Revisiting the Cuban Missile Crisis

Book reviewed in this article:
Ernest R. May and Philip D. Zelikow (eds.), The Kennedy Tapes: Inside the White House during the Cuban Missile Crisis     

Craniofacial morphology,diet and incisor use in three native American populations

It has been difficult to infer behaviours related to anterior tooth use in fossil hominids owing to a lack of broad‐based comparative studies of the relationships between front tooth use and craniodental features in modern humans. In this study, we seek to establish such a neontological baseline by exploring aspects of craniodental form among populations of recent humans that are inferred to have differed in habitual levels of ingestive and paramasticatory processing using the incisors. Morphometric data are compared among samples from three human populations: Aleutian Islanders (n=25); Arikara from the Mobridge Site, SD (n=19); and a late Woodland Bluff group from Jersey County, IL (n=24). Data for various biomechanically relevant dimensions were collected using standard callipers, while three‐dimensional (3D) coordinate data for 28 cranial landmarks were recorded using a video‐based image analysis system. These data indicate that several aspects of cranial form thought to influence the efficiency of force production on the incisors differ among the examined groups, including the positions and size of the primary masticatory muscles and the position of the dentition. For most dimensions, the Aleut sample exhibits the most mechanically advantageous configuration, the Illinois Bluff group the least advantageous configuration, while the Arikara are intermediate in form. This pattern corresponds to differences in the intensity of incisor loading among these groups, as inferred from both ethnographic reports and incisor microwear data. These results, therefore, suggest that cranial form in humans varies with subsistence‐related differences in masticatory force production and that quantitative studies of masticatory system configuration may provide useful information for studies of hominid palaeobiology. Copyright © 2000 John Wiley & Sons, Ltd.     

Probabilistic Regionalization via Evidence Accumulation with Random Spanning Trees as Weak Spatial Representations

Spatial clusters contain biases and artifacts, whether they are defined via statistical algorithms or via expert judgment. Graph-based partitioning of spatial data and associated heuristics gained popularity due to their scalability but can define suboptimal regions due to algorithmic biases such as chaining. Despite the broad literature on deterministic regionalization methods, approaches that quantify regionalization probability are sparse. In this article, we propose a local method to quantify regionalization probabilities for regions defined via graph-based cuts and expert-defined regions. We conceptualize spatial regions as consisting of two types of spatial elements: core and swing. We define three distinct types of regionalization biases that occur in graph-based methods and showcase the use of the proposed method to capture these types of biases. Additionally, we propose an efficient solution to the probabilistic graph-based regionalization problem via performing optimal tree cuts along random spanning trees within an evidence accumulation framework. We perform statistical tests on synthetic data to assess resulting probability maps for varying distinctness of underlying regions and regionalization parameters. Lastly, we showcase the application of our method to define probabilistic ecoregions using climatic and remotely sensed vegetation indicators and apply our method to assign probabilities to the expert-defined Bailey's ecoregions.     

Domestic fowl on Romano-British sites: inter-site comparisons of abundance

The paper compares the abundance of domestic fowl bones with those of sheep/goat and pig from published and unpublished reports on Romano-British animal bones assemblages. The samples are drawn from 68 urban, military, nucleated, villa and unromanized rural settlements. Variations in abundance of domestic fowl within and between these settlement types are noted. Some methodological problems encountered in this survey are discussed and suggestions are made about how more reliable results could be achieved. © 1997 John Wiley & Sons, Ltd.     

Frontier Stories： Periphery as Center in Qing History

Since at least the 1960s, the importance of the tremendous territorial expansion under Qing role to the modem history of China has been generally acknowledged. Indeed, one can say that the frontier story is one of the things that makes the Qing ＂Qing.＂ However, only in the last twenty years has the study of what is now termed the ＂borderlands＂ come into its own as a sub-field. This essay begins by describing some key concepts and terms in the study of the Qing frontier, including the Manchu wordjecen. It then raises the problem of narrative fiameworks, asking how we might best contextualize the growth of the empire, before going on to explore the implications of the discursive shift represented by the ＂New Qing History＂ and the extensive research on Qing borderlands associated therewith. A poem by the Mongol poet Na-xun Lan-bao provides the focus for a concluding discussion of a distinctive Qing frontier sensibility.     

A Combined Cluster and Interaction Model: The Hierarchical Assignment Problem

This article presents a new spatial modeling approach that deals with interactions between individual geographic entities. The developed model represents a generalization of the transportation problem and the classical assignment problem and is termed the hierarchical assignment problem (HAP). The HAP optimizes the spatial flow pattern between individual origin and destination locations, given that some grouping, or aggregation of individual origins and destinations is permitted to occur. The level of aggregation is user specified, and the aggregation step is endogenous to the model itself. This allows for the direct accounting of aggregation costs in pursuit of optimal problem solutions. The HAP is formulated and solved with several sample data sets using commercial optimization software. Trials illustrate how HAP solutions respond to changes in levels of aggregation, as well as reveal the diverse network designs and allocation schemes obtainable with the HAP. Connections between the HAP and the literature on the p-median problem, cluster analysis, and hub-and-spoke networks are discussed and suggestions for future research are made.     

A Genetic Approach to Detecting Clusters in Point Data Sets

Spatial analysis techniques are widely used throughout geography. However, as the size of geographic data sets increases exponentially, limitations to the traditional methods of spatial analysis become apparent. To overcome some of these limitations, many algorithms for exploratory spatial analysis have been developed. This article presents both a new cluster detection method based on a genetic algorithm, and Programs for Cluster Detection, a toolkit application containing the new method as well as implementations of three established methods: Openshaw's Geographical Analysis Machine (GAM), case point-centered searching (proposed by Besag and Newell), and randomized GAM (proposed by Fotheringham and Zhan). We compare the effectiveness of cluster detection and the runtime performance of these four methods and Kulldorf's spatial scan statistic on a synthetic point data set simulating incidence of a rare disease among a spatially variable background population. The proposed method has faster average running times than the other methods and significantly reduces overreporting of the underlying clusters, thus reducing the user's postprocessing burden. Therefore, the proposed method improves upon previous methods for automated cluster detection. The results of our method are also compared with those of Map Explorer (MAPEX), a previous attempt to develop a genetic algorithm for cluster detection. The results of these comparisons indicate that our method overcomes many of the problems faced by MAPEX, thus, we believe, establishing that genetic algorithms can indeed offer a viable approach to cluster detection.