Levi John Wolf – författare

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.Key Features:Showcases the excellent data science environment in PythonProvides examples for readers to replicate, adapt, extend, and improveCovers the crucial knowledge needed by geographic data scientistsIt presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.

This book provides the tools, the methods, and the theory to meet the challenges of contemporary data science applied to geographic problems and data. In the new world of pervasive, large, frequent, and rapid data, there are new opportunities to understand and analyze the role of geography in everyday life. Geographic Data Science with Python introduces a new way of thinking about analysis, by using geographical and computational reasoning, it shows the reader how to unlock new insights hidden within data.Key Features:Showcases the excellent data science environment in PythonProvides examples for readers to replicate, adapt, extend, and improveCovers the crucial knowledge needed by geographic data scientistsIt presents concepts in a far more geographic way than competing textbooks, covering spatial data, mapping, and spatial statistics whilst covering concepts, such as clusters and outliers, as geographic concepts.Intended for data scientists, GIScientists, and geographers, the material provided in this book is of interest due to the manner in which it presents geospatial data, methods, tools, and practices in this new field.

Elgar Research Agendas outline the future of research in a given area. Leading scholars are given the space to explore their subject in provocative ways, and map out the potential directions of travel. They are relevant but also visionary.This Research Agenda explores the future of spatial analysis, and how the field informs and challenges the policy landscape. A wide range of contributors from different intellectual communities address the problem of causality in geographic analysis, arguing that diversity is crucial for the future success of the discipline.Chapters define and explore specific concepts and practices within the field, for instance data science and geosimulation, providing perspectives on the current state of the art of these areas within geography, and how they will shift in the future. In the first section, contributors cover the fundamentals of the topic, as well as various ways to handle the ‘spatial variable’, including the concept of space, the scale of spatial patterns and what those patterns reveal. The book then analyses schools of practice, including geographical data science, causality, generative modelling and machine learning.A Research Agenda for Spatial Analysis will prove an invaluable resource for spatial analysts and geographic information scientists interested in learning about the direction of future developments in the field. Additionally, scholars and students of human and urban geography and geographic research methods will benefit from this crucial overview of the topic.