Spatial Exploration Based Cnns

This volume is a critical book that provides different methodologies that combine the potential data (including Big Data) analysis with GIS applications. It gives readers a comprehensive overview of the current state-of-the-art methods of spatial analysis, focusing both on the new philosophical and theoretical foundations for spatial analysis and on a flexible framework for analysis in the real world, for problems such as complexity and uncertainty.

This book explores social mechanisms that drive network change and link them to computationally sound models of changing structure to detect patterns. This text identifies the social processes generating these networks and how networks have evolved.

Our society has entered a data-driven era, one in which not only are enormous amounts of data being generated daily but there are also growing expectations placed on the analysis of this data. Some data have become simply too large to be displayed and some have too short a lifespan to be handled properly with classical visualization or analysis methods. In order to address these issues, this book explores the potential solutions where we not only visualize data, but also allow users to be able to interact with it. Therefore, this book will focus on two main topics: large dataset visualization and interaction. Graphic cards and their image processing power can leverage large data visualization but they can also be of great interest to support interaction.

Data usually comes in a plethora of formats and dimensions, rendering the information extraction and exploration processes challenging. Thus, being able to perform exploratory analyses of the data with the intent of having an immediate glimpse of some of the data properties is becoming crucial. Exploratory analyses should be simple enough to avoid complicated declarative languages (such as SQL) and mechanisms, while at the same time retaining the flexibility and expressiveness of such languages. Recently, we have witnessed a rediscovery of the so-called example-based methods, in which the user, or analyst, circumvents query languages by using examples as input.