Modeling of spatial distributions of farmland density and its temporal change using geographically weighted regression model

This study used spatial autoregression（SAR） model and geographically weighted regression（GWR） model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity. Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased. SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices（i.e.,farmland density and temporal change）. The GWR model has smooth process when constructing the farmland spatial model. The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations. The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious. The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.     

中国地级市人为热总量的估算及驱动因素分析

人为热一定程度上影响着城市的局地环境和微气候.以2016年中国地级市为研究对象,首先采用了能源消耗清单法结合Suomi-NPP (National Polar-orbiting Partnership)VIIRS (Visible Infrared Imaging Radiometer Suite)夜间灯光数据的方法估...     

Rank-size distributions of Chinese cities: Macro and micro patterns

A large number of studies have been conducted to find a better fit for city rank-size distributions in different countries. Many theoretical curves have been proposed, but no consensus has been reached. This study argues for the importance of examining city rank-size distribution across different city size scales. In addition to focusing on macro patterns, this study examines the micro patterns of city rank-size distributions in China. A moving window method is developed to detect rank-size distributions of cities in different sizes incrementally. The results show that micro patterns of the actual city rank-size distributions in China are much more complex than those suggested by the three theoretical distributions examined(Pareto, quadratic, and q-exponential distributions). City size distributions present persistent discontinuities. Large cities are more evenly distributed than small cities and than that predicted by Zipf′s law. In addition, the trend is becoming more pronounced over time. Medium-sized cities became evenly distributed first and then unevenly distributed thereafter. The rank-size distributions of small cities are relatively consistent. While the three theoretical distributions examined in this study all have the ability to detect the overall dynamics of city rank-size distributions, the actual macro distribution may be composed of a combination of the three theoretical distributions.     

基于地理神经网络加权回归的中国PM2.5浓度空间分布估算方法

中国空气污染问题日益严重,为获得连续的PM_2.5浓度空间分布,现有研究建立了多种基于统计回归的PM_2.5估算模型。然而,由于PM_2.5回归关系显著的空间非平稳性和复杂的非线性特征,如何实现高精度、高合理性的PM_2.5浓度空间大面估计仍然面临挑战,尤其在地形变化复杂、覆盖范围广阔的中国地区更为突出。本文引入了一种将普通线性回归（OLR）和神经网络结合的地理神经网络加权回归（GNNWR）模型,通过集成遥感数据、气象数据和地理信息数据建立了基于GNNWR的PM_2.5浓度空间估算方法。文章以中国2017年PM_2.5年平均浓度估算为例,开展了该模型与OLR、地理加权回归（GWR）的比较实验。实验结果表明,基于GNNWR的PM_2.浓度估算性能指标均明显优于OLR和GWR,且预测精度显著高于GWR。此外,GNNWR获得的PM_2.5浓度空间分布也更为合理,较为细致地刻画了中国地区PM_2.5浓度的局部空间变化和细节层次。     

Environmental effects of foreign trade and its spatial variations in mid-eastern provinces and cities of China

The studies on environmental effects of foreign trade and its spatial variations are helpful to design and implement environmental protection countermeasures.In order to eliminate the adverse effects of insufficient observation values on the accuracy of regression results and dynamic information quantity of fitting equation during empirical study,panel data of the mid-eastern provinces and cities of China from 1985 to 2007 were selected based on the adjustment of classical regression model in this paper.Panel unit root test and panel cointegration analysis method were applied to investigating the environmental effects of foreign trade and its spatial variations in the mid-eastern provinces and cities of China and its three groups divided by foreign trade dependence.The results show that all scale effects are positive,while all technical effects are negative and unable to counteract positive scale effects.Foreign trade development is regarded as an important cause for outstanding eco-environmental problems in the mid-eastern provinces and cities of China.Total effects and structural effects are significantly different among different groups because of spatial variations in environmental policies,export destinations,source of FDI,etc.Following the principle of′coordinating generality and considering differences comprehensively′,it is essential to issue a series of policies and countermeasures corresponding to differences in regional environmental effect of foreign trade,in order to coordinate the relationship between foreign trade development and eco-environment in each region.     

陕西省HFRS疫情时空分异特征及影响要素研究

肾综合征出血热（HFRS）主要是由鼠类携带传播汉坦病毒而引起的一类自然疫源性传染病,严重危害着人类健康。陕西省是我国HFRS疫情最严重的省份之一,发病率居全国前列,研究其疫情时空分异和影响要素对指导当地疫情防控具有重要意义。本研究基于2005—2017年县区尺度HFRS发病率数据,采用空间自相关、热点分析等方法分析陕西省疫情时空分异特征,并利用地理探测器探究影响疫情的主要自然环境和社会经济要素。结果表明：2005—2017年陕西省HFRS发病率明显高于全国水平,同时呈现明显的时间波动和空间聚集,平原面积占比、建设用地面积占比、人口密度等因素可以解释约20%的HFRS疫情空间分异;关中平原聚集了陕西省90%以上的高发病县区,其疫情亦呈现明显的空间分异性,主要受降水量、 NDVI、土地利用类型等因素的影响。由此可知,高发病县区聚集、且自然环境和社会经济条件明显不同的关中平原是陕西省HFRS疫情流行的关键地区。因此,建议陕西省HFRS疫情防控应当重点关注降水量、植被状况以及土地利用类型,特别是在土地城镇化水平较高、人口密度较大的关中平原进行有效的防控干预。     

耦合卡尔曼滤波和多层次聚类的中国PM2.5时空分布分析

近年来,细颗粒物污染尤其是PM_2.5受到人们越来越多的关注,研究PM_2.5的时空分布规律也具有越来越重大的意义。传统的遥感反演方法模型复杂,且不能揭示近地表面的PM_2.5分布规律。地面监测站的建设为PM_2.5的研究提供了更实时的观测数据,但由于测量噪声的影响,观测数据存在不准确的极端异常值。为了揭示中国PM_2.5的时空分布特征,本研究采用Kalman滤波对2015年中国338个城市的空气质量监测网络大数据进行最佳估计,并分析其时空特征。同时,根据中国各城市的PM_2.5浓度的时序分布,采用基于DTW的K-Medoids聚类方法将其分为4个等级,并采用q统计量来评估PM_2.5浓度分布的空间分层异质性。结果表明,采用Kalman滤波能有效去除数据噪声,峰值信噪比（PSNR）明显增大。在时空分布上,中国PM_2.5时间分布曲线呈现“U”形,冬季PM_2.5浓度明显高于夏季,且日变化曲线呈现“W”形;秋冬季PM_2.5浓度的空间分层异质性非常显著,且空间分布呈现“双核分布”,重污染区主要分布在华北平原、新疆等地,西藏、广东、云南等地是稳定的空气质量优良区。     

江苏省商业网点的空间分布特征及其区域差异因素

以江苏省商业网点POI（兴趣点）数据为基础,结合经济社会统计数据,分别对批发零售业、住宿业和餐饮业三种业态类型,运用标准差椭圆、核密度、最近邻指数以及多元线性回归等分析方法,探讨不同发展水平、发展阶段城市商业网点的空间分布模式和业态结构特征,并揭示其区域差异的影响因素。研究发现：江苏省商业网点总体上具有西北走向分布态势,呈现相对集中分布格局,表现出在长江以南地区集聚和在地级城市中心城区密集布局的特征;不同业态类型网点空间分布具有差异,批发零售业网点的空间集聚特征最显著,住宿业网点的分布相对均衡;各市商业业态结构呈现出批发零售业和餐饮业网点数量为主,以及批发零售业销售额独大的特征;地区生产总值、人口规模、居民人均可支配收入、城市综合可达性以及第三次产业产值比重是影响商业网点分布的重要因素,不同因素对商业网点分布的影响程度差异性较大,居民消费能力和地区发展水平是江苏省商业网点分布差异的主要驱动力。     

广东区域公路网络的城市通达性度量及空间特征分析

以广东省各地级市辖区和县域中心点为公路网络节点,通过对各节点城市的中心职能强度指数分析及其相互之间在公路网络的连通关系为基础,综合应用日通达性模型、通达性度量模型和潜力模型,对广东区域通达性进行分析,并以不同指标和度量模型的广东区域通达性的空间格局和分异特征进行GIS分析。结果表明,广东省的城市通达性空间格局,总体上呈现出一种圈层空间分布结构,以广州市核心区域和佛山市中心区域(禅城区和顺德区)为中心向外辐射。其影响通达性区域差异的主要因素包括城市经济水平、交通条件和地理区位等,其中,在交通条件影响下的通达性区域差异,在空间上表现出高等级公路网络指向性。研究结果反映出广东省省域公路网络通达性的空间分异规律,诠释了城市的经济发展水平、交通条件和地理区位对通达性的综合作用,从而为广东省城镇网络体系发育、区域经济协调及珠三角产业转移规划优化提供科学支撑。     

Variation of pH in atmospheric precipitation in cities of Jilin province

Acidrainhasbeenaglobalenvironmentalprob-lem.SouthChinaandthesouthwestpartarethetwomainacidrainareasinChinawiththerangeof'pHvariationfrom4.5to4.O,andseientificstudiesonacidrainfocusontheseareas(WANGetal.,l997).Inre-centyears,however,inthenorthpartofChinaacidrainoccurred'notunusuallyandevenmoreseriouslysuchasincitiesofTumenandHunchuninJilinProvince.Itisessentialtodelveintothetemporalandspatialvariationsofatmosphericprecipitationacidityandthevariationscausesintheseareastoprovidesci-entificba…     

面向土地覆盖变化信息局域精度预测的逻辑回归协变量选用的直接策略与组合策略

土地覆盖信息是地理国情监测、生态环境建模等领域的重要组成。逻辑回归方法是土地覆盖变化信息局域精度量化颇有潜力的方法之一。在逻辑回归建模中,可从待检验地图提取若干像素尺度的景观指数(如像素类别、同质性、异质性、斑块大小等),用作协变量;基于验证样本,可估计逻辑回归系数,从而进行未采样点位的的精度预测。关于协变量的选择有两种策略:一是直接使用土地覆盖变化图中的若干景观指数作为协变量(直接策略),二是使用两时相的土地覆盖图中相应的景观指数的组合作为协变量(组合策略)。基于武汉部分城区的验证结果显示,两种策略下的逻辑回归预测结果的差异不明显。因此,从提高计算效率的角度,推荐采用直接策略进行土地覆盖变化信息的局域精度预测。     

Geography of Talent in China During 2000–2015: An Eigenvector Spatial Filtering Negative Binomial Approach

The increase in China's skilled labor force has drawn much attention from policymakers, national and international firms and media. Understanding how educated talent locates and re-locates across the country can guide future policy discussions of equality, firm localization and service allocation. Prior studies have tended to adopt a static cross-national approach providing valuable insights into the relative importance of economic and amenity differentials driving the distribution of talent in China. Yet, few adopt longitudinal analysis to examine the temporal dynamics in the stregnth of existing associations. Recently released official statistical data now enables space-time analysis of the geographic distribution of talent and its determinants in China. Using four-year city-level data from national population censuses and 1% population sample surveys conducted every five years between 2000 and 2015, we examine the spatial patterns of talent across Chinese cities and their underpinning drivers evolve over time. Results reveal that the spatial distribution of talent in China is persistently unequal and spatially concentrated between 2000 and 2015. It also shows gradually strengthened and significantly positive spatial autocorrelation in the distribution of talent. An eigenvector spatial filtering negative binomial panel is employed to model the spatial determinants of talent distribution. Results indicate the influences of both economic opportunities and urban amenities,particularly urban public services and greening rate, on the distribution of talent. These results highlight that urban economic-and amenity-related factors have simultaneously driven China's talent's settlement patterns over the first fifteen years of the 21 st century.     

A bi-modal model for Chinese cities: City size,car use and land rent

China is experiencing rapid urbanization and motorization. Urban transport congestion poses a challenge to the cities of China. Policies have been made trying to control the car use and the land use in Chinese cities without sound modelling researches. The existing literature on monocentric city modelling has shown that the parameters are critical for the outcome of the modelling. Following the Alonso-type monocentric model, this paper introduces a bi-modal model to simulate the city size, the distribution of land rent and the modal substitution in Chinese cities. We set the key parameters according to the recent available data of China′s cities, and re-explain the hypothesis of the model. Then we make a sensitivity analysis to reveal the impacts of key parameters on the Chinese cities. According to the results, we find that the wage, the price of car use and the agriculture rent have significant impacts on city size. The land supply for the private transport or the public transport has the strongest impacts on car use and the level of transport congestion. The total population of the city and the wage level have strong impacts on land rent. Some results are counter-intuitive, but explainable. We also discuss implication of these results for policy making.     

中国居民预期寿命及其影响因素的空间差异分析

良好的健康和人类福祉是联合国提出的可持续发展目标之一,提高人口预期寿命是迈向此目标的重要一步。由于中国城市在自然环境和社会发展方面有所差异,因此理解不同城市居民的预期寿命主要受何种因素的影响是制定城市公共卫生策略的关键。本研究基于2015年中国286个城市的有效数据,利用探索性回归、普通最小二乘回归、地理加权回归筛选与预期寿命最相关的影响因素并探索其空间差异,再通过二阶聚类将城市分类,以针对性地提出每类城市政策建议。结果显示：① 经济发展,教育条件和医疗设施条件对预期寿命有显著的积极影响,平均海拔和环境污染则具有负面影响;② 东南地区的经济发展对当地居民的预期寿命影响程度更大;东北和西南地区的医疗设施条件对其居民预期寿命促进程度更高;北部地区的教育条件对当地居民预期寿命影响比其他地区更高;平均海拔对西部地区居民预期寿命的影响最大;西北地区居民的预期寿命则更易受到环境污染带来的负面影响;③ 根据空间差异将城市分为3类,其居民预期寿命关键影响因素依次是经济发展和环境污染、教育条件、医疗设施,每类城市的城市管理者应重点关注不同因素来提升居民的预期寿命。     

Impacts of Migration on Urban Environmental Pollutant Emissions in China: A Comparative Perspective

In recent years,researchers have devoted considerable attention to identifying the causes of urban environmental pollution.To determine whether migrant populations significantly affect urban environments,we examined the relationship between urban environmental pollutant emissions and migrant populations at the prefectural level using data obtained for 90 Chinese cities evidencing net in-migration.By dividing the permanent populations of these cities into natives and migrants in relation to the population structure,we constructed an improved Stochastic Impacts by Regression on Population,Affluence and Technology model(STIRPAT)that included not only environmental pollutant emission variables but also variables on the cities’attributes.We subsequently conducted detailed analyses of the results of the models to assess the impacts of natives and migrants on environmental pollutant emissions.The main findings of our study were as follows:1)Migrant populations have significant impacts on environmental emissions both in terms of their size and concentration.Specifically,migrant populations have negative impacts on Air Quality Index(AQI)as well as PM2.5 emissions and positive impacts on emissions of NO2 and CO2.2)The impacts of migrant populations on urban environmental pollutant emissions were 8 to 30 times weaker than that of local populations.3)Urban environmental pollutant emissions in different cities differ significantly according to variations in the industrial structures,public transportation facilities,and population densities.     

Evolution and spatial patterns of spheres of urban influence in China

This article presents the findings of a study of the spheres of urban influence with regard to all cities in China（not including Hong Kong,Macau and Taiwan Province of China）in the years 1990,2000 and 2009.An optimized gravity model with comprehensive time distance was used to carry out a detailed analysis of the spatial patterns of Chinese spheres of urban influence and the spatial characteristics of urban agglomerations.Such urban agglomerations are characterized by high density population and a developed economy,which are also considered as the national competition unit.This paper initially identifies four spatial patterns of urban agglomerations based on the spatial layout of city groups during their evolution.Some basic characteristics of urban agglomerations are outlined,including the number of cities,the size of cities and the functions of urban centers.These characteristics are examined by using statistical methods and Geographic Information System（GIS）.The main findings from this research are that the development stages and structures of urban agglomerations in China vary significantly.It is also clear that the stages and evolution of spatial patterns are strongly affected and dominated by both policy and location factors.     

中国市级人口增长的多因素空间建模分析

本文以中国市域单元为研究对象,利用1990-2010年人口普查数据,采用探索性空间数据分析的方法,分析了过去20 年间中国市级人口增长率的空间分布特征和多变量的空间依赖关系。使用空间计量模型和空间滤波后的经典线性回归模型分别探究了经济、气候、地形、社会文化等因素对中国市级人口增长率的影响。模型对比结果显示,经过空间滤波后的经典线性回归模型能够更好的模拟中国市级人口增长率的变化。在该模型中,经济因素是影响中国城市人口增长率的主要因素,例如代表城市经济发展水平的城市夜光指数密度。气候因素对人口增长率也有着不可忽视的作用,如七月热指数随着等级的提升对人口增长率有着越来越强的负向影响。研究结果表明：人口的区域增长模式是多要素综合作用的结果,在相关建模研究和政策制定中需要重点考虑经济发展水平和气候条件因素对人口增长趋势的不同影响。     

耦合FLUS和Markov的快速发展城市土地利用空间格局模拟方法

模拟城市土地利用空间变化格局的研究,对未来区域规划以及实现可持续发展具有十分积极的作用。以往基于FLUS的研究栅格尺度较大,如何模拟快速发展中城市的复杂土地利用变化过程,挖掘土地利用变化驱动机制值得进一步探讨。本文构建了耦合FLUS和Markov的城市土地利用格局拟合框架,创新性地引入房价指标表征社会经济属性,以深圳为研究区,基于30 m空间分辨率小栅格尺度的土地利用分类数据和基础地理、路网河网、感兴趣点等多源空间变量,模拟不同发展情景下的未来城市土地利用空间格局,并通过随机森林进行土地利用变化驱动因素分析。研究结果表明:本文提出的耦合FLUS和Markov方法相较于传统CA模型（RFA-CA和Logistic-CA）精度更高（FoM=0.22）,能更准确地模拟快速发展中城市的土地利用变化过程;多情景土地利用格局制图结果验证了城市发展过程中生态控制线的重要性,进一步说明本文拟合框架在未来城市规划布局中的参考价值;医院、娱乐场所等的基础设施和公交、路网密度等的基础交通比自然因素（高程、坡度）对城市发展的影响更大,到海岸线距离会在一定程度上限制深圳内部土地利用变化过程。本研究所构建模型及精细制图结果,可为城市区域规划和空间格局模拟等相关研究提供参考依据和理论基础。     

基于Globcover和DMSP/OLS数据的中国大陆城市潜能分析

城市潜能作为城市体系空间相互作用的代表性指标,反映了城市与其所在的城市体系内所有城市间相互作用的强度,即城市所具备的的集聚能力。合理分析城市空间布局及结构、探寻城市发展的空间差异,以及制定区域发展政策日益迫切,研究城市空间相互作用具有重要意义。本文以Globcover数据集提取中国大陆范围内26 619个城市斑块,获取了城市潜能计算时所需的城市斑块规模总量;同时,以DMSP/OLS夜间灯光影像为数据,提取能综合反映人类社会经济活动的城市斑块夜间灯光总强度,参与城市潜能的计算。利用潜能模型,依据城市斑块分层结果,以城市斑块规模和时间成本为参数,计算得到公里格网的城市潜能值模拟分布图,并分析了中国大陆城市潜能的空间分异特征。