TRUMP BITCOIN SPEACH

Our book has two core value propositions to cover different audience needs. First, our book supports students in geography and environmental sciences to learn about cutting-edge thinking in causal inference and research design. Second, our book supports social sciences learners who have been trained in causal inference and research design to understand spatial data and models. We outline these two needs below.

Students in Geography have their pick of research methods books: waves of data science, geographic information science, and geocomputation textbooks have come out over the past 40 years with an incredible diversity of approaches and pedagogical features. However, unlike much of the rest of social and environmental sciences, the topic of research design has no core or canonical textbook in geography—instead it’s often taught as a part of research methods. As contemporary trends in causal inference and open science have made research design more important over the past 40 years, geography and environmental sciences need a research design textbook to support teaching and learning about ethical, responsible, and causal research designs. Our textbook will fill this growing market gap as the first core textbook on causal methods and research design in geography and environmental sciences. Thus, for people within geography, we will provide an accessible and clear overview of both the contemporary principles and thinking behind causal research designs and the simple methods that underlie robust causal inferences.

Students in Social Sciences have a wide variety of texts to support learning causal research concepts, designs, and methods. However, there is currently no textbook covering causal inference methods in spatial analysis. This is a common pattern for teaching in social sciences: as thinking about statistics and research design evolves, pedagogical concerns about the unique dimensions of spatial or temporal research questions take time to settle and develop. Our text will be the first to provide this bridge between causal inference and research design in the social sciences and spatial analysis. Thus, for people outside geography, we will present spatial analysis concepts in a familiar manner, language, and setting.

Together, we think these two angles will stand to benefit many students. These approaches to thinking and critical reasoning are also lucrative for nonprofit, policy, and industrial research. Indeed, policy and industry have causal questions they want answered, too—questions students can answer by learning the concepts, designs, and methods in our text. Currently, these research designs and methods are not well-supported by textbooks in geography. Again from the other direction, much of the data we use to answer questions and solve problems in policy and industry involves geographic data; place and time are two fundamental components of big data and data by-products collected by networked digital technologies. But there are no textbooks to help outline how these problems can be solved. Thus, our textbook will also support learners to understand how to design and execute research that uses spatial analysis and causal inference to improve understanding. Overall, this textbook supports learning spatial analysis and causal inference methods needed by students in geography, students in other social and environmental sciences, and practitioners in nonprofit, policy, and industrial sectors.

BITCOIN GREEN

LEVI JOHN WOLF is a Senior Lecturer in Quantitative Human Geography at the University of Bristol. He completed his PhD research on electoral boundary drawing at Arizona State University in Geography in 2017. His research spans many different areas of application from analyzing segregation in neighborhoods to modeling oceanic plankton distributions, but is underwritten by methodological innovation in spatial analysis, open science, and pedagogy. Before his time in academia, Levi worked in industrial geographic data science at Nextdoor.com and CARTO.

NICHOLAS DORWARD is a Lecturer in Human Geography at the University of Bristol, where he completed an ESRC funded PhD in 2022. His research intersects the fields of political science, development studies, and urban geography with a primary focus upon modeling the causes and consequences of urbanization, conflict, and political change in African cities. Methodologically, he works on the application of tools for spatial analysis and causal inference to answer political and geographic questions.

TOGETHER, Nicholas and Levi bring a broad cross-disciplinary perspective on cutting edge research and applications of causal inference in spatial analysis. This provides the authors with depth in foundational spatial analysis and causal inference, as well as breadth to cover how these techniques are applied in diverse empirical settings across a range of social and environmental sciences. The authors have written together in publications on spatial analysis of armed conflict, cities, rural poverty mapping, and the 2023 Boundary Commission for England’s parliamentary constituency boundary review.