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 Analysiswill 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.