Название: Data Science and Human-Environment SystemsАвтор: Steven M. Manson
Издательство: Cambridge University Press
Год: 2023
Страниц: 279
Язык: английский
Формат: pdf
Размер: 10.0 MB
Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data Science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.
Data Science and cognate fields like Big Data and Artificial Intelligence hold great promise for studying human-environment systems. At the same time, human-environment research offers much to data science in terms of sophisticated approaches to compelling real-world problems. Serious shortcomings exist in social and environmental data, some of which we are only beginning to see. There are many ways that we can make our methods better and, at the same time, work on the theories underlying the data science of human-environment systems.
Data Science deals with data, unsurprisingly. Data Science has subsumed many aspects of Big Data as a scholarly endeavor, but it is important to consider data and big data on their own. Most scholarly work relies on data harnessed to various methods and concepts. Most researchers can readily point to the kinds of data they use. The simple notion of data as measures of phenomena that we find interesting (e.g., temperature, population counts, or interviews) suffices for most conversations about data science. However, it is essential to dig a little deeper at times and recognize the long and fraught history of data in science. A note on terminology – we will use big data as a plural noun when speaking of the data as such (e.g., “Big Data are collected”) and as a singular noun when speaking of the larger field of Big Data (e.g., “Big Data offers perils and promise”).
Contents:
1 Data Science and Human-Environment Systems 1
2 Data Gaps and Potential 27
3 Big Methods, Big Messes, Big Solutions 76
4 Theory and the Perils of Black Box Science 120
5 Policy Dilemmas 161
6 Ways Forward for the Data Science of Human-Environment Systems 198
References 213
Index 252
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