Spatial Data Usage,Spatial Thinking and Spatial Knowledge Generation: The Case of Planning Practitioners in Arequipa,Peru

Spatial thinking and spatial knowledge generation in decision-making are still not mature fields of study in planning research, despite these being crucial elements in addressing the issues of the twenty-first-century city. This article contributes to their understanding by exploring their interrelationships with spatial data usage. Through storylines, it analyzes the arguments that planning practitioners offer in support of infrastructure-led decisions in Arequipa (Peru), before and after spatial data usage. The article concludes that spatial data usage improve spatial thinking to different extents, yet suggests aligning spatial data generation and the inclusion of GIS-based spatial analyses with the spatial knowledge needed by routine planning practice.     

基于矩阵奇异值分解的空间谱估计算法

本文提出了一种利用矩阵奇异值分解来作空间谱估计的方法,即对由天线阵获取的数据所构成的数据矩阵作奇异值分解、删除来自噪声的贡献的诸最小奇异值以改善信噪比,并利用噪声奇异向量和天线阵的方向向量正交的性质来计算空间谱。除了奇异值分解算法本身给计算稳定性带来好处外,本方法的谱估计性能和计算量均优于近几年来国外广泛关注的一种谱估计算法——MUSIC算法。本方法可用于高分辨的测向系统中。     

在公平与效率之间:对北京市养老资源的空间分析

基于网络采集的养老机构数据和第六次全国人口普查数据，以公共设施区位理论为框架，使用空间分析方法，本文对北京市养老资源的分布模式进行了研究。研究发现，在乡镇/街道层面，整体上公办养老资源分布相对均衡，呈现正的空间依赖，民办养老资源则局部呈现负的空间相关性特征。空间回归分析表明，尽管两者布局均受到辖域内服务对象绝对数量这一因素的影响，但民办养老资源分布对老年人口密度这一指标更为敏感。不同类型的养老资源分散与集聚的空间模式，体现了公共设施供给问题上效率与公平原则之间的对比关系，这是行政与市场力量形塑的结果。     

ESTIMATION OF SPATIAL ARMA MODELS

Spatial ARMA models are considered using the nonsymmetric half plane ordering on a lattice of data. A method is given for the estimation of the orders and the coefficients of such models under an identifiability condition and the condition that the beat linear predictor is the best predictor in the mean square sense. Under these conditions, the strong consistency of the estimators ia established. The usual methods for ARMA modelling in Time Series Analysis require estimation of the innovations. The method of this paper introduces an inveree model complementary to the original model so that the estimation of the innovations is avoided. This leads to a substantial reduction in the computational complexity in the two-dimensional case.     

地方政府环境规制竞争背景下地区间的企业污染排放行为

针对地方政府环境规制竞争背景下中国污染排放空间异质性溢出问题,构建中国污染排放空间异质性溢出推拉模型,理论探讨中国区域污染排放在地方政府环境规制竞争背景下空间异质性溢出的形成机理:当由市场效应决定的拉力大于由学习效应决定的推力时,中国污染排放在空间上呈现负溢出效应;当由学习效应决定的推力大于由市场效应决定的拉力时,中国污染排放在空间上呈现正溢出效应。使用中国285个地级市2003-2015年数据,构建异质性空间自回归模型,使用准极大似然估计方法实证检验中国污染排放的空间异质性溢出特征。研究结果显示:（1）中国区域污染排放确实存在空间溢出效应,并且这种溢出效应呈现异质性特征。这种异质性是由不同区域内部不同的拉力与推力较量结果导致;（2）中国工业烟（粉）尘排放强度在空间上主要呈现出正的溢出效应,即中国大部分区域工业烟（粉）尘排放强度受学习效应影响。     

中国城市民生发展的区域差异测度、来源分解与形成机理

本文基于熵值法构建城市民生发展指数,利用Dagum基尼系数和方差分解方法首次从空间和结构两个视角考察我国城市民生发展的差异大小及来源,并运用关系数据分析范式研究其形成机理。研究发现:我国城市民生发展水平持续向好,但2010年之后民生发展不均衡现象显著;民生发展总体差异主要来源于区域间差异,其中东部和中部区域间差异贡献最大,东部区域内差异贡献显著高于中部、西部地区;社会发展差异和生态建设差异是民生发展差异的主要结构来源;人民生活差异、科技创新差异对民生发展差异的影响均从东向西依次减弱,社会发展差异的作用则依次增强,生态建设差异的影响在中部地区最为突出,经济发展差异对东部地区的影响较弱;人民生活差异、科技创新差异上升是导致2010年之后民生发展差异扩大的主要原因。本研究丰富了城市民生评价体系的构建方案,为揭示我国城市民生发展差异状况、探索协同提升对策提供了重要借鉴经验。     

Complexity in applying spatial analysis to describe heterogeneous air-trapping in thoracic imaging data

In this paper we consider a novel approach to analyzing medical images by applying a concept typically employed in geospatial studies. For certain diseases, such as asthma, there is a relevant distinction between the heterogeneity of constriction in airways for patients compared to healthy individuals. In order to describe such heterogeneities quantitatively, we utilize spatial correlation in the realm of lung computer tomography (CT). Specifically, we apply the approximate profile-likelihood estimator (APLE) to simulated lung air-trapping data selected based on potential interest to pulmonologists, and we explore reference values obtainable through this statistic. Results indicate that APLE values are independent of air-trapping values, and can provide useful insight into spatial patterns of these values within the lungs in situations where other common metrics, such as the coefficient of variation, reveal little. The APLE relies on a neighborhood weights matrix to define spatial relatedness of considered regions, and among a few weight structures explored, a working optimal choice seems to be one based on the inverse distance squared between regions of interest. The application yields a new method to help analyze the degree of heterogeneity in lung CT images, which can be generalized to other medical images as well.     

Spatio-Temporal Modeling of Residential Sales Data

This article focuses on the location, time, and spatio-temporal components associated with suitably aggregated data to improve prediction of individual asset values. Such effects are introduced in the context of hierarchical models, which we find more natural than attempting to model covariance structure. Indeed, our cross-sectional database, a sample of 7,936 transactions for 49 subdivisions over a 10-year period in Baton Rouge, Louisiana, precludes covariance modeling. A wide range of models arises, each fitted using sampling-based methods because likelihood-based fitting may not be possible. Choosing among an array of nonnested models is carried out using a posterior predictive criterion. In addition, one year of data is held out for model validation. A thorough analysis of the data incorporating all of the aforementioned issues is presented.     

A First-Order Spatial Integer-Valued Autoregressive SINAR(1, 1) Model

Binomial thinning operator has a major role in modeling one-dimensional integer-valued autoregressive time series models. The purpose of this article is to extend the use of such operator to define a new stationary first-order spatial non negative, integer-valued autoregressive SINAR(1, 1) model. We study some properties of this model like the mean, variance and autocorrelation function. Yule-Walker estimator of the model parameters is also obtained. Some numerical results of the model are presented and, moreover, this model is applied to a real data set.