Some results on unilateral ARMA lattice processes

This paper presents several linked results on unilateral autoregressive moving average processes on a rectangular lattice. It is shown that axially symmetric two-dimensional quadrant processes must be separable. Exact forms for the inverse variance matrix are obtained in some cases, which allow exact Gaussian maximum likelihood estimation and simulation. It is shown that generating functions can be used for extrapolation. The herringbone simulation method is discussed.     

Network autocorrelation models with egocentric data

Network autocorrelation models have been widely used for decades to model the joint distribution of the attributes of a network's actors. This class of models can estimate both the effect of individual characteristics as well as the network effect, or social influence, on some actor attribute of interest. Collecting data on the entire network, however, is very often infeasible or impossible if the network boundary is unknown or difficult to define. Obtaining egocentric network data overcomes these obstacles, but as of yet there has been no clear way to model this type of data and still appropriately capture the network effect on the actor attributes in a way that is compatible with a joint distribution on the full network data. This paper adapts the class of network autocorrelation models to handle egocentric data. The proposed methods thus incorporate the complex dependence structure of the data induced by the network rather than simply using ad hoc measures of the egos’ networks to model the mean structure, and can estimate the network effect on the actor attribute of interest. The vast quantities of unknown information about the network can be succinctly represented in such a way that only depends on the number of alters in the egocentric network data and not on the total number of actors in the network. Estimation is done within a Bayesian framework. A simulation study is performed to evaluate the estimation performance, and an egocentric data set is analyzed where the aim is to determine if there is a network effect on environmental mastery, an important aspect of psychological well-being.     

A simulation comparison on spatial Poisson mixed models

The statistical methods for analyzing spatial count data have often been based on random fields so that a latent variable can be used to specify the spatial dependence. In this article, we introduce two frequentist approaches for estimating the parameters of model-based spatial count variables. The comparison has been carried out by a simulation study. The performance is also evaluated using a real dataset and also by the simulation study. The simulation results show that the maximum likelihood estimator appears to be with the better sampling properties.     

Estimating Spatial Autocorrelation With Sampled Network Data

Spatial autocorrelation is a parameter of importance for network data analysis. To estimate spatial autocorrelation, maximum likelihood has been popularly used. However, its rigorous implementation requires the whole network to be observed. This is practically infeasible if network size is huge (e.g., Facebook, Twitter, Weibo, WeChat, etc.). In that case, one has to rely on sampled network data to infer about spatial autocorrelation. By doing so, network relationships (i.e., edges) involving unsampled nodes are overlooked. This leads to distorted network structure and underestimated spatial autocorrelation. To solve the problem, we propose here a novel solution. By temporarily assuming that the spatial autocorrelation is small, we are able to approximate the likelihood function by its first-order Taylor’s expansion. This leads to the method of approximate maximum likelihood estimator (AMLE), which further inspires the development of paired maximum likelihood estimator (PMLE). Compared with AMLE, PMLE is computationally superior and thus is particularly useful for large-scale network data analysis. Under appropriate regularity conditions (without assuming a small spatial autocorrelation), we show theoretically that PMLE is consistent and asymptotically normal. Numerical studies based on both simulated and real datasets are presented for illustration purpose.     

环境规制的经济效应：“减排”还是“增效”

文章构建了一个关于环境规制经济效应的理论分析框架,并利用中国城市面板数据和动态空间面板模型实证检验了环境规制的“污染减排”和“提质增效”效应。研究结论显示,中国城市的环境规制具有“只减排、不增效”的经济效应及空间溢出效应,在考虑了一系列稳健性检验后结论仍然成立。进一步异质性讨论揭示出：环境规制“只减排、不增效”的经济效应在东、中、西三个地区均得到验证;环境规制的“减排”效应在国际金融危机以后得到明显增强,但仅增加了企业的“合规成本”,而尚未形成“创新效应”;通过加快产业结构调整可以有效改善环境规制“只减排、不增效”的经济效应;经济发展与能源效率呈U”型变化趋势,并且中国正处于“U”型结构的左侧,而库兹涅茨环境曲线并未得到中国城市数据的支持。     

空间外溢视角下的协同创新与区域产业升级

本文从空间维度廓清了协同创新对区域产业升级的空间外溢机制和外溢效应边界形成机理。采用中国分省区面板数据,利用地理距离和社会经济特征多种空间权重矩阵设定的空间杜宾(Durbin)模型实证检验了协同创新对产业升级的影响。结果表明：协同创新对产业升级既有区域内溢出效应,也有区域间溢出效应。空间外溢效应在很大程度上通过创新要素区际流动来实现,且呈现出地理距离衰减特征。进一步考虑地理区位特征差异发现：空间外溢对产业升级的带动效应在中部地区最强,西部地区次之,东部地区最弱。本研究结果有助于全面认识协同创新作用机制,对推动区域间良性互动,实现产业升级发展具有重要现实意义。     

Potentials,problems and possibilities for developing New Zealand’s built environment through high-value geospatial information

Gaining useful information about our built, social and natural environments is vital and possible. The value of data for cities is more informed, more effective management and development choices resulting in more benefits to a greater range of stakeholders. This article explores the state-of-play of data infrastructures in New Zealand to identify the challenges to gaining better value from geospatial information. Following engagement with built environment data custodians and information users, it analyses how digital information infrastructure can support the sharing and use of richer and more comprehensive information by weaving a supportive fabric across data. The analysis reveals that data infrastructures primarily need to facilitate agreement on data management, sharing and use; the sharing of costs and benefits across stakeholders; and emphasise place as a powerful driver to richer information. An overarching sociotechnical approach to digital information infrastructure weaves data platforms, policy and technology together.     

Using geographically weighted regression for environmental justice analysis: Cumulative cancer risks from air toxics in Florida

Previous quantitative research on environmental justice has been limited by simplistic assumptions used to measure health risks and traditional regression techniques that fail to discern spatial variations in statistical relationships. We address these gaps through a case study that examines: (a) whether potential health risks from exposure to hazardous air pollutants in Florida are related to race/ethnicity and socioeconomic status, and (b) how the significance of statistical associations between health risk and race/ethnicity or socioeconomic status vary across the state. This study integrates census tract level estimates of cumulative cancer risk compiled by the EPA with Census 2000 data and a spatial statistical technique known as geographically weighted regression that allows us to explore spatial variability in analytical results. Our findings indicate that while race and ethnicity are significantly related to cancer risks in Florida, conventional regression can hide important local variations in statistical relationships relevant to environmental justice analysis.     

Residential segregation in metropolitan established immigrant gateways and new destinations, 1990-2000

In the 1990s, many immigrants bypassed established gateways like Los Angeles, New York, Chicago, and Miami to create new immigrant destinations across the US. In this paper, we examine how segregation and spatial assimilation might differ between established gateways and new destinations among the 150 largest metropolitan areas. Using data from the 1990 and 2000 censuses, we calculate levels of dissimilarity for Hispanics and Asians by nativity for these two gateway types. Our findings show that segregation levels are consistently lower in new destinations. However, Hispanics in new destinations experienced significant increases segregation during the 1990s, suggesting a convergence in residential patterns by destination type. Nevertheless, in both destinations the native-born are less segregated than the foreign born—consistent with immigrant spatial incorporation. Finally, socioeconomic indicators are generally consistent with predictions of spatial assimilation.     

Inference with a contrast-based posterior distribution and application in spatial statistics

The likelihood function is often used for parameter estimation. Its use, however, may cause difficulties in specific situations. In order to circumvent these difficulties, we propose a parameter estimation method based on the replacement of the likelihood in the formula of the Bayesian posterior distribution by a function which depends on a contrast measuring the discrepancy between observed data and a parametric model. The properties of the contrast-based (CB) posterior distribution are studied to understand what the consequences of incorporating a contrast in the Bayes formula are. We show that the CB-posterior distribution can be used to make frequentist inference and to assess the asymptotic variance matrix of the estimator with limited analytical calculations compared to the classical contrast approach. Even if the primary focus of this paper is on frequentist estimation, it is shown that for specific contrasts the CB-posterior distribution can be used to make inference in the Bayesian way.The method was used to estimate the parameters of a variogram (simulated data), a Markovian model (simulated data) and a cylinder-based autosimilar model describing soil roughness (real data). Even if the method is presented in the spatial statistics perspective, it can be applied to non-spatial data.