Muutke küpsiste eelistusi

Spatial Network Data: Concepts and Techniques for Summarization 1st ed. 2016 [Pehme köide]

  • Formaat: Paperback / softback, 50 pages, kõrgus x laius: 235x155 mm, kaal: 1124 g, 19 Illustrations, black and white; XII, 50 p. 19 illus., 1 Paperback / softback
  • Sari: SpringerBriefs in Computer Science
  • Ilmumisaeg: 28-Jun-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331939620X
  • ISBN-13: 9783319396200
  • Pehme köide
  • Hind: 48,70 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Tavahind: 57,29 €
  • Säästad 15%
  • Raamatu kohalejõudmiseks kirjastusest kulub orienteeruvalt 2-4 nädalat
  • Kogus:
  • Lisa ostukorvi
  • Tasuta tarne
  • Tellimisaeg 2-4 nädalat
  • Lisa soovinimekirja
  • Formaat: Paperback / softback, 50 pages, kõrgus x laius: 235x155 mm, kaal: 1124 g, 19 Illustrations, black and white; XII, 50 p. 19 illus., 1 Paperback / softback
  • Sari: SpringerBriefs in Computer Science
  • Ilmumisaeg: 28-Jun-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 331939620X
  • ISBN-13: 9783319396200
This brief explores two of the main challenges of spatial network data analysis: the many connected components in the spatial network and the many candidates that have to be processed. Within this book, these challenges are conceptualized, well-defined problems are explored, and critical techniques are discussed.
The process of summarizing spatial network data entails finding a compact description or representation of observations or activities on large spatial or spatiotemporal networks. However, summarizing spatial network data can be computationally challenging for various reasons, depending on the domain. The content has applications for professionals, organizations, and researchers in transportation safety, public safety, public health, disaster response, and related fields.

1 Introduction
1(8)
1.1 Summarizing Different Genres of Data
1(3)
1.2 Illustrative Application Domains
4(2)
1.3 Computational Challenges
6(3)
References
6(3)
2 Many Connected Components
9(22)
2.1 Introduction
9(5)
2.1.1 An Illustrative Application Domain: Crime Analysis
11(1)
2.1.2 State of the Art
12(2)
2.1.3 Outline of the
Chapter
14(1)
2.2 Basic Concepts and Problem Statement
14(3)
2.2.1 Basic Concepts
14(1)
2.2.2 Problem Statement
15(2)
2.3 Spatial Network Activity Summarization
17(9)
2.3.1 Computational Structure of Spatial Network Activity Summarization
17(1)
2.3.2 Proof of NP-Completeness
18(2)
2.3.3 Trend: The K-Main Routes Algorithm
20(6)
2.4 Case Study
26(1)
2.5 Summary
27(4)
References
27(4)
3 Many Candidates
31(18)
3.1 Introduction
31(3)
3.1.1 Challenges
32(1)
3.1.2 Current State-of-the-Art
32(2)
3.1.3 Outline of the
Chapter
34(1)
3.2 Basic Concepts and Problem Statement
34(3)
3.2.1 Basic Concepts
34(1)
3.2.2 Problem Statement
35(2)
3.3 Trends
37(6)
3.3.1 Naive Significant Route Miner (NaiveSRM)
37(1)
3.3.2 Significant Route Miner with Likelihood Pruning and Monte Carlo Speedup (SRM)
38(4)
3.3.3 Dynamic Segmentation
42(1)
3.4 Case Study
43(1)
3.5 Discussion
44(1)
3.6 Summary
45(4)
References
46(3)
4 Summary
49