Sparse Big Data Problem. A Case Study of Czech Graffiti Crimes View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Jiří Horák , Igor Ivan , Tomáš Inspektor , Jan Tesla

ABSTRACT

Sparse data sets may be considered as a one of the issues of big data generating extremely uneven frequency distribution. To deal with this issue, special methods must be applied. The study is focused on the Czech graffiti crimes and selected factors (property offences, buildings, flats, garages, educational facilities, and gambling clubs) which may influence the graffiti crimes occurrence. For regression analysis decision trees with the exhaustive CHAID growing method were applied. Grid models with 100, 500 and 1000 m cells were tested. The model of 1 km grid was evaluated as the best. The most influencing factors are the occurrence of secondary schools and gambling devices enhanced for several territorial units. The results of the decision tree for 1 km grid are validated using alternative models of data aggregation—aggregation around the randomly selected building and randomly distributed points. More... »

PAGES

85-106

Book

TITLE

The Rise of Big Spatial Data

ISBN

978-3-319-45122-0
978-3-319-45123-7

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-45123-7_7

DOI

http://dx.doi.org/10.1007/978-3-319-45123-7_7

DIMENSIONS

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