在我们信任的数据中

Data Lives Pub Date : 2021-02-03 DOI:10.2307/j.ctv1c9hmnq.9
Rob Kitchin
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引用次数: 4

摘要

本章讨论开放数据集的数据质量和准确性问题,使用来自爱尔兰数据系统的各种示例。这些例子包括住宅财产价格登记册(RPPR)、都柏林仪表板项目、TRIPS数据库和爱尔兰犯罪数据。爱尔兰的犯罪数据存在许多问题,例如犯罪记录与处理犯罪的警察局有关,而不是与犯罪发生的地点有关。在犯罪分类的标准化方面也存在问题,一些警察对同一犯罪的记录方式略有不同,而且记录的及时性也存在问题。此外,从犯罪管理软件系统中检索数据也存在困难。除了误差之外,每个数据集都有代表性的问题——也就是说,数据忠实地代表它所要测量的东西的程度。在生成数据的过程中,提取、抽象、概化和抽样过程会引入测量误差、噪声、不精确和偏差。然而,在国际上,在制定数据质量指导方针和标准,试图让那些生成和共享数据的人遵守这些指导方针和标准,以及促进向用户报告这些信息的重要性方面已经花费了大量的工作。
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In Data We Trust
This chapter discusses issues of data quality and veracity in open datasets, using a variety of examples from the Irish data system. These examples include the Residential Property Price Register (RPPR), the Dublin Dashboard project, the TRIPS database, and Irish crime data. There are a number of issues with Irish crime data, such as crimes being recorded in relation to the police stations that handle them, rather than the location they are committed. There are also issues in the standardization of crime categorization, with some police officers recording the same crimes in slightly different ways, and also in timeliness of recording. Moreover, there are difficulties of retrieving data from the crime management software system. In addition to errors, every dataset has issues of representativeness — that is, the extent to which the data faithfully represents that which it seeks to measure. In generating data, processes of extraction, abstraction, generalization and sampling can introduce measurement error, noise, imprecision and bias. Yet internationally, there has been much work expended on formulating data-quality guidelines and standards, trying to get those generating and sharing data to adhere to them, and promoting the importance of reporting this information to users.
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