj201807015.
[28]	曹伟征, 邢贞相, 李衡, 等. 佳木斯地区1956—2010年径流量时间序列变异分析[J]. 水土保持研究, 2018, 25(6):126-130.
	CAO W Z, XING Z X, LI H, et al. Time series analysis on runoff in Jiamusi area from 1956 to 2010[J]. Res Soil Water Conserv, 2018, 25(6):126-130. DOI:10.13869/j.cnki.rswc.2018.06.019.
[29]	马新萍, 白红英, 冯海鹏, 等. 52 a来秦岭南北径流变化对比及影响因素[J]. 干旱区地理, 2013, 36(6):1032-1040.
	MA X P, BAI H Y, FENG H P, et al. Runoff variation and its influencing factors compared between south and north of Qinling Mountains during 50 years[J]. Arid Land Geogr, 2013, 36(6):1032-1040. DOI:10.13826/j.cnki.cn65-1103/x.2013.06.011.
[30]	罗润, 周年兴, 谢秋逸, 等. 基于小波分析的不同类型旅游景区旅游流时间特征研究:以江苏省125家高等级旅游景区为例[J]. 地域研究与开发, 2022, 41(6):99-104.
	LUO R, ZHOU N X, XIE Q Y, et al. Study on the temporal characteristics of tourist flow in different types of scenic spots based on wavelet analysis :a case study of 125 high-level scenic spots in Jiangsu Province[J]. Areal Res Dev, 2022, 41(6):99-104. DOI:10.3969/j.issn.1003-2363.2022.06.017.
[31]	王劲峰, 徐成东. 地理探测器:原理与展望[J]. 地理学报, 2017, 72(1):116-134.
	WANG J F, XU C D. Geodetector: principle and prospective[J]. Acta Geogr Sin, 2017, 72(1):116-134. DOI:10.11821/dlxb201701010.
[32]	张瑞, 李朝奎, 姚思妤, 等. 融合地理探测器和地理加权回归的太原市建设用地变化因素研究[J]. 测绘通报, 2022(5):106-109, 119.
	ZHANG R, LI C K, YAO S Y, et al. Study on the change factors of construction land in Taiyuan by integrating geographic detector and geographically weighted regression[J]. Bull Surv Mapp, 2022(5):106-109, 119. DOI:10.13474/j.cnki.11-2246.2022.0150.
[33]	文妙霞, 何学高, 刘欢, 等. 基于地理探测器的宁夏草地植被覆被时空分异及驱动因子[J]. 干旱区研究, 2023, 40(8):1322-1332.
	WEN M X, HE X G, LIU H, et al. Analysis of the spatiotemporal variation characteristics and driving factors of grassland vegetation cover in Ningxia based on geographical detectors[J]. Arid Zone Res, 2023, 40(8):1322-1332. DOI:10.13866/j.azr.2023.08.13.
[34]	凡来, 张大玉. 基于MGWR的华北地区传统村落空间分异特征及影响因素研究[J]. 中国园林, 2022, 38(10):56-61.
	FAN L, ZHANG D Y. Study on spatial differentiation characteristics and influencing factors of traditional villages in north China based on MGWR model[J]. Chin Landsc Archit, 2022, 38(10):56-61. DOI:10.19775/j.cla.2022.10.0056.
[35]	黄颙昊, 杨新苗, 岳锦涛. 基于多尺度地理加权回归模型的城市道路骑行流量分析[J]. 清华大学学报(自然科学版), 2022, 62(7):1132-1141.
	HUANG Y H, YANG X M, YUE J T. Urban street bicycle flow analysis based on multi-scale geographically weighted regression model[J]. J Tsinghua Univ (Sci Technol), 2022, 62(7):1132-1141. DOI:10.16511/i.cnki.qhdxxb.2022.26.014.
[36]	王强, 崔璐明, 鄢慧丽. 黄河流域区域经济差异影响因素的空间异质性:基于MGWR模型的实证研究[J]. 地域研究与开发, 2023, 42(2):7-13.
	WANG Q, CUI L M, YAN H L. Spatial heterogeneity of driving factors of the regional economic disparity in the Yellow River basin : a multiscale geographically weighted regression approach[J]. Areal Res Dev, 2023, 42(2):7-13. DOI:10.3969/j.issn.1003-2363.2023.02.002.
[37]	孙烨, 张宏磊, 刘培学, 等. 基于旅游者网络关注度的旅游景区日游客量预测研究:以不同客户端百度指数为例[J]. 人文地理, 2017, 32(3):152-160.
	SUN Y, ZHANG H L, LIU P X, et al. Forecast of tourism flow volume of tourist attraction based on degree of tourist attention of travel network: a case study of Baidu Index of different clients[J]. Hum Geogr, 2017, 32(3):152-160. DOI:10.13959/j.issn.1003-2398.2017.03.020.

Metrics

Comments

www.nldxb.njfu.edu.cn

Edited ＆ Published by Editorial Department of Nanjing Forestry University(Natural Sciences Edition)
Address： No.159 Longpan Road,Nanjing 210037 Jiangsu,P.R.China
E-mail ：xuebao@njfu.edu.cn , xuebao@njfu.com.cn
Telephone +86-25-85428247,85427076
Distributed by China International Book Trading Corporation P.O. Box 399, Beijing ,P.R. China

因子类型factor type	变量variable	q	P
城市区位特征(G₂) characteristics of the city location	与中心区距离(X₈)	0.217	0.000
	平均出行时间成本(X₉)	0.120	0.009
	周边房价(X₁₀)	0.148	0.008
	建筑平均高度(X₁₁)	0.125	0.004
周边空间特征(G₃) surrounding spatial features	道路网密度(X₁₄)	0.363	0.000
公园自身属性(G₄) park own attributes	公园面积(X₁₅)	0.270	0.009
区域分布质量(G₅) regional distribution quality	停车场数量(X₁₉)	0.160	0.007

排序 sort	交互类型 interaction type	q
1	X₁₁∩X₁₄	0.643
2	X₁₃∩X₁₄	0.635
3	X₁₁∩X₁₅	0.620
4	X₈∩X₁₅	0.611
5	X₁₃∩X₁₅	0.606
6	X₁₀∩X₁₄	0.586
7	X₆∩X₁₅	0.583
8	X₁₄∩X₁₈	0.577
9	X₁∩X₁₅	0.572
10	X₃∩X₁₅	0.571
11	X₃∩X₁₄	0.557
12	X₆∩X₁₄	0.551

Spatial and temporal characteristics and factors influencing the network attention degree of urban parks based on multi-source data

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 37

Related Articles 13

Recommended Articles

Metrics

Comments

Author

Reader

Reviewer

[1]	LIU Yanfen, HUANG Ruxian, AI Jinwen, YANG Liuqing, YU Kunyong, LIU Jian. The cold island effect and its influence on urban parks in Fuzhou City, China [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(3): 295-303.
[2]	YANG He, MI Feng. Associations between urban green space accessibility and mental health of residents from the perspective of socio-economic status [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2024, 48(1): 248-256.
[3]	SONG Qing, LI Chaoqun, CHEN Junyu. Spatio-temporal evolution and influencing factors of carbon emission efficiency in the Yangtze River Delta region at the city scale [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(6): 251-262.
[4]	YANG Yunfeng, YANG Jiaqi. Ecological renovation design of urban parks in subtropical regions based on mosquito prevention and control [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(6): 211-218.
[5]	ZHANG Yinfeng, CAI Hongyue, PENG Jingen, LIU Xuejun, XIE Lijuan, ZHANG Hua, WANG Yanmei. Effects of different planting environments on the growth of Rhododendron moulmainense in Shenzhen urban parks [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2023, 47(2): 197-204.
[6]	HUANG Shuo, ZHNEG Yu, CHENG Linli, JI Chunyue, WANG Shan, DONG Jianwen. The impact mechanism of urban park health benefit evaluation based on landscape preference [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(5): 221-228.
[7]	HOU Xiujuan, YAN Xiaoyun, WANG Bo, LI Xinyuan, BAO Hongguang. Variation characteristics of the air anion and air particulate matter in arid and semi-arid urban park green spaces during summer [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2022, 46(4): 212-220.
[8]	WANG Zhipeng, WANG Wei, XING Siyi. A study on the relationship between the characteristics and usage of urban park green space and population health [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2021, 45(5): 223-231.
[9]	ZHENG Lingyu, PU Haixia, JIANG Zeping. A study on spatial satisfaction of urban parks based on visible green index [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(4): 199-204.
[10]	ZHANG Jinguang, WEI Wei, CHENG Yingyi, ZHAO Bing. Study on site selection of small and medium-sized city parks based on GIS suitability evaluation [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2020, 44(1): 171-178.
[11]	YE Jienan, WANG Hao. Feasibility analysis of the function structure and spatial layout of green space for urban park in disaster [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2018, 42(03): 175-181.
[12]	WANG Qian, WANG Cheng^{2 Symbolj@@}, DU Wanguang, XU Chao, GUO Junqi. Exploring the ecological healthy function of Phyllostachys pubescens forest in Qishan Park of Fuzhou in summer [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2018, 42(02): 120-126.
[13]	HONG Zhimeng1,2. Investigation on species abundances of plant communities at Xiamen city parks [J]. JOURNAL OF NANJING FORESTRY UNIVERSITY, 2009, 33(02): 51-.