THE LIMITS OF OTTOMAN PRAGMATISM

In this paper I reflect critically on the concept of pragmatism as it is used in Ottoman historiography. Pragmatism has gained increasing currency over the last ten to fifteen years as one of the defining features of the Ottoman polity. I argue that unless it is properly defined from a theoretical‐philosophical perspective, and carefully contextualized from a historical perspective, pragmatism cannot be used as an explanatory or comparative category. When used as a framework of explanation for historical change, pragmatism blurs more than it clarifies an essential aspect of the Ottoman polity that it seeks to define, namely, the political. It is essential to reflect on the difference between the political and politics because whereas the political refers to the configuration of the power relations that organize a society as a legitimate entity, politics refers to the strategies, practices, institutions, or discourses whose purpose is to construct and retain hegemony within a polity. Through an analysis of the concept of pragmatism in Ottoman historiography, I show that for most proponents of Ottoman pragmatism, pragmatism pertains to politics rather than to the political. From a perspective rigorously confined to political theory, I argue that much like the discourse of modern tolerance, pragmatism in Ottoman historiography posits a problematic periodization, relegates the political to the background, and depoliticizes essential power relations.     

The boundary value problem in spatial statistical analysis

"The primary objective of this paper is to investigate procedures for detecting and handling existing border biasing in spatial statistical analysis." Six conventional solutions to the boundary value problem are criticized, and three alternate statistical solutions are proposed.     

Some Remarks About the Future of Geographical Analysis: The Journal and the Sub‐Discipline

This paper expands upon a GA 50^th Anniversary panel presentation made at the 2018 New Orleans American Association of Geographers meeting. It notes that retrospectives for this topic already exist, both in terms of the journal and the sub‐discipline. This paper presents discussion about the following selected prominent transitional themes bridging the past and the future of geographical analysis: space–time data; geographic uncertainty and error; and, dimensional reduction spatial statistical modeling. It also presents discussion about the following three themes that are expected to receive considerable future attention: negative spatial autocorrelation; synergies between spatial statistics and spatial optimization; and, big spatial data. The first two of these topics essentially have been ignored in the literature to date.     

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.”     

Impacts of Spatial Autocorrelation in Georeferenced Beta and Multinomial Random Variables

The literature is replete with acknowledgments that spatial autocorrelation (SA) inflates the variance of a random variable (RV), and that it also may alter other RV distributional properties. In most studies, impacts of SA have been examined only for the three most commonly used distributions: the normal, Poisson (and its negative binomial counterpart), and binomial distributions; much less is known about its effects on two other RVs that are utilized in GIScience research: the beta and the multinomial. The beta distribution—which is considered to be very flexible because it can mimic a uniform, exponential, sinusoidal, and normal RV—can be utilized to analyze the radiance of a remotely sensed image, for example. The multinomial distribution, a generalization of the binomial distribution, has been widely used for land use classification, and to describe land use change. The literature also suggests that RV impacts of negative SA, a neglected topic in spatial analysis, may differ from those of positive SA, at least for some RVs (e.g., the Poisson RV). The purpose of this article is to extend the investigation of effects of SA to beta and multinomial RVs, with both positive SA and negative SA assessed and contrasted with each other, using simulation experiments. The simulation experiments are designed to support this assessment. One of the major discoveries is that impacts of positive SA and negative SA behave similarly when a RV conforms to a normal distribution; however, maximum negative SA is unable to materialize for asymmetric RV, whereas positive SA always converges upon its maximum.     

Experiencing refugees: A review essay

MacDonald, Jeffery L. and Amy Zaharlick, eds. Selected Papers on Refugee Issues: III. Arlington, VA: Committee on Refugee Issues, General Anthropological Division, American Anthropological Association, 1994. ii +188 pp. $10.00 paper ($8.00 for AAA Members).

Donnelly, Nancy D. Changing Lives of Refugee Hmong Women. Seattle: University of Washington Press. 1994. viii + 224 pp. including maps, figures, tables, photographs, notes, references, and index. $30.00 cloth.     

TECHNOLOGICAL CATCH‐UP AND GEOGRAPHIC PROXIMITY*

ABSTRACT This paper examines productivity catch‐up as a source of establishment productivity growth. We present evidence that, other things equal, establishments further behind the industry frontier experience faster rates of productivity growth. Geographic proximity to frontier firms makes catch‐up faster. Our econometric specification implies a long‐run relationship between productivity levels, where nonfrontier establishments lie a steady‐state distance behind the frontier such that their rate of productivity growth including catch‐up equals productivity growth at the frontier. We use our econometric estimates to quantify the implied contribution to productivity growth of catch‐up to both the national and regional productivity frontiers.