中国地级城市建成区蓝绿空间时空格局及其气候影响因素分析

蓝绿空间是城市生态安全的重要保障。本文通过谷歌地球引擎（GEE）提取2005、2010、2015与2020年的植被与水体指数数据,构建了包含272个地级城市建成区的蓝绿空间数据库。运用蓝绿空间覆盖率、300 m服务半径覆盖率、分维数与分离度4个指标分析其时空格局与演化模式,并进一步探讨其气候影响因素。结果表明：(1)地级城市建成区蓝绿空间整体呈现为“南高北低”的格局,且南方“西高东低”、北方“东高西低”,但环渤海地区为蓝绿空间覆盖率“洼地”;时间格局为“总体增长、局部降低”,华中地区呈下降趋势的城市最多;(2)就分区而言,西南地区城市建成区蓝绿空间覆盖率最高（平均高于65%）、分离度最低（平均低于0.60）,西北地区覆盖率分异较大,华北地区覆盖率最低（平均10%～30%）且分离度最高（平均约0.98）;(3)采用多尺度地理加权回归模型所得R2为0.85（校正R2为0.83）,其中,降水量对城市建成区蓝绿空间的影响最显著,降水量变化成正相关,气温则成负相关;整体上气候影响与比人为影响相当,但在某些时段间更大。     

Spatial distribution and the scale effect of influencing factors of specialized and sophisticated enterprises that produce new and unique products in the Yangtze River Delta region: Based on the buffer and MGWR analyses北大核心CSCD

High-quality cultivation of specialized and sophisticated enterprises that produce new and unique products is an important starting point for consolidating the security foundation of China's industrial chain and supply chain. Using buffer zone analysis and the multi-scale geographically weighted regression (MGWR) model, this study explored the spatial distribution of specialized and sophisticated enterprises that produce new and unique products in the Yangtze River Delta region and influencing factors in 2021. The study found that: 1) Spatially, Shanghai is the main area where specialized and sophisticated enterprises that produce new and unique products are concentrated, followed by provincial capitals and cities on the coast and along rivers; The overall composition of the industry is unbalanced, and the real economic industries such as machinery and equipment manufacturing and high-tech manufacturing account for a relatively high proportion, but there are differences in different regions. 2) In terms of spatial agglomeration, differences between circles and the scale effect are obvious. Within each province, the spatial distribution of specialized and sophisticated enterprises that produce new and unique products in different cities is uneven. Region-wide, the overall spatial distribution pattern of "one pole and multiple cores" is evident. Shanghai is the main agglomeration area for these enterprises, and the provincial capital cities and cities that are the regional economic centers are the secondary agglomeration areas of these enterprises. 3) The four dimensions of influencing factors—physical geography, government, market, and society—have a scale effect on the spatial distribution of specialized and sophisticated enterprises that produce new and unique products. The degree of land development acts at a small scale, which is a local variable, and shows a large difference in the impact on the spatial distribution of these enterprises across the region. Factors such as elevation, government-business relationship, degree of marketization, number and scale of enterprises, degree of openness, logistics development level, and innovation environment are global variables, and except that the degree of marketization and the number of enterprises have a significant negative impact on the spatial distribution of these enterprises, the impact of all other factors is significantly positive. The research results can provide support for the optimization of the layout of new special expertise enterprise space in the Yangtze River Delta region, in order to provide reference for the formulation of new special expertise policies and industrial planning. © 2023, Editorial office of PROGRESS IN GEOGRAPHY. All rights reserved.     

多尺度地理加权回归的地表温度降尺度研究

由于星载热红外传感器研发技术的局限性,单一传感器尚不能提供兼具高频次、高空间分辨率地表温度数据。协同其他遥感辅助数据,对低空间分辨率、高时间频次地表温度产品开展降尺度研究成为了解决这一难题的有效途径。然而由于现有地表温度降尺度方法未充分考虑不同地表状态参数对地表温度空间分异格局的尺度影响差异,降尺度后的地表温度数据在异质性景观区域存在精度较差和空间纹理不清晰的问题。鉴于此,本文以北京和张掖地区的8期MODIS地表温度产品为例,通过引入多尺度地理加权回归MGWR（Multiscale Geographically Weighted Regression）来分析归一化植被指数NDVI、数字高程模型DEM、坡度和经纬度对地表温度空间格局影响的尺度差异,提出一种针对MODIS地表温度产品的空间降尺度算法,并与TsHARP算法、多元线性回归算法、地理加权回归算法和随机森林回归算法进行定量对比。结果表明,基于MGWR模型的地表温度降尺度转换函数能够良好地揭示多种地表状态参数与地表温度间的不同作用关系,其中NDVI和坡度对地表温度分布具有全局影响,DEM和经纬度对地表温度呈现出了局域性作用。与4种代表性方法相比,基于MGWR算法降尺度后的100 m分辨率地表温度数据具有更好的空间纹理,在城镇和沙漠等温度异质性明显地区保障了清晰的景观纹理;另外,对于所选研究区的8期MODIS地表温度产品而言,利用MGWR算法降尺度后的地表温度均拥有更好的精度,在0—1 K误差级别下的面积占比均大于57%,均方根误差RMSE（Root-Mean-Square Error）均小于2.85 K,决定系数R²（coefficient of determination）均大于0.88。     

城市群水污染物排放的驱动因素及尺度效应

揭示水污染物排放的驱动因素及尺度效应是研究城市群演化环境效应的重要议题,也是实现城市群水污染物协同减排和水环境协同治理的重要前提。以长三角城市群的305个县域为例,选取化学需氧量（COD）和氨氮（NH₃-N）两项特征污染物指标,在2011—2016年水污染物排放时空格局分析基础上,采用空间滞后模型（SLM）和空间误差模型（SEM）评估城市群水污染物排放的驱动因素,构建多尺度地理加权回归（MGWR）模型诊断驱动力的尺度效应及空间异质性。结果显示：常住人口规模、城镇化水平和经济发展水平对水污染物排放呈现全域层面的正向驱动,而工业化水平、社会固定资产投资、外商直接投资、地方财政分权程度以局部的微观作用为主;固定资产投资在杭嘉湖及沿海地区对COD和NH₃-N排放均呈较强正向驱动,工业化水平在太湖流域及浙江全域对COD排放呈正向驱动;外商直接投资对NH₃-N排放呈局部抑制作用,外资投入的“污染光环”效应在苏北、皖北、浙南等城市群边缘区较为突出;地方财政分权在城市群中心区对COD排放具有明显抑制作用,反映出地方环保意识提升和多层级环境规制强约束的积极影响。建议引导绿色生产生活方式以降低水污染物排放基数,在工业生产、资本投入和财政收支等领域嵌入环境倒逼机制,系统构建与水环境承载力相适应的城市群高质量发展格局。     

流动人口购房意愿影响因素的空间异质性——基于MGWR模型的研究

基于2016年中国流动人口动态监测数据,运用多尺度地理加权回归模型对流动人口流入地购房意愿影响因素的空间异质性进行分析。研究发现：① 中国流动人口在流入地城市的购房意愿整体偏低,尤其在流动人口聚集的东南沿海地区,流动人口购房意愿最低。② 多尺度地理加权回归（multi-scale geographically weighted regression,MGWR）模型能识别出不同因素对购房意愿的影响具有空间尺度差异,其中户口类型、流动范围、流动次数等显著变量对不同区域流动人口购房意愿的影响存在明显的区域差异。③ 各影响因素呈现显著的空间分异格局,其中婚姻状况、户口类型、职业类型、房价、公共服务等因素对东南地区购房意愿的影响较大,收入、流动次数、已购住房、随迁子女等因素对东北地区购房意愿的影响更大,西北地区平均受教育年限和拥有住房公积金对流动人口购房意愿的正向促进作用显著,而跨省流动的负向影响由西北向中部地区梯度递减。     

建成环境对城市不同年龄群体活力的时空异质性影响研究

城市活力是城市空间质量的重要体现,对构建可持续发展的城市空间具有重要参考意义。该文以西宁市主城区为例,利用手机信令数据探索城市不同年龄群体活力的空间分布模式,采用多元线性逐步回归模型和多尺度地理加权回归(MGWR)模型定量研究建成环境对城市不同年龄群体活力的时空异质性影响。研究发现:1)根据不同年龄群体活动能力和需求的差异可将活力空间分布模式划分为“离核紧凑型”“离核延伸型”“聚核延伸型”“聚核紧凑型”4种群体活力分布模式。2)建成环境对群体活力影响的群体差异和时空异质性显著。区位条件普遍影响各群体活力且具有稳健的时间变化特征;道路交叉口密度有利于青年和老年群体活力的提升,且白天影响力高于夜晚;公交线路数量仅与老年群体白天活力呈正相关关系;白天时段建筑密度对少年和老年群体活力的积极影响高于夜晚;容积率对青年群体夜晚活力和老年群体白天活力的影响完全相反;相比功能混合度,具体配套设施密度对各群体活力影响更突出。研究结果可为城市精准设计和差异化设施布局提供借鉴,促进城市空间协调发展。     

Drivers of water pollutant discharge in urban agglomerations and their scale effects: Empirical analysis of 305 counties in the Yangtze River Delta

Revealing the drivers and scale effects of water pollutant discharges is an important issue in the study of the environmental consequences during urban agglomeration evolution. It is also a prerequisite for realizing collaborative water pollutant reduction and environmental governance in urban agglomerations. This paper takes 305 counties in the Yangtze River Delta (YRD) as an example and selects chemical oxygen demand (COD) and ammonia nitrogen (NH₃-N) as two distinctive pollutant indicators, using the Spatial Lag Model (SLM) and Spatial Error Model (SEM) to estimate the drivers of water pollutant discharges in 2011 and 2016. Then the Multiscale Geographically Weighted Regression (MGWR) model is constructed to diagnose the scale effect and spatial heterogeneity of the drivers. The findings show that the size of population, the level of urbanization, and the economic development level show global-level increase impacts on water pollutant discharges, while the level of industrialization, social fixed assets investment, foreign direct investment, and local fiscal decentralization are local-level impacts. The spatial heterogeneity of local drivers presents the following characteristics: Social fixed assets investment has a strong promoting effect on both COD and NH₃-N discharges in the Hangzhou-Jiaxing-Huzhou region and the coastal area of the YRD; industrialization has a promoting effect on COD discharges in the Taihu Lake basin and Zhejiang province; foreign direct investment has a local inhibitory effect on NH₃-N discharge, and the pollution halo effect is more prominent in the marginal areas of the YRD such as northern Jiangsu, northern Anhui, and southern Zhejiang; local fiscal decentralization has a noticeable inhibitory effect on COD discharge in the central areas of the YRD, reflecting the positive impacts on improved local environmental awareness and stronger constraints of multilevel environmental regulations in the urban agglomeration. Therefore, it is recommended to guide greener development to reduce the water pollutant discharge; to embed an environmental push-back mechanism in the fields of industrial production, capital investment, and financial income and expenditure; and to establish a high-quality development pattern of urban agglomerations systematically compatible with the carrying capacity of the water environment.     

长三角城市网络外部性的空间异质性

城市网络中是否存在“低端锁定”现象,低等级城市能否从网络中获益,高等级城市是否发挥了辐射带动作用,这些问题关系城市群发展的战略导向,当前仍缺乏深入论证。城市网络外部性是指城市因联系而得到的效益,为回答上述问题提供了理论依据,已成为城市网络研究的新热点。但现有研究主要关注网络外部性的整体特征,忽视区域内城市间的网络外部性差异。本文使用地理加权回归模型将网络外部性测度精确到每个城市,揭示网络外部性的空间异质性特征。研究以长三角地区41个城市为研究对象,按城市汇总企业之间相互投资规模构建城市网络,分析网络联系对城市发展水平的影响,证实了长三角地区存在网络外部性。研究发现：① 虽然网络外部性未成为主导,但嵌入城市网络已经对江浙沪城市发展起到了积极影响。② 网络外部性的地域差异较大,江苏省城市的网络外部性普遍强于浙江省,安徽省的网络外部性不强,城市发展仍主要由人、土地和资本的本地集聚决定。③ 长三角地区的网络外部性主要是在上海的辐射带动作用下形成的,3个省会城市南京、合肥、杭州对周边城市的辐射带动作用有限。④ 低等级城市虽未从网络中获得明显的效益,但也未被“低端锁定”,高等级城市没有在周边形成“集聚阴影”。本文希望能有助于完善城市网络外部性理论,为区域网络化发展提供理论支撑。