GRASS GIS at ICC 2017 - Revision history

Annakrat: /* Using space-time cube for visualization and analysis of active transportation patterns derived from public webcams */

2017-07-18T01:42:27Z

Using space-time cube for visualization and analysis of active transportation patterns derived from public webcams

← Older revision		Revision as of 01:42, 18 July 2017
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	=== Using space-time cube for visualization and analysis of active transportation patterns derived from public webcams ===		=== Using space-time cube for visualization and analysis of active transportation patterns derived from public webcams ===
			[[File:ICC_STC_illustration.png\|400px\|thumb\|right\|''Using space-time cube for visualization and analysis of active transportation patterns derived from public webcams:'' Space time cube representation of pedestrian density in Ehingen, Germany]]
	by Anna Petrasova, Aaron Hipp, and Helena Mitasova from North Carolina State University		by Anna Petrasova, Aaron Hipp, and Helena Mitasova from North Carolina State University

Annakrat: /* Using space-time cube for visualization and analysis of active transportation patterns derived from public webcams */

2017-07-18T01:35:52Z

Using space-time cube for visualization and analysis of active transportation patterns derived from public webcams

← Older revision		Revision as of 01:35, 18 July 2017
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	* Software used: Jupyter, Python, Matplotlib, GRASS GIS, Paraview		* Software used: Jupyter, Python, Matplotlib, GRASS GIS, Paraview
			* Slides: https://petrasovaa.github.io/amos-STC-presentation/icc_presentation.html, also [https://github.com/petrasovaa/amos-STC-presentation/raw/gh-pages/ICC_2017_presentation.pdf PDF version]
	* Extended abstract:		* Extended abstract:
	** Introduction: Active transportation such as walking has been shown to provide many benefits from improved health to reduced traffic problems. Adopting active transportation behavior by citizens is influenced by the built environment and consequently, better understanding the link between them would allow urban planners to more effectively design public spaces and transportation networks. AMOS (The Archive of Many Outdoor Scenes) is a database of images captured by publicly available webcams since 2006, providing an unprecedented time series of data about the dynamics of public spaces. Previous studies have demonstrated that using webcams together with crowdsourcing platform, such as Amazon Mechanical Turk (MTurk), to locate pedestrians in the captured images is a promising technique for analyzing active transportation behavior (Hipp et al., 2016). However, it is challenging to efficiently extract relevant information, such as geospatial patterns of pedestrian density, and visualize it in a meaningful way that can inform urban design or decision making. In this study we propose using a space-time cube (STC) representation of active transportation data to analyze the behavior patterns of pedestrians in public spaces. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time-series of pedestrian occurrences captured by webcam images.		** Introduction: Active transportation such as walking has been shown to provide many benefits from improved health to reduced traffic problems. Adopting active transportation behavior by citizens is influenced by the built environment and consequently, better understanding the link between them would allow urban planners to more effectively design public spaces and transportation networks. AMOS (The Archive of Many Outdoor Scenes) is a database of images captured by publicly available webcams since 2006, providing an unprecedented time series of data about the dynamics of public spaces. Previous studies have demonstrated that using webcams together with crowdsourcing platform, such as Amazon Mechanical Turk (MTurk), to locate pedestrians in the captured images is a promising technique for analyzing active transportation behavior (Hipp et al., 2016). However, it is challenging to efficiently extract relevant information, such as geospatial patterns of pedestrian density, and visualize it in a meaningful way that can inform urban design or decision making. In this study we propose using a space-time cube (STC) representation of active transportation data to analyze the behavior patterns of pedestrians in public spaces. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time-series of pedestrian occurrences captured by webcam images.

Annakrat: /* Immersive Tangible Modelling with Geospatial Data */

2017-07-18T01:32:42Z

Immersive Tangible Modelling with Geospatial Data

← Older revision		Revision as of 01:32, 18 July 2017
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	* Software used: GRASS GIS, Blender, Tangible Landscape		* Software used: GRASS GIS, Blender, Tangible Landscape
			* Slides: https://ncsu-geoforall-lab.github.io/tangible-landscape-talk/ICC_2017.html
	* Extended abstract:		* Extended abstract:
	** Recently, the importance of integrating human perceptions and preferences in geospatial modeling has been broadly acknowledged. Therefore, a new type of interfaces is needed to enable experts and non-experts to collaboratively model landscapes and explore the impacts of “what if” scenarios on both environmental and experiential dimensions. Separate modes of representation, however, are needed for more effective communication of these two dimensions. While perceptions of landscapes are best represented through photorealistic renderings viewed at human-scale perspectives, geospatial simulations are best represented more abstractly to enhance readability.		** Recently, the importance of integrating human perceptions and preferences in geospatial modeling has been broadly acknowledged. Therefore, a new type of interfaces is needed to enable experts and non-experts to collaboratively model landscapes and explore the impacts of “what if” scenarios on both environmental and experiential dimensions. Separate modes of representation, however, are needed for more effective communication of these two dimensions. While perceptions of landscapes are best represented through photorealistic renderings viewed at human-scale perspectives, geospatial simulations are best represented more abstractly to enhance readability.

⚠️Wenzeslaus: /* OSGeo Live introduction */

2017-06-26T15:48:46Z

OSGeo Live introduction

← Older revision		Revision as of 15:48, 26 June 2017
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	* Format: Talk		* Format: Talk
			* Slides: https://wenzeslaus.github.io/osgeolive-presentation

	=== Analytical data visualizations with GRASS GIS and Blender ===		=== Analytical data visualizations with GRASS GIS and Blender ===

⚠️Wenzeslaus: add workshop image

2017-06-22T21:46:24Z

add workshop image

← Older revision		Revision as of 21:46, 22 June 2017
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	=== Analytical data visualizations with GRASS GIS and Blender ===		=== Analytical data visualizations with GRASS GIS and Blender ===

			[[File:Cumulative viewshed.png\|300px\|thumb\|right\|''Analytical data visualizations with GRASS GIS and Blender:'' Cumulative viewshed computed from views along a road on DSM (3D view in GRASS GIS)]]

	by Vaclav Petras, Anna Petrasova, Payam Tabrizian, Brendan Harmon, and Helena Mitasova from North Carolina State University		by Vaclav Petras, Anna Petrasova, Payam Tabrizian, Brendan Harmon, and Helena Mitasova from North Carolina State University

⚠️Wenzeslaus: /* Mapping open data with open source geospatial tools */

2017-06-19T16:55:59Z

Mapping open data with open source geospatial tools

← Older revision		Revision as of 16:55, 19 June 2017
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	* Description: Participants interactively visualize open data and design maps with open source geospatial tools including Tangible Landscape system		* Description: Participants interactively visualize open data and design maps with open source geospatial tools including Tangible Landscape system
	* Format: Hands-on		* Format: Hands-on
			* Materials: [[Analytical data visualizations at ICC 2017]]

	== Presentations ==		== Presentations ==

⚠️Wenzeslaus: /* Analytical data visualizations with GRASS GIS and Blender */

2017-06-19T16:55:43Z

Analytical data visualizations with GRASS GIS and Blender

← Older revision		Revision as of 16:55, 19 June 2017
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	* Format: Talk and demonstration		* Format: Talk and demonstration
			* Materials: [[Analytical data visualizations at ICC 2017]]

	=== Mapping open data with open source geospatial tools ===		=== Mapping open data with open source geospatial tools ===

⚠️Wenzeslaus: /* Presentations */

2017-06-02T15:42:06Z

Presentations

← Older revision		Revision as of 15:42, 2 June 2017
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	by Payam Tabrizian, Brendan Harmon, Anna Petrasova, Vaclav Petras, Helena Mitasova, and Ross Meentemeyer from North Carolina State University		by Payam Tabrizian, Brendan Harmon, Anna Petrasova, Vaclav Petras, Helena Mitasova, and Ross Meentemeyer from North Carolina State University

			* Software used: GRASS GIS, Blender, Tangible Landscape
	* Extended abstract:		* Extended abstract:
	** Recently, the importance of integrating human perceptions and preferences in geospatial modeling has been broadly acknowledged. Therefore, a new type of interfaces is needed to enable experts and non-experts to collaboratively model landscapes and explore the impacts of “what if” scenarios on both environmental and experiential dimensions. Separate modes of representation, however, are needed for more effective communication of these two dimensions. While perceptions of landscapes are best represented through photorealistic renderings viewed at human-scale perspectives, geospatial simulations are best represented more abstractly to enhance readability.		** Recently, the importance of integrating human perceptions and preferences in geospatial modeling has been broadly acknowledged. Therefore, a new type of interfaces is needed to enable experts and non-experts to collaboratively model landscapes and explore the impacts of “what if” scenarios on both environmental and experiential dimensions. Separate modes of representation, however, are needed for more effective communication of these two dimensions. While perceptions of landscapes are best represented through photorealistic renderings viewed at human-scale perspectives, geospatial simulations are best represented more abstractly to enhance readability.
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	by Anna Petrasova, Aaron Hipp, and Helena Mitasova from North Carolina State University		by Anna Petrasova, Aaron Hipp, and Helena Mitasova from North Carolina State University

			* Software used: Jupyter, Python, Matplotlib, GRASS GIS, Paraview
	* Extended abstract:		* Extended abstract:
	** Introduction: Active transportation such as walking has been shown to provide many benefits from improved health to reduced traffic problems. Adopting active transportation behavior by citizens is influenced by the built environment and consequently, better understanding the link between them would allow urban planners to more effectively design public spaces and transportation networks. AMOS (The Archive of Many Outdoor Scenes) is a database of images captured by publicly available webcams since 2006, providing an unprecedented time series of data about the dynamics of public spaces. Previous studies have demonstrated that using webcams together with crowdsourcing platform, such as Amazon Mechanical Turk (MTurk), to locate pedestrians in the captured images is a promising technique for analyzing active transportation behavior (Hipp et al., 2016). However, it is challenging to efficiently extract relevant information, such as geospatial patterns of pedestrian density, and visualize it in a meaningful way that can inform urban design or decision making. In this study we propose using a space-time cube (STC) representation of active transportation data to analyze the behavior patterns of pedestrians in public spaces. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time-series of pedestrian occurrences captured by webcam images.		** Introduction: Active transportation such as walking has been shown to provide many benefits from improved health to reduced traffic problems. Adopting active transportation behavior by citizens is influenced by the built environment and consequently, better understanding the link between them would allow urban planners to more effectively design public spaces and transportation networks. AMOS (The Archive of Many Outdoor Scenes) is a database of images captured by publicly available webcams since 2006, providing an unprecedented time series of data about the dynamics of public spaces. Previous studies have demonstrated that using webcams together with crowdsourcing platform, such as Amazon Mechanical Turk (MTurk), to locate pedestrians in the captured images is a promising technique for analyzing active transportation behavior (Hipp et al., 2016). However, it is challenging to efficiently extract relevant information, such as geospatial patterns of pedestrian density, and visualize it in a meaningful way that can inform urban design or decision making. In this study we propose using a space-time cube (STC) representation of active transportation data to analyze the behavior patterns of pedestrians in public spaces. While STC has already been used to visualize urban dynamics, this is the first study analyzing the evolution of pedestrian density based on crowdsourced time-series of pedestrian occurrences captured by webcam images.

⚠️Wenzeslaus at 15:37, 2 June 2017

2017-06-02T15:37:31Z

← Older revision		Revision as of 15:37, 2 June 2017
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	== Presentations ==		== Presentations ==

	=== Immersive Tangible Modelling with Geospatial ~~data~~ ===		=== Immersive Tangible Modelling with Geospatial Data ===

			[[File:Tangible landscape and blender with water and trees.jpg\|300px\|thumb\|right\|''Immersive Tangible Modelling with Geospatial Data:'' Blender on screen with terrain and ponds computed in GRASS GIS, and a sand model augmented by Tangible Landscape]]

	by Payam Tabrizian, Brendan Harmon, Anna Petrasova, Vaclav Petras, Helena Mitasova, and Ross Meentemeyer from North Carolina State University		by Payam Tabrizian, Brendan Harmon, Anna Petrasova, Vaclav Petras, Helena Mitasova, and Ross Meentemeyer from North Carolina State University
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	** Conclusion: By representing pedestrian density as a georeferenced STC, we were able to generate visualizations that represent complex, spatio-temporal information in a concise, readable way. By harnessing the power of crowdsourced and open data, this study opens new possibilities for the analysis of the dynamics of public spaces within their geospatial context.		** Conclusion: By representing pedestrian density as a georeferenced STC, we were able to generate visualizations that represent complex, spatio-temporal information in a concise, readable way. By harnessing the power of crowdsourced and open data, this study opens new possibilities for the analysis of the dynamics of public spaces within their geospatial context.
	** References: Hipp, J. A., Manteiga, A., Burgess, A., Stylianou, A., & Pless, R. (2016). Webcams, crowdsourcing, and enhanced crosswalks: Developing a novel Method to analyze active Transportation. Frontiers in Public Health, 4(97).		** References: Hipp, J. A., Manteiga, A., Burgess, A., Stylianou, A., & Pless, R. (2016). Webcams, crowdsourcing, and enhanced crosswalks: Developing a novel Method to analyze active Transportation. Frontiers in Public Health, 4(97).

			== See also ==

			* [[GRASS GIS at FOSS4G Boston 2017]]
			* [[GRASS GIS at US-IALE 2017 Annual Meeting]]

			[[Category: Conferences]]
			[[Category: Workshops]]
			[[Category: 2017]]

⚠️Wenzeslaus: /* Workshop */

2017-06-02T15:32:02Z

Workshop

← Older revision		Revision as of 15:32, 2 June 2017
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	== Workshop ==		== Workshop ==

	All the following sessions are part of a workshop titled ''Spatial data infrastructures, standards, open source and open data for geospatial (SDI-Open 2017)'' jointly organized by the ''Commission on Open Source Geospatial Technologies'' and the ''Commission on SDI and Standards''.		All the following sessions are part of a workshop titled ''Spatial data infrastructures, standards, open source and open data for geospatial (SDI-Open 2017)'' jointly organized by the ''Commission on Open Source Geospatial Technologies'' and the ''Commission on SDI and Standards'' (http://sdistandards.icaci.org/).

	* Description: Spatial data infrastructures aim to make spatial data findable, accessible and usable. Open source software and open data portals help to make this possible. In this workshop, participants will be introduced to SDIs, standards, open source and open data in a fun way. The workshop comprises invited presentations to provide an introduction and background to SDIs, standards, open source and open data. A number of hands-on introductions to open source geospatial software together with mapping tasks will give participants the opportunity to explore these tools.		* Description: Spatial data infrastructures aim to make spatial data findable, accessible and usable. Open source software and open data portals help to make this possible. In this workshop, participants will be introduced to SDIs, standards, open source and open data in a fun way. The workshop comprises invited presentations to provide an introduction and background to SDIs, standards, open source and open data. A number of hands-on introductions to open source geospatial software together with mapping tasks will give participants the opportunity to explore these tools.
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	by Vaclav Petras from North Carolina State University		by Vaclav Petras from North Carolina State University

			* Format: Talk

	=== Analytical data visualizations with GRASS GIS and Blender ===		=== Analytical data visualizations with GRASS GIS and Blender ===

	by Vaclav Petras, Anna Petrasova, Payam Tabrizian, Brendan Harmon, and Helena Mitasova from North Carolina State University		by Vaclav Petras, Anna Petrasova, Payam Tabrizian, Brendan Harmon, and Helena Mitasova from North Carolina State University

			* Format: Talk and demonstration

	=== Mapping open data with open source geospatial tools ===		=== Mapping open data with open source geospatial tools ===
Line 26:		Line 30:

	* Description: Participants interactively visualize open data and design maps with open source geospatial tools including Tangible Landscape system		* Description: Participants interactively visualize open data and design maps with open source geospatial tools including Tangible Landscape system
			* Format: Hands-on

	== Presentations ==		== Presentations ==