{"title":"一种探索性数据分析协议,用于识别连续运动数据中的问题","authors":"A. Graser","doi":"10.1080/17489725.2021.1900612","DOIUrl":null,"url":null,"abstract":"ABSTRACT Movement datasets are often complex and require sophisticated processing and analysis. A thorough understanding of the dataset is needed to choose the right methods and to interpret their results. Misunderstandings and violations of assumptions about dataset characteristics can lead to flawed analysis results and wrong conclusions. To address this challenge, we propose a novel protocol for the systematic exploration of movement datasets. The individual protocol steps address the different types of movement data problems. The exploration tools recommended at each step are specifically tailored to identifying potential problems and avoiding common pitfalls when working with global navigation satellite system (GNSS) tracking data, commonly referred to as GPS tracks. However, the general steps should be transferable to continuous movement datasets with different characteristics, such as video trajectories. Furthermore, we provide an open-source implementation of our protocol in the form of a Jupyter notebook accompanying this paper.","PeriodicalId":1,"journal":{"name":"Accounts of Chemical Research","volume":null,"pages":null},"PeriodicalIF":16.4000,"publicationDate":"2021-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/17489725.2021.1900612","citationCount":"8","resultStr":"{\"title\":\"An exploratory data analysis protocol for identifying problems in continuous movement data\",\"authors\":\"A. Graser\",\"doi\":\"10.1080/17489725.2021.1900612\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"ABSTRACT Movement datasets are often complex and require sophisticated processing and analysis. A thorough understanding of the dataset is needed to choose the right methods and to interpret their results. Misunderstandings and violations of assumptions about dataset characteristics can lead to flawed analysis results and wrong conclusions. To address this challenge, we propose a novel protocol for the systematic exploration of movement datasets. The individual protocol steps address the different types of movement data problems. The exploration tools recommended at each step are specifically tailored to identifying potential problems and avoiding common pitfalls when working with global navigation satellite system (GNSS) tracking data, commonly referred to as GPS tracks. However, the general steps should be transferable to continuous movement datasets with different characteristics, such as video trajectories. Furthermore, we provide an open-source implementation of our protocol in the form of a Jupyter notebook accompanying this paper.\",\"PeriodicalId\":1,\"journal\":{\"name\":\"Accounts of Chemical Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":16.4000,\"publicationDate\":\"2021-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1080/17489725.2021.1900612\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Accounts of Chemical Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/17489725.2021.1900612\",\"RegionNum\":1,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Accounts of Chemical Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/17489725.2021.1900612","RegionNum":1,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
An exploratory data analysis protocol for identifying problems in continuous movement data
ABSTRACT Movement datasets are often complex and require sophisticated processing and analysis. A thorough understanding of the dataset is needed to choose the right methods and to interpret their results. Misunderstandings and violations of assumptions about dataset characteristics can lead to flawed analysis results and wrong conclusions. To address this challenge, we propose a novel protocol for the systematic exploration of movement datasets. The individual protocol steps address the different types of movement data problems. The exploration tools recommended at each step are specifically tailored to identifying potential problems and avoiding common pitfalls when working with global navigation satellite system (GNSS) tracking data, commonly referred to as GPS tracks. However, the general steps should be transferable to continuous movement datasets with different characteristics, such as video trajectories. Furthermore, we provide an open-source implementation of our protocol in the form of a Jupyter notebook accompanying this paper.
期刊介绍:
Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance.
Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.