Difference: Papers20100623105936 (1 vs. 3)

Revision 32010-08-13 - TheresiaFreska

 
META TOPICPARENT name="AI_GEOSTATSPapers"
Title: Bridging the Gap Between GIS and Solid Spatial Statistics
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Date: 10 May 2002
  Authors: Konstantin Krivoruchko
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Link: http://www.dpi.inpe.br/gilberto/csiss/papers/krivoruchko.pdf
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Link: http://www.dpi.inpe.br/gilberto/csiss/papers/krivoruchko.pdf - link is broken, will be checked soon
  Abstract:
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REFERENCE: In: Proceedings of the SCISS Specialist Meeting "New Tools for Spatial Data Analysis". Santa Barbara, California, USA. May 10-11, 2002.
 INTRODUCTION
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Over the past few decades, GIS and statistical spatial data analysis tools have been successfully applied in the fields of meteorology, environmental monitoring, epidemiology, and mineral exploration, to name but a few. While these tools operate on spatial data, they have as yet not been incorporated into a single software environment. For this reason, statistical methods remain somewhat of a mystery to most GIS practitioners. To widen the use of spatial statistics, it is argued that statistical software packages, such as SAS, SPLUS, and GSLIB should incorporate three essential components that are provided by a GIS: a robust spatial database (with associated geographic coordinate systems), spatial models, and visualization algorithms. An alternative solution is to incorporate statistical algorithms into GIS software. The Geostatistical Analyst, an extension to ESRI's ArcGIS 8.1, is an example of the latter approach. Released in May 2001, the Geostatistical Analyst has two main components, the exploratory spatial data analysis toolbox and the interpolation and statistical modeling wizard. In the simplest application, users can select default values to create maps from point samples. As their level of knowledge improves, users can use a wide range of processing and post-processing (validation and cross-validation diagnostic) options to explore the properties of the data and hence create a more optimal and possibly more accurate map. All tools in the Geostatistical Analyst are fully compatible with the base product. The views in exploratory spatial data analysis tools are interactive with all of the other tools provided with ArcGIS. Geostatistical layers naturally interact with other GIS features and options, such as projection change, clipping, querying, and exporting. Future software developments will add more statistical tools that GIS practitioners need. In this paper, we discuss the philosophy behind first version of the Geostatistical Analyst and plan for the future implementation of spa tial statistics into ESRI's ArcGIS
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Over the past few decades, GIS and statistical spatial data analysis tools have been successfully applied in the fields of meteorology, environmental monitoring, epidemiology, and mineral exploration, to name but a few. While these tools operate on spatial data, they have as yet not been incorporated into a single software environment. For this reason, statistical methods remain somewhat of a mystery to most GIS practitioners. To widen the use of spatial statistics, it is argued that statistical software packages, such as SAS, SPLUS, and GSLIB should incorporate three essential components that are provided by a GIS: a robust spatial database (with associated geographic coordinate systems), spatial models, and visualization algorithms. An alternative solution is to incorporate statistical algorithms into GIS software. The Geostatistical Analyst, an extension to ESRI's ArcGIS 8.1, is an example of the latter approach. Released in May 2001, the Geostatistical Analyst has two main components, the exploratory spatial data analysis toolbox and the interpolation and statistical modeling wizard. In the simplest application, users can select default values to create maps from point samples. As their level of knowledge improves, users can use a wide range of processing and post-processing (validation and cross-validation diagnostic) options to explore the properties of the data and hence create a more optimal and possibly more accurate map. All tools in the Geostatistical Analyst are fully compatible with the base product. The views in exploratory spatial data analysis tools are interactive with all of the other tools provided with ArcGIS. Geostatistical layers naturally interact with other GIS features and options, such as projection change, clipping, querying, and exporting. Future software developments will add more statistical tools that GIS practitioners need. In this paper, we discuss the philosophy behind first version of the Geostatistical Analyst and plan for the future implementation of spatial statistics into ESRI's ArcGIS.
 
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-- TWikiAdminUser - 2010-06-16
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REFERENCE: In: Proceedings of the SCISS Specialist Meeting "New Tools for Spatial Data Analysis". Santa Barbara, California, USA. May 10-11, 2002.
 

Revision 22010-07-23 - TWikiAdminUser

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META TOPICPARENT name="GeostatisticsPapers"
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META TOPICPARENT name="AI_GEOSTATSPapers"
 Title: Bridging the Gap Between GIS and Solid Spatial Statistics

Date:

Authors: Konstantin Krivoruchko

Link: http://www.dpi.inpe.br/gilberto/csiss/papers/krivoruchko.pdf

Abstract:

REFERENCE: In: Proceedings of the SCISS Specialist Meeting "New Tools for Spatial Data Analysis". Santa Barbara, California, USA. May 10-11, 2002.

INTRODUCTION

Over the past few decades, GIS and statistical spatial data analysis tools have been successfully applied in the fields of meteorology, environmental monitoring, epidemiology, and mineral exploration, to name but a few. While these tools operate on spatial data, they have as yet not been incorporated into a single software environment. For this reason, statistical methods remain somewhat of a mystery to most GIS practitioners. To widen the use of spatial statistics, it is argued that statistical software packages, such as SAS, SPLUS, and GSLIB should incorporate three essential components that are provided by a GIS: a robust spatial database (with associated geographic coordinate systems), spatial models, and visualization algorithms. An alternative solution is to incorporate statistical algorithms into GIS software. The Geostatistical Analyst, an extension to ESRI's ArcGIS 8.1, is an example of the latter approach. Released in May 2001, the Geostatistical Analyst has two main components, the exploratory spatial data analysis toolbox and the interpolation and statistical modeling wizard. In the simplest application, users can select default values to create maps from point samples. As their level of knowledge improves, users can use a wide range of processing and post-processing (validation and cross-validation diagnostic) options to explore the properties of the data and hence create a more optimal and possibly more accurate map. All tools in the Geostatistical Analyst are fully compatible with the base product. The views in exploratory spatial data analysis tools are interactive with all of the other tools provided with ArcGIS. Geostatistical layers naturally interact with other GIS features and options, such as projection change, clipping, querying, and exporting. Future software developments will add more statistical tools that GIS practitioners need. In this paper, we discuss the philosophy behind first version of the Geostatistical Analyst and plan for the future implementation of spa tial statistics into ESRI's ArcGIS

-- TWikiAdminUser - 2010-06-16

Revision 12010-06-23 - TWikiAdminUser

 
META TOPICPARENT name="GeostatisticsPapers"
Title: Bridging the Gap Between GIS and Solid Spatial Statistics

Date:

Authors: Konstantin Krivoruchko

Link: http://www.dpi.inpe.br/gilberto/csiss/papers/krivoruchko.pdf

Abstract:

REFERENCE: In: Proceedings of the SCISS Specialist Meeting "New Tools for Spatial Data Analysis". Santa Barbara, California, USA. May 10-11, 2002.

INTRODUCTION

Over the past few decades, GIS and statistical spatial data analysis tools have been successfully applied in the fields of meteorology, environmental monitoring, epidemiology, and mineral exploration, to name but a few. While these tools operate on spatial data, they have as yet not been incorporated into a single software environment. For this reason, statistical methods remain somewhat of a mystery to most GIS practitioners. To widen the use of spatial statistics, it is argued that statistical software packages, such as SAS, SPLUS, and GSLIB should incorporate three essential components that are provided by a GIS: a robust spatial database (with associated geographic coordinate systems), spatial models, and visualization algorithms. An alternative solution is to incorporate statistical algorithms into GIS software. The Geostatistical Analyst, an extension to ESRI's ArcGIS 8.1, is an example of the latter approach. Released in May 2001, the Geostatistical Analyst has two main components, the exploratory spatial data analysis toolbox and the interpolation and statistical modeling wizard. In the simplest application, users can select default values to create maps from point samples. As their level of knowledge improves, users can use a wide range of processing and post-processing (validation and cross-validation diagnostic) options to explore the properties of the data and hence create a more optimal and possibly more accurate map. All tools in the Geostatistical Analyst are fully compatible with the base product. The views in exploratory spatial data analysis tools are interactive with all of the other tools provided with ArcGIS. Geostatistical layers naturally interact with other GIS features and options, such as projection change, clipping, querying, and exporting. Future software developments will add more statistical tools that GIS practitioners need. In this paper, we discuss the philosophy behind first version of the Geostatistical Analyst and plan for the future implementation of spa tial statistics into ESRI's ArcGIS

-- TWikiAdminUser - 2010-06-16

 
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