Applied spatial statistics and econometrics: data analysis in R
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| Format: | Book |
| Language: | English |
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London
Routledge
2021
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| Series: | Routledge advanced texts in economics and finance
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxv, 593 Seiten Illustrationen, Diagramme, Karten |
| ISBN: | 9780367470777 9780367470760 |
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| 245 | 1 | 0 | |a Applied spatial statistics and econometrics |b data analysis in R |c Katarzyna Kopczewska |
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| adam_text | Contents List of figures List of tables List of contributors Introduction Statement by the American Statistical Association on statistical significance and p-value - use in the book Acknowledgements 1 Basic operations in the Rsoftware xj xvii xix xxj xxiii xxv 1 MATEUSZ KOPYT 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 About the R software The R software interface 1 1 1.2.1 R Commander 1.2.2 RStudio 2 3 Using help Additional packages R language ֊ basic features Defining and loading data Basic operations on objects Basic statistics of the dataset Basic visualisations 4 7 9 9 11 18 24 1.9.1 1.9.2 1.9.3 1.9.4 24 27 29 29 Scatterplot and line chart Column chart Pie chart Boxplot 1.10 Regression in examples 31 Data, spatial classesand basicgraphics 37 KATARZYNA KOPCZEWSKA 2.1 2.2 2.3 2.4 Loading and basic operations on spatial vector data Creating, checking and converting spatial classes Selected colour palettes Basic contour maps with a colour layer Scheme Scheme Scheme Scheme Scheme 2.5 1 ֊ with 2 - with 3 - with 4 - with 5 - with colorRampPaletteO from the grDevices:: package choroplethO fromthe GISTools:: package findlntervalO from the base:: package findColoursO from the classine, package spplotO from the sp:: package 37 48 53 57 57 58 59 60 61 Basic operations and graphs for pointdata 62 Scheme 1 ֊ with pointsO from the graphics:: package - locations only 62 v
Contents 2.6 2.7 2.8 3 Scheme 2 - with spplotO from the sp:: package - locations and values Scheme 3 - with findlntervalO from the base:: package ֊ locations, values, different size of symbols Basic operations on rasters Basic operations on grids Spatial geometries Spatial data with Web APIs 63 64 67 73 80 87 MATEUSZ KOPYT AND KATARZYNA KOPCZEWSKA 3.1 3.2 What is an application programming interface (API)? Creating background maps with use of an application programming interface 3.3 Ways to visualise spatial data ֊ maps for point and regional data Scheme 1 - with bubbleMapO from the RgoogleMaps:: package Scheme 2 ֊ with ggmapO from the ggmap:: package Scheme 3 - with PlotOnStaticMapO from the RgoogleMaps:: package Scheme 4 - with RGoogleMaps : GotMapO and conversion of staticMap into a rastta Spatial data in vector format - example of theOSM database Access to non-spatial internet databases and resources via application programming interface examples Geocoding of data 3.4 3.5 3.6 4 Spatial weights matrix, distance measurement, tessellation, spatial statistics 87 88 102 102 104 109 109 110 117 133 151 KATARZYNA KOPCZEWSKA AND MARIA KUBARA 4.1 4.2 4.3 4.4 4.5 VI Introduction to spatial data analysis Spatial weights matrix 4.2.1 General framework for creating spatial weights matrices 4.2.2 Selection of a neighbourhood matrix 4.2.3 Neighbourhood matrices according to the contiguity criterion 4.2.4 Matrix of к nearest neighbours (knn) 4.2.5 Matrix based on distance criterion (neighbours in a radius of Ժ km) 4.2.6 Inverse distance matrix 4.2.7 Summarising and editing spatial
weights matrix 4.2.8 Spatial lags and higher-order neighbourhoods 4.2.9 Creating weights matrix based on groupmembership ### Example ### ### Example ### Distance measurement and spatial aggregation ### Example ### Tessellation Spatial statistics 4.5.1 Global statistics 4.5.1.1 Global Moran s / statistics 4.5.1.2 Global Geary s C statistics 4.5 1.3 Join-count statistics 4.5.2 Local spatial autocorrelation statistics 4.5.2.2 Local Moran s / statistics (local indicator of spatial association) 4.5.2.3 Local Geary s C statistics 151 153 153 155 156 159 161 163 164 169 170 170 173 174 177 182 185 188 188 194 195 199 199 201
Contents 4.5.2.4 Local Getis-Ord G. statistics 4.5.2.5 Local spatial heteroscedasticity 4.6 4.7 5 Spatial cross-correlations for two variables Correlogram Applied spatial econometrics 202 203 206 208 213 KATARZYNA KOPCZEWSKA 5.1 5.2 5.3 5.4 6 Added value from spatial modelling and classes of models Basic cross-sectional models 213 216 5.2.1 Estimation ### Example ### 5.2.2 Quality assessment of spatial models 5.2.2.1 Information criteria and pseudo-/?2 in assessing model fit 5.2.2.2 Test for heteroscedasticity of model residuals 5.2.2.3 Residual autocorrelation tests 5.2.2.4 Lagrange multiplier tests for model type selection 5.2.2.5 Likelihood ratio and Wald tests for model restrictions 5.2.3 Selection of spatial weights matrix and modelling of diffusion strength 5.2.4 Forecasts in spatial models 5.2.5 Causality 216 219 230 230 232 234 236 238 240 243 245 Selected specifications of cross-sectional spatial models 246 5.3.1 Unidirectional spatial interaction models 5.3.2 Cumulative models 5.3.3 Bootstrapped models for big data ### Example ### 5.3.4 Models for grid data ### Example ### 246 255 261 261 269 269 Spatial panel models 274 ### Example### 278 Geographically weighted regression - modelling spatial heterogeneity 289 PIOTR ĆWIAKOWSKI 6.1 6.2 Geographically weighted regression Basic estimation of geographically weighted regression model 289 291 6.2.1 6.2.2 6.2.3 6.2.4 6.2.5 291 292 295 297 Estimation of the reference ordinary least squares model Choosing the optimal bandwidth for a dataset Local geographically weighted statistics Geographically weighted regression
estimation Basic diagnostic tests of the geographically weighted regression model 6.2.6 Testing the significance of parameters in geographically weighted regression 6.2.7 Selection of the optimal functional form of the model 6.2.8 Geographically weighted regression with heteroscedastic random error 6.3 6.4 6.5 6.6 298 304 305 307 The problem of collinearity in geographically weighted regression models 308 6.3.1 Diagnosing collinearity in geographically weighted regression 308 Mixed geographically weighted regression Robust regression in the geographically weighted regression model Geographically and temporally weighted regression 316 318 319 VII
Contents 7 Spatial unsupervised learning 323 KATARZYNA KOPCZEWSKA 7.1 7.2 7.3 7.4 7.5 7.6 8 Clustering of spatial points with к-means, PAM (partitioning around medoids) and CLARA (clustering large applications) algorithms 323 ### Example ### ### Example ### 326 333 Clustering with the density-based spatial clustering of applications with noise algorithm 336 ### Example ### 337 Spatial principal component analysis 345 ### Example ### 346 Spatial drift 349 ### Example ### 349 Spatial hierarchical clustering 356 ### Example ### ### Example ### 358 362 Spatial oblique decision tree 364 ### Example ### 364 Spatial point pattern analysis and spatial interpolation 371 KATERYNA ŽABAŘINA 8.1 8.2 8.3 8.4 8.5 8.6 VII! Introduction and main definitions 373 8.1.1 8.1.2 8.1.3 8.1.4 Dataset Creation of window and (joint pattern Marks Covariates ### Example ### 8.1.5 Duplicated points 8.1.6 Projection and rescaling 373 374 375 381 381 382 383 Intensity-based analysis of unmarkedpointpattern 386 8.2.1 Quadrat test 8.2.2 Tests with spatial covar iates 387 388 Distance-based analysis of the unmarkedpointpattern 391 8.3.1 Distance-based measures 8.3 11 Ripley s К function 8 3 1.2 F function 8 3 13 G function 8.3.1 4 J function 8 3 1 5 Distance-based complete spatial randomness tests 8.3.2 Monte Carlo tests 8.3.3 Envelopes 8.3.4 Non-graphical tests 392 392 393 393 393 393 396 396 397 Selection and estimation of a proper model forunmarked point pattern 398 8.4.1 8.4.2 8.4.3 8.4.4 399 400 401 404 Theoretical note Choice of parameters Estimation and results Conclusions Intensity-based analysis of
marked pointpattern 404 8.5.1 Segregation test 404 Correlation and spacing analysis of themarkedpoint pattern 405
Contents 8.7 8.8 9 8.6.1 Analysis under assumption of stationarity 8.6.1.1 К function variations for multitype pattern 8.6.1.2 Mark connection function 8.6.1.3 Analysis of within- and between-type dependence 8.6.1.4 Randomisation test of components independence 8.6.2 Analysis under assumption of non-stationarity 8.6.2.1 Inhomogeneous К function variations for multitype pattern Selection and estimation of a proper model for unmarked point pattern 8.7.1 Theoretical note 8.7.2 Choice of optimal radius 8.7.3 Within-industry interaction radius 8.7.4 Between-industry interaction radius 8.7.5 Estimation and results 8.7.6 Model with no between-industry interaction 8.7.7 Model with all possible interactions Spatial interpolation methods ֊ kriging 8.8.1 Basic definitions 8.8.2 Description of chosen kriging methods 8.8.3 Data preparation for the study 8.8.4 Estimation and discussion Spatial sampling and bootstrapping 405 405 407 409 409 410 410 410 412 412 412 414 415 415 418 421 421 424 424 425 433 KATARZYNA KOPCZEWSKA AND PIOTR CWIAKOWSKI 9.1 9.2 9.3 9.4 Spatial point data - object classes and spatial aggregation Spatial sampling ֊ randomisation/generation of new points on the surface Spatial sampling - sampling of sub-samples from existing points 9.3.1 Simple sampling 9.3.2 The options of the sperrorest:: package 9.3.3 Sampling points from areas determined by the /c-means algorithm block bootstrap 9.3.4 Sampling points from moving blocks (moving block bootstrap) Use of spatial sampling and bootstrapping in cross-validation of models ### Example ### 10 Spatial big data 434 437 440
441 443 448 456 462 462 477 PIOTR WÓJCIK 10.1 Examples of big data applications 10.2 Spatial big data 10.2.1 Spatial data types 10.2.2 Challenges related to the use of spatial big data 10.2.2.1 Processing of large datasets 10.2.2.2 Mapping and reduction 10.2.2.3 Spatial data indexing 10.3 The sd:: package - simple features 10.3.1 sf class - a special data frame 10.3.2 Data with POLYGON geometry 10.3.3 Data with POINT geometry 10.3.4 Visualisation using the ggplot2:: package 10.3.5 Selected functions for spatial analysis 478 478 479 479 479 480 480 481 481 482 488 489 490 IX
Contents 10.4 Use the dplyr:: package functions 10.5 Sample analysis of large raster data 10.5.1 Measurement of economic inequalities from space 10.5.2 Analysis using the raster:: package functions 10.5.3 Other functions of the raster:: package 10.5.4 Potential alternative ֊ stars:: package 11 Spatial unsupervised learning ֊ applications of market basket analysis in geomarketing 494 505 505 507 514 515 517 ALESSANDRO FESTI 11.1 11.2 11.3 11.4 Introduction to market basket analysis Data needed in spatial market basket analysis Simulation of data The market basket analysis technique applied to geolocation data 11.5 Spatial association rules 11.6 Applications to geomarketing 11.6.1 Finding the best location for abusiness 11.6.2 Targeting 11.6.3 Discovery of competitors 11.7 Conclusions and further approaches Appendix A: Datasets used in examples A1. Dataset no. 1 / datasetl/- poviat panel data with many variables A2. Dataset no. 2 / dataset2/ - geolocated point data A3. Dataset no. 3 / dataset3/ ֊ monthly unemployment rate in poviats (NTS4) A4. Dataset no. 4 / dataset4/ - grid data for population A5. Shapefiles of contour maps - for poviats (NTS4), regions (NTS2), country (NTS0) and registration areas A6. Raster data on night light intensity onEarth in 2013 A7. Population in cities in Poland Appendix B: Links between packages References Index x 517 518 520 526 530 534 534 536 538 538 541 541 544 548 549 551 552 553 555 561 577
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| adam_txt |
Contents List of figures List of tables List of contributors Introduction Statement by the American Statistical Association on statistical significance and p-value - use in the book Acknowledgements 1 Basic operations in the Rsoftware xj xvii xix xxj xxiii xxv 1 MATEUSZ KOPYT 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 2 About the R software The R software interface 1 1 1.2.1 R Commander 1.2.2 RStudio 2 3 Using help Additional packages R language ֊ basic features Defining and loading data Basic operations on objects Basic statistics of the dataset Basic visualisations 4 7 9 9 11 18 24 1.9.1 1.9.2 1.9.3 1.9.4 24 27 29 29 Scatterplot and line chart Column chart Pie chart Boxplot 1.10 Regression in examples 31 Data, spatial classesand basicgraphics 37 KATARZYNA KOPCZEWSKA 2.1 2.2 2.3 2.4 Loading and basic operations on spatial vector data Creating, checking and converting spatial classes Selected colour palettes Basic contour maps with a colour layer Scheme Scheme Scheme Scheme Scheme 2.5 1 ֊ with 2 - with 3 - with 4 - with 5 - with colorRampPaletteO from the grDevices:: package choroplethO fromthe GISTools:: package findlntervalO from the base:: package findColoursO from the classine, package spplotO from the sp:: package 37 48 53 57 57 58 59 60 61 Basic operations and graphs for pointdata 62 Scheme 1 ֊ with pointsO from the graphics:: package - locations only 62 v
Contents 2.6 2.7 2.8 3 Scheme 2 - with spplotO from the sp:: package - locations and values Scheme 3 - with findlntervalO from the base:: package ֊ locations, values, different size of symbols Basic operations on rasters Basic operations on grids Spatial geometries Spatial data with Web APIs 63 64 67 73 80 87 MATEUSZ KOPYT AND KATARZYNA KOPCZEWSKA 3.1 3.2 What is an application programming interface (API)? Creating background maps with use of an application programming interface 3.3 Ways to visualise spatial data ֊ maps for point and regional data Scheme 1 - with bubbleMapO from the RgoogleMaps:: package Scheme 2 ֊ with ggmapO from the ggmap:: package Scheme 3 - with PlotOnStaticMapO from the RgoogleMaps:: package Scheme 4 - with RGoogleMaps : GotMapO and conversion of staticMap into a rastta Spatial data in vector format - example of theOSM database Access to non-spatial internet databases and resources via application programming interface examples Geocoding of data 3.4 3.5 3.6 4 Spatial weights matrix, distance measurement, tessellation, spatial statistics 87 88 102 102 104 109 109 110 117 133 151 KATARZYNA KOPCZEWSKA AND MARIA KUBARA 4.1 4.2 4.3 4.4 4.5 VI Introduction to spatial data analysis Spatial weights matrix 4.2.1 General framework for creating spatial weights matrices 4.2.2 Selection of a neighbourhood matrix 4.2.3 Neighbourhood matrices according to the contiguity criterion 4.2.4 Matrix of к nearest neighbours (knn) 4.2.5 Matrix based on distance criterion (neighbours in a radius of Ժ km) 4.2.6 Inverse distance matrix 4.2.7 Summarising and editing spatial
weights matrix 4.2.8 Spatial lags and higher-order neighbourhoods 4.2.9 Creating weights matrix based on groupmembership ### Example ### ### Example ### Distance measurement and spatial aggregation ### Example ### Tessellation Spatial statistics 4.5.1 Global statistics 4.5.1.1 Global Moran's / statistics 4.5.1.2 Global Geary's C statistics 4.5 1.3 Join-count statistics 4.5.2 Local spatial autocorrelation statistics 4.5.2.2 Local Moran's / statistics (local indicator of spatial association) 4.5.2.3 Local Geary's C statistics 151 153 153 155 156 159 161 163 164 169 170 170 173 174 177 182 185 188 188 194 195 199 199 201
Contents 4.5.2.4 Local Getis-Ord G. statistics 4.5.2.5 Local spatial heteroscedasticity 4.6 4.7 5 Spatial cross-correlations for two variables Correlogram Applied spatial econometrics 202 203 206 208 213 KATARZYNA KOPCZEWSKA 5.1 5.2 5.3 5.4 6 Added value from spatial modelling and classes of models Basic cross-sectional models 213 216 5.2.1 Estimation ### Example ### 5.2.2 Quality assessment of spatial models 5.2.2.1 Information criteria and pseudo-/?2 in assessing model fit 5.2.2.2 Test for heteroscedasticity of model residuals 5.2.2.3 Residual autocorrelation tests 5.2.2.4 Lagrange multiplier tests for model type selection 5.2.2.5 Likelihood ratio and Wald tests for model restrictions 5.2.3 Selection of spatial weights matrix and modelling of diffusion strength 5.2.4 Forecasts in spatial models 5.2.5 Causality 216 219 230 230 232 234 236 238 240 243 245 Selected specifications of cross-sectional spatial models 246 5.3.1 Unidirectional spatial interaction models 5.3.2 Cumulative models 5.3.3 Bootstrapped models for big data ### Example ### 5.3.4 Models for grid data ### Example ### 246 255 261 261 269 269 Spatial panel models 274 ### Example### 278 Geographically weighted regression - modelling spatial heterogeneity 289 PIOTR ĆWIAKOWSKI 6.1 6.2 Geographically weighted regression Basic estimation of geographically weighted regression model 289 291 6.2.1 6.2.2 6.2.3 6.2.4 6.2.5 291 292 295 297 Estimation of the reference ordinary least squares model Choosing the optimal bandwidth for a dataset Local geographically weighted statistics Geographically weighted regression
estimation Basic diagnostic tests of the geographically weighted regression model 6.2.6 Testing the significance of parameters in geographically weighted regression 6.2.7 Selection of the optimal functional form of the model 6.2.8 Geographically weighted regression with heteroscedastic random error 6.3 6.4 6.5 6.6 298 304 305 307 The problem of collinearity in geographically weighted regression models 308 6.3.1 Diagnosing collinearity in geographically weighted regression 308 Mixed geographically weighted regression Robust regression in the geographically weighted regression model Geographically and temporally weighted regression 316 318 319 VII
Contents 7 Spatial unsupervised learning 323 KATARZYNA KOPCZEWSKA 7.1 7.2 7.3 7.4 7.5 7.6 8 Clustering of spatial points with к-means, PAM (partitioning around medoids) and CLARA (clustering large applications) algorithms 323 ### Example ### ### Example ### 326 333 Clustering with the density-based spatial clustering of applications with noise algorithm 336 ### Example ### 337 Spatial principal component analysis 345 ### Example ### 346 Spatial drift 349 ### Example ### 349 Spatial hierarchical clustering 356 ### Example ### ### Example ### 358 362 Spatial oblique decision tree 364 ### Example ### 364 Spatial point pattern analysis and spatial interpolation 371 KATERYNA ŽABAŘINA 8.1 8.2 8.3 8.4 8.5 8.6 VII! Introduction and main definitions 373 8.1.1 8.1.2 8.1.3 8.1.4 Dataset Creation of window and (joint pattern Marks Covariates ### Example ### 8.1.5 Duplicated points 8.1.6 Projection and rescaling 373 374 375 381 381 382 383 Intensity-based analysis of unmarkedpointpattern 386 8.2.1 Quadrat test 8.2.2 Tests with spatial covar iates 387 388 Distance-based analysis of the unmarkedpointpattern 391 8.3.1 Distance-based measures 8.3 11 Ripley's К function 8 3 1.2 F function 8 3 13 G function 8.3.1 4 J function 8 3 1 5 Distance-based complete spatial randomness tests 8.3.2 Monte Carlo tests 8.3.3 Envelopes 8.3.4 Non-graphical tests 392 392 393 393 393 393 396 396 397 Selection and estimation of a proper model forunmarked point pattern 398 8.4.1 8.4.2 8.4.3 8.4.4 399 400 401 404 Theoretical note Choice of parameters Estimation and results Conclusions Intensity-based analysis of
marked pointpattern 404 8.5.1 Segregation test 404 Correlation and spacing analysis of themarkedpoint pattern 405
Contents 8.7 8.8 9 8.6.1 Analysis under assumption of stationarity 8.6.1.1 К function variations for multitype pattern 8.6.1.2 Mark connection function 8.6.1.3 Analysis of within- and between-type dependence 8.6.1.4 Randomisation test of components'independence 8.6.2 Analysis under assumption of non-stationarity 8.6.2.1 Inhomogeneous К function variations for multitype pattern Selection and estimation of a proper model for unmarked point pattern 8.7.1 Theoretical note 8.7.2 Choice of optimal radius 8.7.3 Within-industry interaction radius 8.7.4 Between-industry interaction radius 8.7.5 Estimation and results 8.7.6 Model with no between-industry interaction 8.7.7 Model with all possible interactions Spatial interpolation methods ֊ kriging 8.8.1 Basic definitions 8.8.2 Description of chosen kriging methods 8.8.3 Data preparation for the study 8.8.4 Estimation and discussion Spatial sampling and bootstrapping 405 405 407 409 409 410 410 410 412 412 412 414 415 415 418 421 421 424 424 425 433 KATARZYNA KOPCZEWSKA AND PIOTR CWIAKOWSKI 9.1 9.2 9.3 9.4 Spatial point data - object classes and spatial aggregation Spatial sampling ֊ randomisation/generation of new points on the surface Spatial sampling - sampling of sub-samples from existing points 9.3.1 Simple sampling 9.3.2 The options of the sperrorest:: package 9.3.3 Sampling points from areas determined by the /c-means algorithm block bootstrap 9.3.4 Sampling points from moving blocks (moving block bootstrap) Use of spatial sampling and bootstrapping in cross-validation of models ### Example ### 10 Spatial big data 434 437 440
441 443 448 456 462 462 477 PIOTR WÓJCIK 10.1 Examples of big data applications 10.2 Spatial big data 10.2.1 Spatial data types 10.2.2 Challenges related to the use of spatial big data 10.2.2.1 Processing of large datasets 10.2.2.2 Mapping and reduction 10.2.2.3 Spatial data indexing 10.3 The sd:: package - simple features 10.3.1 sf class - a special data frame 10.3.2 Data with POLYGON geometry 10.3.3 Data with POINT geometry 10.3.4 Visualisation using the ggplot2:: package 10.3.5 Selected functions for spatial analysis 478 478 479 479 479 480 480 481 481 482 488 489 490 IX
Contents 10.4 Use the dplyr:: package functions 10.5 Sample analysis of large raster data 10.5.1 Measurement of economic inequalities from space 10.5.2 Analysis using the raster:: package functions 10.5.3 Other functions of the raster:: package 10.5.4 Potential alternative ֊ stars:: package 11 Spatial unsupervised learning ֊ applications of market basket analysis in geomarketing 494 505 505 507 514 515 517 ALESSANDRO FESTI 11.1 11.2 11.3 11.4 Introduction to market basket analysis Data needed in spatial market basket analysis Simulation of data The market basket analysis technique applied to geolocation data 11.5 Spatial association rules 11.6 Applications to geomarketing 11.6.1 Finding the best location for abusiness 11.6.2 Targeting 11.6.3 Discovery of competitors 11.7 Conclusions and further approaches Appendix A: Datasets used in examples A1. Dataset no. 1 / datasetl/- poviat panel data with many variables A2. Dataset no. 2 / dataset2/ - geolocated point data A3. Dataset no. 3 / dataset3/ ֊ monthly unemployment rate in poviats (NTS4) A4. Dataset no. 4 / dataset4/ - grid data for population A5. Shapefiles of contour maps - for poviats (NTS4), regions (NTS2), country (NTS0) and registration areas A6. Raster data on night light intensity onEarth in 2013 A7. Population in cities in Poland Appendix B: Links between packages References Index x 517 518 520 526 530 534 534 536 538 538 541 541 544 548 549 551 552 553 555 561 577 |
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| discipline_str_mv | Wirtschaftswissenschaften |
| format | Book |
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| genre | (DE-588)4123623-3 Lehrbuch gnd-content |
| genre_facet | Lehrbuch |
| id | DE-604.BV047071119 |
| illustrated | Illustrated |
| index_date | 2024-07-03T16:13:44Z |
| indexdate | 2024-07-10T09:01:46Z |
| institution | BVB |
| isbn | 9780367470777 9780367470760 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032478125 |
| oclc_num | 1225628493 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-188 DE-N2 DE-473 DE-BY-UBG DE-521 |
| owner_facet | DE-355 DE-BY-UBR DE-188 DE-N2 DE-473 DE-BY-UBG DE-521 |
| physical | xxv, 593 Seiten Illustrationen, Diagramme, Karten |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | Routledge |
| record_format | marc |
| series2 | Routledge advanced texts in economics and finance |
| spelling | Applied spatial statistics and econometrics data analysis in R Katarzyna Kopczewska London Routledge 2021 xxv, 593 Seiten Illustrationen, Diagramme, Karten txt rdacontent n rdamedia nc rdacarrier Routledge advanced texts in economics and finance Datenanalyse (DE-588)4123037-1 gnd rswk-swf Raumdaten (DE-588)4206012-6 gnd rswk-swf R Programm (DE-588)4705956-4 gnd rswk-swf (DE-588)4123623-3 Lehrbuch gnd-content Raumdaten (DE-588)4206012-6 s Datenanalyse (DE-588)4123037-1 s R Programm (DE-588)4705956-4 s b DE-604 Kopczewska, Katarzyna (DE-588)1142159701 edt aut Erscheint auch als Online-Ausgabe 978-1-003-03321-9 (DE-604)BV048256917 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032478125&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kopczewska, Katarzyna Applied spatial statistics and econometrics data analysis in R Datenanalyse (DE-588)4123037-1 gnd Raumdaten (DE-588)4206012-6 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4123037-1 (DE-588)4206012-6 (DE-588)4705956-4 (DE-588)4123623-3 |
| title | Applied spatial statistics and econometrics data analysis in R |
| title_auth | Applied spatial statistics and econometrics data analysis in R |
| title_exact_search | Applied spatial statistics and econometrics data analysis in R |
| title_exact_search_txtP | Applied spatial statistics and econometrics data analysis in R |
| title_full | Applied spatial statistics and econometrics data analysis in R Katarzyna Kopczewska |
| title_fullStr | Applied spatial statistics and econometrics data analysis in R Katarzyna Kopczewska |
| title_full_unstemmed | Applied spatial statistics and econometrics data analysis in R Katarzyna Kopczewska |
| title_short | Applied spatial statistics and econometrics |
| title_sort | applied spatial statistics and econometrics data analysis in r |
| title_sub | data analysis in R |
| topic | Datenanalyse (DE-588)4123037-1 gnd Raumdaten (DE-588)4206012-6 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | Datenanalyse Raumdaten R Programm Lehrbuch |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032478125&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kopczewskakatarzyna appliedspatialstatisticsandeconometricsdataanalysisinr |