This study introduces a new measure of urban centrality. The proposed urban centrality index (UCI) constitutes an extension to the spatial separation index. Urban structure should be more accurately analyzed when considering a centrality scale (varying from extreme monocentricity to extreme polycentricity) than when considering a binary variable (monocentric or polycentric). The proposed index controls for differences in size and shape of the geographic areas for which data are available, and can be calculated using different variables such as employment and population densities, or trip generation rates. The properties of the index are illustrated with simulated artificial data sets and are compared with other similar measures proposed in the existing literature. The index is then applied to the urban structure of four metropolitan areas: Pittsburgh and Los Angeles in the United States; São Paulo, Brazil; and Paris, France. The index is compared with other traditional spatial agglomeration measures, such as global and local Moran's I, and density gradient estimations. El presente estudio introduce una nueva medida de centralidad. El índice de centralidad urbana propuesto (UCI, por sus siglas en inglés) es una extensión al índice de separación espacial (spatial separation index)(Midelfart‐Knarvik et al. 2000). El análisis de la estructura urbana resulta más preciso al usar el índice cuando se toma en cuenta una escala de continua de centralidad (que puede variar de un monocentrismo extremo a un policentrismo extremo) que cuando se considera una variable binaria (monocéntrica o policéntrica). El índex propuesto controla las diferencias de tamaño y forma de las áreas geográficas de las que se tienen datos, y puede ser calculada utilizando diferentes variables, como empleo y densidad poblacional, o tasas de generación de viajes. Las propiedades del índice se ilustran con conjuntos de datos artificiales simulados, y se comparan con otras mediciones similares en la literatura ya existente. Posteriormente, el índice es aplicado a la estructura urbana de cuatro áreas metropolitanas: Pittsburgh y Los Ángeles, en EEUU; San Pablo, en Brasil; y París, Francia. Finalmente, se compara el índice con otras mediciones tradicionales de aglomeración espacial, como el índice de Moran local y global, y estimaciones de gradiente de densidad. 本文介绍了一种度量城市中心性的新方法,提出的城市中心性指数(UCI)是对空间分离指数的扩展。当涉及到中心性规模(从极单中心到极多中心),不仅仅是二元变量(单中心或多中心),城市结构则需更加精确的测度。本文构建的指数可以通过数据可获取的不同大小和形状的地理单元控制,并通过不同变量(如就业与人口密度或者旅次产生率)测算得到。该指数的属性可以通过人工数据集的模拟示例说明,或者通过对比已有文献对相似指数的阐述加以说明。然后,通过将该指数应用于全球四个大都市区(美国匹兹堡和洛杉矶、巴西圣保罗和法国巴黎)的城市结构中进行检验。最后,将该指数与其他测度传统空间集聚指数如全局和局部Moran指数及密度梯度估计进行对比.
Understanding how species composition varies across space and time is fundamental to ecology. While multiple methods having been created to characterize this variation through the identification of groups of species that tend to co-occur, most of these methods unfortunately are not able to represent gradual variation in species composition. The Latent Dirichlet Allocation (LDA) model is a mixed-membership method that can represent gradual changes in community structure by delineating overlapping groups of species, but its use has been limited because it requires abundance data and requires users to a priori set the number of groups. We substantially extend LDA to accommodate widely available presence/absence data and to simultaneously determine the optimal number of groups. Using simulated data, we show that this model is able to accurately determine the true number of groups, estimate the underlying parameters, and fit with the data. We illustrate this method with data from the North American Breeding Bird Survey (BBS). Overall, our model identified 18 main bird groups, revealing striking spatial patterns for each group, many of which were closely associated with temperature and precipitation gradients. Furthermore, by comparing the estimated proportion of each group for two time periods (1997-2002 and 2010-2015), our results indicate that nine (of 18) breeding bird groups exhibited an expansion northward and contraction southward of their ranges, revealing subtle but important community-level biodiversity changes at a continental scale that are consistent with those expected under climate change. Our proposed method is likely to find multiple uses in ecology, being a valuable addition to the toolkit of ecologists.
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