局部空间自相关统计洪水灾害影像分析

针对洪水灾害分析在速度、准确性和及时性等方面的需求,该文在研究空间自相关分析的基础上,提出了一种基于局部自相关统计的洪水灾害影像分析方法。首先,对影像进行掩膜处理,去除云层干扰;其次,采用局部空间统计的方法对影像进行统计分析;然后,通过密度分割的方法提取水体,将影像分为水体和陆地两类;最后,将3幅影像分类的结果进行空间叠加分析,分析洪水灾害影响情况。以2013年嫩江流域3个时期的影像为实验数据,设计了仿真实验。实验结果表明,该方法可以较准确地对大面积洪水影响区域进行分析。     

Developing local measures of spatial association for categorical data

This paper describes a procedure for extending local statistics to categorical spatial data. The approach is based on the notion that there are two fundamental characteristics of categorical spatial data; composition and configuration. Further, it is argued that, when considered locally, the latter should be measured conditionally with respect to the former. These ideas are developed for binary, gridded data. Local composition is measured by counting the numbers of cells of a particular type, while local configuration is measured by join counts. The approach is illustrated using a small, empirical data set and an ad hoc procedure is developed to deal with the impact of global spatial autocorrelation on the local statistics.The author gratefully acknowledges financial support from the GEOIDE Network of Centres of Excellence (ENV #4) and the helpful comments of three anonymous reviewers.     

地球化学的空间自相关异常信息提取方法

针对地球化学数据存在的空间分布相关性特征,该文提出了一种基于空间自相关统计的地球化学异常提取方法。以内蒙古浩布高矿床外围的土壤地球化学数据为例,通过对Sn、Cu元素地球化学数据在不同空间间隔上的全局自相关计算,测算其空间聚集的程度,选取聚集程度最高时的间隔距离作为局部空间自相关的参数,通过局部Moran’s I值研究元素的空间分布特征,分析空间聚类和异常值,从而提取地球化学异常。结果表明,局部空间自相关分析可以揭示Sn、Cu地球化学数据的空间分布特征,能够更好地提取地球化学弱缓异常,说明空间自相关是一种有效的地球化学异常识别方法。     

A linear regression solution to the spatial autocorrelation problem

The Moran Coefficient spatial autocorrelation index can be decomposed into orthogonal map pattern components. This decomposition relates it directly to standard linear regression, in which corresponding eigenvectors can be used as predictors. This paper reports comparative results between these linear regressions and their auto-Gaussian counterparts for the following georeferenced data sets: Columbus (Ohio) crime, Ottawa-Hull median family income, Toronto population density, southwest Ohio unemployment, Syracuse pediatric lead poisoning, and Glasgow standard mortality rates, and a small remotely sensed image of the High Peak district. This methodology is extended to auto-logistic and auto-Poisson situations, with selected data analyses including percentage of urban population across Puerto Rico, and the frequency of SIDs cases across North Carolina. These data analytic results suggest that this approach to georeferenced data analysis offers considerable promise. Received: 18 February 1999/Accepted: 17 September 1999     

基于空间自相关的河南省城镇化水平空间格局研究

城镇化水平是衡量一个地区经济发展状况的重要指标。本文基于空间自相关分析方法,利用2007年河南省各市城镇化数据,分析了城镇化水平的空间分布规律。结果表明,河南省城镇化水平全局Moran’sI指数为0.299,达到显著正相关,呈现明显的全局空间集聚现象;其次,河南省城镇化水平分布的局部空间集聚现象亦十分显著。总体上,城镇化水平分布呈正关联的地市数量明显高于呈负关联的数量。根据计算结果,提出了促进河南省城镇化水平快速全面发展的对策和建议。     

山东省人口分布及空间相关性研究

借助MapInfo7.0平台及其二次开发语言MapBasic对山东省108个县市的人口分布及其空间相关性进行了研究,结果表明:1995-2006年间,山东省各县市人口分布的不均衡指数和集中指数分别由0.0178和0.1306增长到0.0198和0.1454,人口稠密区和人口稀疏区的密度差在不断增大;人口密度分布呈现显著...     

我国东部沿海三大经济区区域经济的空间自相关分析

本文以我国东部沿海三大经济区域——环渤海、长三角、珠三角为研究对象,依据空间统计学的空间自相关方法,采用以高速公路里程数为影响因子改进的空间权重矩阵,以及全局和局部Moran I指数模型,通过ArcToolbox空间分析工具分析三大区域经济空间格局。研究结果表明,长三角与珠三角区域发展呈显著的空间正相关,长三角地区较之其他两地区发展最为成熟,发展极辐射大。同时地理环境对于区域经济发展作用明显,发展核心城市集中于沿江地区或平原地区。     

无障碍设施空间布局核密度热点探测和空间自相关分析——以北京市核心区室外公共空间为例

城市无障碍设施在区域空间中往往呈现聚集分布的特征,通常利用核密度估计方法分析总体空间分布形态,研究区域空间分布的数量差异,探测分布热点;同时通过分析无障碍设施空间自相关性特征,反映无障碍设施服务的聚集特点。将空间分析方法引入到无障碍环境评估当中,可以优化无障碍环境发展空间。结果表明,北京市核心区无障碍设施总体呈现出"多核分布"的态势。无障碍设施的总体分布存在空间差异性,局部无障碍设施空间分布存在聚集特性。     

Modeling spatial dependence in high spatial resolution hyperspectral data sets

 As either the spatial resolution or the spatial scale for a geographic landscape increases, both latent spatial dependence and spatial heterogeneity also will tend to increase. In addition, the amount of georeferenced data that results becomes massively large. These features of high spatial resolution hyperspectral data present several impediments to conducting a spatial statistical analysis of such data. Foremost is the requirement of popular spatial autoregressive models to compute eigenvalues for a row-standardized geographic weights matrix that depicts the geographic configuration of an image's pixels. A second drawback arises from a need to account for increased spatial heterogeneity. And a third concern stems from the usefulness of marrying geostatistical and spatial autoregressive models in order to employ their combined power in a spatial analysis. Research reported in this paper addresses all three of these topics, proposing successful ways to prevent them from hindering a spatial statistical analysis. For illustrative purposes, the proposed techniques are employed in a spatial analysis of a high spatial resolution hyperspectral image collected during research on riparian habitats in the Yellowstone ecosystem. Received: 25 February 2001 / Accepted: 2 August 2001     

Identifying local spatial association in flow data

In this paper we develop a spatial association statistic for flow data by generalizing the statistic of Getis-Ord, G _i (and G _i ^*). This local measure of spatial association, G _ij, is associated with each origin-destination pair. We define spatial weight matrices with different metrics in flow space. These spatial weight matrices focus on different aspects of local spatial association. We also define measures which control for generation or attraction nonstationarity. The measures are implemented to examine the spatial association of residuals from two different models. Using the permutation approach, significance bounds are computed for each statistic. In contrast to the G _i statistic, the normal approximation is often appropriate, but the statistics are still correlated. Small sample properties are also briefly discussed. Received: 18 February 1998/Accepted: 29 September 1998     

基于BP网络与空间统计分析的山东人口空间分布模式预测研究

运用BP网络对山东省17地市未来人口总量进行预测,在预测数据的基础上运用空间自相关方法对未来人口的空间分布模式进行分析。研究表明:2005-2010年山东省人口密度的空间分布模式总体呈现"西南-东北"模式,存在着空间集聚现象;2006-2010年17地市局部空间关联类型基本没有发生变化,西部和南部8个地区存在着明显的"高-高"集聚;北部5个地区存在着明显的"低-低"集聚;淄博和青岛存在着两个"高-低"关联的孤立点。     

Learning in neural spatial interaction models: A statistical perspective

 In this paper we view learning as an unconstrained non-linear minimization problem in which the objective function is defined by the negative log-likelihood function and the search space by the parameter space of an origin constrained product unit neural spatial interaction model. We consider Alopex based global search, as opposed to local search based upon backpropagation of gradient descents, each in combination with the bootstrapping pairs approach to solve the maximum likelihood learning problem. Interregional telecommunication traffic flow data from Austria are used as test bed for comparing the performance of the two learning procedures. The study illustrates the superiority of Alopex based global search, measured in terms of Kullback and Leibler's information criterion. Received: December 2001 / Accepted: August 2002 The author gratefully thanks Martin Reismann (Department of Economic Geography & Geoinformatics) for his valuable research assistance.     

西安市住宅价格空间结构和分异规律分析

利用ESDA方法对西安市城区的291个普通住宅项目均价数据进行研究,通过计算Moran指数和半变异函数分析了其空间自相关性和变异性,并进行了趋势分析。应用Kriging空间插值方法对西安市普通住宅价格空间分布进行了模拟。研究结果表明:西安市房价存在显著的空间自相关性,大部分住宅价格呈空间集聚格局,少部分因存在空间异质性而呈离散分布;房价变异函数表现出各向异性,不同方向有不同结构特征,空间自相关尺度为14.2km;西安市房价空间分异规律明显,房价分布格局受城市功能区划和交通影响较大。     

Analysis of human spatial behavior in a GIS environment: Recent developments and future prospects

In recent years, comprehensive geographic data sets of metropolitan areas and individual-level, georeferenced data are becoming more available to social scientists. At the same time, tools for performing spatial analysis in a GIS environment have also become more available. These developments provide many new opportunities for the analysis and theoretical understanding of disaggregate human spatial behavior. This paper examines how these developments may enable the researcher to represent complex urban and cognitive environments more realistically, and to overcome the limitations of aggregate spatial data framework. It explores their implications for the theoretical and methodological development in geography and other social science disciplines.     

统计遥感空间基础框架的研究与应用

研究通过建立多尺度、多分辨率,多种类的空间基础框架数据库体系,根据地理空间位置实现统计信息资源的整合与共享.针对统计遥感业务对空间基础框架的需求,提出了统计遥感空间基础框架建设的目标与任务,研究了框架建设中框架构成,数据体系、标准体系、技术体系和组织运行体系等关键技术,并展示了统计遥感空间基础框架在国家统计遥感粮食作物...     

安徽省近50余年降水量时空变化特征分析

利用安徽省内15个和周边17个气象台站1957-2008年的降水资料,运用线性倾向估计和ArcGIS软件的空间分析等方法,分析从1957-2008年安徽省降水量的时空分布规律和长期变化趋势,并选取900mm,1200mm,1500mm为特定等值线,通过对特定等值线的变化来分析不同年代降水量的空间差异.研究结果表明：安徽省近50余年来降水整体上呈上升趋势,省内各区域降水差异较大,呈南多北少,山区多平原少的特点;从各区域降水的变化趋势来看,南方山区逐步减少,而北方平原呈逐步增加的趋势,南北降水差距在不断缩小.     

山地丘陵与平原地区城乡聚落空间分布分形特征研究

本文以河南省区域系统的不同地域单元为例,运用城镇体系研究的空间关联维数模型对山地丘陵区及平原地区城乡聚落体系空间结构进行分析,结果表明农村聚落具有与城市聚落相同的空间分布分形特征;山地丘陵城市的城乡聚落空间可达性不及平原城市;城乡聚落空间分布的乌鸦维数集中在1左右,差别不大,表明河南省作为中原地区所具有的城乡聚落空间布局的总体均衡性。基于此提出了中部农区城乡聚落空间优化、撤乡并镇、新农村社区建设应结合地域城乡聚落分布特点制定适宜的规划方案等有益的建议。     

Modeling propensity to move after job change using event history analysis and temporal GIS

The research presented in this paper analyzes the emergent residential behaviors of individual actors in a context of profound social changes in the work sphere. It incorporates a long-term view in the analysis of the relationships between social changes in the work sphere and these behaviors. The general hypothesis is that social changes produce complex changes in the long-term dynamics of residential location behavior. More precisely, the objective of this paper is to estimate the propensity for professional workers to move house after a change of workplace. Our analysis draws on data from a biographical survey using a retrospective questionnaire that enables a posteriori reconstitution of the familial, professional and residential lifelines of professional workers since their departure from their parents’ home. The survey was conducted in 1996 in the Quebec City Metropolitan Area, which, much like other Canadian cities, has experienced a substantial increase in “unstable” work, even for professionals. The approach is based on event history analysis, a Temporal Geographic Information System and exploratory spatial analysis of model’s residuals. Results indicate that 48.9% of respondents moved after a job change and that the most important factors influencing the propensity to move house after a job change are home tenure (for lone adults as for couple) and number of children (for couples only). We also found that moving is associated with changing neighborhood for owners while tenants or co-tenants tend to stay in the same neighborhood. The probability of moving 1 year after a job change is 0.10 for lone adults and couples while after 2 years, the household structure seems to have an impact: the probability increased to 0.23 for lone adults and to 0.21 for couples. The outcome of this research contributes to furthering our understanding of a familial decision (to move) following a professional event (change of job), controlling for household structure, familial, professional and spatial contexts.