Characterization of lime carbonates in plasters from Teotihuacan,Mexico: preliminary results of cathodoluminescence and carbon isotope analyses

This study characterizes the degree of calcination of lime in lime plaster samples from Teotihuacan, the capital of a regional state in prehispanic Central Mexico. Lime plaster production consists of multiple steps, from the firing of raw materials to the mixing of lime and aggregate and the final application. While previous studies have focused on the compositional variability, specifically the recipe of lime plasters and mortars, the characterization of lime itself has not been sufficiently addressed. In this study, cathodoluminescence analysis coupled with petrographic and image analyses were employed to examine the degree of calcination of lime. The results of cathodoluminescence petrography were further examined through stable carbon isotope and ¹⁴C measurements. It appeared that the results of cathodoluminescence analysis are consistent with those of other analytical methods and that there are diachronic changes in the degree of calcination of lime among lime plaster samples. This implies changes in the organization of lime production, specifically the consistency in the control of firing temperature.     

A Multi-Method Approach to Reconstructing Occupational History and Activity Areas: A Case Study at the Formative Site of Tlalancaleca,Central Mexico

While surface collection constitutes an important component of field research at the site and regional levels, the association between surface and sub-surface materials needs to be examined through independent lines of evidence. This study employed manual bucket auger probing and soil geochemistry at the Formative site of Tlalancaleca, Central Mexico (800 b.c.–a.d. 250), to evaluate the results of surface collection and to understand the formation process of the anthropogenic landscape. The combined results at one of the largest architectural complexes at Tlalancaleca indicate that it was built during the Late Formative period (500–100 b.c.) and rebuilt during the subsequent Terminal Formative period (100 b.c.–a.d. 250), which is corroborated by radiocarbon dates. Moreover, the results suggest segregated uses of space such as cooking/storage, waste disposal, and craft production areas within the complex. Overall results demonstrate the effectiveness of this multi-method approach for reconstructing occupational history and activity areas.     

Balancing Spatial and Non-Spatial Variation in Varying Coefficient Modeling: A Remedy for Spurious Correlation

This study discusses the importance of balancing spatial and non-spatial variation in spatial regression modeling. Unlike spatially varying coefficients (SVC) modeling, which is popular in spatial statistics, non-spatially varying coefficients (NVC) modeling has largely been unexplored in spatial fields. Nevertheless, as we will explain, consideration of non-spatial variation is needed not only to improve model accuracy but also to reduce spurious correlation among varying coefficients, which is a major problem in SVC modeling. We consider a Moran eigenvector approach modeling spatially and non-spatially varying coefficients (S&NVC). A Monte Carlo simulation experiment comparing our S&NVC model with existing SVC models suggests both modeling accuracy and computational efficiency for our approach. Beyond that, somewhat surprisingly, our approach identifies true and spurious correlations among coefficients nearly perfectly, even when usual SVC models suffer from severe spurious correlations. It implies that S&NVC model should be used even when the analysis purpose is modeling SVCs. Finally, our S&NVC model is employed to analyze a residential land price data set. Its results suggest existence of both spatial and non-spatial variation in regression coefficients in practice. The S&NVC model is now implemented in the R package spmoran.     

Eigenvector Spatial Filtering for Large Data Sets: Fixed and Random Effects Approaches

Eigenvector spatial filtering (ESF) is a spatial modeling approach, which has been applied in urban and regional studies, ecological studies, and so on. However, it is computationally demanding, and may not be suitable for large data modeling. The objective of this study is developing fast ESF and random effects ESF (RE‐ESF), which are capable of handling very large samples. To achieve it, we accelerate eigen‐decomposition and parameter estimation, which make ESF and RE‐ESF slow. The former is accelerated by utilizing the Nyström extension, whereas the latter is by small matrix tricks. The resulting fast ESF and fast RE‐ESF are compared with nonapproximated ESF and RE‐ESF in Monte Carlo simulation experiments. The result shows that, while ESF and RE‐ESF are slow for several thousand samples, fast ESF and RE‐ESF require only several seconds for the samples. It is also suggested that the proposed approaches effectively remove positive spatial dependence in the residuals with very small approximation errors when the number of eigenvectors considered is 200 or more. Note that these approaches cannot deal with negative spatial dependence. The proposed approaches are implemented in an R package “spmoran.”     

POPULATION'S URBAN ENVIRONMENT EVALUATION MODEL AND ITS APPLICATION*

We propose the Population's Urban Environment Evaluation Model that quantitatively presents inhabitants’ evaluations of urban environments. Our target is to establish firmly a method to effectively reflect the inhabitants’ consciousness in a regional planning process. The approach taken follows a group decision-making theory and a multi-attribute utility theory: a kind of social welfare function (of additive form) is identified by using questionnaire data. The results of application of our model to Kitakyushu show that it presents several useful insights for regional planning.     

Microstructural and elemental analyses of slags excavated from Xuxiebian iron‐smelting site,Sichuan, China

Xuxiebian is an iron‐smelting site located in Pujiang county of Sichuan province, China. Two excavations were carried out in 2007 and 2011, and four bowl‐shaped furnaces were excavated. Ten slag samples from the site were prepared and analysed. The present paper reports the results of metallographic and elemental examination of the specimens. It is concluded that both pig‐iron‐smelting and refining process were applied at the Xuxiebian site, and the bowl‐shaped furnaces are possibly refining furnaces. The function of the site was probably to produce pig iron and refine the pig iron into wrought iron.     

Application of LASSO to the Eigenvector Selection Problem in Eigenvector‐based Spatial Filtering

Eigenvector‐based spatial filtering is one of the often used approaches to model spatial autocorrelation among the observations or errors in a regression model. In this approach, a subset of eigenvectors extracted from a modified spatial weight matrix is added to the model as explanatory variables. The subset is typically specified via the selection procedure of the forward stepwise model, but it is disappointingly slow when the observations n take a large number. Hence, as a complement or alternative, the present article proposes the use of the least absolute shrinkage and selection operator (LASSO) to select the eigenvectors. The LASSO model selection procedure was applied to the well‐known Boston housing data set and simulation data set, and its performance was compared with the stepwise procedure. The obtained results suggest that the LASSO procedure is fairly fast compared with the stepwise procedure, and can select eigenvectors effectively even if the data set is relatively large (n = 10⁴), to which the forward stepwise procedure is not easy to apply.     

A Route Map for Successful Applications of Geographically Weighted Regression

Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows spatial heterogeneities in processes and relationships to be investigated through a series of local regression models rather than a single global one. Standard GWR assumes that relationships between the response and predictor variables operate at the same spatial scale, which is frequently not the case. To address this, several GWR variants have been proposed. This paper describes a route map to decide whether to use a GWR model or not, and if so which of three core variants to apply: a standard GWR, a mixed GWR or a multiscale GWR (MS-GWR). The route map comprises 3 primary steps that should always be undertaken: (1) a basic linear regression, (2) a MS-GWR, and (3) investigations of the results of these in order to decide whether to use a GWR approach, and if so for determining the appropriate GWR variant. The paper also highlights the importance of investigating a number of secondary issues at global and local scales including collinearity, the influence of outliers, and dependent error terms. Code and data for the case study used to illustrate the route map are provided.