Beginner's Tutorial to GRASS GIS in Python - Revision history

Collin Godsell: /* Conclusion */

2023-12-22T21:07:43Z

Conclusion

Collin Godsell at 20:56, 22 December 2023

2023-12-22T20:56:37Z

Collin Godsell: /* Results */

2023-12-22T20:56:11Z

Results

Collin Godsell: /* Results */

2023-12-22T20:45:06Z

Results

Collin Godsell: /* Sample Analysis */

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Sample Analysis

Collin Godsell: /* Sample Analysis */

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Sample Analysis

Collin Godsell: /* Sample Analysis */

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Sample Analysis

Collin Godsell: /* Introduction & Purpose */

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Introduction & Purpose

Collin Godsell: /* Introduction & Purpose */

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Introduction & Purpose

Collin Godsell: /* Results */

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Results

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 ==Conclusion==
+* To conclude this tutorial I would like to thank all participants or tutorial follower's for choosing this tutorial as an introduction to Python in the realm of GIS using entirely open source software and data. We have covered a lot of content in a condensed format over the span of the tutorial, as well as skipped many frustrating troubleshooting moments on my end when creating this resource. We have briefly covered the usefulness behind an IDE and a virtual environment and why this is a good place to start when embarking on an introductory Python journey. Next, we outlined using the IDE for some simple mathematical commands such as creating an example dataset with numpy and visually representing this dataset (sine wave) using matplotlib.
+*After gaining a brief foundation on Python, Spyder and the nature of both, we moved on to creating a few scripts for execution within GRASS. Remember when creating your own scripts do not be discouraged by error messages as there will bound to be small and slight errors that happen on your part, the computer's part and miscommunication between both! When creating your own scripts I advise consulting the programmers manual I linked in the beginning of the tutorial which I will link again for convenience ([https://grass.osgeo.org/grass83/manuals/libpython/script_intro.html]). I consulted this manual for how to get started using '''grass.script''' as there are a few differences between regular python and grass.script. In addition see the other Grass manuals for more specific resources such as function parameters!
+*Finally, after some troubleshooting and a semi-steep learning curve, we were able to export our scripts from Spyder, navigate to GRASS GIS and launch them using the Python Script Launcher. It is useful to note that even once we have achieved this step there is still a possibility for errors within the GIS and the code, and most commonly they stem from what you name the files, so be cautious and stay consistent! Throughout the entire tutorial process we learned the values of keeping an organized workspace when programming, the values of efficiency and interoperability with open source software and data, as well as the deeply rooted foundation that GIS (especially GRASS) finds in coding languages such as Python, as well as what unique capabilities they hold over using the GUI (functions such as r.univar or vectstatistics).
+*I hope you found use in this tutorial and are comfortable starting your Python-GRASS journey!

← Older revision		Revision as of 20:56, 22 December 2023
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	*Note the values are returned on the left hand side, and were completed in 57 seconds, which is much more efficient than manually attempting to calculate each statistic. An added benefit to this is that the code is easy to reference and find in compassion to designing a methodology to effectively handle the task of computing the average elevation as well as the standard error of the surface!		*Note the values are returned on the left hand side, and were completed in 57 seconds, which is much more efficient than manually attempting to calculate each statistic. An added benefit to this is that the code is easy to reference and find in compassion to designing a methodology to effectively handle the task of computing the average elevation as well as the standard error of the surface!

			==Conclusion==

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 [[File:OutputforScript1.png|frame|center|This image is the visual output of running the first script. ]]
+'''Note''' the selection of the colour table is crucial in visually representing your data. This is displayed in the difference between the output of script 1 and 2 where I modify the colour table.
+: '''3.''' The output of script 2 will yield the user a new polygon shapefile of all the selected areas that are connected to the cycling network. The script ranks them by shade of green based on how well connected they are (how much of the network fell within the polygon).
+[[File:VectorOutput.png|frame|center|The output of script 2, the vector analysis]]
+'''Note''' the difference in colours between the cycling network in script 1 and 2 in your own analysis (my photos are not contingent).
+: '''4''' Finally, the output of script 3 will yield the user two values outlining the average elevation values as well as the standard error of the DEM providing accuracy to the elevation attribute under focus.
+[[File:RasterAnalysisOutput1.png|frame|center|The output of the raster analysis script ]]
+*Note the values are returned on the left hand side, and were completed in 57 seconds, which is much more efficient than manually attempting to calculate each statistic. An added benefit to this is that the code is easy to reference and find in compassion to designing a methodology to effectively handle the task of computing the average elevation as well as the standard error of the surface!

@@ Line 198: / Line 198: @@
 [[File:Importpanel.png|frame|center|Displaying the layers created via script ]]
+: '''2.''' Upon adding all of the created layers from the first script, the user will see the visual representations of their data displayed in "Map Display 1".
+[[File:OutputforScript1.png|frame|center|This image is the visual output of running the first script. ]]

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 [[File:RasterAnalysis1.png|frame|center|The code I scripted for the raster analysis portion]]
+* In the raster analysis code I have used the '''r.univar''' function which allows the users to derive the descriptive statistics of a raster using only one variable. The benefit to using a command such as r.univar rather than manually deriving the statistics is the efficiency at which they are calculated and how easy it is to get them.
+'''Note''' I include a section for standard error as analyzing the accuracy of the attributes you wish to express is just as important as expressing the attributes themselves. It is imperative to always state or seek out any forms of uncertainty in your data and/or analysis.
 ==Execution==

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 * '''Furthermore''' I created a script for a simple Raster Analysis that makes use of statistical analysis rather than a visual representation. One of the few advantages to loading a python script in GRASS rather than manually is the efficiency in querying and calculating statistics of layers and datasets. The Raster analysis highlights the efficiency of python when calculating a value such as average elevation or standard error of the surface. For the purpose of this tutorial I only show you the methods to calculate the average elevation but it is encouraged to expand upon your knowledge with the resources/documentation provided.
-[[File:RasterAnalysis.png|frame|left|A simple analysis calculating the average elevation of the study zone ]]
+[[File:RasterAnalysis1.png|frame|center|The code I scripted for the raster analysis portion]]
 ==Execution==

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 * '''Next''' I created a script that joins the boundary polygon and the cycling network shapefile to create a polygon layer that only has polygons that intersect with a cycling network (which is all of them). Next, I take the newly created '''selected_zones''' and use it as an area input for the next command which counts the number of bike_repair_stations that fall within the polygons.
-*To accomplish this analysis which visualizes the sectors of Ottawa with access to bike repair shops, I make use of the v.select command which selects all polygons that intersect with the cycling network layer. Next, I create a new layer using the selected features, and I take this layer and make use of v.vect.stats function. This is a very useful function that allows the users to make statistical analysis, such as count used here, with an input point layer and an area layer (polygon).
+*To accomplish this analysis which visualizes the sectors of Ottawa with access to bike repair shops, I make use of the v.select command which selects all polygons that intersect with the cycling network layer. Next, I create a new layer using the selected features, and I take this layer and make use of '''v.vect.stats function'''. This is a very useful function that allows the users to make statistical analysis, such as count used here, with an input point layer and an area layer (polygon).
-*Lastly I make use of v.colours to adjust the colour table of each raster layer to give it more meaningful and classified results. '''Pay close attention to the parameters as it describes 'modifiers' of the function'''
+*Lastly I make use of '''v.colours''' to adjust the colour table of each raster layer to give it more meaningful and classified results. '''Pay close attention to the parameters as it describes 'modifiers' of the function'''
 [[File:VectorAnalysis.png|frame|center|The code used to perform the simple Vector Analysis]]

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 Throughout my brief years of study at Carleton University in the Geomatics discipline, I have noticed a shift towards a more ‘user-friendly’ focus in developing GIS tools. A great example of this initiative is the recent updates done to GRASS GIS with GRASS GIS83 improving the user GUI exceptionally with a single panel housing all the tools rather than many separate windows.
-This apparent shift away from the traditional coding roots of GIS to a GUI dominant interface comes with upsides, downsides as well as an entirely new range of audience members. The focus of this tutorial is to explore the simple use of Python scripts written in Python 83 (8.3.10) using an open source IDE, in GRASS GIS83 using the pre-installed grass.script package in GRASS. The tutorial will consist of a few simple analyses using data from a few sources such as Open Ottawa and Ontario Geohub to demonstrate the capabilities of using GRASS with script rather than individual tools and the time it saves a user, especially when completing repetitive tasks. I am to highlight the importance of efficiency of processing as well as the additional avenues opened by interoperability. When I say this I mean unique commands that are available to you when using Python rather than just the GUI.
+This apparent shift away from the traditional coding roots of GIS to a GUI dominant interface comes with upsides, downsides as well as an entirely new range of audience members. The focus of this tutorial is to explore the simple use of Python scripts written in Python 83 (8.3.10) using an open source IDE, in GRASS GIS83 using the pre-installed grass.script package in GRASS. The tutorial will consist of a few simple analyses using data from a few open data sources such as Open Ottawa and USGS's Earth Explorer to demonstrate the capabilities of using GRASS with script rather than individual tools and the time it saves a user, especially when completing repetitive tasks. I am to highlight the importance of efficiency of processing as well as the additional avenues opened by interoperability. When I say this I mean unique commands that are available to you when using Python rather than just the GUI.
 From my personal experience the hardest part to kick-start your GIS coding experience is finding the resources and knowing where to start. This tutorial aims to fill the gaps in knowledge of a beginner GIS user looking to use code in GIS through a user friendly method available in GRASS. Therefore, the audience to this tutorial is marketed at beginner users that know the basic working principles of GIS, and are looking for a taste of scripting in there analysis process.

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 ==Results==
-: '''1.''' The results of the first operation (the import and clip) are the importing of the 3 data layers into the GIS.
+: '''1.''' The results of the first operation (the import and clip) are the importing of the 3 data layers into the GIS and are displayed in the centre screen after you;ve added them in the layers tab. To display your results navigate to the layers tab and select '''add multiple raster or vector layers'''. This will open a menu that allows you to select the layers you wish to add, having a separate section for raster and vector layers.
+[[File:Importpanel.png|frame|center|Displaying the layers created via script ]]