城市地价空间自相关分析——以宿州市为例

运用全局和局域自相关统计量及地统计学半变异函数分析方法,对宿州市商业和住宅地价的全局自相关、各项异性及局域自相关特征进行了全面分析.结果表明:在2 800m范围内,宿州市商业和住宅地价均存在较强的正自相关性且趋于高值集聚,随着相关距离的增大,地价半方差按球状模型曲线增长,自相关性逐渐减弱;在不同方向上,地价的空间自相关特征不尽相同,南北方向上的空间自相关范围要大于东西方向,但随机性较强,自相关程度较东西方向弱;在全局呈显著自相关的情形下局域地价也主要呈空间集聚格局,但部分地区表现出异质性.     

Cluster-mapping procedure for tourism regions based on geostatistics and fuzzy clustering: example of Polish districts

ABSTRACT

In tourism, the phenomenon of spatial agglomeration (concentration of economic activity) spreads beyond the borders of the territorial units. It is referred to as geographic ‘spillovers’ that enhances spatial interdependence and functional relationship of neighbouring regions. Within the standard procedure of cluster-mapping, only concentration inside a territorial unit may be analysed which is a source of biased results in tourism studies. However, tourist agglomeration centres sometimes occur at the juncture of territorial units and the economic entities located in them form spatial clusters with a different degree of membership to several centres of agglomerations located in the neighbourhood. Therefore, we propose to measure ‘inter-regional’ agglomeration in tourism providing modification of spatial autocorrelation measures (regarding neighbourhood and distance). In order to measure neighbourhood more precisely, we use geostatistical information (global positioning system (GPS) coordinates of tourism entities) and geographically weighted fuzzy clustering approach (FGWC). We examine the method on the example of Polish districts (NUTS-4) using database of 131,338 firms registered in section I (accommodation and food services) of Polish Classification of Activity in 2015. The results proved that a novel method of cluster-mapping considering spatial dependency combined with geographic information system (GIS) and FGWC method increases the accuracy of the identification of tourism clusters (inter-regional agglomeration).     

Modelling the Ecological Comorbidity of Acute Respiratory Infection,Diarrhoea and Stunting among Children Under the Age of 5 Years in Somalia

The aim of this study was to assess spatial co‐occurrence of acute respiratory infections (ARI), diarrhoea and stunting among children of the age between 6 and 59 months in Somalia. Data were obtained from routine biannual nutrition surveys conducted by the Food and Agriculture Organization 2007–2010. A Bayesian hierarchical geostatistical shared component model was fitted to the residual spatial components of the three health conditions. Risk maps of the common spatial effects at 1×1 km resolution were derived. The empirical correlations of the enumeration area proportion were 0.37, 0.63 and 0.66 for ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. Spatially, the posterior residual effects ranged 0.03–20.98, 0.16–6.37 and 0.08–9.66 for shared component between ARI and stunting, diarrhoea and stunting and ARI and diarrhoea, respectively. The analysis showed clearly that the spatial shared component between ARI, diarrhoea and stunting was higher in the southern part of the country. Interventions aimed at controlling and mitigating the adverse effects of these three childhood health conditions should focus on their common putative risk factors, particularly in the South in Somalia.     

Covariance functions that are stationary or nonstationary in space and stationary in time

There is a great demand for statistical modelling of phenomena that evolve in both space and time, and thus, there is a growing literature on covariance function models for spatio-temporal processes. Although several nonseparable space–time covariance models are available in the literature, very few of them can be used for spatially anisotropic data. In this paper, we propose a new class of stationary nonseparable covariance functions that can be used for both geometrically and zonally anistropic data. In addition, we show some desirable mathematical features of this class. Another important aspect, only partially covered by the literature, is that of spatial nonstationarity. We show a very simple criteria allowing for the construction of space–time covariance functions that are nonseparable, nonstationary in space and stationary in time. Part of the theoretical results proposed in the paper will then be used for the analysis of Irish wind speed data as in HASLETT and RAFTERY ( Applied Statistics , 38 , 1989, 1).     

GIS在微生物监测中的应用探讨

文章提出了GIS在微生物监测中的设想及操作步骤。通过实验得出微生物数据,并建立微生物空间数据库,利用ARCGIS软件中地统计模块对微生物进行空间自相关分析,进行环境分析。     

Hierarchical clustering of spatially correlated functional data

Classification problems of functional data arise naturally in many applications. Several approaches have been considered for solving the problem of finding groups based on functional data. In this paper, we are interested in detecting groups when the functional data are spatially correlated. Our methodology allows to find spatially homogeneous groups of sites when the observations at each sampling location consist of samples of random functions. In univariable and multivariable geostatistics, various methods of incorporating spatial information into the clustering analysis have been considered. Here, we extend these methods to the functional context to fulfil the task of clustering spatially correlated curves. In our approach, we initially use basis functions to smooth the observed data, and then, we weight the dissimilarity matrix among curves by either the trace‐variogram or the multivariable variogram calculated with the coefficients of the basis functions. This paper contains a simulation study as well as the analysis of a real data set corresponding to average daily temperatures measured at 35 Canadian weather stations.     

地质统计学及其在地质工程中的应用分析

随着科技日益发展,地质统计学在地质工程中也发挥着重要的作用,这就对地质统计提出了更高的要求。本文从地质统计方法入手,简要阐述了地质统计学发展的现状,并分析了统计方法在地质工程中的重要作用及其应用。     

A kernel-based method for nonparametric estimation of variograms

Variogram estimation plays an important role in many areas of spatial statistics. Potential areas of application include biology, ecology, economics and meteorology. However, it is common that, for example under highly correlated patterns, traditional estimators can not reflect all the spatial features or dependencies. In this paper, we present an alternative distribution-free estimator based on nearest-neighbour estimation with a non-constant smoothing field that is better able to adapt to spatially varying features of the data pattern. We present a simulation study to compare our new estimator to a nearest-neighbour estimator built with a constant smoothing parameter and to the classical variogram estimator. We apply our method to analyze two ecological data sets.     

指示克立格法及其在城市地价评估中的应用

目前地价评估中常用的模型法没有考虑地价样点的自身特点,存在一定的缺陷。将非参数的地质统计学的指示克立格法引用到地价评估领域,计算出地价评估中的克立格函数,据此分析地价分布的空间变异特征,估算出各评估单元的地估算价值,并给出了估计误差。     

基于因子分析法的交通污染源分析研究

随着城镇化、工业化的发展,交通污染问题愈发严重,重金属在表层土壤的污染程度也愈演愈烈,已经严重威胁到人类的生产和生活。因此研究交通活动影响下城市的地质环境显得尤为重要。本文基于环境单元数据,运用地统计学软件计算区域土壤重金属的半变异函数,研究区域土壤重金属含量的空间结构特征。并利用多个实测变量之间的相互关系,运用数学变换,将多个变量转变为少数几个线性不相关的综合指标,用较少的有代表性的因子来说明众多变量所提取的主要信息,从而有效地确定交通污染源。