“历史区域统计”的发展与数据利用

Hiromasa Watanabe, Y. Murayama, K. Fujita
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引用次数: 2

摘要

自日本近代以来,大量的统计数据被公布出来。在这些统计中,明治时代出版的近代统计是掌握日本历史地理的基础。地理信息系统是将现代统计应用于历史区域分析的有力分析工具。虽然地理信息系统具有利用现代日本统计进行历史区域分析的潜力,但目前的研究尚未取得重大进展。一个背景因素是,日本近代的市政多边形数据和数字化统计无法向公众开放。因此,在2004年,作者建立了一个名为“历史区域统计”的开放式网络数据库,其中包含了各种城市多边形数据和现代数字化统计数据。本研究的目的是回顾“历史区域统计”中包含的一些数字化统计和市政多边形数据,并通过案例研究讨论它们的可用性。《历史区域统计》包含8组统计(39幅)和4组市域图(213幅)。在这些资料中,有军事统计资料、《明治24年征令表》、《县统计》、《市府合并数据库》等,具有通用性。本个案研究运用1890年“市县合并数据库”及军事统计资料《明治24 nen Chohatsu Bukken Ichiranhyo(1891年征用令清单)》,分析明治中期日本中部地区的地域结构。运用因子分析和聚类分析对区域结构进行了解释。在因子分析中,从35个变量中提炼出8个因子。然后,通过对因子矩阵的聚类分析,将日本中部地区划分为6个区域类型。数字化统计和市政多边形减少了处理或构建数据的复杂研究过程。案例分析的区域结构可以从日本历史地理学已有的研究成果中得到理解。这些点显示了利用GIS进行历史区域分析的“历史区域统计”的可能可用性。另一方面,案例研究中使用的数据也存在一定的误差。这一点与“历史区域统计”中的其他数据相同,需要用户配合进行修正。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of “Historical Regional Statistics” and Utilization of the Data
Enormous amounts of statistics have been published since the start of the Japanese modern era. Among all of these statistics, modern statistics published in the Meiji era are fundamental for grasping the historical geography of Japan. GIS can be powerful analytical tool for applying such modern statistics to historical regional analyses. Although GIS has potential for historical regional analyses using modern Japanese statistics, studies are not making significant progress at the present time. A background factor is that municipal polygon data and digitized statistics in the Japanese modern era are not available to the public. As a result, in 2004, the authors established the open web-based database titled “Historical regional statistics,” which contains a variety of municipal polygon data and digitized statistics from the modern era. The purpose of this study is to review some digitized statistics and municipal polygon data contained in “Historical regional statistics,” and discus their availability through a case study. “Historical regional statistics” contains eight groups of statistics (39 statistics) and four groups of municipal maps (213 maps). Among these data, military statistics, “Meiji 24 Nen Chohatsu Bukken Ichiranhyo (Requisition Order List in 1891)”, “Fuken Tokei Hyo (Prefectural Statistics)” and “Consolidation of municipalities database” are available and provide versatility. The case study, which analyzes the regional structure of central Japan in the mid-Meiji era, applies the 1890 “Consolidation of municipalities database” and military statistics, “Meiji 24 nen Chohatsu Bukken Ichiranhyo (Requisition order list in 1891)”. Factor and cluster analyses are applied to explain the regional structure. In the factor analysis, eight factors are abstracted from 35 variables. Then, by applying the cluster analysis to the factor matrix, central Japan is classified into six regional types. Complicated research processes for handling or building of data are reduced by digitized statistics and municipal polygons. The regional structure analyzed in the case study can be understood from existing findings of historical geography in Japan. These points show the possible availability of “Historical regional statistics” for historical regional analyses with GIS. On the other hand, it is shown that data used in the case study contain some errors. This point is common to other data in “Historical regional statistics,” and needs to be corrected with the user's cooperation.
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