Preface

Spatial analysis and geographic information are interconnected themes that complement each other in the study and understanding of space and territory, allowing, among other things, to analyze and understand spatial patterns and territorial dynamics of phenomena and processes.

Geographic information is the basis for spatial analysis. Its quality is a critical aspect of making informed decisions and obtaining reliable results. It is essential to ensure the precision, accuracy, reliability, completeness, consistency, timeliness, readability, and comprehensibility of the geographic data used. This can be achieved through proper data collection, validation, standardization, and updating techniques.

In this context, the increasing availability of Big Data (structured or not) has allowed obtaining more detailed representations of human behavior and movement patterns, environmental changes, and other complex spatial dynamics. The seven Vs (Volume, Variety, Velocity, Variability, Veracity, Visualization, and Value) enabled the creation of more accurate complex models and the understanding of phenomena that were previously difficult to analyze at different scales.

Through the complex systems approach, it is possible to understand the interactions and feedback between the elements that make up space and territory and how these interactions influence spatial patterns and territorial dynamics. The spatially explicit models, with special emphasis on those supported by artificial intelligence (where machine learning fits), help the understanding and prediction of spatiotemporal dynamics, allowing us to simulate future and/or past scenarios, identify patterns, and test hypotheses. Its areas of application are quite diverse, covering themes such as urban expansion, the spread of diseases, the distribution of natural resources, and transport and mobility, among others, as long as they have a spatial dimension.

Thus, with the simultaneous emergence of new data sources and new methods of analysis, new concepts and tools have emerged, such as the smart city, circularity and urban metabolism, mixed models (qualitative/quantitative), multiverse, agnostics and agents, virtual and augmented reality, digital twins, and the metaverse. Despite being different in substance and area of application, all these approaches aim to support the process of territorial analysis, allowing for understanding the organization and transformations of space and identifying the interdependent relationships between the different elements that compose it. The territorial analysis is fundamental for understanding spatial inequalities and for adequate planning and management of the territory, which is essential for supporting decision-making.

In short, spatial analysis and modeling cover a wide range of themes and topics, as the theme is interdisciplinary and comprises several domains of Geographic Information Science (GISc). As such, this book about spatial analysis in Geographic Information

Systems (GIS) is a powerful tool, as it is a comprehensive resource for students, researchers, and practitioners who are interested in learning about spatial analysis and its applications in GIS.

This book covers a wide range of topics, including the fundamentals of GIS and spatial data, various types of spatial analysis techniques, and their applications in different fields. It also provides examples of how GIS and spatial analysis have been used to solve real-world problems, such as exploring new data sources (e.g., light detection and ranging [LiDar]), analyzing health and environment relationships, mapping land use/land cover changes, modeling transports and infrastructures, or assessing and monitoring the impacts of mining activity.

This volume is a valuable resource for anyone who wants to learn more about spatial analysis in GIS. It provides a clear and concise explanation of complex concepts as well as practical guidance on how to use GIS and spatial analysis tools effectively. Included case studies and examples help readers apply what they have read to develop their own skills in spatial analysis. We hope this book contributes to the field and helps others learn about the power and potential of spatial analysis.
