数字与历史:近代潮汕地区海外移民规模再考察——以数据处理方法为视角

论文对近代潮汕地区海外移民相关数据的统计出处、统一口径及可信度进行了分析,认为数据的解读需要方法论的指导,必须清楚数据的"统计出处"和"统计口径"。统计出处不同,相关数据的性质和可信度迥异;统计口径不同,统计标值的内容和范围也大不一样。近代潮汕地区海外移民相关数据出处有三:海关系统、侨务系统和领事系统。其中海关统计数据基于税收目的而产生,可信度较差。在统计对象上,海关系统和侨务系统统计的为"出入口数字",而领事系统统计的是"侨民户口数字",只有后者才是真正的"移民数据"。遗憾的是,今人在计算或估测近代潮汕地区海外移民规模时,往往径直采用海关数据,忽视海关数据的可信度问题;在数据解读的时候,又不加校正,甚至不考虑数据的统计口径而误读数字,结果夸大了近代潮汕地区海外移民的规模。     

海关视野里的历史镜像——《中国旧海关与近代社会图史》评介

《中国旧海关与近代社会图史》不仅直观、生动地揭示了中国近代海关的百年沧桑,也形象、全面地再现了近代中国社会的方方面面,反映了当时的重大历史事件及社会状况。该书可谓学术价值和社会功能完美结合的有益尝试。     

奢华的背后:论近代东北大开发中的毛皮贸易

19世纪末期和20世纪初期,在中国东北地区对外开放和全面开发之时,恰逢欧美社会盛行消费皮草。本文着重利用中国旧海关贸易统计和贸易报告等一手资料,分析近代东北皮毛外向型市场体系的构建过程以及该贸易所造成的东北野生动物资源迅速减少、流行性大鼠疫等严重的生态和社会影响。落后地区在开发原始资源的同时,应当防御发达国家环境成本的转移,这是近代东北毛皮贸易带来的历史启示。     

宋子文与近代中国海关

宋子文自出任南京国民政府财政部长后,便与中国近代海关结下不解之缘,在恢复关税自主、海关的改革、维护海关制度完整等诸多方面都不乏建树,奠定了其在中国近代海关史上的重要历史地位。     

中国旧海关出版物评述——以美国哈佛燕京图书馆收藏为中心

总数达一千余期(卷)的旧海关出版物,是研究近代中国最大、最系统、最科学且内容极为广泛的资料宝库。它包括七大系列,还有七十余本未纳入任何系列的书。要充分利用这些出版物,必须探讨其复杂的结构、丰富的内容和学术价值。美国哈佛大学哈佛燕京图书馆是该出版物全球最大的收藏单位,几乎占了总量的三分之二。由于170册的《中国旧海关史料》已经出版,而哈佛所收的旧海关出版物未刊部分二百余册将在我国出版,利用哈佛的丰富收藏进行的对各系列尤其是对学界知之不多的第二至第七系列及系列外之书的介绍,有利于学界对这一资料宝库的开发利用。     

1884—1939年中药材埠际贸易的变迁研究

中药材作为近代中国贸易格局中的重要商品之一,在口岸-腹地的经济互动中位置显要,研究中药材埠际贸易网络对理解其流通的历史具有重要意义。由于受到调查数据相对零散和研究方法相对传统的限制,该网络的复原少有实质性推进。本文主要利用中国旧海关史料的中药材埠际贸易数据,辅以其他对外贸易数据,对近代的中药材埠际贸易流通网络进行数据分析研究,发现中药材埠际贸易格局的演变与通商口岸开埠的空间进程密切相关。从19世纪末到20世纪20年代,中药材埠际贸易网络呈现逐渐加密的态势,1927年后,因为战争,中药材埠际贸易网络摧毁严重;同时,中药材国际贸易的区域分布却较为广阔,从以中国为中心的东亚区域延伸到欧洲、美洲部分地区。     

近代福建华侨出入国规模及其发展变化

众所周知,福建是我国华侨人数最多的省份之一。据1955年的统计,福建的海外华侨人数为3 676 693人,占全国华侨总数的30.4％。但由于完整统计资料的匮乏,关于近代福建华侨出入国规模及其发展变化情况,至今仍未得其详。笔者不揣简陋,以所见之海关关册     

区域经济史研究中的一个讹误——对近代广西龙州海关贸易的再认识

近代龙州海关贸易中的进出口与一般意义上的进出口是完全相异的两个概念。澄清此一问题可以匡正某些成见,同时也为重新评价近代龙州海关的地位、作用和龙州开埠的意义提供了可能。     

评陈诗启著《中国近代海关史》

陈诗启先生是中国近代海关史研究的开拓者。从70年代初,他便开始了中国近代海关史的研究工作,自1980年起,相继发表多篇专题论文,并出版了具有开拓意义的专著《中国近代海关史问题初探》。今天呈现在读者面前的《中国近代海关史》,是陈先生20余年潜心研究的结晶。     

试论近代中国海关完全殖民地化的特征

试论近代中国海关完全殖民地化的特征康之国从１８４２年至１９２７年间,中国近代海关经历了半殖民地化的形成、发展及完全殖民地化几个阶段。以往,学术界仅就中国近代海关半殖民地化问题进行了研究,本文则拟从海关的行政管理权、税则税率的制订权、关税的征收、保管及...     

Statistics as the science of government: The stillborn British Empire statistical bureau, 1918–20

The 1920 British Empire Statistical Conference was the direct outcome of the Dominions Royal Commission's Final Report, which had spelt out the need to increase the uniformity and comparability of statistics originating from various parts of the Empire and had proposed setting up an imperial central statistical office. Over 24 days, delegates debated a large number of topics, ranging from the practical and empirical subject matters of statistical inquiry to more abstract issues such as the nature and object of statistical data collection and analysis, and to the problems raised by the establishment of a statistical bureau that would operate on an unprecedented scale. This article seeks to understand why, despite apparently favourable conditions, this project soon ended in complete failure. The reasons must be sought in the neatly distinctive outlooks held by the British government and Dominion representatives as regards the function of statistics for the purpose of government, in the quite different bureaucratic settings that embodied and sustained these views, as well as in the tensions and centrifugal pressures that acted upon inter-imperial relations following the Great War.     

Spatial Autocorrelation Statistics of Areal Prevalence Rates under High Uncertainty in Denominator Data

We propose a new estimator of spatial autocorrelation of areal incidence or prevalence rates in small areas, such as crime and health indicators, for correcting spatially heterogeneous sampling errors in denominator data. The approach is dubbed the heteroscedasticity‐consistent empirical Bayes (HC‐EB) method. As American Community Survey (ACS) data have been released to the public for small census geographies, small‐area estimates now form the demographic landscape of neighborhoods. Meanwhile, there is growing awareness of the diminished statistical validity of global and local Moran’s I when such small‐area estimates are used in denominator data. Using teen birth rates by census tracts in Mecklenburg County, North Carolina, we present comparisons of conventional and new HC‐EB estimates of Global and Local Moran’s I statistics created on ACS data, along with estimates on ground truth values from the 2010 decennial census. Results show that the new adjustment method dramatically enhances the statistical validity of global and local spatial autocorrelation statistics.     

THE BAYESIAN APPROACH TO THE INTERPRETATION OF ARCHAEOLOGICAL DATA

Archaeologists are increasingly becoming aware of an approach to data investigation known as Bayesian statistics. In this paper we outline both the philosophical and statistical background to the approach. We show that it provides a logical and coherent framework in which to make inferences on the basis of both data and a priori expert knowledge. We note that adoption of the Bayesian framework is particularly timely since there have been recent dramatic developments in numerical methods which mean that a number of previous implementation problems have now been solved. As a result, many questions of archaeological interest, which require the use of complex statistical models, are being investigated using this methodology. We use a variety of recently published examples from a range of archaeological areas to illustrate the type of questions that can be answered and the nature of the methodologies used, and we make comparisons with the results obtained using more traditional statistical techniques.     

历史建筑木柱防腐措施研究

本工作针对在浙江温州地区历史木构建筑修缮中发现的木柱根部腐朽严重的问题,提出对其的保护措施,分析其腐朽原因。为此,通过查阅相关国内外规范及经验做法、实地调研、勘察检测、实验室进行材料试验、数据统计分析等办法进行综合分析,得出木柱腐朽的主要原因为冷凝水。采用通过对比实验研究发现,传统历史建筑中的木柱础做法和对木柱进行防水处理可以较好地解决木柱腐朽的问题。     

统计视角下的旅游业思考

从统计视角来界定旅游概念是旅游研究中达成共识的一种可行选择。本文以统计为视角,对旅游的基本概念进行了分析并对这一概念的优缺点进行了说明。然后从中国旅游统计的基本情况、主要特点、面临的困难、旅游卫星账户以及中国旅游统计发展的大体思路等几个方面对中国旅游业的测度进行了阐释。最后,从统计视角出发,对旅游研究中的一些错误观点进行了纠正。     

Maximum Getis–Ord Statistic Adjusted for Spatially Autocorrelated Data

Local statistics test the null hypothesis of no spatial association or clustering around the vicinity of a location. To carry out statistical tests, it is assumed that the observations are independent and that they exhibit no global spatial autocorrelation. In this article, approaches to account for global spatial autocorrelation are described and illustrated for the case of the Getis–Ord statistic with binary weights. Although the majority of current applications of local statistics assume that the spatial scale of the local spatial association (as specified via weights) is known, it is more often the case that it is unknown. The approaches described here cover the cases of testing local statistics for the cases of both known and unknown weights, and they are based upon methods that have been used with aspatial data, where the objective is to find changepoints in temporal data. After a review of the Getis–Ord statistic, the article provides a review of its extension to the case where the objective is to choose the best set of binary weights to estimate the spatial scale of the local association and assess statistical significance. Modified approaches that account for spatially autocorrelated data are then introduced and discussed. Finally, the method is illustrated using data on leukemia in central New York, and some concluding comments are made.     

一座尚未充分利用的近代史资料宝库--中国旧海关系列出版物评述

中国旧海关留存的卷帙浩繁的海关文献,是近百年社会经济史研究中一项最为完整、系统的统计数据和文字资料。哈佛燕京所藏的海关资料和京华版《中国旧海关史料》所收录的出版物具有重要的文献价值,以季报、年报、十年报告、国内贸易册、月报等为层次,将其宝贵内容作以介绍,弥补以往简单地介绍海关文献之不足,研究者若能利用这一丰富的资源,将会拓宽和深入中国近代史的研究。     

Using Spatial Filters and Exploratory Data Analysis to Enhance Regression Models of Spatial Data

Residual spatial autocorrelation is a situation frequently encountered in regression analysis of spatial data. The statistical problems arising due to this phenomenon are well‐understood. Original developments in the field of statistical analysis of spatial data were meant to detect spatial pattern, in order to assess whether corrective measures were required. An early development was the use of residual autocorrelation as an exploratory tool to improve regression analysis of spatial data. In this note, we propose the use of spatial filtering and exploratory data analysis as a way to identify omitted but potentially relevant independent variables. We use an example of blood donation patterns in Toronto, Canada, to demonstrate the proposed approach. In particular, we show how an initial filter used to rectify autocorrelation problems can be progressively replaced by substantive variables. In the present case, the variables so retrieved reveal the impact of urban form, travel habits, and demographic and socio‐economic attributes on donation rates. The approach is particularly appealing for model formulations that do not easily accommodate positive spatial autocorrelation, but should be of interest as well for the case of continuous variables in linear regression.     

Spatial Autocorrelation in Ecological Studies: A Legacy of Solutions and Myths

A major aim of including the spatial component in ecological studies is to characterize the nature and intensity of spatial relationships between organisms and their environment. The growing awareness by ecologists of the importance of including spatial structure in ecological studies (for hypothesis development, experimental design, statistical analyses, and spatial modeling) is beneficial because it promotes more effective research. Unfortunately, as more researchers perform spatial analysis, some misconceptions about the virtues of spatial statistics have been carried through the process and years. Some of these statistical concepts and challenges were already presented by Cliff and Ord in 1969. Here, we classify the most common misconceptions about spatial autocorrelation into three categories of challenges: (1) those that have no solutions, (2) those where solutions exist but are not well known, and (3) those where solutions have been proposed but are incorrect. We conclude in stressing where new research is needed to address these challenges.