南京市住宅价格的探索性空间数据分析

应用探索性空间数据分析方法中的空间相关性分析,考察南京市住宅小区均价的全局空间相关性和局部空间相关性,得出各期数据的Moran’sI指数及其变化趋势、各住宅小区均价局部Moran’sI指数、住宅小区均价LISA聚类图,证明住宅小区均价之间存在明显的空间相关性。     

生产性服务业FDI空间集聚的经济增长效应——基于省级面板数据的空间计量分析

利用我国24个省市2004—2011年的数据,借助空间计量方法对生产性服务业FDI空间集聚的经济增长效应进行实证分析。从全局Moran’s I指数分析来看,生产性服务业FDI和经济增长均具有较强的空间相关性并呈现空间集聚的特征,某个地区与邻近地区生产性服务业FDI和经济增长的水平有关。从空间滞后模型分析看,生产性服务业FDI对经济的促进作用总体上小于经济开放度对经济增长的促进作用,但是经济开放度对经济增长的作用有弱化的趋势,而生产性服务业FDI对经济增长的促进作用在不断提升。     

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

以华北地区823个传统村落为研究对象,利用核密度分析法分析其空间分布特征,并通过空间自相关分析各传统村落在空间分布上的依赖性,选取影响传统村落分布的地形因素、气候因素、生态因素、交通因素、经济因素、人口因素、文化因素和城市因素作为自变量,运用地理探测器和多尺度地理加权回归(MGWR),对各影响因素进行交互作用探测和空间异质性探讨,为传统村落的保护与发展提供定量依据。结果显示：1)华北地区传统村落呈现“多核集聚”的分布特征,并沿太行山脉呈“线状”集中分布,其Moran’s I结果显示华北地区传统村落存在高度空间自相关性;2)各影响因素的两两交互配合共同影响传统村落的集中度;3)平均海拔、年均气温、人口密度、文物保护单位集中度和大城市集中度5个因素对华北地区传统村落分布的影响存在明显的空间分异特征。     

基于ESDA的中原经济区城镇化空间关联研究

运用探索性空间数据分析(ESDA)方法,在工具型软件ArcGIS 9.3与空间统计分析软件GeoDa的支持下,对中原经济区28个地级市的城镇化水平进行空间相关性与空间差异分析.通过计算局部空间自相关,揭示了中原经济区城镇化水平空间异质性,及各地级市之间的相互作用在区域差异中的影响.据此,提出了提高城镇化水平的建议.     

基于POI的游憩空间分异特征及其相关性因素研究——以广州市为例

游憩空间是城市居民消遣娱乐的主要场所。随着居民对美好生活需求的不断提升,游憩空间的需求增长与供给不足之间的矛盾也日渐凸显,因此研究游憩空间的规划布局和优化具有重要意义。该文以广州市为例,使用核密度分析、Moran’s I指数等方法,对广州市游憩空间的空间分布差异以及与自然、社会要素的空间关系进行了测量分析,揭示了游憩空间现存问题及成因。结果显示：（1）广州市各地区游憩空间的利用率存在明显差异,反映了游憩空间缺少合理的统筹规划;（2）不同类型的游憩空间的数量、规模、等级和空间特征差异明显;（3）游憩空间的分布在总体上与社会经济、人口分布、交通因素存在一定的依存性;（4）自然旅游类游憩空间与地形、海拔、坡度等自然要素相关,并且与社会经济等人文因素具有反作用关系。     

地表温度与植被指数相关性的空间尺度特征——以重庆市为例

为了揭示植被改善城市热岛的量化关系,以重庆市主城区为例,在不同的尺度上分析了地表温度与归一化植被指数之间的相关性,分别从全局和局部出发分析了两者之间的线性关系.结果发现,在120m尺度上两者之间的相关性最强,空间相关场表明相关性在空间上呈现较强的异质性;全局普通线性回归和地理加权回归分析发现,两者之间没有显著的线性关系,这说明可能还存在其他多种因素的复杂影响.     

基于POI大数据的扬州市房价空间分异及影响因素研究

以扬州市住宅房价POI大数据为研究对象,利用空间相关性研究房价空间分异特征,进而采用地理加权模型分析影响因素对房价异质性的作用机制。结果显示：扬州市房价在空间上具有集聚特征,在中心城区具有显著的空间异质性;从城市区划角度来看,中心城区为房价高值聚集区,其余4个区为房价低值聚集区;整个市域内,小学、公园、医院等对房价有提升作用,商场、公交站、中学、金融服务等对房价有抑制作用;影响因素对房价空间异质作用表现为圈层结构,影响强度沿外渐变。     

基于GIS的污水管网易爆气体时空分布特征研究

针对城市污水管道爆炸、气体中毒等问题,以重庆"一小时经济圈"地区污水管网为研究对象,基于重庆市地下管网危险源物联网监测平台,通过时空模型挖掘技术与GIS方法,研究某小区污水管道中的易爆气体变化特征及其影响因子,同时分析了其在2016年—2019年的易爆气体时空分布特征。结果表明,该小区污水管道中的易爆气体浓度峰值时间段为:02:00—04:00、07:00—09:00、15:00—17:00;根据污水来源,易爆气体浓度大小排序为:商业区居民住宅区公厕;易爆气体浓度受污水量和COD浓度的中等强度的正向影响;当温度35℃时,温度越高则易爆气体浓度越大;接近检查井顶部的空间易爆气体浓度更大,且沿着污水管道呈现减小的趋势;管径越大、管龄越长,易爆气体浓度就越大;研究区域污水管道中的易爆气体浓度整体上呈现逐年降低的趋势,但在西部地区存在上升趋势;Moran’s I空间自相关分析结果显示,p值为0.036 8,z得分为2.087 6,Moran’s I指数为0.783 8,数据呈现聚类模式,表明易爆气体浓度倾向于发生空间聚类现象。     

中国近年火灾的空间聚集趋势分析

通过全局自相关分析,发现中国近年来火灾存在空间自相关和空间聚集现象。2002年以前,火灾空间聚集趋于加强;2003年以后,火灾空间聚集趋于减弱。进一步分析火灾局部自相关,火灾的热点区域主要存在于东北地区、河套地区、新疆地区,以及环渤海、长三角部分地区,但河套地区、新疆地区的热点范围有扩大趋势,其他地区的热点范围则逐渐消失。     

金佛山国家级风景名胜区人类活动强度与景观格局时空变化响应研究

随着社会经济快速发展,风景名胜区人类活动日益增强,往往带来景观格局变化等潜在威胁。为揭示景观格局与人类活动强度的响应关系,识别二者耦合作用形成的生态环境破碎区域,以金佛山国家级风景名胜区为例,基于2000年、2010年、2020年三期土地利用数据,通过构建人类活动强度模型及景观格局指数计算,对金佛山国家级风景名胜区景观时空变化进行定量分析,同时利用空间自相关分析,探索人类活动强度与景观格局在空间上的响应关联,进而叠加耦合出人地矛盾突出区域,作为潜在的重点关注区域。研究结果表明：自2000年以来,金佛山国家级风景名胜区景观格局发生明显变化,建设用地面积持续扩张,人类活动干扰增多,景观破碎度增加,景观异质性增强;近20年来人类活动强度高值区域面积逐年扩大且分布于低海拔区域,聚集在研究区域的北部三泉镇及南部金山镇等村镇聚落,高聚类区主要为建设用地与耕地区域;人类活动强度与景观格局变化存在显著的空间依赖性,呈现出较强烈的空间相关性,在空间分布中变化具有高度的一致性;金佛山风景名胜区人类活动强度与景观格局指数耦合的人地矛盾突出区域在空间分布中呈围绕南部及北部村镇扩张趋势。     

A spatio‐temporal measure of spatial dependence: An example using real estate data*

The computation of Moran's I index and his statistics test relies mainly on an exogenous specification of a spatial weights matrix. However, the exogenous weights matrix is usually developed in a strictly spatial context, even when data are collected over time. This paper develops a spatio‐temporal weights matrix and uses the new definition to evaluate spatial dependence using Moran's I index applied to real estate data for Québec City from 1986 to 1996. The results are compared with the original Moran's I index using a strictly spatial weights matrix specification based on Euclidian distance or contiguity. The findings suggest that ignoring the temporal dimension could lead to misinterpretation of the ‘real’ measure of spatial dependence over time. However, the time dimension cannot explain the total spatial autocorrelation since the Moran's I index is still significant even when adjusting for time consideration. The differences between the estimated indices and statistics depend on the structure of the spatial and the temporal weights matrices that are used to construct the complete spatio‐temporal weights matrix.     

Spatial analyses for copper,lead and zinc contents in sediments of the Yangtze River basin

Three kinds of spatial analysis methods (geostatistics, spatial autocorrelation and fractal) were used for Cu, Pb and Zn contents in sediments of the Yangtze River basin. The ranges of spatial correlation revealed by variograms are approximately 1000 km for Cu and Zn, while some drift has been detected for Pb. The ranges show correlation among patches, while ranges of positive spatial autocorrelation detected by spatial correlogram based on Moran's I are approximately 500 km for the three elements, showing the average size of patches in the distribution maps. Directional features of Cu, Pb and Zn have been disclosed by variogram and spatial autocorrelation index, which are in accordance with their spatial distribution maps. All of the three indices (variogram, Moran's I and fractal dimension) have shown that the spatial structure of Cu is the best among the three elements, which is mainly controlled by rock type. The spatial structures of Pb and Zn are disturbed by mineralization and possibly by human activities.     

厦门市房价空间分布与基础设施水平关系的研究

为了探究厦门市岛内普通住宅房价的空间分布与基础设施水平的关系,运用 ArcGIS 得出最邻近指数、全局的 MoranI 指数和局部聚类与异常值情况,揭示普通住宅的空间聚集和房价空间自相关现象;通过数据统计分析、克里金插值和趋势分析,得到厦门岛内房价多中心分布状态及其演变规律。实现了基础设施水平空间结构的地理表达及整体评价,并基于双对数回归模型和地理加权回归模型,揭示影响房价空间分异的各类基础设施因子的不同影响情况。     

Finite sample properties of Moran's I test for spatial autocorrelation in tobit models

In this note, we investigate the finite‐sample properties of Moran's I test statistic for spatial autocorrelation in tobit models suggested by Kelejian and Prucha. We fill a void in the theoretical literature by investigating the finite sample properties of this test statistic in a series of Monte Carlo simulations, using data sets ranging from 49 to 15,625 observations. We find that the test is unbiased, has considerable power and approximates the asymptotic normal distribution even for medium‐sized sample sizes, empirically confirming the theoretical results of Kelejian and Prucha. However, some caution is needed, since the statistic turns out to be sensitive to misspecification in the form of heteroscedasticity. In such instances the test over‐rejects the null hypothesis, mistaking heteroscedasticity for spatial autocorrelation.     

于GWR模型的广州市住宅价格影响因素研究

以广州市“都会区”为例,基于2016~2019年住宅小区均价及POI数据,综合运用kriging插值、GWR模型对广州市住宅价格空间分布结构及其影响因素进行研究。研究结果表明:2016~2019年广州市住宅价格整体呈上升趋势,沿珠江水系发展的双核、多次中心、组团式的空间结构已基本成型;高速/快速路口、三甲医院、省一级小学对住宅价格的回归系数在中心城区与近郊区域间呈明显的正负差异,绿化率、地铁、房龄等单向影响因素回归系数强弱渐变;房龄、CBD、省一级小学、三甲医院、绿化率、休闲配套对中心城区住宅价格影响较大,而近郊区域主要受地铁、高速/快速路口的影响;较全局OLS模型,GWR模型的拟合优度大幅提高19.9%,能够更加精确地研究住宅价格影响因素的空间异质性。     

城市快速发展进程中土地价格的空间性状及其演变分析——以北京市朝阳区、海淀区为例

由于影响地价的外部作用力在空间分布上明显不均衡,造成城市土地价格空间分异的性状,同时,随着城市化进程加快,土地价格的空间性状表现出一定的演化规律。本文以北京市海淀区、朝阳区为例,系统分析了商业、住宅、工业用地的价格空间性状及其演化规律。     

Ghost cities of China: Identifying urban vacancy through social media data

“Ghost Cities” have become a phenomenon of global interest since 2009 when popular media highlighted the existence of Ordos, a large Chinese city that was almost entirely vacant. The term is used to describe housing vacancy associated with overdevelopment and can refer to small communities, neighborhoods, or even whole cities that lie vacant. Ghost Cities are particularly prevalent in China where housing vacancy has become a serious concern for many second and third-tier cities. Measuring the extent of these vacant areas has been challenging due to Chinese data restrictions. This research tests whether it is possible to collect data, scraped from Chinese social media open access API's including Dianping (Chinese Yelp), Amap (Chinese MapQuest), Fang (Chinese Zillow), and Baidu (Chinese Google Maps) to develop a computational model to identify areas considered to be Ghost Cities. The model created for this study is based on the idea that thriving communities need access to basic amenities. Therefore Hansen's gravitational model was applied to give an “amenities score” for residential locations based on their accessibility to restaurants, banks, grocery stores, beauty salons, KTV, medical facilities, schools, and malls Moran's I spatial autocorrelation was applied to the amenity scores below the mean to determine spatially clustered residential locations with low scores. The results were considered potential Ghost Cities and were visited in Chengdu and Shenyang to confirm the accuracy of the model. These site visits showed that the model identified transitional, underperforming, or vacant housing in these cities, illustrating that it is possible to use data scraped from social media to identify underused residential developments.     

A Moran eigenvector spatial filtering specification of entropy measures

Regional science investigations of geographical disparities in socio-economic development sometimes utilize entropy, which measures a phenomenon's distributional uniformity across geographical space. Entropy also is widely utilized to measure random phenomenon dispersion, and often used to identify the most probable allocation of a phenomenon in space. Its common formulation is with empirical frequencies, following Shannon. Batty introduces spatial entropy assuming equal probability over space. His specification considers probabilities as fundamentally being spatially independent, which does not hold in most empirical geographical analyses. Hence, an entropy measure can be further modified by controlling extra variation caused by spatial autocorrelation. This paper proposes a Moran eigenvector spatial filtering (MESF) entropy specification that accounts for spatial autocorrelation when modelling georeferenced data. Using eigenvectors from a transformed spatial weights matrix, MESF identifies and isolates spatially autocorrelated components within a georeferenced variable. Coupling it with a non-normal distribution, such as a binomial or beta probability model, which researchers often employ to describe empirical probabilities, expands its utility. The proposed method is examined with an application to regional income inequality in Poland during 2005–2012. This application demonstrates that accounting for spatial autocorrelation further enhances an entropy measure, showing that the MESF specification provides a flexible method for controlling spatial autocorrelation in an entropy formulation.     

居住与工业用地比例变化及其引发的问题思考

本文对我国城市居住与工业用地比例进行了统计分析。近20年来我国城市居住与工业用地的面积比呈"倒V"型,曲线转折点为2001年。用地出让制度改革、政绩考核及现有的税收体制导致地方政府2002年之后的供地偏好转变,产生了低价、超量供应工业用地与高价、缩量供应居住用地的供地倾向,致使我国城市工业与居住用地供应出现结构性失衡。在对近10年全国居住用地比例、商品住宅供求关系、商品住宅平均价格回归分析和相关研究的基础上,笔者从实证角度验证了居住用地比例下降(即居住用地供应不足)在房价过快上涨过程中所起到的基础性作用。最后,论文针对城市建设用地的结构优化及居住用地供应保障问题提出了相关建议。     

The puzzling heterogeneity of amenity capitalization effects on land markets

Government‐funded urban amenities provide significant values to land markets. This paper uses spatial econometric techniques to recover the puzzling heterogeneity in capitalized values of local amenities such as higher education facilities, transport and environmental amenities. On top of its policy implications, this paper differentiates between non‐spatial models and spatial models to allow spatially varying marginal‐price estimates and reflect the subtle ways that land markets capitalize local amenities. Our results provide heterogeneous estimates of capitalized amenity values that would affect land development, illustrating the linkage between government‐funded amenities and the intensity of land development.