Spatial and Temporal Differentiation of Urban Park Land Growth and Its Driving Factors:Empirical Analysis Based on Urban Panel Data of Hunan Province
Received date: 2021-05-22
Revised date: 2021-10-27
Online published: 2025-03-31
Under the background of ecological civilization strategy,the land management of urban park plays more and more important role in urban land use benefit,urban social transformation and sustainable development. Based on the panel data of 13 cities (autonomous prefecture) in Hunan from 2001 to 2016,this paper conducts an empirical study on the spatial and temporal evolution characteristics and driving factors of urban park land growth using ArcGIS spatial analysis,variation coefficient,Teil index and panel regression model. The results show that: 1) The urban park land growth in Hunan Province was on the rise,and the per capita urban park area was 2.509 m2 in 2016. It had significant differences among cities in Hunan and showed the characteristic of coherence in term of spatial distribution,high-value areas mainly distributed in the north and east of Hunan. 2) From 2001 to 2016,the regional differences in urban park land use growth in Hunan Province were gradually decreasing,the relative development speed was significantly different among different cities,Changde and Zhuzhou developed fastest. 3) Real estate investment,urban transportation and ecological environment had positive driving effect on land use growth of urban park,while population density and economic growth had negative driving effect on land use growth of urban park. 4)The driving mechanism of urban park land growth was as follows: the five factors of ecological environment, real estate investment, urban transportation, economic growth and population size jointly drive the growth of urban park land through their different attribute characteristics and action process. The urban park land has gradually become an important land demand for high quality urban life,and the urban development of Hunan Province is still in the pre-transformation stage of economic growth inhibiting park land growth.
LUO Wenbin , CHU Xuelian , TANG Pei , DING Dexiao , WU Cifang , WANG Zhao . Spatial and Temporal Differentiation of Urban Park Land Growth and Its Driving Factors:Empirical Analysis Based on Urban Panel Data of Hunan Province[J]. Economic geography, 2021 , 41(12) : 74 -83 . DOI: 10.15957/j.cnki.jjdl.2021.12.008
表1 2001—2016年湖南省13个地级市城市公园用地面积变化(单位:m2/人)Tab.1 Change of urban park area in 13 prefecture-level cities of Hunan Province from 2001 to 2016 |
年份 | 长沙 | 株洲 | 湘潭 | 衡阳 | 邵阳 | 岳阳 | 常德 | 张家界 | 益阳 | 郴州 | 永州 | 怀化 | 娄底 | 平均值 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2001 | 2.728 | 1.279 | 1.064 | 0.497 | 0.523 | 0.435 | 0.390 | 0.228 | 1.189 | 1.505 | 0.600 | 0.566 | 0.857 | 0.912 |
2002 | 2.567 | 1.647 | 2.335 | 0.577 | 0.903 | 0.638 | 1.098 | 0.373 | 1.349 | 1.267 | 0.556 | 0.521 | 1.902 | 1.210 |
2003 | 3.056 | 1.609 | 2.332 | 0.574 | 0.964 | 0.667 | 1.403 | 0.638 | 1.317 | 1.098 | 0.522 | 1.030 | 1.511 | 1.286 |
2004 | 2.893 | 2.848 | 2.014 | 0.741 | 1.252 | 1.189 | 1.858 | 0.382 | 1.739 | 1.399 | 0.800 | 0.902 | 1.541 | 1.505 |
2005 | 3.418 | 2.752 | 1.884 | 0.689 | 1.212 | 2.361 | 1.770 | 0.358 | 1.596 | 1.003 | 0.765 | 1.063 | 1.119 | 1.538 |
2006 | 3.406 | 2.92 | 0.663 | 0.625 | 2.290 | 1.003 | 1.977 | 0.690 | 1.469 | 1.280 | 0.682 | 1.455 | 1.546 | 1.539 |
2007 | 3.393 | 3.132 | 0.646 | 0.559 | 2.115 | 0.919 | 2.048 | 0.657 | 1.363 | 1.232 | 0.590 | 1.359 | 1.453 | 1.497 |
2008 | 3.295 | 3.127 | 0.949 | 0.778 | 2.066 | 0.907 | 1.988 | 0.625 | 1.280 | 1.208 | 0.578 | 1.294 | 1.410 | 1.500 |
2009 | 3.242 | 3.635 | 0.936 | 0.526 | 2.000 | 0.879 | 3.103 | 0.705 | 1.613 | 1.420 | 0.574 | 1.249 | 1.383 | 1.636 |
2010 | 2.776 | 5.005 | 1.030 | 0.528 | 2.011 | 0.762 | 3.300 | 0.841 | 2.376 | 1.062 | 0.696 | 1.344 | 1.814 | 1.811 |
2011 | 2.724 | 4.801 | 0.986 | 0.499 | 2.185 | 1.441 | 3.189 | 0.923 | 2.301 | 0.913 | 0.647 | 1.284 | 1.329 | 1.786 |
2012 | 3.172 | 4.639 | 1.065 | 2.738 | 2.050 | 1.388 | 3.056 | 1.043 | 2.786 | 2.088 | 0.625 | 1.605 | 1.264 | 2.117 |
2013 | 3.101 | 4.832 | 1.070 | 2.214 | 2.081 | 1.604 | 3.051 | 1.268 | 2.788 | 2.22 | 0.596 | 1.532 | 1.204 | 2.120 |
2014 | 3.364 | 5.129 | 1.045 | 2.405 | 1.978 | 1.589 | 2.937 | 1.442 | 2.610 | 2.344 | 0.572 | 1.487 | 1.920 | 2.217 |
2015 | 3.273 | 5.028 | 1.020 | 2.374 | 2.068 | 1.530 | 2.826 | 1.397 | 2.464 | 2.937 | 1.582 | 1.427 | 1.844 | 2.290 |
2016 | 3.446 | 5.382 | 0.996 | 2.304 | 2.267 | 1.530 | 4.617 | 1.467 | 2.589 | 2.975 | 1.514 | 1.510 | 2.022 | 2.509 |
表2 因子得分系数矩阵表Tab.2 Coefficient matrix of factor score |
变量 | 估计系数 | 标准差 | t检验值 | P值 | R2值 | 调整后R2值 | F检验值(p值) | 样本数 |
---|---|---|---|---|---|---|---|---|
C | -3.2832 | 0.6061 | -5.4170*** | 0.0000 | 0.4649 | 0.4517 | 35.1030*** (0.0000) | 208 |
PCGDP | -0.3211 | 0.1113 | -2.8849*** | 0.0043 | ||||
POD | -0.0929 | 0.0364 | -2.5488** | 0.0116 | ||||
REI | 0.3158 | 0.0636 | 4.9610*** | 0.0000 | ||||
GBD | 0.7130 | 0.1944 | 3.6673*** | 0.0003 | ||||
URA | 0.1410 | 0.0737 | 2.0343** | 0.0432 |
注:*、**、***分别代表 10%、5%、1%显著性水平。 |
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