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| − | <b>Data Understanding</b> | + | <b>Boonsong Lekagul waterways readings</b> |
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| | <b>i. Read in Raster Layer (Lekagul Roadways Map)</b> | | <b>i. Read in Raster Layer (Lekagul Roadways Map)</b> |
| | * It is a single layer raster file. 200x200. | | * It is a single layer raster file. 200x200. |
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| − | class : RasterLayer
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| − | <br> dimensions : 200, 200, 40000 (nrow, ncol, ncell)
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| − | <br> resolution : 1, 1 (x, y)
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| − | <br> extent : 0, 200, 0, 200 (xmin, xmax, ymin, ymax)
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| − | <br> coord. ref. : NA
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| − | <br> names : Lekagul_Roadways_2018
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| − | <br> values : 0, 255 (min, max)
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| | <b>ii. Find out structure of Raster Layer</b> | | <b>ii. Find out structure of Raster Layer</b> |
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| − | <b>Data Cleaning</b> | + | <b>chemical units of measure</b> |
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| | <b>i. Import two CSV Files (Birds)</b> | | <b>i. Import two CSV Files (Birds)</b> |
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| | * Create a new column for Quarter (Q1,Q2,Q3,Q4) & Season (Spring, Summer, Fall, Winter) | | * Create a new column for Quarter (Q1,Q2,Q3,Q4) & Season (Spring, Summer, Fall, Winter) |
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| − | <b>iv. Geospatial File Compatibility</b>
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| − | * Convert CSV file (2081 birds) into the following:
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| − | ** spatial point data frame
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| − | ** sp format
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| − | ** shp format
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| − | ** st_read compatible format
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| − | ** readOGR compatible format
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| − | ** ppp format (for spatstat compatibility)
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| − |
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| − | <b>v. Data Overview & Exploration</b>
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| − | * Overlay 2081 Birds, Raster Map & Dumping Site, for an integrated overview using plot()
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| − | * Use facet_wrap() to visualise location of clustering across species, across time, and across season, and by call/song in a trellis plot
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| − |
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| − | <b>vi. Selection of Treatment & Control Groups</b>
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| − | * Use ‘Rose Pipits’ as Treatment Group
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| − | * Use ‘Ordinary Snape’ and ‘Lesser Birchbeere’ as Control Groups to see if dumping were the cause
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| − | * Use ‘All Birds’ as third control to see if external factors were the cause
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| − | |-
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| − | <b>Geospatial Visualisation </b>
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| − | <b><u>Spatial Point Pattern Visualisation</u></b>
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| − | <b>i. Prepare polygon layer </b>
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| − | * Create a 200x200 spatial polygon to depict the boundaries of Lekagul raster map
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| − | * Merge Raster Polygon with Rose Pipit Layer, using owin() from spatstat package
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| − |
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| − |
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| − | <b>ii. Kernel Density Plot </b>
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| − | * First, set sigma=bw.diggle (Uses cross-validation to select a smoothing bandwidth for the kernel estimation of point process intensity)
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| − | * Apply the Kernel Density Plot (By Year; 2012-2017)
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| − | ** For All Birds
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| − | ** For Rose Pipits only (Treatment Group)
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| − | ** For OS & LB only (Control Groups)
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| − |
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| − |
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| − | <b>iii. Adjust Parameters (sigma) </b>
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| − | * Adjust the plots by using the sigma of the most dense cluster
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| − | ** This is typically the largest sigma
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| − |
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| − |
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| − | <b>iv. Fine-Tune for Clearer Visualisation </b>
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| − | * Then add in the dumping site & adjust the colour/size
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| − | * So that we can visualize the clusters relative to the dumping site
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| − | |-
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| − | |
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| − | <b>Statistical Confirmation </b>
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| − | <b><u>Spatial Point Pattern Analysis & Cluster Confirmation </u></b>
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| − | <b>i. Quadrat Analysis (Density Based Measure) </b>
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| − | * Apply Monti-Carlo Simulation
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| − | * Followed by Quadrat Test to test for clustering
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| − |
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| − |
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| − | <b>ii. Nearest Neighbour (Density Based Measure) </b>
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| − | * Apply Monti-Carlo Simulation
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| − | * Followed by Clark-Evans Test to test for clustering
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| − |
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| − |
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| − | <b>iii. K-Function (Distance Based Measure) </b>
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| − | * Apply Monti-Carlo simulation
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| − | * Visualise significance based on confidence envelope
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| − |
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| − |
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| − | <b>iv. K-Cross (for bivariate analysis) </b>
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| − | * Apply Monti-Carlo simulation
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| − | * Visualise spatial dependence (significance) based on confidence envelope
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| − |
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| − | |-
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| − | |
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| − | <b>Audio Processing</b>
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| − | <b>i. Data Preparation </b>
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| − | * Read in MP3 Files (Training & Testing Data)
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| − | * Convert to .wav format using writeWav()
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| − | * Convert .wav files to data frame using analyzeFolder()
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| − | * Read in data frame
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| − |
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| − |
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| − | <b>ii. Audio Extraction & Manipulation </b>
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| − | * Extract only 1 of 2 channels (choose left).
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| − | * Convert each sound array to floating point values ranging from -1 to 1.
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| − |
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| − |
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| − | <b>iii. Adjust Parameters (sigma) </b>
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| − | * Adjust the plots by using the sigma of the most dense cluster
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| − | ** This is typically the largest sigma
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| − |
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| − |
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| − | <b>iv. Fine-Tune for Clearer Visualisation </b>
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| − | * Then add in the dumping site & adjust the colour/size
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| − | * So that we can visualize the clusters relative to the dumping site
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| − | |-
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| − | |
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| − | <b>Audio Visualisation</b>
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| − | <b>i. Amplitude Envelope Plot</b>
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| − | * Use env() to plot the envelopes of the amplitude plots
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| − | * Do this for all the 15 test birds
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| − | * Do this for 5 training birds per species and select most representative plot as ‘dictionary’
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| − |
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| − |
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| − | <b>ii. Oscillogram Plot</b>
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| − | * Use seewave package to plot oscillogram
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| − | * Do this for all the 15 test birds
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| − | * Do this for 5 of the training birds, per species and select most representative plot as ‘dictionary’
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| − |
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| − |
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| − | <b>iii. Distribution of audio parameters, using Trellis Plot</b>
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| − | * Run analyzeFolder() from soundgen library to the entire collections to extract dataframe from .wav files
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| − | * Find the acoustic parameters that are particularly relevant i.e. mean>standard deviation
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| − | * Out of the 15 <b>median</b> (mean and sd not used) attributes available after extracting the dataframe, the following 7 will be used for analysis as they produced greatest variation across species:
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| − | ** dom_median,HNR_median, meanFreq_median, peakFreq_median, pitch_median, pitchAutocor_median, pitchSpec_median
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| − | * These were selected as they vary across the species more, than the other of the 8 variables
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| − | * Use ggplot() to plot a trellis plot using the 19 training species
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| − | * Label the mean using black solid line
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| − | * Use ggplot() to insert the 15 testing birds, with blue dotted line as the testing bird's mean
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| − | * Visualise and identify the top 3 closest species to the mean, by parameter
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| − | * Select the species based on most no. of parameters selected as closest
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| − | |-
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| − | <b>Audio Classification</b>
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| − | <b>i. Decision Tree</b>
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| − | * Use rpart, caret and e1071 libraries
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| − | * Using the extracted dataframe with analyzeFolder(), out of the 2081 birds, set 70% as training data, 30% as validation data
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| − | * Build decision tree model using training data. Evaluate misclassification rate.
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| − | * Apply model to 15 testing birds to predict the species
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| − | <b>ii. Random Forest</b>
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| − | * Use randomForest library to create a Random Forest model with default parameters
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| − | * Then we will fine tune the model by changing 'mtry'
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| − | * We can tune the random forest model by changing the number of trees (ntree) and the number of variables randomly sampled at each stage (mtry).
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| − | ** Ntree: Number of trees to grow. This should not be set to too small a number, to ensure that every input row gets predicted at least a few times.
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| − | ** Mtry: Number of variables randomly sampled as candidates at each split. Default value for classification is sqrt(p) where p is number of variables in x.
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| − | * Evaluate the RF's misclassification rate, with the Decision Tree
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| − | * Compare with visualisation plots, to see if the prediction matches
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| | | | |
| | |} | | |} |
| | </div> | | </div> |
VAST Challenge: Mini Challenge 2
