Myth,Primogeniture and Long Distance Trade‐Friends in Northwest New Britain,Papua New Guinea

ABSTRACT The origins of ceremonies for firstborn children and long distance trade networks are embedded in Bariai mythology and cosmology. Based on my ethnographic research and the ethnographic reportage contained in the Australian colonial Patrol Officers' Reports, this paper explores the pre‐ and post‐contact trade networks of Bariai parents as they pursue a reputation for ‘renown’ by entering into complex trade‐friendships (sobo) and exchanges for the necessary wealth to undertake one (of seventeen) firstborn ceremony, the mata pau or ‘new eye.’ My intent in this paper is to (1) reiterate that a people and their culture can only be understood within regional systems of relationships; (2) indicate the manner in which long distance trade‐friendships were created and maintained over a long period of time; (3) show how these socio‐economic institutions are embedded in Bariai cosmology and thus made meaningful; (4) attest to the vitality and importance of these systems despite the impact of modernity, missionization and money.     

GUERRILLA WARFARE IN THE PLANNING SYSTEM: REVOLUTIONARY PROGRESS TOWARDS SUSTAINABILITY?

This article extends Qviström's (2007; Geografiska Annaler: Series B, Human Geography 89 (3): 269–282) ideas concerning “landscapes out of order” within a re‐discovering and re‐imagining of spatial planning theory and practice. Taking the viewpoint that planners and decision‐makers order and manage space in prescribed and constrained ways, the article argues that this can hinder innovative practices which have the potential to deliver significant societal and environmental benefits. Using case studies from permaculture and guerrilla gardening, we illustrate how planning practice can be rooted in confrontation and legal challenge rather than with more positive and inclusive approaches, as is envisaged within spatial planning theory. Clearly, the ways in which such initiatives intersect with the planning system raise important questions about joined‐up policy across scales and sectors, and the ability of planning to be a proactive vehicle of environmental and social change. Our findings confirm that spatial planning theory is largely “disintegrated” (Scott et al. 2013; Progress in Planning 83: 1–52) from much contemporary planning and environmental practice and wider discourses of sustainability. This suggests an urgent re‐examination of the spirit and purpose of planning to embrace and promote the new even where they challenge established orthodoxy and planning order.     

SPATIAL ECONOMETRIC MODELING OF ORIGIN‐DESTINATION FLOWS*

ABSTRACT Standard spatial autoregressive models rely on spatial weight structures constructed to model dependence among n regions. Ways of parsimoniously modeling the connectivity among the sample of N=n² origin‐destination (OD) pairs that arise in a closed system of interregional flows has remained a stumbling block. We overcome this problem by proposing spatial weight structures that model dependence among the N OD pairs in a fashion consistent with standard spatial autoregressive models. This results in a family of spatial OD models introduced here that represent an extension of the spatial regression models described in Anselin (1988) .     

Using Spatial Filters and Exploratory Data Analysis to Enhance Regression Models of Spatial Data

Residual spatial autocorrelation is a situation frequently encountered in regression analysis of spatial data. The statistical problems arising due to this phenomenon are well‐understood. Original developments in the field of statistical analysis of spatial data were meant to detect spatial pattern, in order to assess whether corrective measures were required. An early development was the use of residual autocorrelation as an exploratory tool to improve regression analysis of spatial data. In this note, we propose the use of spatial filtering and exploratory data analysis as a way to identify omitted but potentially relevant independent variables. We use an example of blood donation patterns in Toronto, Canada, to demonstrate the proposed approach. In particular, we show how an initial filter used to rectify autocorrelation problems can be progressively replaced by substantive variables. In the present case, the variables so retrieved reveal the impact of urban form, travel habits, and demographic and socio‐economic attributes on donation rates. The approach is particularly appealing for model formulations that do not easily accommodate positive spatial autocorrelation, but should be of interest as well for the case of continuous variables in linear regression.     

A Wavelet‐Based Extension of Generalized Linear Models to Remove the Effect of Spatial Autocorrelation. 基于小波扩展广义线性模型消除空间自相关的影响

Biogeographical studies are often based on a statistical analysis of data sampled in a spatial context. However, in many cases standard analyses such as regression models violate the assumption of independently and identically distributed errors. In this article, we show that the theory of wavelets provides a method to remove autocorrelation in generalized linear models (GLMs). Autocorrelation can be described by smooth wavelet coefficients at small scales. Therefore, data can be decomposed into uncorrelated and correlated parts. Using an appropriate linear transformation, we are able to extend GLMs to autocorrelated data. We illustrate our new method, called the wavelet‐revised model (WRM), by applying it to multiple regression with response variables conforming to various distributions. Results are presented for simulated data and real biogeographical data (species counts of the plant genus Utricularia [bladderworts] in grid cells throughout Germany). The results of our WRM are compared with those of GLMs and models based on generalized estimating equations. We recommend WRMs, especially as a method that allows for spatial nonstationarity. The technique developed for lattice data is applicable without any prior knowledge of the real autocorrelation structure.     

Model Uncertainty in Matrix Exponential Spatial Growth Regression Models

This article considers the most important aspects of model uncertainty for spatial regression models, namely, the appropriate spatial weight matrix to be employed and the appropriate explanatory variables. We focus on the spatial Durbin model (SDM) specification in this study that nests most models used in the regional growth literature, and develop a simple Bayesian model‐averaging approach that provides a unified and formal treatment of these aspects of model uncertainty for SDM growth models. The approach expands on previous work by reducing the computational costs through the use of Bayesian information criterion model weights and a matrix exponential specification of the SDM model. The spatial Durbin matrix exponential model has theoretical and computational advantages over the spatial autoregressive specification due to the ease of inversion, differentiation, and integration of the matrix exponential. In particular, the matrix exponential has a simple matrix determinant that vanishes for the case of a spatial weight matrix with a trace of zero. This allows for a larger domain of spatial growth regression models to be analyzed with this approach, including models based on different classes of spatial weight matrices. The working of the approach is illustrated for the case of 32 potential determinants and three classes of spatial weight matrices (contiguity‐based, k‐nearest neighbor, and distance‐based spatial weight matrices), using a data set of income per capita growth for 273 European regions.     

INCORPORATING TECHNOLOGY DIFFUSION,FACTOR MOBILITY AND STRUCTURAL CHANGE INTO CROSS‐REGION GROWTH REGRESSION: AN APPLICATION TO CHINA*

ABSTRACT This paper advocates a spatial dynamic model that introduces technology diffusion, factor mobility, and structural change into the cross‐region growth regression. The spatial setting is derived from theory rather than spatial statistical tests. An application of this model to the study of cross‐province growth in China over the period 1980–2005 indicates that incomes are spatially correlated, which highlights the significance of technology diffusion and factor mobility. Furthermore, the integration of neoclassical growth empirics and the structural change perspective of development economics provide a much improved account of interprovincial variations in income levels and economic growth.     

Potential Use of Radiometric Data for Wildlife Habitat Modelling

The potential use of existing radiometric data sets, previously collected for prospecting purposes, has very rarely been used as a variable predictor in wildlife habitat modelling. The utility of radiometric data for predicting vegetation community patterns and wildlife habitat was investigated in the Australian arid zone using the Burt Plain bioregion as a case study. Using spatial datasets and a Species Distribution Modelling Toolkit, arid zone vertebrate species were modelled with Generalised Linear Modelling (GLM) regression modelling techniques. These models were used to predict the probability of occurrence of a species at any given location, defined in terms of its environmental attributes. A statistical correlation between the radioactive elements uranium, thorium and potassium, and terrain aspect was found. No statistical correlations were established between the radioactive elements and vegetation patterns; although we suspect these exist at finer scales of mapping. Radiometric data were identified as explanatory variables in the habitat models of all of the 32 vertebrate species examined, and used as illustration in the development of probabilistic spatial predictions of three species (Red Kangaroo, Macropus rufus; Lesser Hairy‐footed Dunnart, Sminthopsis youngsoni; and Rabbit, Oryctolagus cuniculus) in the bioregion. Our analyses suggest that radiometric data sets involving the radioactive elements: (uranium, thorium, and potassium), and vegetation could be used as predictors of biodiversity patterns at the bioregional and landscape level. This is an important finding given the challenges posed in undertaking broad‐scale biological surveys in the arid zone of Australia.     

30年来国际“贸易-交通-旅游”(3T)互动的统计分析

经济全球化背景下,国际"贸易-交通-旅游"(3T)互动日益增强。采用1982-2011年时间序列数据,分析国际贸易、交通和旅游总量的发展历程,从进、出口分量视角考察三者相关关系,运用最小二乘法构建回归方程。结果发现:①30年来国际贸易、交通和旅游的发展呈持续增长态势。国际贸易和旅游日益重要。②国际贸易、交通和旅游增长过程具有同步性,表现为波动周期同涨同落。③所构建的回归方程决断系数R²均在0.99以上,国际贸易、交通和旅游存在显著相关性。国际货运与国际贸易,国际客运与国际旅游之间具有确定、直接的因果关系;国际贸易与国际旅游的因果性尚待探究。     

NORDEN AND THE "SUBSTANTIVE LANDSCAPE": A PERSONAL ACCOUNT

This commentary takes its point of departure in the debate provoked by the landscape theory that Denis Cosgrove presented in his seminal book, Social Formation and Symbolic Landscape (1984), in which he defined landscape in terms of scenic, spatial forms of representation. By unpacking the evolution of the concept of landscape the author proposed an alternative interpretation to Cosgrove's, emphasizing differing conceptions of lawfulness, in which the “substantive” platial landscape was introduced as a counterpoint to the scenic spatial concept. Initially, and for two particular reasons, the substantive landscape became theoretically placed in Norden: first, due to the historical and contemporary territorial/platial connotations of the term in the Nordic realm, and second, because the interest in a substantive landscape has been considerable among Nordic scholars. Today, this interest goes well beyond a Nordic context. In commenting on the contributions in this special issue the paper points at how different engagements with a “substantive landscape” might inform present and future scholarly debate concerning the role and meaning of landscape.     

Comparative Spatial Filtering in Regression Analysis

One approach to dealing with spatial autocorrelation in regression analysis involves the filtering of variables in order to separate spatial effects from the variables’ total effects. In this paper we compare two filtering approaches, both of which allow spatial statistical analysts to use conventional linear regression models. Getis’ filtering approach is based on the autocorrelation observed with the use of the G_i local statistic. Griffith's approach uses an eigenfunction decomposition based on the geographic connectivity matrix used to compute a Moran's I statistic. Economic data are used to compare the workings of the two approaches. A final comparison with an autoregressive model strengthens the conclusion that both techniques are effective filtering devices, and that they yield similar regression models. We do note, however, that each technique should be used in its appropriate context.     

Maps of Interrelations and their Application in Geographic Research

The basic problem of statistical indicators of the degree of correspondence between geographic phenomena is that they are not sufficiently differentiated from place to place. There is a need for special maps of interrelations showing the correspondence of spatial distribution of two or more related phenomena. Five techniques are proposed: (1) The delimitation of regions distinguished by varying degrees of correspondence; (2) the compilation of cartograms of interrelations; (3) the compilation of maps of isocorrelates; (4) the compilation of maps of deviation from a regression; (5) the compilation of contour-entropy maps. The author thus expands on the 1957 paper of Robinson and Bryson on a method for describing quantitatively the correspondence of geographical distributions.     

不同模型在镇域空间相互作用中的应用与比较分析——以湖北省大冶市为例

镇域尺度的空间相互作用研究对促进城乡统筹和城镇体系规划具有重要意义。本文借鉴最新研究成果,从理论及实践层面对镇域空间相互作用研究进行了探索,提出一种基于空间可达性的辐射模型,并与传统重力模型进行比较分析。以大冶市16个乡镇为例进行实例研究,结果表明:1辐射模型与实测数据的相似度较高,可以更好地反映镇域空间相互作用的客观规律;2两种模拟方法在体现空间相互作用的方向性以及结构特征方面具有显著差异;3辐射模型中影响范围的引入以及空间可达性对区域异质性的体现是其与重力模型的主要区别。     

An Introduction to the Network Weight Matrix

This study introduces the network weight matrix as a replacement for the spatial weight matrix to measure the spatial dependence between links of a network. This matrix stems from the concepts of betweenness centrality and vulnerability in network science. The elements of the matrix are a function not simply of proximity, but of network topology, network structure, and demand configuration. The network weight matrix has distinctive characteristics, which are capable of reflecting spatial dependence between traffic links: (1) elements are allowed to have negative and positive values capturing the competitive and complementary nature of links, (2) diagonal elements are not fixed to zero, which takes the self‐dependence of a link upon itself into consideration, and (3) elements not only reflect the spatial dependence based on the network structure, but they acknowledge the demand configuration as well. We verify the network weight matrix by modeling traffic flows in a 3 × 3 grid test network with 9 nodes and 24 directed links connecting 72 origin‐destination (OD) pairs. Models encompassing the network weight matrix outperform both models without spatial components and models with the spatial weight matrix. The network weight matrix represents a more accurate and defensible spatial dependency between traffic links, and offers the potential to augment traffic flow prediction.     

A Simulation Study on Specifying a Regression Model for Spatial Data: Choosing between Autocorrelation and Heterogeneity Effects

In this simulation study, regressions specified with autocorrelation effects are compared against those with relationship heterogeneity effects, and in doing so, provides guidance on their use. Regressions investigated are: (1) multiple linear regression, (2) a simultaneous autoregressive error model, and (3) geographically weighted regression. The first is nonspatial and acts as a control, the second accounts for stationary spatial autocorrelation via the error term, while the third captures spatial heterogeneity through the modeling of nonstationary relationships between the response and predictor variables. The geostatistical‐based simulation experiment generates data and coefficients with known multivariate spatial properties, all within an area‐unit spatial setting. Spatial autocorrelation and spatial heterogeneity effects are varied and accounted for. On fitting the regressions, that each have different assumptions and objectives, to very different geographical processes, valuable insights to their likely performance are uncovered. Results objectively confirm an inherent interrelationship between autocorrelation and heterogeneity, that results in an identification problem when choosing one regression over another. Given this, recommendations on the use and implementation of these spatial regressions are suggested, where knowledge of the properties of real study data and the analytical questions being posed are paramount.     

A Geostatistical Linear Regression Model for Small Area Data. 一种适用于小区域数据的地统计线性回归模型

We present a new linear regression model for use with aggregated, small area data that are spatially autocorrelated. Because these data are aggregates of individual‐level data, we choose to model the spatial autocorrelation using a geostatistical model specified at the scale of the individual. The autocovariance of observed small area data is determined via the natural aggregation over the population. Unlike lattice‐based autoregressive approaches, the geostatistical approach is invariant to the scale of data aggregation. We establish that this geostatistical approach also is a valid autoregressive model; thus, we call this approach the geostatistical autoregressive (GAR) model. An asymptotically consistent and efficient maximum likelihood estimator is derived for the GAR model. Finite sample evidence from simulation experiments demonstrates the relative efficiency properties of the GAR model. Furthermore, while aggregation results in less efficient estimates than disaggregated data, the GAR model provides the most efficient estimates from the data that are available. These results suggest that the GAR model should be considered as part of a spatial analyst's toolbox when aggregated, small area data are analyzed. More important, we believe that the GAR model's attention to the individual‐level scale allows for a more flexible and theory‐informed specification than the existing autoregressive approaches based on an area‐level spatial weights matrix. Because many spatial process models, both in geography and in other disciplines, are specified at the individual level, we hope that the GAR covariance specification will provide a vehicle for a better informed and more interdisciplinary use of spatial regression models with area‐aggregated data.     

Commerce and trade in Viking Age Norway. Exchange of products or organized transactions?

In this paper the difficulties of distinguishing between commerce in the sense of exchange of products (i.e. more locally organized trade between neighbours/districts) and trade carried out as the main occupation for certain people, are discussed. These are questions very closely connected with the urbanization process of Norway. With the material from Kaupang (Skiringssal) as a point of departure, the author discusses two ways of finding out what type of trade was carried out in Viking Age Norway: A) One may look for the same combination of names found at Kaupang in other places. These seem to reveal ‘town‐making’ features. B) Ask whether any of the still existing medieval towns in Norway have had a pre‐urban stage. At the end of the paper the author discusses the possibilities of tracing different commodities which were traded over the different ports or market‐places.     

The Forgotten Semantics of Regression Modeling in Geography

This article is concerned with the semantics associated with the statistical analysis of spatial data. It takes the simplest case of the prediction of variable y as a function of covariate(s) x, in which predicted y is always an approximation of y and only ever a function of x, thus, inheriting many of the spatial characteristics of x, and illustrates several core issues using “synthetic” remote sensing and “real” soils case studies. The outputs of regression models and, therefore, the meaning of predicted y, are shown to vary due to (1) choices about data: the specification of x (which covariates to include), the support of x (measurement scales and granularity), the measurement of x and the error of x, and (2) choices about the model including its functional form and the method of model identification. Some of these issues are more widely recognized than others. Thus, the study provides definition to the multiple ways in which regression prediction and inference are affected by data and model choices. The article invites researchers to pause and consider the semantic meaning of predicted y, which is often nothing more than a scaled version of covariate(s) x, and argues that it is naïve to ignore this.     

The Changing Geography of Violence in Russia's North Caucasus, 1999-2011: Regional Trends and Local Dynamics in Dagestan,Ingushetia, and Kabardino-Balkaria

Three U.S. geographers analyze the temporal and spatial trends of 17,438 violent events in Russia's North Caucasus region from August 1999 to July 2011, demonstrating that the diffusion of conflict away from Chechnya intensified during the period 2007-2011, as levels of violence rose in neighboring republics. An increasing number of casualties are civilians in Dagestan, Ingushetia, and Kabardino-Balkaria, the three republics that are the focus of the paper. Employing multiple methods of spatial pattern analysis and geographically sensitive regression models, the authors examine the spatial fragmentation of violence from the perspective of rebel groups operating in the three republics. The analysis documents how the incidence of violence varies dramatically over space (i.e., reflecting the influence of urbanization, strategic location, and physical geographic factors such as elevation and extent of forest cover). Although violence in the North Caucasus region as a whole has declined in absolute terms over the past four years, the authors show how new geographies of violence are developing in the region, underscoring the emergence of republic-based insurgent operations against the various organs of the Russian state. Journal of Economic Literature, Classification Numbers: H560, H770, O180. 9 figures, 3 tables, 1 appendix, 103 references: