000			04207nam a22003975i 4500
001			279069
003			MX-SnUAN
005			20160429153930.0
007			cr nn 008mamaa
008			150903s2008 xxu| o |||| 0|eng d
020			_a9780387781716 _99780387781716
024	7		_a10.1007/9780387781716 _2doi
035			_avtls000332899
039		9	_a201509030759 _bVLOAD _c201404122306 _dVLOAD _c201404092040 _dVLOAD _y201402041058 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aRA648.5-654
100	1		_aBivand, Roger S. _eautor _9303185
245	1	0	_aApplied Spatial Data Analysis with R / _cby Roger S. Bivand, Edzer J. Pebesma, Virgilio Gómez-Rubio.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_axiv, 378 páginas _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aUse R!
500			_aSpringer eBooks
505	0		_aHandling Spatial Data in R -- Hello World: Introducing Spatial Data -- Classes for Spatial Data in R -- Visualising Spatial Data -- Spatial Data Import and Export -- Further Methods for Handling Spatial Data -- Customising Spatial Data Classes and Methods -- Analysing Spatial Data -- Spatial Point Pattern Analysis -- Interpolation and Geostatistics -- Areal Data and Spatial Autocorrelation -- Modelling Areal Data -- Disease Mapping.
520			_aApplied Spatial Data Analysis with R is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information systems, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where coloured figures, complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003. Roger Bivand is Professor of Geography in the Department of Economics at Norges Handelshøyskole, Bergen, Norway. Edzer Pebesma is Professor of Geoinformatics at Westfälische Wilhelms-Universität, Münster, Germany. Virgilio Gómez-Rubio is Research Associate in the Department of Epidemiology and Public Health, Imperial College London, London, United Kingdom.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aPebesma, Edzer J. _eautor _9303186
700	1		_aGómez-Rubio, Virgilio. _eautor _9303187
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387781709
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-78171-6 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942			_c14
999			_c279069 _d279069