Analytical data visualizations at ICC 2017 - Revision history

Annakrat at 18:10, 14 September 2020

2020-09-14T18:10:03Z

← Older revision		Revision as of 18:10, 14 September 2020
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	We start the workshop with a [https://ncsu-geoforall-lab.github.io/ICC-workshop-presentation/index.html#/ presentation] about the overview of geospatial tools used in this workshop.		We start the workshop with a [https://ncsu-geoforall-lab.github.io/ICC-workshop-presentation/index.html#/ presentation] about the overview of geospatial tools used in this workshop.

			Note: Screenshots of GUI may be outdated, however the rest of the tutorial is updated to work with GRASS GIS 7.8.

	== Data ==		== Data ==

Annakrat: /* Importing data */

2020-01-21T18:54:58Z

Importing data

← Older revision		Revision as of 18:54, 21 January 2020
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	==== Importing data ====		==== Importing data ====
	[[File:Import_raster_7.2.1.png\|400px\|thumb\|right\|Import raster data]]		[[File:Import_raster_7.2.1.png\|400px\|thumb\|right\|Import raster data]]
	In this step we import the provided data into GRASS GIS. In menu ''File - Import raster data'' select ''Common formats import'' and in the dialog browse to find <tt>dsm.tif</tt> and click button ''Import''. Repeat for raster file <tt>ortho.tif</tt>.		In this step we import the provided data into GRASS GIS. In menu ''File - Import raster data'' select ''Common formats import'' and in the dialog browse to find <tt>dsm.tif</tt> and click button ''Import''. Repeat for raster file <tt>ground.tif</tt> and <tt>ortho.tif</tt>.

	Similarly, go to ''File - Import vector data - Common formats import'' and import files <tt>roads.geojson</tt> and <tt>buildings.geojson</tt>. '''Note that in this case we need to change the output name from default <tt>OGRGeoJSON</tt> to <tt>roads</tt> and <tt>buildings</tt>, respectively.''' This vector data has different CRS, so a dialog informing you about the need to reproject the data appears and we confirm it.		Similarly, go to ''File - Import vector data - Common formats import'' and import files <tt>roads.geojson</tt> and <tt>buildings.geojson</tt>. '''Note that in this case we need to change the output name from default <tt>OGRGeoJSON</tt> to <tt>roads</tt> and <tt>buildings</tt>, respectively.''' This vector data has different CRS, so a dialog informing you about the need to reproject the data appears and we confirm it.

Annakrat: /* Session 2: Scripting of rendering in GRASS GIS */ gscript to gs

2020-01-21T18:46:24Z

Session 2: Scripting of rendering in GRASS GIS: gscript to gs

← Older revision		Revision as of 18:46, 21 January 2020
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	#!/usr/bin/env python		#!/usr/bin/env python

	import grass.script as ~~gscript~~		import grass.script as gs

	viewsheds = ~~gscript~~.list_strings(type=['raster'], pattern='viewshed*')		viewsheds = gs.list_strings(type=['raster'], pattern='viewshed*')

	for viewshed in viewsheds:		for viewshed in viewsheds:
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	filename = '{}.png'.format(viewshed.split('@')[0])		filename = '{}.png'.format(viewshed.split('@')[0])
	# begin rendering		# begin rendering
	~~gscript~~.run_command('d.mon', start='cairo', output=filename)		gs.run_command('d.mon', start='cairo', output=filename)
	# rendering commands		# rendering commands
	~~gscript~~.run_command('d.rast', map='ortho')		gs.run_command('d.rast', map='ortho')
	~~gscript~~.run_command('d.vect', map='umstead_drive', color='232:232:232', width=3)		gs.run_command('d.vect', map='umstead_drive', color='232:232:232', width=3)
	~~gscript~~.run_command('d.rast', map=viewshed)		gs.run_command('d.rast', map=viewshed)
	# end rendering		# end rendering
	~~gscript~~.run_command('d.mon', stop='cairo')		gs.run_command('d.mon', stop='cairo')
	</source>		</source>

Annakrat: /* Session 1: Batch processing using Python */ compute dsm - dem

2020-01-21T18:45:35Z

Session 1: Batch processing using Python: compute dsm - dem

← Older revision		Revision as of 18:45, 21 January 2020
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	We will use the difference between the DSM and ground elevation to filter out points that happen to lie on some vegetation covering the road. This is to avoid computing viewsheds from the top of a tree,		We will use the difference between the DSM and ground elevation to filter out points that happen to lie on some vegetation covering the road. This is to avoid computing viewsheds from the top of a tree,
	which would significantly distort viewshed size.		which would significantly distort viewshed size.

			r.mapcalc "diff = dsm - ground"
			r.colors map=diff color=differences

	From these viewsheds we can then easily compute ''Cumulative viewshed'' (how much a place is visible).		From these viewsheds we can then easily compute ''Cumulative viewshed'' (how much a place is visible).

Annakrat: /* Session 1: Introduction to scripting in GRASS GIS with Python */ gscript to gs

2020-01-21T18:43:52Z

Session 1: Introduction to scripting in GRASS GIS with Python: gscript to gs

← Older revision		Revision as of 18:43, 21 January 2020
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	It starts with importing GRASS GIS Python Scripting Library:		It starts with importing GRASS GIS Python Scripting Library:
	<source lang="python">		<source lang="python">
	import grass.script as ~~gscript~~		import grass.script as gs
	</source>		</source>

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	<source lang="python">		<source lang="python">
	~~gscript~~.run_command('g.region', flags='p')		gs.run_command('g.region', flags='p')
	</source>		</source>

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	<source lang="python">		<source lang="python">
	~~gscript~~.run_command('g.region', raster='dsm')		gs.run_command('g.region', raster='dsm')
	</source>		</source>

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	<source lang="python">		<source lang="python">
	~~gscript~~.run_command('r.viewshed', input='dsm', output='python_viewshed', coordinates=(640645, 224035), overwrite=True)		gs.run_command('r.viewshed', input='dsm', output='python_viewshed', coordinates=(640645, 224035), overwrite=True)
	</source>		</source>

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	<source lang="python">		<source lang="python">
	univar = ~~gscript~~.parse_command('r.univar', map='python_viewshed', flags='g')		univar = gs.parse_command('r.univar', map='python_viewshed', flags='g')
	print univar['n']		print(univar['n'])
	</source>		</source>

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	GRASS GIS Python Scripting Library also provides several wrapper functions for often called modules. List of convenient wrapper functions with examples includes:		GRASS GIS Python Scripting Library also provides several wrapper functions for often called modules. List of convenient wrapper functions with examples includes:
	* Raster metadata using {{pyapi\|script\|script.raster\|raster_info}}: <code>~~gscript~~.raster_info('viewshed_python')</code>		* Raster metadata using {{pyapi\|script\|script.raster\|raster_info}}: <code>gs.raster_info('viewshed_python')</code>
	* Vector metadata using {{pyapi\|script\|script.vector\|vector_info}}: <code>~~gscript~~.vector_info('roads')</code>		* Vector metadata using {{pyapi\|script\|script.vector\|vector_info}}: <code>gs.vector_info('roads')</code>
	* List raster data in current location using {{pyapi\|script\|script.core\|list_grouped}}: <code>~~gscript~~.list_grouped(type=['raster'])</code>		* List raster data in current location using {{pyapi\|script\|script.core\|list_grouped}}: <code>gs.list_grouped(type=['raster'])</code>
	* Get current computational region using {{pyapi\|script\|script.core\|region}}: <code>~~gscript~~.region()</code>		* Get current computational region using {{pyapi\|script\|script.core\|region}}: <code>gs.region()</code>

	Here are two commands (to be executed in the ''Console'') often used when working with scripts. First is setting the computational region. We can do that in a script, but it is better and more general to do it before executing the script (so that the script can be used with different computational region settings):		Here are two commands (to be executed in the ''Console'') often used when working with scripts. First is setting the computational region. We can do that in a script, but it is better and more general to do it before executing the script (so that the script can be used with different computational region settings):

Annakrat: /* Session 1: Batch processing using Python */ fix viewshed computing on top of a tree

2020-01-21T18:42:36Z

Session 1: Batch processing using Python: fix viewshed computing on top of a tree

← Older revision		Revision as of 18:42, 21 January 2020
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	We compute viewshed from each point using a Python script.		We compute viewshed from each point using a Python script.
			We will use the difference between the DSM and ground elevation to filter out points that happen to lie on some vegetation covering the road. This is to avoid computing viewsheds from the top of a tree,
			which would significantly distort viewshed size.

	From these viewsheds we can then easily compute ''Cumulative viewshed'' (how much a place is visible).		From these viewsheds we can then easily compute ''Cumulative viewshed'' (how much a place is visible).
	For that we will use module {{cmd\|r.series}}, which overlays the viewshed rasters and computes how many times each cell is visible in all the rasters.		For that we will use module {{cmd\|r.series}}, which overlays the viewshed rasters and computes how many times each cell is visible in all the rasters.
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	<source lang="python">		<source lang="python">
	#!/usr/bin/env ~~python~~		#!/usr/bin/env python3

	import grass.script as ~~gscript~~		import grass.script as gs

	def main():		def main():
	# obtain the coordinates of viewpoints		# obtain the coordinates of viewpoints
	viewpoints = ~~gscript~~.read_command('v.out.ascii', input='viewpoints',		viewpoints = gs.read_command('v.out.ascii', input='viewpoints',
	separator='comma', layer=2).strip()		separator='comma', layer=2).strip()
	# loop through the viewpoints and compute viewshed from each of them		# loop through the viewpoints and compute viewshed from each of them
	for point in viewpoints.splitlines():		for point in viewpoints.splitlines():
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	continue		continue
	x, y, cat = point.split(',')		x, y, cat = point.split(',')
	~~gscript~~.run_command('r.viewshed', input='dsm', output='viewshed' + cat,		diff = gs.raster_what(map='diff', coord=(float(x), float(y)))[0]['diff']['value']
	coordinates=(x, y), max_distance=300, overwrite=True)		if float(diff) > 2:
			continue
			gs.run_command('r.viewshed', input='dsm', output='viewshed' + cat,
			coordinates=(x, y), max_distance=300, overwrite=True)
	# obtain all viewshed results and set their color to yellow		# obtain all viewshed results and set their color to yellow
	# export the list of viewshed map names to a file		# export the list of viewshed map names to a file
	maps_file = 'viewsheds.txt'		maps_file = 'viewsheds.txt'
	~~gscript~~.run_command('g.list', type='raster', pattern='viewshed*', output=maps_file)		gs.run_command('g.list', type='raster', pattern='viewshed*', output=maps_file)
	~~gscript~~.write_command('r.colors', file=maps_file, rules='-',		gs.write_command('r.colors', file=maps_file, rules='-',
	stdin='0% yellow \n 100% yellow')		stdin='0% yellow \n 100% yellow')
	# cumulative viewshed		# cumulative viewshed
	~~gscript~~.run_command('r.series', file='viewsheds.txt', output='cumulative_viewshed', method='count')		gs.run_command('r.series', file='viewsheds.txt', output='cumulative_viewshed', method='count')
	# set color of cumulative viewshed from grey to yellow		# set color of cumulative viewshed from grey to yellow
	~~gscript~~.write_command('r.colors', map='cumulative_viewshed', rules='-',		gs.write_command('r.colors', map='cumulative_viewshed', rules='-',
	stdin='0% 70:70:70 \n 100% yellow')		stdin='0% 70:70:70 \n 100% yellow')

	if __name__ == '__main__':		if __name__ == '__main__':

⚠️Wenzeslaus: fix non-ascii quotes in Overpass query

2017-11-02T03:02:20Z

fix non-ascii quotes in Overpass query

← Older revision		Revision as of 03:02, 2 November 2017
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	// gather results		// gather results
	(		(
	// query part for: ~~“highway~~=*”		// query part for: "highway=*"
	way["highway"](35.76599,-78.66249,35.77230,-78.65261);		way["highway"](35.76599,-78.66249,35.77230,-78.65261);
	);		);
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	// gather results		// gather results
	(		(
	// query part for: ~~“building~~=*”		// query part for: "building=*"
	way["building"](35.76599,-78.66249,35.77260,-78.65261);		way["building"](35.76599,-78.66249,35.77260,-78.65261);
	relation["building"](35.76599,-78.66249,35.77260,-78.65261);		relation["building"](35.76599,-78.66249,35.77260,-78.65261);

⚠️Wenzeslaus: /* GRASS GIS modules overview */

2017-07-18T17:00:19Z

GRASS GIS modules overview

← Older revision		Revision as of 17:00, 18 July 2017
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	=== GRASS GIS modules overview ===		=== GRASS GIS modules overview ===

	One of the advantages of GRASS is the diversity and number of modules that let you analyze all manners of spatial and temporal data. GRASS GIS has over [https://grass.osgeo.org/grass72/manuals/full_index.html 500 different modules}} in the core distribution and over [https://grass.osgeo.org/grass72/manuals/addons/ 300 addon modules] that can be used to prepare and analyze data.		One of the advantages of GRASS is the diversity and number of modules that let you analyze all manners of spatial and temporal data. GRASS GIS has over [https://grass.osgeo.org/grass72/manuals/full_index.html 500 different modules] in the core distribution and over [https://grass.osgeo.org/grass72/manuals/addons/ 300 addon modules] that can be used to prepare and analyze data.

	GRASS functionality is available through ''modules'' (tools, functions). Modules respect the following naming conventions:		GRASS functionality is available through ''modules'' (tools, functions). Modules respect the following naming conventions:

⚠️Wenzeslaus: /* Software */

2017-07-18T02:58:09Z

Software

← Older revision		Revision as of 02:58, 18 July 2017
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	''' Linux '''		''' Linux '''

	~~Install GRASS GIS from packages:~~
	~~<pre>~~
	~~sudo add-apt-repository ppa:ubuntugis/ubuntugis-unstable~~
	~~sudo apt-get update~~
	~~sudo apt-get install grass~~
	~~</pre>~~

	For other Linux distributions other then Ubuntu, please try to find GRASS GIS in their package managers.		For other Linux distributions other then Ubuntu, please try to find GRASS GIS in their package managers.

⚠️Wenzeslaus: two 2017 in the workshop name

2017-07-12T02:27:15Z

two 2017 in the workshop name

← Older revision		Revision as of 02:27, 12 July 2017
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	[[File:Blender-plain.png\|300px\|right\|none]]		[[File:Blender-plain.png\|300px\|right\|none]]

	This is material for two sessions at the [[GRASS GIS at ICC 2017\|ICC 2017]] SDI-Open ~~2017~~ (Spatial data infrastructures, standards, open source and open data for geospatial) workshop called ''Analytical data visualizations with GRASS GIS and Blender'' and ''Mapping open data with open source geospatial tools'' held in Washington, DC, July 1-2, 2017. These two sessions introduce GRASS GIS and Blender, example of their processing capabilities and visualization techniques. Participants interactively visualize open data and design maps with several open source geospatial tools including Tangible Landscape system.		This is material for two sessions at the [[GRASS GIS at ICC 2017\|ICC 2017]] SDI-Open (Spatial data infrastructures, standards, open source and open data for geospatial) workshop called ''Analytical data visualizations with GRASS GIS and Blender'' and ''Mapping open data with open source geospatial tools'' held in Washington, DC, July 1-2, 2017. These two sessions introduce GRASS GIS and Blender, example of their processing capabilities and visualization techniques. Participants interactively visualize open data and design maps with several open source geospatial tools including Tangible Landscape system.

	Authors: [[User:Wenzeslaus\|Vaclav Petras]], [[User:Annakrat\|Anna Petrasova]], Payam Tabrizian, Brendan Harmon, and Helena Mitasova		Authors: [[User:Wenzeslaus\|Vaclav Petras]], [[User:Annakrat\|Anna Petrasova]], Payam Tabrizian, Brendan Harmon, and Helena Mitasova

← Older revision		Revision as of 02:27, 12 July 2017
Line 3:		Line 3:
	[[File:Blender-plain.png\|300px\|right\|none]]		[[File:Blender-plain.png\|300px\|right\|none]]

	This is material for two sessions at the [[GRASS GIS at ICC 2017\|ICC 2017]] SDI-Open ~~2017~~ (Spatial data infrastructures, standards, open source and open data for geospatial) workshop called ''Analytical data visualizations with GRASS GIS and Blender'' and ''Mapping open data with open source geospatial tools'' held in Washington, DC, July 1-2, 2017. These two sessions introduce GRASS GIS and Blender, example of their processing capabilities and visualization techniques. Participants interactively visualize open data and design maps with several open source geospatial tools including Tangible Landscape system.		This is material for two sessions at the [[GRASS GIS at ICC 2017\|ICC 2017]] SDI-Open (Spatial data infrastructures, standards, open source and open data for geospatial) workshop called ''Analytical data visualizations with GRASS GIS and Blender'' and ''Mapping open data with open source geospatial tools'' held in Washington, DC, July 1-2, 2017. These two sessions introduce GRASS GIS and Blender, example of their processing capabilities and visualization techniques. Participants interactively visualize open data and design maps with several open source geospatial tools including Tangible Landscape system.

	Authors: [[User:Wenzeslaus\|Vaclav Petras]], [[User:Annakrat\|Anna Petrasova]], Payam Tabrizian, Brendan Harmon, and Helena Mitasova		Authors: [[User:Wenzeslaus\|Vaclav Petras]], [[User:Annakrat\|Anna Petrasova]], Payam Tabrizian, Brendan Harmon, and Helena Mitasova