基于GWR模型的成渝城市群生态系统服务时空演变及驱动因素研究

邵明; 董宇翔; 林辰松

doi:10.12171/j.1000-1522.20200217

基于GWR模型的成渝城市群生态系统服务时空演变及驱动因素研究

邵明^1,,
董宇翔²,
林辰松^1, ,

1.
北京林业大学园林学院，北京 100083
2.
同济大学建筑与城市规划学院，上海 200092

基金项目: 中央高校基本科研业务费专项（2017ZY11），北京市共建项目（2015BLUREE01），北京市重点研发计划项目（D171100007117003），国家自然科学基金项目（51908036）

详细信息

作者简介:
邵明，博士生。主要研究方向：风景园林规划设计与理论研究。 Email：shao_0701@foxmail.com　地址：100083北京市海淀区清华东路35号北京林业大学园林学院

责任作者:
林辰松，讲师。主要研究方向：风景园林规划设计与理论研究。 Email：7884231@qq.com　地址：同上

中图分类号: X171.1;S718.5
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出版历程
- 收稿日期: 2020-07-12
- 修回日期: 2020-10-08
- 网络出版日期: 2020-10-28
- 发布日期: 2020-12-13

Spatio-temporal evolution and driving factors of ecosystem services in Chengdu-Chongqing urban agglomeration of southwestern China based on GWR model

1.
School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
2.
College of Architecture and Urban Planning, Tongji University, Shanghai 200092, China

摘要

摘要:
目的对成渝城市群自然生态系统服务时空演变特征与驱动因素进行研究，有助于规划者从宏观尺度上掌握其自然资源存量情况与发展趋势，合理拟定成渝城市群规划建设策略，从而推动我国西部大开发平台建设。
方法选择成渝城市群为研究对象，以144个市、县、区作为研究单元，通过当量因子法量化其生态系统服务，建立“自然—社会”驱动因子指标体系，在此基础上运用最小二乘法（OLS）模型筛选具有显著相关性的驱动因子，运用地理加权回归（GWR）构建回归模型，研究成渝城市群自然社会因素对生态系统服务的驱动作用。
结果（1）成渝城市群生态系统服务总量劣化趋势显著，1995—2015年间（1995、2000、2005、2010、2015年，共5期）成渝城市群生态系统服务价值分别为3 470.53亿、3 464.08亿、3 452.43亿、3 438.02亿和3 423.92亿元，分别比前一期下降6.45亿、11.65亿、14.40亿和14.10亿元；（2）成渝城市群生态系统服务空间分布以成都、重庆两处核心城市中心区与缙云—中梁山脉中心区呈现显著区域化分布特征，全域超过90.38%的区域生态系统服务均处于下降趋势；（3）降雨量、温度、单位面积平均GDP、林业用地比例、人口密度5项驱动因子对成渝城市群生态系统服务具有显著影响。其中，降雨量、单位面积平均GDP呈现较明显的负相关驱动特征，林地覆盖比例呈现较明显的正相关驱动特征，降雨量与人口密度则呈现较明显的两极化趋势。
结论本研究明确了在研究期限内成渝城市群生态系统服务逐渐劣化的发展状态，所构建的地理加权回归（GWR）模型在空间层面上量化降雨量、温度、单位面积平均GDP、林业用地比例、人口密度5项主要驱动因素对成渝城市群生态系统服务所呈现的不同驱动特征与驱动强度，为成渝城市群长远规划提供决策依据。
- 成渝城市群 /
- 生态系统服务 /
- 地理加权回归 /
- 驱动因素
Abstract:
Objective The study on the temporal and spatial evolution characteristics and driving factors of the ecosystem services of Chengdu-Chongqing urban agglomeration can help planners grasp its situation of natural resource stock and development trend on a macro scale, reasonably formulate the planning and construction strategy of Chengdu-Chongqing urban agglomeration of southwestern China, so as to promote the construction of China’s western development platform.
Method In this study, the whole region of Chengdu-Chongqing urban agglomeration was selected as the research object, and 144 districts, counties and regions were taken as the research units. The values of ecosystem services were quantified by equivalent factor method, and the bi-directional driving factor index system of “natural environment-social economy” was established. On this basis, the least square method (OLS) model was used to screen the driving factor models with significant correlation, and the geographical weighted regression (GWR) model was used to study the driving correlation between the ecosystem services, natural environment characteristics and socio-economic factors of Chengdu-Chongqing urban agglomeration.
Result (1) The total amount of ecosystem services of Chengdu-Chongqing urban agglomeration showed a significant trend of deterioration. From 1995 to 2015（totally 5 periods, i.e. 1995, 2000, 2005, 2010, 2015), the ecosystem service values of Chengdu-Chongqing urban agglomeration were 347.053, 346.408, 345.243, 343.802 and 342.392 billion CNY, respectively, showing a value decline of 0.645 billion, 1.165 billion, 1.440 billion and 1.410 billion CNY, respectively compared with the former period. (2) The spatial distribution of ecosystem services in Chengdu-Chongqing urban agglomeration was characterized by significant regional distribution in the core urban centers of Chengdu and Chongqing and the central area of Jinyun-Zhongliang Mountains. More than 90.38% of the regional ecosystem services in the whole region were in a downward trend. (3) Research results on ecological driving factors of Chengdu-Chongqing urban agglomeration showed that, five driving factors, namely rainfall, temperature, per capita GDP, forestry land ratio and population density, had significant influences on ecosystem services of Chengdu-Chongqing urban agglomeration. Among them, rainfall and land per capita GDP showed obvious negative correlation driving characteristics, the proportion of forest land showed obvious positive correlation driving characteristics, and rainfall and population density showed obvious polarization trend.
Conclusion In this study, the status of deterioration of ecosystem services in Chengdu-Chongqing urban agglomeration was clarified. The GWR (geographic weighted regression) model was constructed to quantify the different driving characteristics and driving intensities of the five main driving factors of rainfall, temperature, land average GDP, forestry land proportion and population density on the ecosystem services of Chengdu-Chongqing urban agglomeration, which provide decision-making basis for the long-term planning of Chengdu-Chongqing urban agglomeration.
- Chengdu-Chongqing urban agglomeration /
- ecosystem service /
- geographically weighted regression /
- driving factor

HTML全文

图 1 成渝城市群生态系统服务评价

Figure 1. Ecosystem service evaluation of Chengdu-Chongqing urban agglomeration

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图 2 地理加权回归（GWR）模型拟合结果

Figure 2. Fitting result of geographically weighted regression (GWR) model

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图 3 成渝城市群温度与生态系统服务回归系数空间分布

Figure 3. Spatial distribution of regression coefficients between temperature and ecosystem service in Chengdu-Chongqing urban agglomeration

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图 7 成渝城市群人口密度与生态系统服务回归系数空间分布

Figure 7. Spatial distribution of regression coefficients between population density and ecosystem service in Chengdu-Chongqing urban agglomeration

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图 4 成渝城市群降雨量与生态系统服务回归系数空间分布

Figure 4. Spatial distribution of regression coefficients between rainfall and ecosystem service in Chengdu-Chongqing urban agglomeration

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图 5 成渝城市群林地覆盖比例与生态系统服务回归系数空间分布

Figure 5. Spatial distribution of regression coefficients between forest land proportion and ecosystem service in Chengdu-Chongqing urban agglomeration

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图 6 成渝城市群单位面积平均GDP与生态系统服务回归系数空间分布

Figure 6. Spatial distribution of regression coefficients between average GDP per unit area and ecosystem service in Chengdu-Chongqing urban agglomeration

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表 1 数据来源及精度表

Table 1 Data sources and accuracy table

数据名称 Data name	数据描述 Data description	数据格式/精度 Data format/accuracy	数据来源 Data source
行政边界 Administrative boundary	正式政府规划边界 Official government planning boundary	Shp	《成渝城市群发展规划》 Chengdu-Chongqing City Cluster Development Plan
土地利用 Land use	土地利用 Land use	Raster/250 m	欧洲航天航空局(ESA) European Space Agency
农产经济数据 Agricultural economic data	四川省粮食经济数据 Grain economic data of Sichuan Province	Excel	《中国农产品成本收益资料汇编》《中国统计年鉴》 Data Collection on the Cost and Benefit of China’s Agricultural Products (China Statistical Yearbook)
高程 Elevation	平均高程数据 Data of average elevation	Raster/30 m	地理空间数据云平台(http://www.gscloud.cn/) Geospatial data cloud platform (http://www.gscloud.cn/)
植被覆盖指数 Vegetation coverage index(NDVI)	年均NDVI Average annual NDVI	Raster/500 m	地理空间数据云平台(http://www.gscloud.cn/) Geospatial data cloud platform (http://www.gscloud.cn/)
降雨量 Rainfall	年均降雨量 Average annual rainfall	Raster/1 km	中国科学院资源环境科学数据中心(www.resdc.cn) Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (www.resdc.cn)
温度 Temperature	年均地表温度 Average annual land surface temperature	Raster/1 km	中国科学院资源环境科学数据中心(www.resdc.cn) Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (www.resdc.cn)
单位面积GDP GDP per unit area	每平方公里平均GDP Average GDP per square kilometer	Raster/1 km	中国科学院资源环境科学数据中心(www.resdc.cn) Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (www.resdc.cn)
林地面积比例 Forestland area ratio	森林生态系统占行政单元总面积比例 Forestland area ratio to the administrative units	Shp	欧洲航天航空局(ESA) European Space Agency
人口密度 Population density	每平方公里平均人口数量 Average population per square kilometer	Raster/1 km	全球人口动态统计分析数据库 Global statistical analysis database of population dynamics
已建成区面积比例 Built-up area ratio	已建成区占行政单元总面积比例 Proportion of built up area to the administrative units	Shp	清华大学地球系统科学数据库 Tsinghua University Earth System Science Database
土壤 Soil	土壤含沙量 Soil sediment concentration	Raster/1 km	世界土壤数据库 Harmonized World Soil Database
基础设施密度 Infrastructure density	基础设施在行政单元内比例 Infrastructure ratio to the administrative units	Shp	高德地图POI接口 AutoNavi POI interface

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表 2 驱动因子统计学分析表

Table 2 Statistical analysis on driving factors

驱动因子 Driving factor		平均值 Average value	标准差 Standard deviation	最大值 Maximum value	最小值 Minimum value
自然环境因子 Natural environmental factor	高程 Elevation/m	608.477 5	391.038 9	2 466.168 1	243.272 3
	土壤(含沙量) Soil (sediment concentration)/%	11.144 2	19.276 7	90.000 0	0.000 0
	植被覆盖指数 NDVI	0.761 0	0.085 3	0.879 5	0.404 3
	降雨量 Rainfall/mm	1 053.407 4	123.527 2	1 247.849 5	789.998 9
	温度 Temperature/℃	16.927 6	1.725 4	19.226 3	10.279 7
社会经济因子 Socioeconomic factor	单位面积GDP/(元·km⁻²) GDP per unit area/(CNY·km⁻²)	6 745.219 8	18 194.331 5	136 089.924 2	145.433 3
	人口密度/(人·km⁻²) Population density/(person·km⁻²)	893.455 7	2 274.309 1	21 474.381 0	39.794 2
	已建成区面积比例 Built-up area ratio/%	0.071 5	0.152 6	0.863 5	0.000 0
	林地面积比例 Forestland area ratio/%	0.170 4	0.156 9	0.670 9	0.005 2
	基础设施密度/(个·km⁻²) Infrastructure density /(number·km⁻²)	0.339 7	0.784 8	4.027 5	0.003 7

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表 3 基于探索性回归的OLS模型参数因子筛选结果表

Table 3 Filtering results of parameter factor based on exploratory regression

	驱动因素 Driving factor	初始模型 Initial model			筛选后模型 Filtered model
	驱动因素 Driving factor	系数 Coefficient	标准差 Standard deviation	概率/稳健概率 Probability/ robust probability	系数 Coefficient	标准差 Standard deviation	概率/稳健概率 Probability/ robust probability
自然环境因子 Natural environmental factor	高程 Elevation	0.021 3	0.009 5 m	/
	土壤(含沙量) Soil (sediment concentration)	0.054 2	0.049 3%
	植被覆盖指数 NDVI	43.740 9	31.353 2
	降雨量 Rainfall	0.027 0	0.010 0 mm	/	0.064 4	0.009 8 mm	*/*
	温度 Temperature	1.164 0	2.284 4 ℃		−5.725 3	0.705 5 ℃	*/*
社会经济因子 Socioeconomic factor	单位面积GDP GDP per unit area	0.000 0	0.000 1元/km² 0.000 1 CNY/km²		−0.000 6	0.000 1元/km² 0.000 1 CNY/km²	*/*
	人口密度 Population density	0.000 8	0.000 7人/km² 0.000 7 person/km²		−0.003 1	0.000 6人/km² 0.000 6 person/km²	*/
	已建成区面积比例 Built-up area ratio	−138.790 0	34.630 7%	*/*
	林地面积比例 Forestland area ratio	272.574 3	6.440 8%	*/*	282.986 2	7.336 9%	*/*
	基础设施密度 Infrastructure density	4.783 3	5.611 1个/km² 5.611 1 number/km²
注：、、*分别为P < 0.05、P < 0.01、P < 0.001；初始模型与筛选后模型均满足R² > 0.6的回归拟合优度。Notes: ，，* mean P < 0.05, P < 0.01, P < 0.001; both the initial model and the screened model meet R² > 0.6 goodness of regression fitting.

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