UID:
almahu_9949386014902882
Umfang:
1 online resource (xxv, 593 pages) :
,
illustrations (chiefly color)
ISBN:
9781003033219
,
1003033210
,
9781000079784
,
1000079783
,
9781000079760
,
1000079767
,
9781000079746
,
1000079740
Serie:
Routledge advanced texts in economics and finance
Inhalt:
"This textbook is a comprehensive introduction to applied spatial data analysis, using R. Each chapter walks the reader through a different method, explaining how to interpret the results and what conclusions can be drawn. The author team showcase key topics including unsupervised learning, causal inference, spatial weight matrices, spatial econometrics, heterogeneity and bootstrapping. It is accompanied by a suite of data and R code on Github, to help readers practise techniques via replication and exercises. This text will be a valuable resource for advanced students of econometrics, spatial planning and regional science. It will also be suitable for researchers and data scientists working with spatial data"--
Anmerkung:
Basic operations in the R software -- Data, spatial classes and basic graphics -- Spatial data from the Web API -- Spatial weights matrix, distance measurement, tessellation, spatial statistics -- Applied spatial econometrics -- Geographically weighted regression -- modelling spatial heterogeneity -- Spatial unsupervised learning -- Spatial point pattern analysis and spatial interpolation -- Spatial sampling and bootstrapping -- Spatial big data -- Spatial unsupervised learning - applications of market basket analysis in geomarketing.
Weitere Ausg.:
Print version: Applied spatial statistics and econometrics Milton Park, Abingdon, Oxon ; New York, NY : Routledge, [2020] ISBN 9780367470777
Sprache:
Englisch
Schlagwort(e):
Electronic books.
;
Electronic books.
DOI:
10.4324/9781003033219
URL:
https://www.taylorfrancis.com/books/9781003033219
