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The high species richness of tropical forests has long been recognized, yet there remains substantial uncertainty regarding the actual number of tropical tree species. Using a pantropical tree inventory database from closed canopy forests, consisting of 657,630 trees belonging to 11,371 species, we use a fitted value of Fisher's alpha and an approximate pantropical stem total to estimate the minimum number of tropical forest tree species to fall between ∼ 40,000 and ∼ 53,000, i.e., at the high end of previous estimates. Contrary to common assumption, the Indo-Pacific region was found to be as species-rich as the Neotropics, with both regions having a minimum of ∼ 19,000-25,000 tree species. Continental Africa is relatively depauperate with a minimum of ∼ 4,500-6,000 tree species. Very few species are shared among the African, American, and the Indo-Pacific regions. We provide a methodological framework for estimating species richness in trees that may help refine species richness estimates of tree-dependent taxa.

Today, calculus frequently is taught with artificial intelligence in the form of computer algebra systems. Although these software packages may reduce tedium associated with the mechanics of calculus, they may be less effective if not supplemented by the accompanying teaching of calculus theory. This paper presents two examples from spatial statistics in which computer software in an unsupervised auto-execution mode fails, or can fail, to yield correct calculus results. Accordingly, it emphasizes the need to teach calculus theory when using software packages such as Mathematica and Maple. [ABSTRACT FROM AUTHOR]

Both historically and in terms of practiced academic organization, the anticipation should be that a flourishing synergistic interface exists between statistics and operations research in general, and between spatial statistics/econometrics and spatial optimization in particular. Unfortunately, for the most part, this expectation is false. The purpose of this paper is to address this existential missing link by focusing on the beneficial contributions of spatial statistics to spatial optimization, via spatial autocorrelation (i.e., dis/similar attribute values tend to cluster together on a map), in order to encourage considerably more future collaboration and interaction between contributors to their two parent bodies of knowledge. The key basic statistical concept in this pursuit is the median in its bivariate form, with special reference to the global and to sets of regional spatial medians. One-dimensional examples illustrate situations that the narrative then extends to two-dimensional illustrations, which, in turn, connects these treatments to the spatial statistics centrography theme. Because of computational time constraints (reported results include some for timing experiments), the summarized analysis restricts attention to problems involving one global and two or three regional spatialmedians. The fundamental and foundational spatial, statistical, conceptual tool employed here is spatial autocorrelation: geographically informed sampling designs--which acknowledge a non- random mixture of geographic demand weight values that manifests itself as local, homogeneous, spatial clusters of these values--can help spatial optimization techniques determine the spatial optima, at least for location-allocation problems. A valuable discovery by this study is that existing but ignored spatial autocorrelation latent in georeferenced demand point weights undermines spatial optimization algorithms. All in all, this paper should help initiate a dissipation of the existing isolation between statistics and operations research, hopefully inspiring substantially more collaborative work by their professionals in the future. [ABSTRACT FROM AUTHOR]

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Geographical Analysis ( GA), in cooperation with the Wiley- Blackwell publishing business, undertook a project to complete the digitizing of all back issues of the journal and make those for the period 1969-2001 free to the academic community; the volumes for 2002-2005 already were, and continue to be, free. This article commemorates the completion of this event late in 2010, drawing upon this new resource to highlight important contributions made through the journal to the geography and spatial sciences community. Besides summarizing publication trends for the journal, in the tradition of Fenneman's Venn diagram conceptualization of overlaps between geography and its cognate disciplines, this article highlights prominent articles published in the journal that contributed to the four themes of urban and economic geography, transportation geography, spatial statistics, and geographic information science. Geographical Analysis (GA) en cooperación con publicaciones Wiley-Blackwell llevo a cabo el proyecto de digitación de todos los números pasados de la revista, los cuales están ahora a disposición de la comunidad académica sin costo alguno; los volúmenes de los años 2000-5 ya estaban disponibles y continúan siendo gratis. El presente artículo conmemora la culminación de dicho evento a fines de 2010, recogiendo los aportes de esta nueva fuente de información y resaltando varias contribuciones importantes hechas vía GA a la comunidad de geografía y ciencias espaciales. Además de resumir las tendencia de las publicaciones de la revista, en la tradición de la conceptualización de traslapes entre la geografía y disciplinas afines de los diagramas Venn Fanneman, el presente artículo resalta, artículos prominentes publicados en GA que contribuyeron a los cuatro temas principales: geografía urbana y económica, geografía del transporte, estadística especial y ciencias (sistemas) de información geográfica. 地理分析（GA）和Wiley-Blackwell出版商合作完成了对本刊1969-2001年所有刊册数字化的项目，并向学术界免费开放。事实上，从2002年第5卷就已经开始并将一直持续免费。本文记述了2010年末完成的这一事件，并依托这一新的数据资源，重新回顾了本刊对地理学和空间科学研究群体做出的重要贡献。本文总结了本刊论文发表趋势，从Fenneman的韦恩图概念传统来看是地理学及其相关学科的交叉。此外还回顾了本刊发表的对城市与经济地理、交通地理、空间统计以及地理信息科学方法做出卓越贡献的论文。 [ABSTRACT FROM AUTHOR]

Separate space- or time-lags have been considered regularly in data analyses; as space–time models are more recently being studied extensively in data analytic fashion, joint estimation of both lags has to be considered explicitly. This paper addresses this issue, taking into special consideration parametric parsimony together with specification richness; use of the bivariate Poisson frequency distribution is advocated and applied to an empirical case. The relation of this approach to random effects specifications is investigated. Data for Belgian regional products constitute the empirical case study. [ABSTRACT FROM AUTHOR]

Statistics is a branch of mathematics concerned with the collection, quantification, analysis, interpretation, and presentation of real-world data, and the use of probability theory to estimate population parameters with these data. Spatial statistics is a subset of statistics that is concerned with handling the special problems associated with geographically distributed data, which include spatial point patterns, regional and lattice measurement aggregations, irregularly spaced site-specific measurements on a surface, and image analysis. Meanwhile, econometrics is concerned with the application of statistical methods to the study of economic data and problems. When coining the term spatial econometrics in 1979, Paelinck and Klaassen characterized it as a subset of econometrics that is concerned with the role of spatial dependence in regional economic model response and explanatory variables, asymmetries in spatial relationships, the specification of geographic structure governing spatial interactions, and the explicit modeling of space. We outline and discuss principal similarities (e.g., testing for the presence of spatial autocorrelation) and differences (e.g., map generalization) between spatial statistics and spatial econometrics. In doing so, our goal is to help clarify past, present, and future relationships between these two subfields. [ABSTRACT FROM AUTHOR]

The Poisson model, discovered nearly two centuries ago, is the basis for analyses of rare events. Its first applications included descriptions of deaths from mule kicks. More than half a century ago the Poisson model began being used in geographical analysis. Its initial descriptions of geographic distributions of points, disease maps, and spatial flows were accompanied by an assumption of independence. Today this unrealistic assumption is replaced by one allowing for the presence of spatial autocorrelation in georeferenced counts. Contemporary statistical theory has led to the creation of powerful Poisson-based modeling tools for geographically distributed count data. [ABSTRACT FROM AUTHOR]

The eigenfunction spatial filter derives from the Moran Coefficient that indexes spatial autocorrelation. Mean, variance and statistical distribution characterizations and descriptions of georeferenced random variables and their interrelationships are derived in terms of the eigenfunction spatial filter. Selected comparisons are made with spatial autoregressive model results. Implications of the eigenfunction spatial filter are outlined for simulation experiments, variance components of correlation coefficients, missing values estimation, and non-normal georeferenced random variables. Particularly unanticipated findings include: spatial filtering reveals the potential need to employ a spatial autocorrelation reduction stepwise regression variable selection criterion, the standard error of a univariate mean can be expressed in terms of a conventional regression variance inflation factor that captures spatial autocorrelation effects, spatial autocorrelation can simultaneously inflate and deflate a bivariate correlation coefficient, simulation experiments can be easily designed to involve specific map patterns associated with non-zero spatial autocorrelation, spatial filtering furnishes a convenient way to estimate missing georeferenced values, and the asymptotic variance of the Moran Coefficient is unaltered by spatial filtering. [ABSTRACT FROM AUTHOR]

Spatial operations research problems seek “best” locations, often points of minimum aggregate weighted distance, requiring georeferenced data as input. Frequently maps of such data are incomplete, with holes in their geographic distributions. Spatial statistical procedures are available to complete these data sets with best estimates of the missing values. Impacts such imputations have on 2-median facility location–allocation solutions are explored. The sampling distribution of the spatial mean and standard distance of these medians are studied. Population density is used as the weight attribute in determining location-allocation solutions because it can be accurately described with a relatively simple spatial statistical model. [ABSTRACT FROM AUTHOR]

The integration of GIS and spatial statistical analysis can occur in two different ways: embedding spatial statistical analysis into a GIS environment, or embedding selected GIS functions into a spatial statistical analysis environment. Attention has been given almost exclusively to the former in the past. This paper argues that the latter deserves more attention, especially with the rapid development of embeddable GIS component technology. However, because current statistical software packages have not yet provided the environment into which GIS components can be embedded, we explore the possibility of integrating GIS components and spatial statistical analysis in a current proprietary Database Management System (DBMS: Microsoft Access), which can be easily extended to incorporate statistical analysis capabilities. A conceptual framework for this integration is outlined, an interactive spatial statistical analysis module is presented, and an example is used to illustrate the advantage of this module. [ABSTRACT FROM AUTHOR]

The article focuses on spatial statistics and supercomputing. A supercomputer offers enormous central processing unit memory capabilities and achieves increased scientific computing speed in one of two ways. A supercomputer may have vectorization facilities, in which entire arrays, rather than individual scalar numbers, are dealt with in a single operation. Additionally, a supercomputer may use parallelization of program code in which a computer program is decomposed into independent tasks that are executed in parallel by different integrated processors.

Surveys Minitab computer software for teaching spatial statistics to geographers. Background to spatial statistics; Educational benefits arising from using statistics; Features of the statistics. INSET: Student self-assessment Assessed..