论文标题
大数据的大问题：关键空间数据分析的挑战
Big Issues for Big Data: challenges for critical spatial data analytics
论文作者
论文摘要
在本文中，我们考虑了使用大数据和大空间数据的一些问题，并突出了需要开放和关键框架的问题。我们专注于大数据收集和分析的一系列挑战。特别是，我们考虑1）与推断有关的问题通常有偏见的大数据，挑战了与观察，n，接近n，人群（n-> n）的假定的推理性优势，以及对数据科学分析的需求，回答实际意义的问题，或更强调效果的大小，而不是统计陈述的真实性或虚假陈述； 2）需要接受数据中的混乱并记录数据对数据进行的所有操作，因为这种支持和可重复性范例的支持； 3）需要明确地寻求了解数据中偏见，混乱等的原因，以及在分析中使用此类数据的推论后果，通过采用关键方法来进行空间数据科学。特别是，我们认为有必要将单个数据科学研究置于更广泛的社会和经济环境中，沿推理理论在存在大数据的情况下的作用，以及与大数据中的混乱和复杂性有关的问题。
In this paper we consider some of the issues of working with big data and big spatial data and highlight the need for an open and critical framework. We focus on a set of challenges underlying the collection and analysis of big data. In particular, we consider 1) the issues related to inference when working with usually biased big data, challenging the assumed inferential superiority of data with observations, n, approaching N, the population (n->N), and the need for data science analysis that answer questions of practical significance or with greater emphasis n the size of the effect, rather than the truth or falsehood of a statistical statement; 2) the need to accept messiness in your data and to document all operations undertaken on the data because of this support of openness and reproducibility paradigms; and 3) the need to explicitly seek to understand the causes of bias, messiness etc in the data and the inferential consequences of using such data in analyses, by adopting critical approaches to spatial data science. In particular we consider the need to place individual data science studies in a wider social and economic contexts, along the the role of inferential theory in the presence of big data, and issues relating to messiness and complexity in big data.
