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.
Abstract:This paper studies the long-term consequences of the government-sponsored programs of European immigration to Southern Brazil before the Great War. We find that the municipalities closer to the original sites of nineteenth century government sponsored settlements (colônias) have higher per capita income, less poverty and dependence on Bolsa Família cash transfers, better health and education outcomes; and for the areas close to German colonies, also less inequality of income and educational outcomes than otherwise. Since that is a reduced form relationship, we then attempt to identify the relative importance of more egalitarian landholdings and higher initial human capital in determining those outcomes. Our findings are suggestive that more egalitarian land distribution played a more important role than higher initial human capital in achieving the good outcomes associated with closeness to a colônia. Walter Belluzzo and Rafael G. Duarte helped us to obtain and understand the ENEM test scores data. We owe to Cleandro Krause (IPEA) for the valuable information on the location of the official settlements in the present day grid of municípios. We are also grateful to Gabriela Drummond Marques da Silva and Waldery Rodrigues Junior for helping us translating the municipal identifiers in the 1970 Census.
This paper presents a method for classifying the ancestry of Brazilian surnames based on historical sources. The information obtained forms the basis for applying fuzzy matching and machine learning classification algorithms to more than 46 million workers in 5 categories: Iberian, Italian, Japanese, German and East European. The vast majority (96.7%) of the single surnames were identified using a fuzzy matching and the rest using a method proposed by Cavnar and Trenkle (1994). A comparison of the results of the procedures with data on foreigners in the 1920 Census and with the geographic distribution of non-Iberian surnames underscores the accuracy of the procedure. The study shows that surname ancestry is associated with significant differences in wages and schooling.
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