A note on the geomorphological environment of Çatal Hüyük, Turkey

It had previously been proposed (Cohen, 1970) that after large-scale Early Holocene alluviation, no post-occupational sediment accumulated around the neolithic site of Çatal Huyuk in south-central Anatolia. Data presented here, however, indicate that several metres of archaeological deposit at both east and west mounds are overlain by alluvium. This implies that the modern distribution of soil and water resources cannot be used directly to assess past agricultural potential in the vicinity of the site.     

A Hybrid Framework for Space–Time Modeling of Environmental Data. 环境数据时空建模的混合框架

The space–time autoregressive integrated moving average (STARIMA) model family provides useful tools for modeling space–time processes that exhibit stationarity (or near stationarity) in space and time. However, a more general method for routine use and efficient computation is needed to model the nonlinearities and nonstationarities of environmental space–time series. This article presents a hybrid framework combining machine learning and statistical methods to address this issue. It uses an artificial neural network (ANN) to extract global deterministic (nonlinear) space–time trends and a STARIMA model to extract local stochastic space–time variations in data. A four‐stage procedure is proposed for analyzing and modeling space–time series. The proposed framework and procedures are applied to forecast annual average temperature at 137 national meteorological stations in China. The results demonstrate that the hybrid framework achieves better forecasting accuracy than the STARIMA model alone. This finding suggests that the combination of machine learning and statistical methods provides a very powerful tool for analyzing and modeling space–time series of environmental data that have strong spatial nonlinear and nonstationary components.     

Behçet Kemal Çağlar's Kur’ân‐ı Kerîm'den İlhamlar (Inspirations from the Holy Qur’ān): A Kemalist's Personal,Poetic Response to the Qur’ān

Behçet Kemal Ça?lar, 1908–1969, is the author of a commentary of the Qur’ān, Kur’ân‐? Kerîm'den ?lhamlar (‘Inspirations from the Holy Qur’ān’), published in 1966. This work can be described as a poetic reflection on the Qur’ān. It does not adhere to rendering every line or verse, but instead insists on maintaining a rhythmic cadence and end‐rhyme. Although it resembles a translation in some ways, Ça?lar refuses to call his work a translation. This paper begins by introducing Ça?lar and his text, a brief history of Turkish translations of the Qur’ān, then Ça?lar's approach is contrasted with the aims of translators of the Qur’ān. Ça?lar's text is studied in more detail, providing a sample of the Turkish text and a translation of it into English, focusing on Ça?lar's reflection on Sūrat ?aha. Through this study, it becomes clear that as a result of his prioritizing the literary aspects of the Qur’ān in his reflection, Ça?lar's book has an advantage over literal translations of the Qur'an and it can be useful for Qur’ān translation. At the same time, Ça?lar's book is a reflection of a desire to develop a Turkish Islam—a manifestation of Islam that came from Turkey, that reflected its language and culture and that was intelligible to its people.     

Aggregation of Regional Economic Time Series with Different Spatial Correlation Structures. 具有不同空间相关结构的区域经济时间序列集成

In this article, we compare the relative efficiency of different forecasting methods of space‐time series when variables are spatially and temporally correlated. We consider two cases: (1) univariate forecasting (i.e., a space‐time series aggregated into a single time series) and (2) the more general instance of multivariate forecasting (i.e., a space‐time series aggregated into a coarser spatial partition). We extend the results in the literature by including the consideration of larger datasets and the treatment of edge effects and of negative spatial correlation. We first introduce a statistical framework based on the space‐time autoregressive class of random field models, which constitutes the basis of our simulation study, and we present the various alternative forecasting methods considered in the simulation. We then present the results of a Monte Carlo study related to univariate forecasting. In order to allow a comparison with the findings of Giacomini and Granger (2004), we consider the same forecasting strategies and the same combinations of the parameter values used there, but with a larger parametric set. Finally, we extend our analysis to the case of multivariate forecasting. The outcomes obtained provide operational suggestions about how to choose between alternative forecasting methods in empirical circumstances.     

A New Perspective about Moran's Coefficient: Spatial Autocorrelation as a Linear Regression Problem. Moran系数的新视角：空间自相关视为线性回归问题

The computation of Moran's index of spatial autocorrelation requires the definition of a spatial weighting matrix. The eigendecomposition of this doubly centered matrix (i.e., one that forces the sums of all rows and columns to equal zero) has interesting properties that have been exploited in various contexts: distribution properties of the Moran coefficient (MC), spatial filtering in linear models, generalized linear models, and multivariate analysis. In this article, this eigendecomposition is used to propose a new view of MC based on its interpretation in the simple context of linear regression. I use this interpretation to demonstrate the different properties of MC and also the inefficiency of this index in some situations involving simultaneous positive and negative spatial autocorrelation. I propose some new statistics and procedures for testing spatial autocorrelation, and conduct a simulation study to evaluate these new approaches.     

The Scottish Union Bill of 1656–7

The Scottish Union Bill introduced to the second Protectorate Parliament in October 1656 was based on the union ordinance of 1654, but it was then subjected to wide‐ranging amendments over the next few months. These amendments made many concessions to the Scots, including recognizing their separate legal system and the rights of the burghs, and allowing an expansion of free trade. This article explores the implications of these changes to the constitutional relationship between the two nations, and identifies the changes with a programme of reform championed by the Scottish council, led by Lord Broghill. The fate of the Union Bill thus became linked to the wider reform movement that saw the replacement of the Instrument of Government with the more moderate, civilian constitution known as the Humble Petition and Advice in the spring of 1657.     

Sørenga 7: the reconstruction of a 17th‐century boat from Oslo harbour

In 2006, during the Immersed Tunnel Project in the harbour of Oslo, Norway, a c.9.4 m‐long boat was discovered. The boat was found in the area historically known as Sørenga, and was named Sørenga 7, following six other finds in the area excavated from the early 1970s to the 1990s. The boat was documented digitally piece by piece, and a scale model was made in cardboard and polyamide. The deposition of the boat in the transition between the 17th and 18th centuries focuses attention on life in the early modern harbour of Christiania (Oslo).     

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.