ºÝºÝߣshows by User: petzlux / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: petzlux / Tue, 20 Dec 2011 03:10:22 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: petzlux SOTMEU 2011 - OSM Potlatch2 Usability Evaluation /slideshow/sotmeu-2011-osm-potlatch2-usability-evaluation/10644478 sotmeupresentationdraftv2-13243721412854-phpapp01-111220031159-phpapp01
This paper presents one of the first systematic investigations into the usability of Volunteered Geographic Information (VGI) editor front-ends, using established best practice in Human Computer Interaction (HCI) research. The two front-ends evaluated are Potlatch 2 and Google Map Maker, to present contrasting views of the user experience of two major VGI projects. Two user groups with no prior experience of VGI contribution were instructed to enrol and contribute data to both VGI projects, and their interaction with the two services were monitored using a mobile eye tracker and video screen capture software in a computer lab environment. The resulting data was analysed to reveal how users interact and experience VGI editors, as well as highlight deficiencies and differences between Potlatch 2 and Google Map Maker. The results from this research project are a set of recommendations for the future development of these editors, specifically relating to improving the user experience and ease of use of VGI editors.]]>
This paper presents one of the first systematic investigations into the usability of Volunteered Geographic Information (VGI) editor front-ends, using established best practice in Human Computer Interaction (HCI) research. The two front-ends evaluated are Potlatch 2 and Google Map Maker, to present contrasting views of the user experience of two major VGI projects. Two user groups with no prior experience of VGI contribution were instructed to enrol and contribute data to both VGI projects, and their interaction with the two services were monitored using a mobile eye tracker and video screen capture software in a computer lab environment. The resulting data was analysed to reveal how users interact and experience VGI editors, as well as highlight deficiencies and differences between Potlatch 2 and Google Map Maker. The results from this research project are a set of recommendations for the future development of these editors, specifically relating to improving the user experience and ease of use of VGI editors.]]> Tue, 20 Dec 2011 03:10:22 GMT /slideshow/sotmeu-2011-osm-potlatch2-usability-evaluation/10644478 petzlux@slideshare.net(petzlux) SOTMEU 2011 - OSM Potlatch2 Usability Evaluation petzlux This paper presents one of the first systematic investigations into the usability of Volunteered Geographic Information (VGI) editor front-ends, using established best practice in Human Computer Interaction (HCI) research. The two front-ends evaluated are Potlatch 2 and Google Map Maker, to present contrasting views of the user experience of two major VGI projects. Two user groups with no prior experience of VGI contribution were instructed to enrol and contribute data to both VGI projects, and their interaction with the two services were monitored using a mobile eye tracker and video screen capture software in a computer lab environment. The resulting data was analysed to reveal how users interact and experience VGI editors, as well as highlight deficiencies and differences between Potlatch 2 and Google Map Maker. The results from this research project are a set of recommendations for the future development of these editors, specifically relating to improving the user experience and ease of use of VGI editors. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/sotmeupresentationdraftv2-13243721412854-phpapp01-111220031159-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This paper presents one of the first systematic investigations into the usability of Volunteered Geographic Information (VGI) editor front-ends, using established best practice in Human Computer Interaction (HCI) research. The two front-ends evaluated are Potlatch 2 and Google Map Maker, to present contrasting views of the user experience of two major VGI projects. Two user groups with no prior experience of VGI contribution were instructed to enrol and contribute data to both VGI projects, and their interaction with the two services were monitored using a mobile eye tracker and video screen capture software in a computer lab environment. The resulting data was analysed to reveal how users interact and experience VGI editors, as well as highlight deficiencies and differences between Potlatch 2 and Google Map Maker. The results from this research project are a set of recommendations for the future development of these editors, specifically relating to improving the user experience and ease of use of VGI editors.
SOTMEU 2011 - OSM Potlatch2 Usability Evaluation from Patrick Weber
]]> 652 4 https://cdn.slidesharecdn.com/ss_thumbnails/sotmeupresentationdraftv2-13243721412854-phpapp01-111220031159-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Location Intelligence: an innovative approach to business location decision making /slideshow/location-intelligence-an-innovative-approach-to-business-location-decision-making/3132087 casaengdpresentation-12658875448257-phpapp01
As one of the leading ‘world cities’ London is home to a highly internationalised workforce and is particularly reliant on these sources of foreign direct investment (FDI). In the face of increasing global competition and a very difficult economic climate, the capital must compete effectively to encourage and support such investors. Through a collaborative study with London’s official foreign direct investment agency, Think London, the need for a coherent framework for data, methodologies and tools to inform business location decision making became apparent. This presentation will discuss the development of a rich environment to iteratively explore, compare and rank London’s business neighbourhoods alongside ancillary data. This is achieved through the development, integration and evaluation of data and its manipulation to form a model for locational based decision support. Firstly, we discuss the development of a geo-business classification for London which draws upon methods and practices common to many geospatial neighbourhood classifications that are used for profiling consumers. In this instance a geo-business classification is developed by encapsulating relevant location variables using Principal Component Analysis into a set of composite area characteristics. Secondly, we discuss the implementation an appropriate Multi-Criteria Decision Making methodology, in this case Analytical Hierarchy Process (AHP), enabling the aggregation of the geo-business classification and decision makers preferences into discrete decision alternatives (Carver 1991; Jankowski 1995). Lastly, we present the preliminary results of the integration of both data and model through the development and evaluation of a web-based prototype and evaluate its usefulness through scenario testing.]]>
As one of the leading ‘world cities’ London is home to a highly internationalised workforce and is particularly reliant on these sources of foreign direct investment (FDI). In the face of increasing global competition and a very difficult economic climate, the capital must compete effectively to encourage and support such investors. Through a collaborative study with London’s official foreign direct investment agency, Think London, the need for a coherent framework for data, methodologies and tools to inform business location decision making became apparent. This presentation will discuss the development of a rich environment to iteratively explore, compare and rank London’s business neighbourhoods alongside ancillary data. This is achieved through the development, integration and evaluation of data and its manipulation to form a model for locational based decision support. Firstly, we discuss the development of a geo-business classification for London which draws upon methods and practices common to many geospatial neighbourhood classifications that are used for profiling consumers. In this instance a geo-business classification is developed by encapsulating relevant location variables using Principal Component Analysis into a set of composite area characteristics. Secondly, we discuss the implementation an appropriate Multi-Criteria Decision Making methodology, in this case Analytical Hierarchy Process (AHP), enabling the aggregation of the geo-business classification and decision makers preferences into discrete decision alternatives (Carver 1991; Jankowski 1995). Lastly, we present the preliminary results of the integration of both data and model through the development and evaluation of a web-based prototype and evaluate its usefulness through scenario testing.]]> Thu, 11 Feb 2010 05:27:42 GMT /slideshow/location-intelligence-an-innovative-approach-to-business-location-decision-making/3132087 petzlux@slideshare.net(petzlux) Location Intelligence: an innovative approach to business location decision making petzlux As one of the leading ‘world cities’ London is home to a highly internationalised workforce and is particularly reliant on these sources of foreign direct investment (FDI). In the face of increasing global competition and a very difficult economic climate, the capital must compete effectively to encourage and support such investors. Through a collaborative study with London’s official foreign direct investment agency, Think London, the need for a coherent framework for data, methodologies and tools to inform business location decision making became apparent. This presentation will discuss the development of a rich environment to iteratively explore, compare and rank London’s business neighbourhoods alongside ancillary data. This is achieved through the development, integration and evaluation of data and its manipulation to form a model for locational based decision support. Firstly, we discuss the development of a geo-business classification for London which draws upon methods and practices common to many geospatial neighbourhood classifications that are used for profiling consumers. In this instance a geo-business classification is developed by encapsulating relevant location variables using Principal Component Analysis into a set of composite area characteristics. Secondly, we discuss the implementation an appropriate Multi-Criteria Decision Making methodology, in this case Analytical Hierarchy Process (AHP), enabling the aggregation of the geo-business classification and decision makers preferences into discrete decision alternatives (Carver 1991; Jankowski 1995). Lastly, we present the preliminary results of the integration of both data and model through the development and evaluation of a web-based prototype and evaluate its usefulness through scenario testing. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/casaengdpresentation-12658875448257-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> As one of the leading ‘world cities’ London is home to a highly internationalised workforce and is particularly reliant on these sources of foreign direct investment (FDI). In the face of increasing global competition and a very difficult economic climate, the capital must compete effectively to encourage and support such investors. Through a collaborative study with London’s official foreign direct investment agency, Think London, the need for a coherent framework for data, methodologies and tools to inform business location decision making became apparent. This presentation will discuss the development of a rich environment to iteratively explore, compare and rank London’s business neighbourhoods alongside ancillary data. This is achieved through the development, integration and evaluation of data and its manipulation to form a model for locational based decision support. Firstly, we discuss the development of a geo-business classification for London which draws upon methods and practices common to many geospatial neighbourhood classifications that are used for profiling consumers. In this instance a geo-business classification is developed by encapsulating relevant location variables using Principal Component Analysis into a set of composite area characteristics. Secondly, we discuss the implementation an appropriate Multi-Criteria Decision Making methodology, in this case Analytical Hierarchy Process (AHP), enabling the aggregation of the geo-business classification and decision makers preferences into discrete decision alternatives (Carver 1991; Jankowski 1995). Lastly, we present the preliminary results of the integration of both data and model through the development and evaluation of a web-based prototype and evaluate its usefulness through scenario testing.
Location Intelligence: an innovative approach to business location decision making from Patrick Weber
]]> 1311 7 https://cdn.slidesharecdn.com/ss_thumbnails/casaengdpresentation-12658875448257-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 AGI 2008 - Investing in GI - a review of benefits /slideshow/Patrick-Weber-AGI-Presentation-revision-kate/721076 PatrickWeberAGIPresentationrevisionkate-122582711871-phpapp01
This is a presentation given at the Association of Geographic Information Annual Conference in Stratford-upon-Avon, Uk.]]>
This is a presentation given at the Association of Geographic Information Annual Conference in Stratford-upon-Avon, Uk.]]> Tue, 04 Nov 2008 11:33:13 GMT /slideshow/Patrick-Weber-AGI-Presentation-revision-kate/721076 petzlux@slideshare.net(petzlux) AGI 2008 - Investing in GI - a review of benefits petzlux This is a presentation given at the Association of Geographic Information Annual Conference in Stratford-upon-Avon, Uk. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/PatrickWeberAGIPresentationrevisionkate-122582711871-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> This is a presentation given at the Association of Geographic Information Annual Conference in Stratford-upon-Avon, Uk.
AGI 2008 - Investing in GI - a review of benefits from Patrick Weber
]]> 260 3 https://cdn.slidesharecdn.com/ss_thumbnails/PatrickWeberAGIPresentationrevisionkate-122582711871-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-petzlux-48x48.jpg?cb=1522903979 https://cdn.slidesharecdn.com/ss_thumbnails/sotmeupresentationdraftv2-13243721412854-phpapp01-111220031159-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/sotmeu-2011-osm-potlatch2-usability-evaluation/10644478 SOTMEU 2011 - OSM Potl... https://cdn.slidesharecdn.com/ss_thumbnails/casaengdpresentation-12658875448257-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/location-intelligence-an-innovative-approach-to-business-location-decision-making/3132087 Location Intelligence:... https://cdn.slidesharecdn.com/ss_thumbnails/PatrickWeberAGIPresentationrevisionkate-122582711871-phpapp01-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/Patrick-Weber-AGI-Presentation-revision-kate/721076 AGI 2008 - Investing i...