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GIS Algorithms
Geographic information systems (GIS) have become increasingly important in helping us understand complex social, economic, and natural dynamics where spatial components play a key role. The critical algorithms used in GIS, however, are notoriously difficult to both teach and understand, in part due to the lack of a coherent representation. GIS Algorithms attempts to address this problem by combining rigorous formal language with example case studies and student exercises. Using Python code throughout, Xiao breaks the subject down into three fundamental areas: Geometric Algorithms Spatial Indexing Spatial Analysis and Modelling With its comprehensive coverage of the many algorithms involved, GIS Algorithms is a key new textbook in this complex and critical area of geography.
Geographic Information Science
This book constitutes the refereed proceedings of the 7th International Conference on Geographic Information Science, GIScience 2012, held in Columbus, OH, USA in September 2012. The 26 full papers presented were carefully reviewed and selected from 57 submissions. While the traditional research topics are well reflected in the papers, emerging topics that involve new research hot-spots such as cyber infrastructure, big data, web-based computing also occupy a significant portion of the volume.
GeoComputational Analysis and Modeling of Regional Systems
The contributed volume collects cutting-edge research in GeoComputational Analysis of Regional Systems. The contributions emphasize methodological innovations or substantive breakthroughs on many facets of the socio-economic and environmental reality of regional contexts.
Thematic Cartography and Geovisualization, Fourth Edition
This comprehensive and well-established cartography textbook covers the theory and the practical applications of map design and the appropriate use of map elements. It explains the basic methods for visualizing and analyzing spatial data and introduces the latest cutting-edge data visualization techniques. The fourth edition responds to the extensive developments in cartography and GIS in the last decade, including the continued evolution of the Internet and Web 2.0; the need to analyze and visualize large data sets (commonly referred to as Big Data); the changes in computer hardware (e.g., the evolution of hardware for virtual environments and augmented reality); and novel applications of t...
Spatially Explicit Hyperparameter Optimization for Neural Networks
Neural networks as the commonly used machine learning algorithms, such as artificial neural networks (ANNs) and convolutional neural networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a few studies that investigate the parameter settings of neural networks in GIScience. Moreover, the model performance of neural networks often depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the overall running time. Therefore, an automated approach is necessary for addressing these limitations in current studies. This book proposes an automated spatially explicit hyperparameter optimization approach to identify optimal or near-optimal parameter settings for neural networks in the GIScience field. Also, the approach improves the computing performance at both model and computing levels. This book is written for researchers of the GIScience field as well as social science subjects.
Higher-dimensional modelling of geographic information
Higher-dimensional modelling of geographic information
Computation and Visualization for Understanding Dynamics in Geographic Domains
The world is ever changing, and a comprehensive understanding of the world will not be achieved without theoretical and methodological advances to decode complex dynamics in human and environmental systems. Computation and Visualization for the Understanding of Dynamics in Geographic Domains: A Research Agenda synthesizes key ideas and issu
The Changing Alpine Treeline
The alpine treeline ecotone (ATE) is an area of transition high on mountains where closed canopy forests from lower elevations give way to the open alpine tundra and rocky expanses above. Alpine tundra is an island biome and its ecotone with forest is subject to change, and like oceanic islands, alpine tundra is subject to invasion – or the upward advance of treeline. The invasion of tundra by trees will have consequences for the tundra biome as invasion does for other island flora and fauna. To examine the invasibility of tundra we take a plant's-eye-view, wherein the local conditions become extremely important. Among these local conditions, we find geomorphology to be exceptionally impor...
Genetic and Evolutionary Computation — GECCO 2003
The set LNCS 2723 and LNCS 2724 constitutes the refereed proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2003, held in Chicago, IL, USA in July 2003. The 193 revised full papers and 93 poster papers presented were carefully reviewed and selected from a total of 417 submissions. The papers are organized in topical sections on a-life adaptive behavior, agents, and ant colony optimization; artificial immune systems; coevolution; DNA, molecular, and quantum computing; evolvable hardware; evolutionary robotics; evolution strategies and evolutionary programming; evolutionary sheduling routing; genetic algorithms; genetic programming; learning classifier systems; real-world applications; and search based software engineering.
Geographic Data Science with Python
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 Python. ● Provides examples for readers to replicate, adapt, extend, and improve. ● Covers the crucial knowledge needed by geographic data scientists. It 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.
