空间统计学进展及其在经济地理研究中的应用

空间统计是20世纪90年代以后在经济地理.尤其是城市和区域研究领域中发展起来的重要研究方法.这一方法考虑到事物发展的空间依赖性.大大革新了原有经典统计.并借助于地理信息技术增强了可视化效果.丰富了在城市和区域研究中对空间的认识和预期.本文简明地综述了空间统计主要领域和内容.指出探索式空间分析.局部空间统计和空间回归模型是空间统计与经济地理研究主要的结合点,代表了未来发展趋势:并就空间尺度、空间权重矩阵、边缘效应和解释谬误等方面着重阐述了应用时应注意的问题.在此基础上,对近年来空间统计在社会经济要素集聚、土地利用和城市空间结构、交通和房方产研究中的应用进行了回顾,总结其主要应用方面和价值.空间统计将会大大提高对城市微观尺度的研究,为城市研究的基本理论假设和社会经济发展机理提供重要研究手段,但对数据库建设提出了更高的要求.     

元阳县土地利用空间格局及其变化的图谱方法研究

信息图谱为区域土地利用空间格局及变化提供了一种谱系化、定量化与定位化相结合的研究方法.以云南省元阳县为例,在遥感、GIS空间分析和统计分析模型的支持下,从土地利用时空演变模式、空间扩展过程、斑块的空间分布特征三方面建立土地利用空间格局及变化信息图谱,并对其图谱特征进行分析.结果表明:土地利用时空演变征兆图谱更直观形象地揭示了区域土地利用变化的基本模式,提供了时空复合的表达方式;土地利用斑块形态与空间扩展图谱的建立应用空间格局研究中的相关概念和方法,将微观、宏观层次的图形信息与变化过程相结合;利用等步长变距离缓冲区分析法建立的VCM曲线可直观地描述不同土地利用类型斑块的空间分布特征及不同研究时段内其空间分布特征的变化情况.     

中国区域经济增长集聚的空间统计分析

运用空间统计和计量经济学Moran I指数法及时空数据(Panel Data)模型分析了中国31个省级区域经济增长集聚及其影响因素.结果显示:①中国省域经济增长具有明显的空间依赖性,在地理空间上存在集聚现象,区域经济增长在时空上呈现出明显的空间效应,忽视空间效应将造成模型设定的偏差和计量结果的非科学性;②空间相关以及由此带来的国际国内贸易及外资等经济活动频繁程度,在很大程度上引起了31个省域区际经济增长的空间不均衡,空间集聚使得在经济增长过程中地理区位(距离)产生的空间成本降低,但地理特征将深刻作用于区域经济增长空间集聚的中心和外围关系;③外商直接投资、国际与区际贸易、人力资本、技术创新等因素对中国区域经济增长的贡献非常重要,但它却不能轻易改变经济地理的规则,经济增长因素在地理空间上的非均衡集聚导致了迥然不同的区域经济增长格局.     

中国地市旅游经济差异的时空演变特征

利用Theil指数分解及GIS空间分析等方法,比较分析了近年来中国地市入境、国内旅游经济差异的时空演变特征。结果表明,两者的高度空间集中性都具有总体下降的趋势,且其时空演变过程以东部地带内的差异和变化为主要来源;入境旅游经济较之国内旅游经济具更强的空间极化特征及更慢的差异缩小趋势,入境旅游经济的空间极化格局以点状高度集中分布为主要特征,国内旅游经济的空间极化格局主要呈轴状延伸趋势;在缩小东、中、西部地市间社会经济差异的作用上,前者并不明显,后者的空间经济效应相对明显,但作用程度有限。     

基于对象的GIS时空数据模型设计方法

GIS时空数据模型是描述空间实体的时间特性和空间特性的有机体,是GIS存储、再现、分析动态的现实世界的基础.该文采用面向对象的思想将地理实体抽象为空间对象,对空间对象的空间几何信息、属性信息、时间信息进行封装,提出了基于对象的GIS时空数据模型的构建方法.重点探讨了基于对象的地理实体描述方法、空间对象的时态特征和时态数据的存储方法,为时空信息的有机集成、共享管理、决策分析与应用提供了关于时空数据组织与管理模式的新思路.     

中国医疗资源配置与服务利用协调发展的时空演化——基于机构分层分析框架

基于机构分层分析框架,运用统计与空间分析方法,剖析中国省域基层医疗卫生机构和医院医疗资源配置与服务利用协调发展的时空演化特征及其驱动因子。研究发现:2010—2018年2类机构的耦合协调类型由濒临失调衰退型变为勉强协调发展型;总体呈东部经济发达地区水平较高,中部地区居中,西北、东北、西南等经济欠发达沿边地区较低的空间分布格局和以长江中下游地区和青藏高原地区为核心的热、冷点区的空间聚集特征,同时在空间格局改善程度、局部空间集聚效应强度和冷热点区变化范围等方面存在差异;受人口分布与结构、经济发展水平和地理空间等因子驱动,同时存在明显时空异质性特征。     

社会经济统计数据空间化研究进展

社会经济统计数据通常是以各级行政区为单位的汇总数据,它虽然能反映统计单元之间的差异但却不能反映统计单元内部的异质性,在实际应用中,无法满足统计任意区域内的社会经济数据的需求,而社会经济统计数据空间化则是有效解决该问题的一条重要途径。本文对现有社会经济统计数据的空间化方法、社会经济统计数据空间化过程所依赖的辅助数据、现有主要的社会经济空间化数据产品进行了归纳总结,并从空间化方法的制约因素和改进方向、新型辅助数据的探索和多源辅助数据的综合利用、高时空分辨率和高精度数据产品研发3个方面展望了社会经济统计数据空间化的未来发展趋势。研究结果可为社会经济统计数据空间化方法的选择与改进、辅助数据的选择与综合利用、社会经济空间化数据产品的选择与改进提供参考。     

基于多元成土因素的土壤有机质空间分布分析

以陕西省蓝田县2013年667份土壤有机质样本为对象,运用GIS空间分析及遥感数字图像处理收集整理土壤类型、地形、植被等成土因子,利用多元线性回归分析集成所有成土因子对土壤养分进行空间分布预测。结果表明:通过分级统计均值定权法和像元线性拉伸法将所有成土因子统一为相对度量值,并根据成土因子与有机质含量的相关性显著程度进行因子取舍,有利于集成各类成土因子构建多元线性回归模型。预测结果定性分析表明:多元线性回归预测结果与kriging法预测结果在宏观上具有一致的空间分布趋势;但多元线性回归预测结果土壤有机质空间分布特征带有各种成土因子的变化特征,从视觉效果上,克服了传统插值法中存在的斑块状分布现象,更精细的描述了本区域内有机质空间分布趋势; MPE和RMS定量精度分析显示,在集成多元成土因素对有机质进行空间分布分析时,本文方法优于常用kriging插值法,该法可作为集成多元成土因子对土壤养分空间分布预测的有效方法。本区域内土壤有机质高值区域主要集中在地势低平、坡度缓和、湿度适中的农耕区,地势较高、坡度陡的山区有机质含量低。     

对学生描述地理空间分布能力的诊断和提高策略

正地理(课标版)全国高考考试大纲和江苏等省市高考地理考试说明中的"考核目标与要求",明确要求学生能够用准确简洁的文字和图表等表达方式描述地理事物的主要特征、分布和发展变化,掌握地理概念、地理数据、地理事物的主要特征及分布、地理基本原理与规律等知识。地理分布包括空间分布和时间分布。"地理空间分布"就是指地理事物、地理现象、地理性状、地理符号等在地球表层展开的空间范围和位置排列状态。地理     

地理学视角下犯罪者行为研究进展

犯罪地理学以社会问题为导向,关注犯罪现象的格局、过程与机理,沿着“揭示问题、服务安全、解决问题”的思路,去破解复杂的社会难题,并在公共安全和犯罪防控领域贡献力量。本文基于地理学视角,从犯罪出行、犯罪空间决策、重复犯罪三大研究主题出发,综述了国内外犯罪者行为的研究进展。结果表明,国外研究取得了较多成果,如：①犯罪出行方面发现了就近掠夺和外出犯罪的空间模式;不同犯罪类型的出行距离存在显著差异;以及犯罪出行距离受犯罪者个体特征、地理特征、犯罪收益和情感因素的影响。②犯罪空间决策受经济因素、社会因素以及犯罪者空间意识的影响。③重复犯罪存在时空聚集性和时空临近性。国内研究主要集中在国外理论与经验引介,犯罪时空分布、形成机理与空间防控,以及犯罪模拟与预测方面,而地理学视角下犯罪者行为研究尚处于初始阶段,有许多空白亟需填补。总体而言,在犯罪者研究领域,仍存在以下3点不足：①在犯罪出行方面,综合的视角不多;②在犯罪空间决策方面,尚未涉及犯罪空间决策的时间差异及形成机制;③在重复犯罪方面,尚未考虑过去的犯罪活动和经验。最后,论文从视角、内容、方法及应用上提出未来的研究重点：注重综合性视角的实证分析;开展针对犯罪团伙的研究;合理利用大数据分析犯罪者行为的规律、过程和机理,避免导致推理错误;注重理论研究成果的转化,满足国家社会治安的重大需求,并提升犯罪地理学的学科价值。     

Guest editorial

The past decade has witnessed extensive development of measures that examine characteristics of spatial subsets (local spaces) defined with respect to a complete data set (global space). Such procedures have evolved independently in fields such as geography, GIS, cartography, remote sensing, and landscape ecology. Collectively, we label these procedures as local spatial methods. We focus on those methods that share a common goal of identifying subsets whose characteristics are statistically ‘significant’ in some way. We propose the concept of local spatial statistical analysis (LoSSA) both as an integrative structure for existing methods and as a framework that facilitates the development of new local and global statistics. By formalizing what is involved when a particular local statistic is used, LoSSA helps to reveal the key features and limitations of the procedure. These include a consideration of the nature of the spatial subsets, their spatial relationship to the complete data set, and the relationship between a given global statistic and the corresponding local statistics computed for the data set.     

NDVI spatial pattern and its differentiation on the Mongolian Plateau

GIMMS NDVI database and geo-statistics were used to depict the spatial distribution and temporal stability of NDVI on the Mongolian Plateau.The results demonstrated that:(1) Regions of interest with high NDVI indices were distributed primarily in forested mountainous regions of the east and the north,areas with low NDVI indices were primarily distributed in the Gobi desert regions of the west and the southwest,and areas with moderate NDVI values were mainly distributed in a middle steppe strap from northwest to southeast.(2) The maximum NDVI values maintained for the past 22 years showed little variation.The average NDVI variance coefficient for the 22-year period was 15.2%.(3) NDVI distribution and vegetation cover showed spatial autocorrelations on a global scale.NDVI patterns from the vegetation cover also demonstrated anisotropy;a higher positive spatial correlation was indicated in a NW-SE direction,which suggested that vegetation cover in a NW-SE direction maintained increased integrity,and vegetation assemblage was mainly distributed in the same specific direction.(4) The NDVI spatial distribution was mainly controlled by structural factors,88.7% of the total spatial variation was influenced by structural and 11.3% by random factors.And the global autocorrelation distance was 1178 km,and the average vegetation patch length(NW-SE) to width(NE-SW) ratio was approximately 2.4:1.0.     

蒙古高原NDVI的空间格局及分异

GIMMS NDVI database and geo-statistics were used to depict the spatial distribu-tion and temporal stability of NDVI on the Mongolian Plateau. The results demonstrated that: (1) Regions of interest with high NDVI indices were distributed primarily in forested moun-tainous regions of the east and the north, areas with low NDVI indices were primarily distrib-uted in the Gobi desert regions of the west and the southwest, and areas with moderate NDVI values were mainly distributed in a middle steppe strap from northwest to southeast. (2) The maximum NDVI values maintained for the past 22 years showed little variation. The average NDVI variance coefficient for the 22-year period was 15.2%. (3) NDVI distribution and vege-tation cover showed spatial autocorrelations on a global scale. NDVI patterns from the vegetation cover also demonstrated anisotropy; a higher positive spatial correlation was in-dicated in a NW-SE direction, which suggested that vegetation cover in a NW-SE direction maintained increased integrity, and vegetation assemblage was mainly distributed in the same specific direction. (4) The NDVl spatial distribution was mainly controlled by structural factors, 88.7% of the total spatial variation was influenced by structural and 11.3% by random factors. And the global autocorrelation distance was 1178 km, and the average vegetation patch length (NW-SE) to width (NE-SW) ratio was approximately 2.4:1.0.     

Quantifying the spatial heterogeneity of points

Variation in the spatial heterogeneity of points reflects the evolutionary process or mechanism of geographical events. The key to depicting this variation is quantifying spatial heterogeneity. In this paper, the spatial heterogeneity of a point pattern is defined as the degree of aggregation-type deviation from complete spatial randomness. In such a case, a goodness-of-fit-type statistic based on the distribution of nearest-neighbor distances called the level of heterogeneity (LH*) is regarded as a standard measurement, and a normalized version called the normalized level of heterogeneity (NLH*) is proposed for datasets with different point numbers and study region areas. Considering the complex integration calculation of LH* and NLH*, simulation experiments are implemented to test the capability of some classic nearest-neighbor statistics in quantifying spatial heterogeneity. The results showed that except for the standard LH* statistic, only Clark and Evans’ statistic (A-w) and Byth and Ripley’s statistic (H-xw) are robust. Statistics NLH*, (A-w) and (H-xw) are validated by quantifying the spatial heterogeneity of two-dimensional crime events, three-dimensional earthquake events and four-dimensional origin-destination (OD) events. The results indicate that these statistics all have a reasonable explanation in quantifying spatial heterogeneity for real-world geographical events of different types and with different dimensions. Compared with NLH*, Clark and Evans’ (A-w) statistic and Byth and Ripley’s (H-xw) statistic are recommended from the perspective of accessibility.     

自然地理要素空间插值的几个问题

资源管理、灾害管理、生态环境治理以及全球变化研究的需要强化了部分自然地理要素空间插值研究的重要性。这些要素空间插值的核心是建立充分逼近要素空间分布特征的函数方程。对于给定的区域与要素样本值 ,插值函数可以有多种模型形式。各类模型的精度受其理论基础、模型算法、时空尺度效应、样本数据属性等因素的综合影响。通过对国际主要插值研究成果进行分析 ,文章认为各类模型插值精度的差异缘于模型对插值要素空间变异性与空间相关性的反映 ,具体应用中 ,只有对已知样本数据进行变异性与相关性分析才能选出适当的插值方法。     

基于空间自相关的闽台城镇建设用地分布研究

空间自相关是一种重要的空间统计方法, 用来检验某种地理现象或某一属性值的整体分 布状况, 判断此现象或属性值在空间上是否有聚集特性存在。本文利用2002 年ASTER 影像数据 作为遥感数据源提取闽台建设用地信息, 闽台建设用地密度的分布呈现出沿台湾海峡呈对称集 聚分布的态势。通过建设用地密度的空间自相关分析, 显示闽台建设用地空间分布整体上呈显著 的空间正相关, 集聚现象明显; 而在局部上则呈现出不同的空间结构形态, 建设用地高密度区主 要集聚分布在闽东南沿海的闽江口、厦门湾和泉州湾三大城镇密集区及台湾西部的台北、台中和 高雄三大都会区。这种空间相关关系的探讨对于认识闽台人口和社会经济的空间分布及福建省 建设用地未来的发展具有重要的意义, 也对福建省社会经济发展政策的制定提供重要参考依据。     

中国土壤土层厚度的空间变异性特征

以全国第二次土壤普查的1627个土壤剖面资料为基础,在地质统计学和地理信息系统的支持下,以变异函数为基本工具初步分析中国土壤土层厚度的空间变异特征,并应用普通克里格法进行最优无偏线性插值,制作出分辨率为30km×30km的中国土壤土层厚度的空间分布图。结果表明:中国土壤土层厚度具有较好的可迁性和空间结构性特点,实验变异函数值的变化趋势基本上随着距离的增加逐渐上升,拟合变程在680km以上,土壤厚度的相关性可大于680km,土层厚度具有明显的块状或连续分布的特点     

Flow Cross K-function: a bivariate flow analytical method

Spatial flow data represent meaningful interaction activities between pairs of corresponding locations, such as daily commuting, animal migration, and merchandise shipping. Despite recent advances in flow data analytics, there is a lack of literature on detecting bivariate or multivariate spatial flow patterns. In this paper we introduce a new spatial statistical method called Flow Cross K-function, which combines the Cross K-function that detects marked point patterns and the Flow K-function that detects univariate flow clustering patterns. Flow Cross K-function specifically assesses spatial dependence of two types of flow events, in other words, whether one type of flows is spatially associated with the other, and if so, whether this is according to a clustering or dispersion trend. Both a global version and a local version of Flow Cross K-function are developed. The former measures the overall bivariate flow patterns in the study area, while the latter can identify anomalies at local scales that may not follow the global trend. We test our method with carefully designed synthetic data that simulate the extreme situations. We exemplify the usefulness of this method with an empirical study that examines the distributions of taxi trip flows in New York City.     

玉溪市中心城区建设用地集约利用空间差异研究

为科学地分析城市土地集约利用在空间上的分布规律及趋势,以玉溪市中心城区为研究区域,在玉溪市中心城区建设用地集约利用评价的基础上,通过空间相关分析的方法揭示了中心城区建设用地集约度的空间分布特征。结果表明:玉溪市中心城区集约利用度呈现出由城市中心逐渐向外递减的趋势,土地集约利用水平在一定程度上存在集聚效应;从全域空间分析看,各功能区土地利用集约度空间分布在整体上具有较好的正相关性,居住功能区相关性最强,其他功能区最弱;从局部空间的角度来看,集约度的分布既存在空间聚集性又存在空间异质性。     

Using geovisual analytics to compare the performance of geographically weighted discriminant analysis versus its global counterpart,linear discriminant analysis

Geographically weighted spatial statistical methods are a family of spatial statistical methods developed to address the presence of non-stationarity in geographical processes, the so-called spatial heterogeneity. While these methods have recently become popular for analysis of spatial data, one of their characteristics is that they produce outputs that in themselves form complex multi-dimensional spatial data sets. Interpretation of these outputs is therefore not easy, but is of high importance, since spatial and non-spatial patterns in the results of these methods contain clues to causes of underlying non-stationarity. In this article, we focus on one of the geographically weighted methods, the geographically weighted discriminant analysis (GWDA), which is a method for prediction and analysis of categorical spatial data. It is an extension of linear discriminant analysis (LDA) that allows the relationship between the predictor variables and the categories to vary spatially. This produces a very complex data set of GWDA results, which include on top of the already complex discriminant analysis outputs (e.g. classifications and posterior probabilities) also spatially varying outputs (e.g. classification function parameters). In this article, we suggest using geovisual analytics to visualise results from LDA and GWDA to facilitate comparison between the global and local method results. For this, we develop a bespoke visual methodology that allows us to examine the performance of global and local classification method in terms of quality of classification. Furthermore, we are also interested in identifying the presence (or absence) of non-stationarity through comparison of the outputs of both methods. We do this in two ways. First, we visually explore spatial autocorrelation in both LDA and GWDA misclassifications. Second, we focus on relationships between the classification result and the independent variables and how they vary over space. We describe our visual analytic system for exploration of LDA and GWDA outputs and demonstrate our approach on a case study using a data set linking election results with a selection of socio-economic variables.