Exploratory Analysis of Spatial and Temporal Data A Systematic Approach /

Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appe...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριοι συγγραφείς: Andrienko, Natalia (Συγγραφέας), Andrienko, Gennady (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Andrienko, Natalia.  |e author. 
245 1 0 |a Exploratory Analysis of Spatial and Temporal Data  |h [electronic resource] :  |b A Systematic Approach /  |c by Natalia Andrienko, Gennady Andrienko. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2006. 
300 |a XVI, 704 p. 282 illus., 37 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
505 0 |a Data -- Tasks -- Tools -- Principles -- Conclusion. 
520 |a Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing. The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular. They start by developing a general view of data structures and characteristics and then build on top of this a general task typology, distinguishing between elementary and synoptic tasks. This typology is then applied to the description of existing approaches and technologies, resulting not just in recommendations for choosing methods but in a set of generic procedures for data exploration. Professionals practicing analysis will profit from tested solutions – illustrated in many examples – for reuse in the catalogue of techniques presented. Students and researchers will appreciate the detailed description and classification of exploration techniques, which are not limited to spatial data only. In addition, the general principles and approaches described will be useful for designers of new methods for EDA. 
650 0 |a Computer science. 
650 0 |a Earth sciences. 
650 0 |a Information storage and retrieval. 
650 0 |a Application software. 
650 0 |a Geographical information systems. 
650 1 4 |a Computer Science. 
650 2 4 |a Computer Applications. 
650 2 4 |a Geographical Information Systems/Cartography. 
650 2 4 |a Earth Sciences, general. 
650 2 4 |a Information Storage and Retrieval. 
700 1 |a Andrienko, Gennady.  |e author. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783540259947 
856 4 0 |u http://dx.doi.org/10.1007/3-540-31190-4  |z Full Text via HEAL-Link 
912 |a ZDB-2-SCS 
950 |a Computer Science (Springer-11645)