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Abstracting Geographic Information in a Data Rich World Methodologies and Applications of Map Generalisation /
Research in the field of automated generalisation has faced new challenges in recent years as a result of technological developments in web-based processing, new visualisation paradigms and access to very large volumes of multi-source data generated by sensors and humans. In these contexts, map gene...
| Clasificación: | Libro Electrónico |
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| Autor Corporativo: | |
| Otros Autores: | , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2014.
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| Edición: | 1st ed. 2014. |
| Colección: | Publications of the International Cartographic Association (ICA),
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| Temas: | |
| Acceso en línea: | Texto Completo |
| Sumario: | Research in the field of automated generalisation has faced new challenges in recent years as a result of technological developments in web-based processing, new visualisation paradigms and access to very large volumes of multi-source data generated by sensors and humans. In these contexts, map generalisation needs to underpin 'on-demand mapping', a form of mapping that responds to individual user requirements in the thematic selection and visualisation of geographic information. It is this new impetus that drives the research of the ICA Commission on Generalisation and Multiple Representation (for example through its annual workshops, biannual tutorials and publications in international journals). This book has a coherent structure, each chapter focusing on core concepts and tasks in the map generalisation towards on-demand mapping. Each chapter presents a state-of-the-art review, together with case studies that illustrate the application of pertinent generalisation methodologies. The book addresses issues from data gathering to multi scaled outputs. Thus there are chapters devoted to defining user requirements in handling specifications, and in the application and evaluation of map generalisation algorithms. It explores the application of generalisation methodologies in the context of growing volumes of data and the increasing popularity of user generated content. |
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| Descripción Física: | XV, 407 p. 180 illus., 128 illus. in color. online resource. |
| ISBN: | 9783319002033 |
| ISSN: | 2195-1713 |