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Applied Machine Learning Predictive Modelling In Regional Spatial Data Analysis Problem

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dc.title Applied Machine Learning Predictive Modelling In Regional Spatial Data Analysis Problem en
dc.contributor.author Kovářík, Martin
dc.contributor.author Benda, Radek
dc.relation.ispartof Finance and Performance of Firms in Science, Education and Practice 2015
dc.identifier.isbn 978-80-7454-482-8
dc.date.issued 2015
dc.citation.spage 701
dc.citation.epage 715
dc.event.title 7th International Scientific Conference on Finance and Performance of Firms in Science, Education and Practice
dc.event.location Zlín
utb.event.state-en Czech Republic
utb.event.state-cs Česká republika
dc.event.sdate 2015-04-23
dc.event.edate 2015-04-24
dc.type conferenceObject
dc.language.iso en
dc.publisher Univerzita Tomáše Bati ve Zlíně (UTB)
dc.publisher Tomas Bata University in Zlín en
dc.relation.uri http://www.ufu.utb.cz/konference/sbornik2015.pdf
dc.subject Machine Learning en
dc.subject Spatial Data Analysis en
dc.subject Linear Regression en
dc.subject Regression Trees en
dc.subject Random Forests en
dc.subject Support Vector Machines en
dc.subject Cubist Model en
dc.subject k-fold Cross-Validation en
dc.description.abstract Urban and Regional Studies deal with large tables of spatial data obtained from censuses and surveys. It is necessary to simplify the huge amount of detailed information in order to extract the main trends. The main aim of this article is to present and compare machine learning models in spatial data analysis problem. As an example of spatial data modelling we draw upon public-domain data about California housing values. We use a variety of regression modelling techniques, showing how additional information about location (longitude and latitude) can contribute to the analysis. The data comprise observations of housing values, economic covariates, and longitude and latitude. We follow Pace and Barry (1997) in defining response and explanatory variables for a linear regression model. en
utb.faculty Faculty of Management and Economics
dc.identifier.uri http://hdl.handle.net/10563/1006486
utb.identifier.obdid 43873264
utb.identifier.wok 000374107300055
utb.source d-wok
dc.date.accessioned 2016-07-26T14:58:38Z
dc.date.available 2016-07-26T14:58:38Z
utb.contributor.internalauthor Kovářík, Martin
utb.contributor.internalauthor Benda, Radek
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