Difference between revisions of "ISSS608 2016-17 T3 Assign Chan En Ying Grace Methodology"
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<b>Data Understanding</b> | <b>Data Understanding</b> | ||
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− | <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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− | <b> | + | <b>ii. Find out structure of Raster Layer</b> |
<br> Extent : 40000 | <br> Extent : 40000 | ||
<br> CRS arguments : NA | <br> CRS arguments : NA | ||
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<b>Data Cleaning</b> | <b>Data Cleaning</b> | ||
|| | || | ||
− | <b> | + | <b>i. Import two CSV Files (Birds)</b> |
* 2081 Training Birds (Metadata) | * 2081 Training Birds (Metadata) | ||
* 15 Test Birds (Provided by Kasios) | * 15 Test Birds (Provided by Kasios) | ||
− | <b> | + | <b>ii. Fix Data Quality Issues</b> |
* Change File ID from numeric to character | * Change File ID from numeric to character | ||
* Change coordinates to numeric | * Change coordinates to numeric | ||
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− | <b> | + | <b>iii. Data Manipulation</b> |
* Extract out the “Year” and “Month” from the date, as new columns | * Extract out the “Year” and “Month” from the date, as new columns | ||
* 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) | ||
− | <b> | + | <b>iv. Geospatial File Compatibility</b> |
* Convert CSV file (2081 birds) into the following: | * Convert CSV file (2081 birds) into the following: | ||
** spatial point data frame | ** spatial point data frame | ||
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− | <b> | + | <b>v. Data Overview & Exploration</b> |
* Overlay 2081 Birds, Raster Map & Dumping Site, for an integrated overview using `plot()` | * Overlay 2081 Birds, Raster Map & Dumping Site, for an integrated overview using `plot()` | ||
* Use `facet_wrap` to identify location of clustering across species, across time, and across season, and by call/song | * Use `facet_wrap` to identify location of clustering across species, across time, and across season, and by call/song | ||
− | <b> | + | <b>vi. Segregation of Treatment & Control Groups</b> |
* Use ‘Rose Pipits’ as Treatment Group | * Use ‘Rose Pipits’ as Treatment Group | ||
* Use ‘Ordinary Snape’ and ‘Lesse Birchbeere’ as Control Groups | * Use ‘Ordinary Snape’ and ‘Lesse Birchbeere’ as Control Groups | ||
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<b>Geospatial Visualisation </b> | <b>Geospatial Visualisation </b> | ||
|| | || | ||
− | <b>Spatial Point Pattern Visualisation (Density-Based Measure) </b> | + | <b><u>Spatial Point Pattern Visualisation (Density-Based Measure) </u></b> |
− | <b> | + | <b>i. Prepare polygon layer </b> |
* Create a 200x200 spatial polygon to depict the boundaries of Lekagul raster map | * Create a 200x200 spatial polygon to depict the boundaries of Lekagul raster map | ||
* Merge Raster Polygon with Rose Pipit Layer, using `owin` from spatstat package | * Merge Raster Polygon with Rose Pipit Layer, using `owin` from spatstat package | ||
− | <b> | + | |
+ | <b>ii. Kernel Density Plot </b> | ||
* First, set sigma=bw.diggle | * First, set sigma=bw.diggle | ||
* Apply the Kernel Density Plot (By Year; 2012-2017) | * Apply the Kernel Density Plot (By Year; 2012-2017) | ||
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** For OS & LB only (Control Groups) | ** For OS & LB only (Control Groups) | ||
− | <b> | + | |
+ | <b>iii. Adjust Parameters (sigma) </b> | ||
* Adjust the plots by using the sigma of the most dense cluster | * Adjust the plots by using the sigma of the most dense cluster | ||
** This is typically the largest sigma | ** This is typically the largest sigma | ||
+ | |||
+ | <b>iv. Fine-Tune for Clearer Visualisation </b> | ||
+ | * Then add in the dumping site & adjust the colour/size | ||
+ | * So that we can visualize the clusters relative to the dumping site | ||
+ | |- | ||
+ | | | ||
+ | 4. | ||
+ | || | ||
+ | <b>Statistical Confirmation </b> | ||
+ | || | ||
+ | <b><u>Spatial Point Pattern Analysis (Distance-Based Measure)</u></b> | ||
+ | |||
+ | <b>i. Quadrat Analysis </b> | ||
+ | * Apply Monti-Carlo Simulation | ||
+ | * Followed by Quadrat Test to test for clustering | ||
+ | |||
+ | |||
+ | <b>ii. K-Nearest Neighbour </b> | ||
+ | * Apply Monti-Carlo Simulation | ||
+ | * Followed by Clark-Evans Test to test for clustering | ||
+ | |||
+ | |||
+ | <b>iii. K-Function </b> | ||
+ | * Apply Monti-Carlo simulation | ||
+ | * Visualise significance based on grey band | ||
|} | |} | ||
</div> | </div> | ||
<br> | <br> |
Revision as of 21:18, 22 June 2018
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Tools
R is the primary tool used in this analysis. The following lists the packages used for the project’s scope - for data cleaning, data visualisation, geospatial analysis and audio processing.
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Approach Taken
The following outlines the approach used for the analysis.
Step |
Approach |
Description |
1. |
Data Understanding |
i. Read in Raster Layer (Lekagul Roadways Map)
class : RasterLayer
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2. |
Data Cleaning |
i. Import two CSV Files (Birds)
iii. Data Manipulation
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3. |
Geospatial Visualisation |
Spatial Point Pattern Visualisation (Density-Based Measure) i. Prepare polygon layer
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4. |
Statistical Confirmation |
Spatial Point Pattern Analysis (Distance-Based Measure) i. Quadrat Analysis
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