Introduction to environmental data science:
Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models and more
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton
CRC Press
2023
©2023 |
| Schriftenreihe: | Chapman and Hall/CRC Data Science Ser
|
| Online-Zugang: | UBT01 URL des Erstveröffentlichers |
| Zusammenfassung: | Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models and more |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (403 Seiten) |
| ISBN: | 9781000842272 9781003317821 |
| DOI: | 10.1201/9781003317821 |
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| 100 | 1 | |a Davis, Jerry Douglas |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Introduction to environmental data science |
| 264 | 1 | |a Boca Raton |b CRC Press |c 2023 | |
| 264 | 1 | |c ©2023 | |
| 300 | |a 1 Online-Ressource (403 Seiten) | ||
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| 490 | 0 | |a Chapman and Hall/CRC Data Science Ser | |
| 500 | |a Description based on publisher supplied metadata and other sources | ||
| 505 | 8 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Author/editor biographies -- List of Figures -- 1. Background, Goals and Data -- 1.1. Environmental Data Science -- 1.2. Environmental Data and Methods -- 1.3. Goals -- 1.3.1. Some definitions: -- 1.4. Exploratory Data Analysis -- 1.5. Software and Data -- 1.5.1. Data -- 1.6. Acknowledgements -- I. Exploratory Data Analysis -- 2. Introduction to R -- 2.1. Data Objects -- 2.1.1. Scalars and assignment -- 2.2. Functions -- 2.3. Expressions and Statements -- 2.4. Data Classes -- 2.4.1. Integers -- 2.5. Rectangular Data -- 2.6. Data Structures in R -- 2.6.1. Vectors -- 2.6.2. Lists -- 2.6.3. Matrices -- 2.6.4. Data frames -- 2.6.5. Factors -- 2.7. Accessors and Subsetting -- 2.7.1. [] Subsetting -- 2.7.2. [[]] The mysterious double bracket -- 2.7.3. Accessing a vector from a data frame -- 2.8. Programming scripts in RStudio -- 2.8.1. function : creating your own -- 2.8.2. if : conditional operations -- 2.8.3. for loops -- 2.8.4. Subsetting with logic -- 2.8.5. Apply functions -- 2.9. RStudio projects -- 2.9.1. R Markdown -- 2.10. Exercises: Introduction to R -- 3. Data Abstraction -- 3.1. The Tidyverse -- 3.2. Tibbles -- 3.2.1. Building a tibble from vectors -- 3.2.2. tribble -- 3.2.3. read_csv -- 3.3. Summarizing variable distributions -- 3.3.1. Stratifying variables by site using a Tukey box plot -- 3.4. Database operations with dplyr -- 3.4.1. Select, mutate, and the pipe -- 3.4.2. filter -- 3.4.3. Writing a data frame to a csv -- 3.4.4. Summarize by group -- 3.4.5. Count -- 3.4.6. Sorting after summarizing -- 3.4.7. The dot operator -- 3.5. String abstraction -- 3.5.1. Detecting matches -- 3.5.2. Subsetting strings -- 3.5.3. String length -- 3.5.4. Replacing substrings with other text ("mutating" strings) -- 3.5.5. Concatenating and splitting | |
| 505 | 8 | |a 3.6. Dates and times with lubridate -- 3.7. Calling functions explicitly with :: -- 3.8. Exercises: Data Abstraction -- 4. Visualization -- 4.1. plot in base R -- 4.2. ggplot2 -- 4.3. Plotting one variable -- 4.3.1. Histogram -- 4.3.2. Density plot -- 4.3.3. Boxplot -- 4.4. Plotting Two Variables -- 4.4.1. Two continuous variables -- 4.4.2. Two variables, one discrete -- 4.4.3. Color systems -- 4.4.4. Trend line -- 4.5. General Symbology -- 4.5.1. Categorical symbology -- 4.5.2. Log scales instead of transform -- 4.6. Graphs from Grouped Data -- 4.6.1. Faceted graphs -- 4.7. Titles and Subtitles -- 4.8. Pairs Plot -- 4.9. Exercises: Visualization -- 5. Data Transformation -- 5.1. Data joins -- 5.2. Set operations -- 5.3. Binding rows and columns -- 5.4. Pivoting data frames -- 5.4.1. pivot_longer -- 5.4.2. pivot_wider -- 5.4.3. A free_y faceted graph using a pivot -- 5.5. Exercise: Transformation -- II. Spatial -- 6. Spatial Data and Maps -- 6.1. Spatial Data -- 6.1.1. Simple geometry building in sf -- 6.1.2. Building points from a data frame -- 6.1.3. SpatVectors in terra -- 6.1.4. Creating features from shapefiles -- 6.2. Coordinate Referencing Systems -- 6.3. Creating sf Data from Data Frames -- 6.3.1. Removing geometry -- 6.4. Base R's plot() with terra -- 6.4.1. Using maptiles to create a basemap -- 6.5. Raster data -- 6.5.1. Building rasters -- 6.5.2. Vector to raster conversion -- 6.6. ggplot2 for Maps -- 6.6.1. Rasters in ggplot2 -- 6.7. tmap -- 6.8. Interactive Maps -- 6.8.1. Leaflet -- 6.8.2. Mapview -- 6.8.3. tmap (view mode) -- 6.8.4. Interactive mapping of individual penguins abstracted from a big dataset -- 6.9. Exercises: Spatial Data and Maps -- 6.9.1. Project preparation -- 7. Spatial Analysis -- 7.1. Data Frame Operations -- 7.1.1. Using grouped summaries, and filtering by a selection -- 7.2. Spatial Analysis Operations | |
| 505 | 8 | |a 7.2.1. Using topology to subset -- 7.2.2. Centroid -- 7.2.3. Distance -- 7.2.4. Buffers -- 7.2.5. Spatial overlay: union and intersection -- 7.2.6. Clip with st_crop -- 7.2.7. Spatial join with st_join -- 7.2.8. Further exploration of spatial analysis -- 7.3. Exercises: Spatial Analysis -- 8. Raster Spatial Analysis -- 8.1. Terrain functions -- 8.2. Map Algebra in terra -- 8.3. Distance -- 8.4. Extracting Values -- 8.5. Focal Statistics -- 8.6. Zonal Statistics -- 8.7. Exercises: Raster Spatial Analysis -- 9. Spatial Interpolation -- 9.1. Null Model of the Original Data -- 9.2. Voronoi Polygon -- 9.2.1. Cross-validation and relative performance -- 9.3. Nearest Neighbor Interpolation -- 9.3.1. Cross-validation and relative performance of the nearest neighbor model -- 9.4. Inverse Distance Weighted (IDW) -- 9.4.1. Using cross-validation and relative performance to guide inverse-distance weight choice -- 9.4.2. IDW: trying other inverse distance powers -- 9.5. Polynomials and Trend Surfaces -- 9.6. Kriging -- 9.6.1. Create a variogram. -- 9.6.2. Fit the variogram based on visual interpretation -- 9.6.3. Ordinary Kriging -- 9.7. Exercises: Spatial Interpolation -- III. Statistics and Modeling -- 10. Statistical Summaries and Tests -- 10.1. Goals of Statistical Analysis -- 10.2. Summary Statistics -- 10.2.1. Summarize by group: stratifying a summary -- 10.2.2. Boxplot for visualizing distributions by group -- 10.2.3. Generating pseudorandom numbers -- 10.3. Correlation r and Coefficient of Determination r2 -- 10.3.1. Displaying correlation in a pairs plot -- 10.4. Statistical Tests -- 10.4.1. Comparing samples and groupings with a t test and a non-parametric Kruskal-Wallis Rank Sum test -- 10.4.2. Analysis of variance -- 10.4.3. Testing a correlation -- 10.5. Exercises: Statistics -- 11. Modeling -- 11.1. Some Common Statistical Models | |
| 505 | 8 | |a 11.2. Linear Model (lm) -- 11.3. Spatial Influences on Statistical Analysis -- 11.3.1. Mapping residuals -- 11.4. Analysis of Covariance -- 11.5. Generalized linear model (GLM) -- 11.5.1. Binomial family: logistic GLM with streams -- 11.5.2. Logistic landslide model -- 11.5.3. Poisson regression -- 11.5.4. Models employing machine learning -- 11.6. Exercises: Modeling -- 12. Imagery and Classification Models -- 12.1. Reading and Displaying Sentinel-2 Imagery -- 12.1.1. Individual bands -- 12.1.2. Spectral subsets to create three-band R-G-B and NIR-R-G for visualization -- 12.1.3. Crop to study area extent -- 12.1.4. Saving results -- 12.1.5. Band scatter plots -- 12.2. Spectral Profiles -- 12.3. Map Algebra and Vegetation Indices -- 12.3.1. Vegetation indices -- 12.3.2. Histogram -- 12.3.3. Other vegetation indices -- 12.4. Unsupervised Classification with k-means -- 12.5. Machine Learning Classification of Imagery -- 12.5.1. Read imagery and training data and extract sample values for training -- 12.5.2. Training the CART model -- 12.5.3. Prediction using the CART model -- 12.5.4. Validating the model -- 12.6. Classifying with 10 m Sentinel-2 Imagery -- 12.6.1. Subset bands (10 m) -- 12.6.2. Crop to RCV extent and extract pixel values -- 12.6.3. Training the CART model (10 m) and plot the tree -- 12.6.4. Prediction using the CART model (10 m) -- 12.7. Classification Using Multiple Images Capturing Phenology -- 12.7.1. Create a 10-band stack from both images -- 12.7.2. Extract the training data (10 m spring + summer) -- 12.7.3. CART model and prediction (10 m spring + summer) -- 12.8. Conclusions and Next Steps for Imagery Classification -- 12.9. Exercises: Imagery Analysis and Classification Models -- IV. Time Series -- 13. Time Series Visualization and Analysis -- 13.1. Structure, Seasonality, and Decomposition of Time Series | |
| 505 | 8 | |a 13.2. Creation of Time Series (ts) Data -- 13.2.1. Frequency, start, and end parameters for ts() -- 13.2.2. Associating times with time series -- 13.2.3. Subsetting time series by times -- 13.2.4. Changing the frequency to use a different period -- 13.2.5. Time stamps and extensible time series -- 13.3. Data smoothing: moving average (ma) -- 13.4. Decomposition of data logger data: Marble Mountains -- 13.5. Facet Graphs for Comparing Variables over Time -- 13.6. Lag Regression -- 13.6.1. The lag regression, using a lag function in a linear model -- 13.7. Ensemble Summary Statistics -- 13.8. Learning more about Time Series in R -- 13.9. Exercises: Time Series -- V. Communication and References -- 14. Communication with Shiny -- 14.1. Shiny Document -- 14.1.1. Input and output objects in the Old Faithful Eruptions document -- 14.1.2. Input widgets -- 14.1.3. Other input widgets -- 14.2. A Shiny App -- 14.2.1. A brief note on reactivity -- 14.3. Shiny App I/O Methods -- 14.3.1. Data tables -- 14.3.2. Text as character: renderPrint() and verbatimTextOutput() -- 14.3.3. Formatted text -- 14.3.4. Plots -- 14.4. Shiny App in a Package -- 14.5. Components of a Shiny App (sierra) -- 14.5.1. Initial data setup -- 14.5.2. The ui section, with a tabsetPanel structure -- 14.5.3. The server section, including reactive elements -- 14.5.4. Calling shinyApp with the ui and server function results -- 14.6. A MODIS Fire App with Web Scraping and observe with leafletProxy -- 14.6.1. Setup code -- 14.6.2. ui -- 14.6.3. Using observe and leafletProxy to allow changing the date while retaining the map zoom -- 14.7. Learn More about Shiny Apps -- 14.8. Exercises: Shiny -- References -- Index | |
| 520 | |a Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models and more | ||
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Hardcover |z 978-1-032-32218-6 |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe, Paperback |z 978-1-032-33034-1 |
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| 966 | e | |u https://doi.org/10.1201/9781003317821 |l UBT01 |p ZDB-7-TFC |q UBT_Einzelkauf_2023 |x Verlag |3 Volltext | |
Datensatz im Suchindex
| _version_ | 1804184933723275264 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Davis, Jerry Douglas |
| author_facet | Davis, Jerry Douglas |
| author_role | aut |
| author_sort | Davis, Jerry Douglas |
| author_variant | j d d jd jdd |
| building | Verbundindex |
| bvnumber | BV048831697 |
| collection | ZDB-30-PQE ZDB-7-TFC |
| contents | Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Author/editor biographies -- List of Figures -- 1. Background, Goals and Data -- 1.1. Environmental Data Science -- 1.2. Environmental Data and Methods -- 1.3. Goals -- 1.3.1. Some definitions: -- 1.4. Exploratory Data Analysis -- 1.5. Software and Data -- 1.5.1. Data -- 1.6. Acknowledgements -- I. Exploratory Data Analysis -- 2. Introduction to R -- 2.1. Data Objects -- 2.1.1. Scalars and assignment -- 2.2. Functions -- 2.3. Expressions and Statements -- 2.4. Data Classes -- 2.4.1. Integers -- 2.5. Rectangular Data -- 2.6. Data Structures in R -- 2.6.1. Vectors -- 2.6.2. Lists -- 2.6.3. Matrices -- 2.6.4. Data frames -- 2.6.5. Factors -- 2.7. Accessors and Subsetting -- 2.7.1. [] Subsetting -- 2.7.2. [[]] The mysterious double bracket -- 2.7.3. Accessing a vector from a data frame -- 2.8. Programming scripts in RStudio -- 2.8.1. function : creating your own -- 2.8.2. if : conditional operations -- 2.8.3. for loops -- 2.8.4. Subsetting with logic -- 2.8.5. Apply functions -- 2.9. RStudio projects -- 2.9.1. R Markdown -- 2.10. Exercises: Introduction to R -- 3. Data Abstraction -- 3.1. The Tidyverse -- 3.2. Tibbles -- 3.2.1. Building a tibble from vectors -- 3.2.2. tribble -- 3.2.3. read_csv -- 3.3. Summarizing variable distributions -- 3.3.1. Stratifying variables by site using a Tukey box plot -- 3.4. Database operations with dplyr -- 3.4.1. Select, mutate, and the pipe -- 3.4.2. filter -- 3.4.3. Writing a data frame to a csv -- 3.4.4. Summarize by group -- 3.4.5. Count -- 3.4.6. Sorting after summarizing -- 3.4.7. The dot operator -- 3.5. String abstraction -- 3.5.1. Detecting matches -- 3.5.2. Subsetting strings -- 3.5.3. String length -- 3.5.4. Replacing substrings with other text ("mutating" strings) -- 3.5.5. Concatenating and splitting 3.6. Dates and times with lubridate -- 3.7. Calling functions explicitly with :: -- 3.8. Exercises: Data Abstraction -- 4. Visualization -- 4.1. plot in base R -- 4.2. ggplot2 -- 4.3. Plotting one variable -- 4.3.1. Histogram -- 4.3.2. Density plot -- 4.3.3. Boxplot -- 4.4. Plotting Two Variables -- 4.4.1. Two continuous variables -- 4.4.2. Two variables, one discrete -- 4.4.3. Color systems -- 4.4.4. Trend line -- 4.5. General Symbology -- 4.5.1. Categorical symbology -- 4.5.2. Log scales instead of transform -- 4.6. Graphs from Grouped Data -- 4.6.1. Faceted graphs -- 4.7. Titles and Subtitles -- 4.8. Pairs Plot -- 4.9. Exercises: Visualization -- 5. Data Transformation -- 5.1. Data joins -- 5.2. Set operations -- 5.3. Binding rows and columns -- 5.4. Pivoting data frames -- 5.4.1. pivot_longer -- 5.4.2. pivot_wider -- 5.4.3. A free_y faceted graph using a pivot -- 5.5. Exercise: Transformation -- II. Spatial -- 6. Spatial Data and Maps -- 6.1. Spatial Data -- 6.1.1. Simple geometry building in sf -- 6.1.2. Building points from a data frame -- 6.1.3. SpatVectors in terra -- 6.1.4. Creating features from shapefiles -- 6.2. Coordinate Referencing Systems -- 6.3. Creating sf Data from Data Frames -- 6.3.1. Removing geometry -- 6.4. Base R's plot() with terra -- 6.4.1. Using maptiles to create a basemap -- 6.5. Raster data -- 6.5.1. Building rasters -- 6.5.2. Vector to raster conversion -- 6.6. ggplot2 for Maps -- 6.6.1. Rasters in ggplot2 -- 6.7. tmap -- 6.8. Interactive Maps -- 6.8.1. Leaflet -- 6.8.2. Mapview -- 6.8.3. tmap (view mode) -- 6.8.4. Interactive mapping of individual penguins abstracted from a big dataset -- 6.9. Exercises: Spatial Data and Maps -- 6.9.1. Project preparation -- 7. Spatial Analysis -- 7.1. Data Frame Operations -- 7.1.1. Using grouped summaries, and filtering by a selection -- 7.2. Spatial Analysis Operations 7.2.1. Using topology to subset -- 7.2.2. Centroid -- 7.2.3. Distance -- 7.2.4. Buffers -- 7.2.5. Spatial overlay: union and intersection -- 7.2.6. Clip with st_crop -- 7.2.7. Spatial join with st_join -- 7.2.8. Further exploration of spatial analysis -- 7.3. Exercises: Spatial Analysis -- 8. Raster Spatial Analysis -- 8.1. Terrain functions -- 8.2. Map Algebra in terra -- 8.3. Distance -- 8.4. Extracting Values -- 8.5. Focal Statistics -- 8.6. Zonal Statistics -- 8.7. Exercises: Raster Spatial Analysis -- 9. Spatial Interpolation -- 9.1. Null Model of the Original Data -- 9.2. Voronoi Polygon -- 9.2.1. Cross-validation and relative performance -- 9.3. Nearest Neighbor Interpolation -- 9.3.1. Cross-validation and relative performance of the nearest neighbor model -- 9.4. Inverse Distance Weighted (IDW) -- 9.4.1. Using cross-validation and relative performance to guide inverse-distance weight choice -- 9.4.2. IDW: trying other inverse distance powers -- 9.5. Polynomials and Trend Surfaces -- 9.6. Kriging -- 9.6.1. Create a variogram. -- 9.6.2. Fit the variogram based on visual interpretation -- 9.6.3. Ordinary Kriging -- 9.7. Exercises: Spatial Interpolation -- III. Statistics and Modeling -- 10. Statistical Summaries and Tests -- 10.1. Goals of Statistical Analysis -- 10.2. Summary Statistics -- 10.2.1. Summarize by group: stratifying a summary -- 10.2.2. Boxplot for visualizing distributions by group -- 10.2.3. Generating pseudorandom numbers -- 10.3. Correlation r and Coefficient of Determination r2 -- 10.3.1. Displaying correlation in a pairs plot -- 10.4. Statistical Tests -- 10.4.1. Comparing samples and groupings with a t test and a non-parametric Kruskal-Wallis Rank Sum test -- 10.4.2. Analysis of variance -- 10.4.3. Testing a correlation -- 10.5. Exercises: Statistics -- 11. Modeling -- 11.1. Some Common Statistical Models 11.2. Linear Model (lm) -- 11.3. Spatial Influences on Statistical Analysis -- 11.3.1. Mapping residuals -- 11.4. Analysis of Covariance -- 11.5. Generalized linear model (GLM) -- 11.5.1. Binomial family: logistic GLM with streams -- 11.5.2. Logistic landslide model -- 11.5.3. Poisson regression -- 11.5.4. Models employing machine learning -- 11.6. Exercises: Modeling -- 12. Imagery and Classification Models -- 12.1. Reading and Displaying Sentinel-2 Imagery -- 12.1.1. Individual bands -- 12.1.2. Spectral subsets to create three-band R-G-B and NIR-R-G for visualization -- 12.1.3. Crop to study area extent -- 12.1.4. Saving results -- 12.1.5. Band scatter plots -- 12.2. Spectral Profiles -- 12.3. Map Algebra and Vegetation Indices -- 12.3.1. Vegetation indices -- 12.3.2. Histogram -- 12.3.3. Other vegetation indices -- 12.4. Unsupervised Classification with k-means -- 12.5. Machine Learning Classification of Imagery -- 12.5.1. Read imagery and training data and extract sample values for training -- 12.5.2. Training the CART model -- 12.5.3. Prediction using the CART model -- 12.5.4. Validating the model -- 12.6. Classifying with 10 m Sentinel-2 Imagery -- 12.6.1. Subset bands (10 m) -- 12.6.2. Crop to RCV extent and extract pixel values -- 12.6.3. Training the CART model (10 m) and plot the tree -- 12.6.4. Prediction using the CART model (10 m) -- 12.7. Classification Using Multiple Images Capturing Phenology -- 12.7.1. Create a 10-band stack from both images -- 12.7.2. Extract the training data (10 m spring + summer) -- 12.7.3. CART model and prediction (10 m spring + summer) -- 12.8. Conclusions and Next Steps for Imagery Classification -- 12.9. Exercises: Imagery Analysis and Classification Models -- IV. Time Series -- 13. Time Series Visualization and Analysis -- 13.1. Structure, Seasonality, and Decomposition of Time Series 13.2. Creation of Time Series (ts) Data -- 13.2.1. Frequency, start, and end parameters for ts() -- 13.2.2. Associating times with time series -- 13.2.3. Subsetting time series by times -- 13.2.4. Changing the frequency to use a different period -- 13.2.5. Time stamps and extensible time series -- 13.3. Data smoothing: moving average (ma) -- 13.4. Decomposition of data logger data: Marble Mountains -- 13.5. Facet Graphs for Comparing Variables over Time -- 13.6. Lag Regression -- 13.6.1. The lag regression, using a lag function in a linear model -- 13.7. Ensemble Summary Statistics -- 13.8. Learning more about Time Series in R -- 13.9. Exercises: Time Series -- V. Communication and References -- 14. Communication with Shiny -- 14.1. Shiny Document -- 14.1.1. Input and output objects in the Old Faithful Eruptions document -- 14.1.2. Input widgets -- 14.1.3. Other input widgets -- 14.2. A Shiny App -- 14.2.1. A brief note on reactivity -- 14.3. Shiny App I/O Methods -- 14.3.1. Data tables -- 14.3.2. Text as character: renderPrint() and verbatimTextOutput() -- 14.3.3. Formatted text -- 14.3.4. Plots -- 14.4. Shiny App in a Package -- 14.5. Components of a Shiny App (sierra) -- 14.5.1. Initial data setup -- 14.5.2. The ui section, with a tabsetPanel structure -- 14.5.3. The server section, including reactive elements -- 14.5.4. Calling shinyApp with the ui and server function results -- 14.6. A MODIS Fire App with Web Scraping and observe with leafletProxy -- 14.6.1. Setup code -- 14.6.2. ui -- 14.6.3. Using observe and leafletProxy to allow changing the date while retaining the map zoom -- 14.7. Learn More about Shiny Apps -- 14.8. Exercises: Shiny -- References -- Index |
| ctrlnum | (ZDB-30-PQE)EBC7192131 (ZDB-30-PAD)EBC7192131 (ZDB-89-EBL)EBL7192131 (OCoLC)1369652260 (DE-599)BVBBV048831697 |
| doi_str_mv | 10.1201/9781003317821 |
| format | Electronic eBook |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>11037nmm a2200481zc 4500</leader><controlfield tag="001">BV048831697</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20230503 </controlfield><controlfield tag="007">cr|uuu---uuuuu</controlfield><controlfield tag="008">230224s2023 |||| o||u| ||||||eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781000842272</subfield><subfield code="9">978-1-00-084227-2</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781003317821</subfield><subfield code="c">Online</subfield><subfield code="9">978-1-003-31782-1</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1201/9781003317821</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PQE)EBC7192131</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-30-PAD)EBC7192131</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(ZDB-89-EBL)EBL7192131</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1369652260</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV048831697</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-703</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Davis, Jerry Douglas</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Introduction to environmental data science</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton</subfield><subfield code="b">CRC Press</subfield><subfield code="c">2023</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">©2023</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (403 Seiten)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Chapman and Hall/CRC Data Science Ser</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Description based on publisher supplied metadata and other sources</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Author/editor biographies -- List of Figures -- 1. Background, Goals and Data -- 1.1. Environmental Data Science -- 1.2. Environmental Data and Methods -- 1.3. Goals -- 1.3.1. Some definitions: -- 1.4. Exploratory Data Analysis -- 1.5. Software and Data -- 1.5.1. Data -- 1.6. Acknowledgements -- I. Exploratory Data Analysis -- 2. Introduction to R -- 2.1. Data Objects -- 2.1.1. Scalars and assignment -- 2.2. Functions -- 2.3. Expressions and Statements -- 2.4. Data Classes -- 2.4.1. Integers -- 2.5. Rectangular Data -- 2.6. Data Structures in R -- 2.6.1. Vectors -- 2.6.2. Lists -- 2.6.3. Matrices -- 2.6.4. Data frames -- 2.6.5. Factors -- 2.7. Accessors and Subsetting -- 2.7.1. [] Subsetting -- 2.7.2. [[]] The mysterious double bracket -- 2.7.3. Accessing a vector from a data frame -- 2.8. Programming scripts in RStudio -- 2.8.1. function : creating your own -- 2.8.2. if : conditional operations -- 2.8.3. for loops -- 2.8.4. Subsetting with logic -- 2.8.5. Apply functions -- 2.9. RStudio projects -- 2.9.1. R Markdown -- 2.10. Exercises: Introduction to R -- 3. Data Abstraction -- 3.1. The Tidyverse -- 3.2. Tibbles -- 3.2.1. Building a tibble from vectors -- 3.2.2. tribble -- 3.2.3. read_csv -- 3.3. Summarizing variable distributions -- 3.3.1. Stratifying variables by site using a Tukey box plot -- 3.4. Database operations with dplyr -- 3.4.1. Select, mutate, and the pipe -- 3.4.2. filter -- 3.4.3. Writing a data frame to a csv -- 3.4.4. Summarize by group -- 3.4.5. Count -- 3.4.6. Sorting after summarizing -- 3.4.7. The dot operator -- 3.5. String abstraction -- 3.5.1. Detecting matches -- 3.5.2. Subsetting strings -- 3.5.3. String length -- 3.5.4. Replacing substrings with other text ("mutating" strings) -- 3.5.5. Concatenating and splitting</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">3.6. Dates and times with lubridate -- 3.7. Calling functions explicitly with :: -- 3.8. Exercises: Data Abstraction -- 4. Visualization -- 4.1. plot in base R -- 4.2. ggplot2 -- 4.3. Plotting one variable -- 4.3.1. Histogram -- 4.3.2. Density plot -- 4.3.3. Boxplot -- 4.4. Plotting Two Variables -- 4.4.1. Two continuous variables -- 4.4.2. Two variables, one discrete -- 4.4.3. Color systems -- 4.4.4. Trend line -- 4.5. General Symbology -- 4.5.1. Categorical symbology -- 4.5.2. Log scales instead of transform -- 4.6. Graphs from Grouped Data -- 4.6.1. Faceted graphs -- 4.7. Titles and Subtitles -- 4.8. Pairs Plot -- 4.9. Exercises: Visualization -- 5. Data Transformation -- 5.1. Data joins -- 5.2. Set operations -- 5.3. Binding rows and columns -- 5.4. Pivoting data frames -- 5.4.1. pivot_longer -- 5.4.2. pivot_wider -- 5.4.3. A free_y faceted graph using a pivot -- 5.5. Exercise: Transformation -- II. Spatial -- 6. Spatial Data and Maps -- 6.1. Spatial Data -- 6.1.1. Simple geometry building in sf -- 6.1.2. Building points from a data frame -- 6.1.3. SpatVectors in terra -- 6.1.4. Creating features from shapefiles -- 6.2. Coordinate Referencing Systems -- 6.3. Creating sf Data from Data Frames -- 6.3.1. Removing geometry -- 6.4. Base R's plot() with terra -- 6.4.1. Using maptiles to create a basemap -- 6.5. Raster data -- 6.5.1. Building rasters -- 6.5.2. Vector to raster conversion -- 6.6. ggplot2 for Maps -- 6.6.1. Rasters in ggplot2 -- 6.7. tmap -- 6.8. Interactive Maps -- 6.8.1. Leaflet -- 6.8.2. Mapview -- 6.8.3. tmap (view mode) -- 6.8.4. Interactive mapping of individual penguins abstracted from a big dataset -- 6.9. Exercises: Spatial Data and Maps -- 6.9.1. Project preparation -- 7. Spatial Analysis -- 7.1. Data Frame Operations -- 7.1.1. Using grouped summaries, and filtering by a selection -- 7.2. Spatial Analysis Operations</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">7.2.1. Using topology to subset -- 7.2.2. Centroid -- 7.2.3. Distance -- 7.2.4. Buffers -- 7.2.5. Spatial overlay: union and intersection -- 7.2.6. Clip with st_crop -- 7.2.7. Spatial join with st_join -- 7.2.8. Further exploration of spatial analysis -- 7.3. Exercises: Spatial Analysis -- 8. Raster Spatial Analysis -- 8.1. Terrain functions -- 8.2. Map Algebra in terra -- 8.3. Distance -- 8.4. Extracting Values -- 8.5. Focal Statistics -- 8.6. Zonal Statistics -- 8.7. Exercises: Raster Spatial Analysis -- 9. Spatial Interpolation -- 9.1. Null Model of the Original Data -- 9.2. Voronoi Polygon -- 9.2.1. Cross-validation and relative performance -- 9.3. Nearest Neighbor Interpolation -- 9.3.1. Cross-validation and relative performance of the nearest neighbor model -- 9.4. Inverse Distance Weighted (IDW) -- 9.4.1. Using cross-validation and relative performance to guide inverse-distance weight choice -- 9.4.2. IDW: trying other inverse distance powers -- 9.5. Polynomials and Trend Surfaces -- 9.6. Kriging -- 9.6.1. Create a variogram. -- 9.6.2. Fit the variogram based on visual interpretation -- 9.6.3. Ordinary Kriging -- 9.7. Exercises: Spatial Interpolation -- III. Statistics and Modeling -- 10. Statistical Summaries and Tests -- 10.1. Goals of Statistical Analysis -- 10.2. Summary Statistics -- 10.2.1. Summarize by group: stratifying a summary -- 10.2.2. Boxplot for visualizing distributions by group -- 10.2.3. Generating pseudorandom numbers -- 10.3. Correlation r and Coefficient of Determination r2 -- 10.3.1. Displaying correlation in a pairs plot -- 10.4. Statistical Tests -- 10.4.1. Comparing samples and groupings with a t test and a non-parametric Kruskal-Wallis Rank Sum test -- 10.4.2. Analysis of variance -- 10.4.3. Testing a correlation -- 10.5. Exercises: Statistics -- 11. Modeling -- 11.1. Some Common Statistical Models</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">11.2. Linear Model (lm) -- 11.3. Spatial Influences on Statistical Analysis -- 11.3.1. Mapping residuals -- 11.4. Analysis of Covariance -- 11.5. Generalized linear model (GLM) -- 11.5.1. Binomial family: logistic GLM with streams -- 11.5.2. Logistic landslide model -- 11.5.3. Poisson regression -- 11.5.4. Models employing machine learning -- 11.6. Exercises: Modeling -- 12. Imagery and Classification Models -- 12.1. Reading and Displaying Sentinel-2 Imagery -- 12.1.1. Individual bands -- 12.1.2. Spectral subsets to create three-band R-G-B and NIR-R-G for visualization -- 12.1.3. Crop to study area extent -- 12.1.4. Saving results -- 12.1.5. Band scatter plots -- 12.2. Spectral Profiles -- 12.3. Map Algebra and Vegetation Indices -- 12.3.1. Vegetation indices -- 12.3.2. Histogram -- 12.3.3. Other vegetation indices -- 12.4. Unsupervised Classification with k-means -- 12.5. Machine Learning Classification of Imagery -- 12.5.1. Read imagery and training data and extract sample values for training -- 12.5.2. Training the CART model -- 12.5.3. Prediction using the CART model -- 12.5.4. Validating the model -- 12.6. Classifying with 10 m Sentinel-2 Imagery -- 12.6.1. Subset bands (10 m) -- 12.6.2. Crop to RCV extent and extract pixel values -- 12.6.3. Training the CART model (10 m) and plot the tree -- 12.6.4. Prediction using the CART model (10 m) -- 12.7. Classification Using Multiple Images Capturing Phenology -- 12.7.1. Create a 10-band stack from both images -- 12.7.2. Extract the training data (10 m spring + summer) -- 12.7.3. CART model and prediction (10 m spring + summer) -- 12.8. Conclusions and Next Steps for Imagery Classification -- 12.9. Exercises: Imagery Analysis and Classification Models -- IV. Time Series -- 13. Time Series Visualization and Analysis -- 13.1. Structure, Seasonality, and Decomposition of Time Series</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">13.2. Creation of Time Series (ts) Data -- 13.2.1. Frequency, start, and end parameters for ts() -- 13.2.2. Associating times with time series -- 13.2.3. Subsetting time series by times -- 13.2.4. Changing the frequency to use a different period -- 13.2.5. Time stamps and extensible time series -- 13.3. Data smoothing: moving average (ma) -- 13.4. Decomposition of data logger data: Marble Mountains -- 13.5. Facet Graphs for Comparing Variables over Time -- 13.6. Lag Regression -- 13.6.1. The lag regression, using a lag function in a linear model -- 13.7. Ensemble Summary Statistics -- 13.8. Learning more about Time Series in R -- 13.9. Exercises: Time Series -- V. Communication and References -- 14. Communication with Shiny -- 14.1. Shiny Document -- 14.1.1. Input and output objects in the Old Faithful Eruptions document -- 14.1.2. Input widgets -- 14.1.3. Other input widgets -- 14.2. A Shiny App -- 14.2.1. A brief note on reactivity -- 14.3. Shiny App I/O Methods -- 14.3.1. Data tables -- 14.3.2. Text as character: renderPrint() and verbatimTextOutput() -- 14.3.3. Formatted text -- 14.3.4. Plots -- 14.4. Shiny App in a Package -- 14.5. Components of a Shiny App (sierra) -- 14.5.1. Initial data setup -- 14.5.2. The ui section, with a tabsetPanel structure -- 14.5.3. The server section, including reactive elements -- 14.5.4. Calling shinyApp with the ui and server function results -- 14.6. A MODIS Fire App with Web Scraping and observe with leafletProxy -- 14.6.1. Setup code -- 14.6.2. ui -- 14.6.3. Using observe and leafletProxy to allow changing the date while retaining the map zoom -- 14.7. Learn More about Shiny Apps -- 14.8. Exercises: Shiny -- References -- Index</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models and more</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Hardcover</subfield><subfield code="z">978-1-032-32218-6</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Paperback</subfield><subfield code="z">978-1-032-33034-1</subfield></datafield><datafield tag="856" ind1="4" ind2="0"><subfield code="u">https://doi.org/10.1201/9781003317821</subfield><subfield code="x">Verlag</subfield><subfield code="z">URL des Erstveröffentlichers</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield><subfield code="a">ZDB-7-TFC</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-034097275</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://doi.org/10.1201/9781003317821</subfield><subfield code="l">UBT01</subfield><subfield code="p">ZDB-7-TFC</subfield><subfield code="q">UBT_Einzelkauf_2023</subfield><subfield code="x">Verlag</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV048831697 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T21:35:29Z |
| indexdate | 2024-07-10T09:47:13Z |
| institution | BVB |
| isbn | 9781000842272 9781003317821 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034097275 |
| oclc_num | 1369652260 |
| open_access_boolean | |
| owner | DE-703 |
| owner_facet | DE-703 |
| physical | 1 Online-Ressource (403 Seiten) |
| psigel | ZDB-30-PQE ZDB-7-TFC ZDB-7-TFC UBT_Einzelkauf_2023 |
| publishDate | 2023 |
| publishDateSearch | 2023 |
| publishDateSort | 2023 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Chapman and Hall/CRC Data Science Ser |
| spelling | Davis, Jerry Douglas Verfasser aut Introduction to environmental data science Boca Raton CRC Press 2023 ©2023 1 Online-Ressource (403 Seiten) txt rdacontent c rdamedia cr rdacarrier Chapman and Hall/CRC Data Science Ser Description based on publisher supplied metadata and other sources Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Author/editor biographies -- List of Figures -- 1. Background, Goals and Data -- 1.1. Environmental Data Science -- 1.2. Environmental Data and Methods -- 1.3. Goals -- 1.3.1. Some definitions: -- 1.4. Exploratory Data Analysis -- 1.5. Software and Data -- 1.5.1. Data -- 1.6. Acknowledgements -- I. Exploratory Data Analysis -- 2. Introduction to R -- 2.1. Data Objects -- 2.1.1. Scalars and assignment -- 2.2. Functions -- 2.3. Expressions and Statements -- 2.4. Data Classes -- 2.4.1. Integers -- 2.5. Rectangular Data -- 2.6. Data Structures in R -- 2.6.1. Vectors -- 2.6.2. Lists -- 2.6.3. Matrices -- 2.6.4. Data frames -- 2.6.5. Factors -- 2.7. Accessors and Subsetting -- 2.7.1. [] Subsetting -- 2.7.2. [[]] The mysterious double bracket -- 2.7.3. Accessing a vector from a data frame -- 2.8. Programming scripts in RStudio -- 2.8.1. function : creating your own -- 2.8.2. if : conditional operations -- 2.8.3. for loops -- 2.8.4. Subsetting with logic -- 2.8.5. Apply functions -- 2.9. RStudio projects -- 2.9.1. R Markdown -- 2.10. Exercises: Introduction to R -- 3. Data Abstraction -- 3.1. The Tidyverse -- 3.2. Tibbles -- 3.2.1. Building a tibble from vectors -- 3.2.2. tribble -- 3.2.3. read_csv -- 3.3. Summarizing variable distributions -- 3.3.1. Stratifying variables by site using a Tukey box plot -- 3.4. Database operations with dplyr -- 3.4.1. Select, mutate, and the pipe -- 3.4.2. filter -- 3.4.3. Writing a data frame to a csv -- 3.4.4. Summarize by group -- 3.4.5. Count -- 3.4.6. Sorting after summarizing -- 3.4.7. The dot operator -- 3.5. String abstraction -- 3.5.1. Detecting matches -- 3.5.2. Subsetting strings -- 3.5.3. String length -- 3.5.4. Replacing substrings with other text ("mutating" strings) -- 3.5.5. Concatenating and splitting 3.6. Dates and times with lubridate -- 3.7. Calling functions explicitly with :: -- 3.8. Exercises: Data Abstraction -- 4. Visualization -- 4.1. plot in base R -- 4.2. ggplot2 -- 4.3. Plotting one variable -- 4.3.1. Histogram -- 4.3.2. Density plot -- 4.3.3. Boxplot -- 4.4. Plotting Two Variables -- 4.4.1. Two continuous variables -- 4.4.2. Two variables, one discrete -- 4.4.3. Color systems -- 4.4.4. Trend line -- 4.5. General Symbology -- 4.5.1. Categorical symbology -- 4.5.2. Log scales instead of transform -- 4.6. Graphs from Grouped Data -- 4.6.1. Faceted graphs -- 4.7. Titles and Subtitles -- 4.8. Pairs Plot -- 4.9. Exercises: Visualization -- 5. Data Transformation -- 5.1. Data joins -- 5.2. Set operations -- 5.3. Binding rows and columns -- 5.4. Pivoting data frames -- 5.4.1. pivot_longer -- 5.4.2. pivot_wider -- 5.4.3. A free_y faceted graph using a pivot -- 5.5. Exercise: Transformation -- II. Spatial -- 6. Spatial Data and Maps -- 6.1. Spatial Data -- 6.1.1. Simple geometry building in sf -- 6.1.2. Building points from a data frame -- 6.1.3. SpatVectors in terra -- 6.1.4. Creating features from shapefiles -- 6.2. Coordinate Referencing Systems -- 6.3. Creating sf Data from Data Frames -- 6.3.1. Removing geometry -- 6.4. Base R's plot() with terra -- 6.4.1. Using maptiles to create a basemap -- 6.5. Raster data -- 6.5.1. Building rasters -- 6.5.2. Vector to raster conversion -- 6.6. ggplot2 for Maps -- 6.6.1. Rasters in ggplot2 -- 6.7. tmap -- 6.8. Interactive Maps -- 6.8.1. Leaflet -- 6.8.2. Mapview -- 6.8.3. tmap (view mode) -- 6.8.4. Interactive mapping of individual penguins abstracted from a big dataset -- 6.9. Exercises: Spatial Data and Maps -- 6.9.1. Project preparation -- 7. Spatial Analysis -- 7.1. Data Frame Operations -- 7.1.1. Using grouped summaries, and filtering by a selection -- 7.2. Spatial Analysis Operations 7.2.1. Using topology to subset -- 7.2.2. Centroid -- 7.2.3. Distance -- 7.2.4. Buffers -- 7.2.5. Spatial overlay: union and intersection -- 7.2.6. Clip with st_crop -- 7.2.7. Spatial join with st_join -- 7.2.8. Further exploration of spatial analysis -- 7.3. Exercises: Spatial Analysis -- 8. Raster Spatial Analysis -- 8.1. Terrain functions -- 8.2. Map Algebra in terra -- 8.3. Distance -- 8.4. Extracting Values -- 8.5. Focal Statistics -- 8.6. Zonal Statistics -- 8.7. Exercises: Raster Spatial Analysis -- 9. Spatial Interpolation -- 9.1. Null Model of the Original Data -- 9.2. Voronoi Polygon -- 9.2.1. Cross-validation and relative performance -- 9.3. Nearest Neighbor Interpolation -- 9.3.1. Cross-validation and relative performance of the nearest neighbor model -- 9.4. Inverse Distance Weighted (IDW) -- 9.4.1. Using cross-validation and relative performance to guide inverse-distance weight choice -- 9.4.2. IDW: trying other inverse distance powers -- 9.5. Polynomials and Trend Surfaces -- 9.6. Kriging -- 9.6.1. Create a variogram. -- 9.6.2. Fit the variogram based on visual interpretation -- 9.6.3. Ordinary Kriging -- 9.7. Exercises: Spatial Interpolation -- III. Statistics and Modeling -- 10. Statistical Summaries and Tests -- 10.1. Goals of Statistical Analysis -- 10.2. Summary Statistics -- 10.2.1. Summarize by group: stratifying a summary -- 10.2.2. Boxplot for visualizing distributions by group -- 10.2.3. Generating pseudorandom numbers -- 10.3. Correlation r and Coefficient of Determination r2 -- 10.3.1. Displaying correlation in a pairs plot -- 10.4. Statistical Tests -- 10.4.1. Comparing samples and groupings with a t test and a non-parametric Kruskal-Wallis Rank Sum test -- 10.4.2. Analysis of variance -- 10.4.3. Testing a correlation -- 10.5. Exercises: Statistics -- 11. Modeling -- 11.1. Some Common Statistical Models 11.2. Linear Model (lm) -- 11.3. Spatial Influences on Statistical Analysis -- 11.3.1. Mapping residuals -- 11.4. Analysis of Covariance -- 11.5. Generalized linear model (GLM) -- 11.5.1. Binomial family: logistic GLM with streams -- 11.5.2. Logistic landslide model -- 11.5.3. Poisson regression -- 11.5.4. Models employing machine learning -- 11.6. Exercises: Modeling -- 12. Imagery and Classification Models -- 12.1. Reading and Displaying Sentinel-2 Imagery -- 12.1.1. Individual bands -- 12.1.2. Spectral subsets to create three-band R-G-B and NIR-R-G for visualization -- 12.1.3. Crop to study area extent -- 12.1.4. Saving results -- 12.1.5. Band scatter plots -- 12.2. Spectral Profiles -- 12.3. Map Algebra and Vegetation Indices -- 12.3.1. Vegetation indices -- 12.3.2. Histogram -- 12.3.3. Other vegetation indices -- 12.4. Unsupervised Classification with k-means -- 12.5. Machine Learning Classification of Imagery -- 12.5.1. Read imagery and training data and extract sample values for training -- 12.5.2. Training the CART model -- 12.5.3. Prediction using the CART model -- 12.5.4. Validating the model -- 12.6. Classifying with 10 m Sentinel-2 Imagery -- 12.6.1. Subset bands (10 m) -- 12.6.2. Crop to RCV extent and extract pixel values -- 12.6.3. Training the CART model (10 m) and plot the tree -- 12.6.4. Prediction using the CART model (10 m) -- 12.7. Classification Using Multiple Images Capturing Phenology -- 12.7.1. Create a 10-band stack from both images -- 12.7.2. Extract the training data (10 m spring + summer) -- 12.7.3. CART model and prediction (10 m spring + summer) -- 12.8. Conclusions and Next Steps for Imagery Classification -- 12.9. Exercises: Imagery Analysis and Classification Models -- IV. Time Series -- 13. Time Series Visualization and Analysis -- 13.1. Structure, Seasonality, and Decomposition of Time Series 13.2. Creation of Time Series (ts) Data -- 13.2.1. Frequency, start, and end parameters for ts() -- 13.2.2. Associating times with time series -- 13.2.3. Subsetting time series by times -- 13.2.4. Changing the frequency to use a different period -- 13.2.5. Time stamps and extensible time series -- 13.3. Data smoothing: moving average (ma) -- 13.4. Decomposition of data logger data: Marble Mountains -- 13.5. Facet Graphs for Comparing Variables over Time -- 13.6. Lag Regression -- 13.6.1. The lag regression, using a lag function in a linear model -- 13.7. Ensemble Summary Statistics -- 13.8. Learning more about Time Series in R -- 13.9. Exercises: Time Series -- V. Communication and References -- 14. Communication with Shiny -- 14.1. Shiny Document -- 14.1.1. Input and output objects in the Old Faithful Eruptions document -- 14.1.2. Input widgets -- 14.1.3. Other input widgets -- 14.2. A Shiny App -- 14.2.1. A brief note on reactivity -- 14.3. Shiny App I/O Methods -- 14.3.1. Data tables -- 14.3.2. Text as character: renderPrint() and verbatimTextOutput() -- 14.3.3. Formatted text -- 14.3.4. Plots -- 14.4. Shiny App in a Package -- 14.5. Components of a Shiny App (sierra) -- 14.5.1. Initial data setup -- 14.5.2. The ui section, with a tabsetPanel structure -- 14.5.3. The server section, including reactive elements -- 14.5.4. Calling shinyApp with the ui and server function results -- 14.6. A MODIS Fire App with Web Scraping and observe with leafletProxy -- 14.6.1. Setup code -- 14.6.2. ui -- 14.6.3. Using observe and leafletProxy to allow changing the date while retaining the map zoom -- 14.7. Learn More about Shiny Apps -- 14.8. Exercises: Shiny -- References -- Index Introduction to Environmental Data Science focuses on data science methods in the R language applied to environmental research, with sections on exploratory data analysis in R including data abstraction, transformation, and visualization; spatial data analysis in vector and raster models and more Erscheint auch als Druck-Ausgabe, Hardcover 978-1-032-32218-6 Erscheint auch als Druck-Ausgabe, Paperback 978-1-032-33034-1 https://doi.org/10.1201/9781003317821 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Davis, Jerry Douglas Introduction to environmental data science Cover -- Half Title -- Title Page -- Copyright Page -- Contents -- Author/editor biographies -- List of Figures -- 1. Background, Goals and Data -- 1.1. Environmental Data Science -- 1.2. Environmental Data and Methods -- 1.3. Goals -- 1.3.1. Some definitions: -- 1.4. Exploratory Data Analysis -- 1.5. Software and Data -- 1.5.1. Data -- 1.6. Acknowledgements -- I. Exploratory Data Analysis -- 2. Introduction to R -- 2.1. Data Objects -- 2.1.1. Scalars and assignment -- 2.2. Functions -- 2.3. Expressions and Statements -- 2.4. Data Classes -- 2.4.1. Integers -- 2.5. Rectangular Data -- 2.6. Data Structures in R -- 2.6.1. Vectors -- 2.6.2. Lists -- 2.6.3. Matrices -- 2.6.4. Data frames -- 2.6.5. Factors -- 2.7. Accessors and Subsetting -- 2.7.1. [] Subsetting -- 2.7.2. [[]] The mysterious double bracket -- 2.7.3. Accessing a vector from a data frame -- 2.8. Programming scripts in RStudio -- 2.8.1. function : creating your own -- 2.8.2. if : conditional operations -- 2.8.3. for loops -- 2.8.4. Subsetting with logic -- 2.8.5. Apply functions -- 2.9. RStudio projects -- 2.9.1. R Markdown -- 2.10. Exercises: Introduction to R -- 3. Data Abstraction -- 3.1. The Tidyverse -- 3.2. Tibbles -- 3.2.1. Building a tibble from vectors -- 3.2.2. tribble -- 3.2.3. read_csv -- 3.3. Summarizing variable distributions -- 3.3.1. Stratifying variables by site using a Tukey box plot -- 3.4. Database operations with dplyr -- 3.4.1. Select, mutate, and the pipe -- 3.4.2. filter -- 3.4.3. Writing a data frame to a csv -- 3.4.4. Summarize by group -- 3.4.5. Count -- 3.4.6. Sorting after summarizing -- 3.4.7. The dot operator -- 3.5. String abstraction -- 3.5.1. Detecting matches -- 3.5.2. Subsetting strings -- 3.5.3. String length -- 3.5.4. Replacing substrings with other text ("mutating" strings) -- 3.5.5. Concatenating and splitting 3.6. Dates and times with lubridate -- 3.7. Calling functions explicitly with :: -- 3.8. Exercises: Data Abstraction -- 4. Visualization -- 4.1. plot in base R -- 4.2. ggplot2 -- 4.3. Plotting one variable -- 4.3.1. Histogram -- 4.3.2. Density plot -- 4.3.3. Boxplot -- 4.4. Plotting Two Variables -- 4.4.1. Two continuous variables -- 4.4.2. Two variables, one discrete -- 4.4.3. Color systems -- 4.4.4. Trend line -- 4.5. General Symbology -- 4.5.1. Categorical symbology -- 4.5.2. Log scales instead of transform -- 4.6. Graphs from Grouped Data -- 4.6.1. Faceted graphs -- 4.7. Titles and Subtitles -- 4.8. Pairs Plot -- 4.9. Exercises: Visualization -- 5. Data Transformation -- 5.1. Data joins -- 5.2. Set operations -- 5.3. Binding rows and columns -- 5.4. Pivoting data frames -- 5.4.1. pivot_longer -- 5.4.2. pivot_wider -- 5.4.3. A free_y faceted graph using a pivot -- 5.5. Exercise: Transformation -- II. Spatial -- 6. Spatial Data and Maps -- 6.1. Spatial Data -- 6.1.1. Simple geometry building in sf -- 6.1.2. Building points from a data frame -- 6.1.3. SpatVectors in terra -- 6.1.4. Creating features from shapefiles -- 6.2. Coordinate Referencing Systems -- 6.3. Creating sf Data from Data Frames -- 6.3.1. Removing geometry -- 6.4. Base R's plot() with terra -- 6.4.1. Using maptiles to create a basemap -- 6.5. Raster data -- 6.5.1. Building rasters -- 6.5.2. Vector to raster conversion -- 6.6. ggplot2 for Maps -- 6.6.1. Rasters in ggplot2 -- 6.7. tmap -- 6.8. Interactive Maps -- 6.8.1. Leaflet -- 6.8.2. Mapview -- 6.8.3. tmap (view mode) -- 6.8.4. Interactive mapping of individual penguins abstracted from a big dataset -- 6.9. Exercises: Spatial Data and Maps -- 6.9.1. Project preparation -- 7. Spatial Analysis -- 7.1. Data Frame Operations -- 7.1.1. Using grouped summaries, and filtering by a selection -- 7.2. Spatial Analysis Operations 7.2.1. Using topology to subset -- 7.2.2. Centroid -- 7.2.3. Distance -- 7.2.4. Buffers -- 7.2.5. Spatial overlay: union and intersection -- 7.2.6. Clip with st_crop -- 7.2.7. Spatial join with st_join -- 7.2.8. Further exploration of spatial analysis -- 7.3. Exercises: Spatial Analysis -- 8. Raster Spatial Analysis -- 8.1. Terrain functions -- 8.2. Map Algebra in terra -- 8.3. Distance -- 8.4. Extracting Values -- 8.5. Focal Statistics -- 8.6. Zonal Statistics -- 8.7. Exercises: Raster Spatial Analysis -- 9. Spatial Interpolation -- 9.1. Null Model of the Original Data -- 9.2. Voronoi Polygon -- 9.2.1. Cross-validation and relative performance -- 9.3. Nearest Neighbor Interpolation -- 9.3.1. Cross-validation and relative performance of the nearest neighbor model -- 9.4. Inverse Distance Weighted (IDW) -- 9.4.1. Using cross-validation and relative performance to guide inverse-distance weight choice -- 9.4.2. IDW: trying other inverse distance powers -- 9.5. Polynomials and Trend Surfaces -- 9.6. Kriging -- 9.6.1. Create a variogram. -- 9.6.2. Fit the variogram based on visual interpretation -- 9.6.3. Ordinary Kriging -- 9.7. Exercises: Spatial Interpolation -- III. Statistics and Modeling -- 10. Statistical Summaries and Tests -- 10.1. Goals of Statistical Analysis -- 10.2. Summary Statistics -- 10.2.1. Summarize by group: stratifying a summary -- 10.2.2. Boxplot for visualizing distributions by group -- 10.2.3. Generating pseudorandom numbers -- 10.3. Correlation r and Coefficient of Determination r2 -- 10.3.1. Displaying correlation in a pairs plot -- 10.4. Statistical Tests -- 10.4.1. Comparing samples and groupings with a t test and a non-parametric Kruskal-Wallis Rank Sum test -- 10.4.2. Analysis of variance -- 10.4.3. Testing a correlation -- 10.5. Exercises: Statistics -- 11. Modeling -- 11.1. Some Common Statistical Models 11.2. Linear Model (lm) -- 11.3. Spatial Influences on Statistical Analysis -- 11.3.1. Mapping residuals -- 11.4. Analysis of Covariance -- 11.5. Generalized linear model (GLM) -- 11.5.1. Binomial family: logistic GLM with streams -- 11.5.2. Logistic landslide model -- 11.5.3. Poisson regression -- 11.5.4. Models employing machine learning -- 11.6. Exercises: Modeling -- 12. Imagery and Classification Models -- 12.1. Reading and Displaying Sentinel-2 Imagery -- 12.1.1. Individual bands -- 12.1.2. Spectral subsets to create three-band R-G-B and NIR-R-G for visualization -- 12.1.3. Crop to study area extent -- 12.1.4. Saving results -- 12.1.5. Band scatter plots -- 12.2. Spectral Profiles -- 12.3. Map Algebra and Vegetation Indices -- 12.3.1. Vegetation indices -- 12.3.2. Histogram -- 12.3.3. Other vegetation indices -- 12.4. Unsupervised Classification with k-means -- 12.5. Machine Learning Classification of Imagery -- 12.5.1. Read imagery and training data and extract sample values for training -- 12.5.2. Training the CART model -- 12.5.3. Prediction using the CART model -- 12.5.4. Validating the model -- 12.6. Classifying with 10 m Sentinel-2 Imagery -- 12.6.1. Subset bands (10 m) -- 12.6.2. Crop to RCV extent and extract pixel values -- 12.6.3. Training the CART model (10 m) and plot the tree -- 12.6.4. Prediction using the CART model (10 m) -- 12.7. Classification Using Multiple Images Capturing Phenology -- 12.7.1. Create a 10-band stack from both images -- 12.7.2. Extract the training data (10 m spring + summer) -- 12.7.3. CART model and prediction (10 m spring + summer) -- 12.8. Conclusions and Next Steps for Imagery Classification -- 12.9. Exercises: Imagery Analysis and Classification Models -- IV. Time Series -- 13. Time Series Visualization and Analysis -- 13.1. Structure, Seasonality, and Decomposition of Time Series 13.2. Creation of Time Series (ts) Data -- 13.2.1. Frequency, start, and end parameters for ts() -- 13.2.2. Associating times with time series -- 13.2.3. Subsetting time series by times -- 13.2.4. Changing the frequency to use a different period -- 13.2.5. Time stamps and extensible time series -- 13.3. Data smoothing: moving average (ma) -- 13.4. Decomposition of data logger data: Marble Mountains -- 13.5. Facet Graphs for Comparing Variables over Time -- 13.6. Lag Regression -- 13.6.1. The lag regression, using a lag function in a linear model -- 13.7. Ensemble Summary Statistics -- 13.8. Learning more about Time Series in R -- 13.9. Exercises: Time Series -- V. Communication and References -- 14. Communication with Shiny -- 14.1. Shiny Document -- 14.1.1. Input and output objects in the Old Faithful Eruptions document -- 14.1.2. Input widgets -- 14.1.3. Other input widgets -- 14.2. A Shiny App -- 14.2.1. A brief note on reactivity -- 14.3. Shiny App I/O Methods -- 14.3.1. Data tables -- 14.3.2. Text as character: renderPrint() and verbatimTextOutput() -- 14.3.3. Formatted text -- 14.3.4. Plots -- 14.4. Shiny App in a Package -- 14.5. Components of a Shiny App (sierra) -- 14.5.1. Initial data setup -- 14.5.2. The ui section, with a tabsetPanel structure -- 14.5.3. The server section, including reactive elements -- 14.5.4. Calling shinyApp with the ui and server function results -- 14.6. A MODIS Fire App with Web Scraping and observe with leafletProxy -- 14.6.1. Setup code -- 14.6.2. ui -- 14.6.3. Using observe and leafletProxy to allow changing the date while retaining the map zoom -- 14.7. Learn More about Shiny Apps -- 14.8. Exercises: Shiny -- References -- Index |
| title | Introduction to environmental data science |
| title_auth | Introduction to environmental data science |
| title_exact_search | Introduction to environmental data science |
| title_exact_search_txtP | Introduction to environmental data science |
| title_full | Introduction to environmental data science |
| title_fullStr | Introduction to environmental data science |
| title_full_unstemmed | Introduction to environmental data science |
| title_short | Introduction to environmental data science |
| title_sort | introduction to environmental data science |
| url | https://doi.org/10.1201/9781003317821 |
| work_keys_str_mv | AT davisjerrydouglas introductiontoenvironmentaldatascience |