SlideShare une entreprise Scribd logo
1  sur  22
Télécharger pour lire hors ligne
Creating Bathymetry Maps
With Coarse Data -
Bayesian Kriging Using
Open Source Tools
Hal Koike
University of Hawaii,
Hawaii Fisheries Cooperative Research Unit
Why do we need a
       Bathymetry Map?
 Marine resource management is pushed
  toward ecosystem based management (e.g.
  linking with land development, marine
  protected area)
 You need spatial data to fully understand
  the ecosystem of your interest
 Species distribution for marine organisms is
  known to be influenced by depth
Outside the United States…

Most countries do not have a spatial
data repository where bathymetry
data, land cover data, etc. is readily
available to be used for analysis.
If $$ is Limited,
   What are the Options?
 Stick with what you have
 Create a pseudo-bathymetry map
 Some budget friendly data covering the world
 (bathymetry case)
    Navigational chart (low cost)
    MODIS (free)
    Hyperion (free)
Case for Seychelles…
What is available
What I need
Solution


Create a pseudo bathymetry map using
Bayesian Kriging option in GeoR (Rilbeiro jr.,
P.J. and Diggle, P.J. 2001)
What is GeoR?

 Created by Paulo J. Ribeiro Jr. and Peter J.
  Diggle.
 One of the many packages available through
  R-CRAN project
 Operated on R
Step 1. Georectification
Step 2. Enter the Depth Data
What it looks like after
entering all the points
Step 3. Import the Point
     Data to Geo R
Step 4. Find your Range
Step 5. Run the Bayesian
     Kriging Simulation
x <- seq(241472,403019,2000)
y <- seq(9449003,9559751,2000)
d1 <- expand.grid(x=x,y=y)
ex.bayes <-
   krige.bayes(YourData,loc=d1,model=model.control(
   cov.m="matern",kappa=0.5),prior=prior.control(phi.
   discrete=seq(0,80000,l=10),phi.prior="reciprocal"))
Hawaii Pacific GIS Conference 2012: 3D GIS - Creating Bathymetry Maps with Coarse Data - Bayesian Kriging Using Open Source Tools
Predicted Values
Predicted Values
Error of Predicted Values
 (Estimation Variance)
Error of Predicted Values
 (Estimation Variance)
Accuracy Check
Accuracy Comparison



        Bathymetry Map            Standard Deviation
SRTM 30 (1km grid)                      76.89
Bayesian Kriging (2km grid)              9.00
Conventional Kriging (2km grid)          8.30


Statistically simulated bathymetry map had
less deviation then remotely sensed data
Hawaii Pacific GIS Conference 2012: 3D GIS - Creating Bathymetry Maps with Coarse Data - Bayesian Kriging Using Open Source Tools

Contenu connexe

En vedette

RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...
RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...
RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...RuleML
 
Pattern recognition on human vision
Pattern recognition on human visionPattern recognition on human vision
Pattern recognition on human visionGiacomo Veneri
 
Communications for Clean Water
Communications for Clean WaterCommunications for Clean Water
Communications for Clean WaterChoose Clean Water
 
Earth science 14.1
Earth science 14.1Earth science 14.1
Earth science 14.1Tamara
 
Introduction to matlab lecture 4 of 4
Introduction to matlab lecture 4 of 4Introduction to matlab lecture 4 of 4
Introduction to matlab lecture 4 of 4Randa Elanwar
 
Introduction to Neural networks (under graduate course) Lecture 5 of 9
Introduction to Neural networks (under graduate course) Lecture 5 of 9Introduction to Neural networks (under graduate course) Lecture 5 of 9
Introduction to Neural networks (under graduate course) Lecture 5 of 9Randa Elanwar
 
What is pattern recognition (lecture 5 of 6)
What is pattern recognition (lecture 5 of 6)What is pattern recognition (lecture 5 of 6)
What is pattern recognition (lecture 5 of 6)Randa Elanwar
 
Introduction to matlab lecture 2 of 4
Introduction to matlab lecture 2 of 4Introduction to matlab lecture 2 of 4
Introduction to matlab lecture 2 of 4Randa Elanwar
 
Digital library construction
Digital library constructionDigital library construction
Digital library constructionRanda Elanwar
 
The Mapping Network Lake Mapping
The Mapping Network Lake MappingThe Mapping Network Lake Mapping
The Mapping Network Lake MappingThe Mapping Network
 
Do Humans Beat Computers At Pattern Recognition
Do Humans Beat Computers At Pattern RecognitionDo Humans Beat Computers At Pattern Recognition
Do Humans Beat Computers At Pattern RecognitionBitdefender
 
The Object Detection Capabilities of the Bathymetry Systems Utilised for the ...
The Object Detection Capabilities of the Bathymetry Systems Utilised for the ...The Object Detection Capabilities of the Bathymetry Systems Utilised for the ...
The Object Detection Capabilities of the Bathymetry Systems Utilised for the ...Luke Elliott
 
Pattern Recognition and its Application
Pattern Recognition and its ApplicationPattern Recognition and its Application
Pattern Recognition and its ApplicationSajida Mohammad
 
Airborne LiDAR Bathymetry of the Great Barrier Reef
Airborne LiDAR Bathymetry of the Great Barrier ReefAirborne LiDAR Bathymetry of the Great Barrier Reef
Airborne LiDAR Bathymetry of the Great Barrier Reeffungis
 
Managing hydrographic data for multiple usage
Managing hydrographic data for multiple usageManaging hydrographic data for multiple usage
Managing hydrographic data for multiple usageMilan Uitentuis
 
Pattern Recognition: digital identity, digital #curation and digital badges (...
Pattern Recognition: digital identity, digital #curation and digital badges (...Pattern Recognition: digital identity, digital #curation and digital badges (...
Pattern Recognition: digital identity, digital #curation and digital badges (...Joyce Seitzinger
 
What is pattern recognition (lecture 4 of 6)
What is pattern recognition (lecture 4 of 6)What is pattern recognition (lecture 4 of 6)
What is pattern recognition (lecture 4 of 6)Randa Elanwar
 
What is pattern recognition (lecture 3 of 6)
What is pattern recognition (lecture 3 of 6)What is pattern recognition (lecture 3 of 6)
What is pattern recognition (lecture 3 of 6)Randa Elanwar
 

En vedette (20)

RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...
RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...
RuleML2015: Similarity-Based Strict Equality in a Fully Integrated Fuzzy Logi...
 
Pattern recognition on human vision
Pattern recognition on human visionPattern recognition on human vision
Pattern recognition on human vision
 
Hydro2016
Hydro2016Hydro2016
Hydro2016
 
Communications for Clean Water
Communications for Clean WaterCommunications for Clean Water
Communications for Clean Water
 
Earth science 14.1
Earth science 14.1Earth science 14.1
Earth science 14.1
 
Conowingo Presentation- USGS
Conowingo Presentation- USGSConowingo Presentation- USGS
Conowingo Presentation- USGS
 
Introduction to matlab lecture 4 of 4
Introduction to matlab lecture 4 of 4Introduction to matlab lecture 4 of 4
Introduction to matlab lecture 4 of 4
 
Introduction to Neural networks (under graduate course) Lecture 5 of 9
Introduction to Neural networks (under graduate course) Lecture 5 of 9Introduction to Neural networks (under graduate course) Lecture 5 of 9
Introduction to Neural networks (under graduate course) Lecture 5 of 9
 
What is pattern recognition (lecture 5 of 6)
What is pattern recognition (lecture 5 of 6)What is pattern recognition (lecture 5 of 6)
What is pattern recognition (lecture 5 of 6)
 
Introduction to matlab lecture 2 of 4
Introduction to matlab lecture 2 of 4Introduction to matlab lecture 2 of 4
Introduction to matlab lecture 2 of 4
 
Digital library construction
Digital library constructionDigital library construction
Digital library construction
 
The Mapping Network Lake Mapping
The Mapping Network Lake MappingThe Mapping Network Lake Mapping
The Mapping Network Lake Mapping
 
Do Humans Beat Computers At Pattern Recognition
Do Humans Beat Computers At Pattern RecognitionDo Humans Beat Computers At Pattern Recognition
Do Humans Beat Computers At Pattern Recognition
 
The Object Detection Capabilities of the Bathymetry Systems Utilised for the ...
The Object Detection Capabilities of the Bathymetry Systems Utilised for the ...The Object Detection Capabilities of the Bathymetry Systems Utilised for the ...
The Object Detection Capabilities of the Bathymetry Systems Utilised for the ...
 
Pattern Recognition and its Application
Pattern Recognition and its ApplicationPattern Recognition and its Application
Pattern Recognition and its Application
 
Airborne LiDAR Bathymetry of the Great Barrier Reef
Airborne LiDAR Bathymetry of the Great Barrier ReefAirborne LiDAR Bathymetry of the Great Barrier Reef
Airborne LiDAR Bathymetry of the Great Barrier Reef
 
Managing hydrographic data for multiple usage
Managing hydrographic data for multiple usageManaging hydrographic data for multiple usage
Managing hydrographic data for multiple usage
 
Pattern Recognition: digital identity, digital #curation and digital badges (...
Pattern Recognition: digital identity, digital #curation and digital badges (...Pattern Recognition: digital identity, digital #curation and digital badges (...
Pattern Recognition: digital identity, digital #curation and digital badges (...
 
What is pattern recognition (lecture 4 of 6)
What is pattern recognition (lecture 4 of 6)What is pattern recognition (lecture 4 of 6)
What is pattern recognition (lecture 4 of 6)
 
What is pattern recognition (lecture 3 of 6)
What is pattern recognition (lecture 3 of 6)What is pattern recognition (lecture 3 of 6)
What is pattern recognition (lecture 3 of 6)
 

Similaire à Hawaii Pacific GIS Conference 2012: 3D GIS - Creating Bathymetry Maps with Coarse Data - Bayesian Kriging Using Open Source Tools

Big datascienceh2oandr
Big datascienceh2oandrBig datascienceh2oandr
Big datascienceh2oandrSri Ambati
 
Big Data Science with H2O in R
Big Data Science with H2O in RBig Data Science with H2O in R
Big Data Science with H2O in RAnqi Fu
 
Osgeo.wageningen kickoff event nov2012
Osgeo.wageningen kickoff event nov2012Osgeo.wageningen kickoff event nov2012
Osgeo.wageningen kickoff event nov2012pvangenuchten
 
RemoteSensing_DeepLearning_v2.pptx
RemoteSensing_DeepLearning_v2.pptxRemoteSensing_DeepLearning_v2.pptx
RemoteSensing_DeepLearning_v2.pptxElise Colin
 
Drones and A.I in Earth Science
Drones and A.I in Earth ScienceDrones and A.I in Earth Science
Drones and A.I in Earth ScienceARDC
 
Playful Explorations of Public and Personal Data - OSCON Data 2011
Playful Explorations of Public and Personal Data - OSCON Data 2011Playful Explorations of Public and Personal Data - OSCON Data 2011
Playful Explorations of Public and Personal Data - OSCON Data 2011Andrew Turner
 
eMerges - Terra Cognita 2006 Workshop (ISWC)
eMerges - Terra Cognita 2006 Workshop (ISWC)eMerges - Terra Cognita 2006 Workshop (ISWC)
eMerges - Terra Cognita 2006 Workshop (ISWC)Vlad Tanasescu
 
Tutorial on Polynomial Networks at CVPR'22
Tutorial on Polynomial Networks at CVPR'22Tutorial on Polynomial Networks at CVPR'22
Tutorial on Polynomial Networks at CVPR'22Grigoris C
 
Spatial Analysis with R - the Good, the Bad, and the Pretty
Spatial Analysis with R - the Good, the Bad, and the PrettySpatial Analysis with R - the Good, the Bad, and the Pretty
Spatial Analysis with R - the Good, the Bad, and the PrettyNoam Ross
 
[212]big models without big data using domain specific deep networks in data-...
[212]big models without big data using domain specific deep networks in data-...[212]big models without big data using domain specific deep networks in data-...
[212]big models without big data using domain specific deep networks in data-...NAVER D2
 
Finding Meaning in Points, Areas and Surfaces: Spatial Analysis in R
Finding Meaning in Points, Areas and Surfaces: Spatial Analysis in RFinding Meaning in Points, Areas and Surfaces: Spatial Analysis in R
Finding Meaning in Points, Areas and Surfaces: Spatial Analysis in RRevolution Analytics
 
Finding Fallopia: detection of Japanese Knotweed s.l. taxa in the UK using re...
Finding Fallopia: detection of Japanese Knotweed s.l. taxa in the UK using re...Finding Fallopia: detection of Japanese Knotweed s.l. taxa in the UK using re...
Finding Fallopia: detection of Japanese Knotweed s.l. taxa in the UK using re...Daniel Jones
 
Big Data + Big Sim: Query Processing over Unstructured CFD Models
Big Data + Big Sim: Query Processing over Unstructured CFD ModelsBig Data + Big Sim: Query Processing over Unstructured CFD Models
Big Data + Big Sim: Query Processing over Unstructured CFD ModelsUniversity of Washington
 
Gis fandamentals -1
Gis fandamentals -1Gis fandamentals -1
Gis fandamentals -1RJRANJEET1
 
IEEE SIGHT Bombay section webinar talk on GIS & Remote Sensing-Introduction t...
IEEE SIGHT Bombay section webinar talk on GIS & Remote Sensing-Introduction t...IEEE SIGHT Bombay section webinar talk on GIS & Remote Sensing-Introduction t...
IEEE SIGHT Bombay section webinar talk on GIS & Remote Sensing-Introduction t...AdityaAllamraju1
 
The Other HPC: High Productivity Computing
The Other HPC: High Productivity ComputingThe Other HPC: High Productivity Computing
The Other HPC: High Productivity ComputingUniversity of Washington
 
MACHINE LEARNING FOR SATELLITE-GUIDED WATER QUALITY MONITORING
MACHINE LEARNING FOR SATELLITE-GUIDED WATER QUALITY MONITORINGMACHINE LEARNING FOR SATELLITE-GUIDED WATER QUALITY MONITORING
MACHINE LEARNING FOR SATELLITE-GUIDED WATER QUALITY MONITORING VisionGEOMATIQUE2014
 

Similaire à Hawaii Pacific GIS Conference 2012: 3D GIS - Creating Bathymetry Maps with Coarse Data - Bayesian Kriging Using Open Source Tools (20)

Big datascienceh2oandr
Big datascienceh2oandrBig datascienceh2oandr
Big datascienceh2oandr
 
Big Data Science with H2O in R
Big Data Science with H2O in RBig Data Science with H2O in R
Big Data Science with H2O in R
 
Data mining
Data mining Data mining
Data mining
 
Osgeo.wageningen kickoff event nov2012
Osgeo.wageningen kickoff event nov2012Osgeo.wageningen kickoff event nov2012
Osgeo.wageningen kickoff event nov2012
 
RemoteSensing_DeepLearning_v2.pptx
RemoteSensing_DeepLearning_v2.pptxRemoteSensing_DeepLearning_v2.pptx
RemoteSensing_DeepLearning_v2.pptx
 
Drones and A.I in Earth Science
Drones and A.I in Earth ScienceDrones and A.I in Earth Science
Drones and A.I in Earth Science
 
Keller geo edu
Keller geo eduKeller geo edu
Keller geo edu
 
Playful Explorations of Public and Personal Data - OSCON Data 2011
Playful Explorations of Public and Personal Data - OSCON Data 2011Playful Explorations of Public and Personal Data - OSCON Data 2011
Playful Explorations of Public and Personal Data - OSCON Data 2011
 
eMerges - Terra Cognita 2006 Workshop (ISWC)
eMerges - Terra Cognita 2006 Workshop (ISWC)eMerges - Terra Cognita 2006 Workshop (ISWC)
eMerges - Terra Cognita 2006 Workshop (ISWC)
 
Gis lecture #01
Gis lecture #01Gis lecture #01
Gis lecture #01
 
Tutorial on Polynomial Networks at CVPR'22
Tutorial on Polynomial Networks at CVPR'22Tutorial on Polynomial Networks at CVPR'22
Tutorial on Polynomial Networks at CVPR'22
 
Spatial Analysis with R - the Good, the Bad, and the Pretty
Spatial Analysis with R - the Good, the Bad, and the PrettySpatial Analysis with R - the Good, the Bad, and the Pretty
Spatial Analysis with R - the Good, the Bad, and the Pretty
 
[212]big models without big data using domain specific deep networks in data-...
[212]big models without big data using domain specific deep networks in data-...[212]big models without big data using domain specific deep networks in data-...
[212]big models without big data using domain specific deep networks in data-...
 
Finding Meaning in Points, Areas and Surfaces: Spatial Analysis in R
Finding Meaning in Points, Areas and Surfaces: Spatial Analysis in RFinding Meaning in Points, Areas and Surfaces: Spatial Analysis in R
Finding Meaning in Points, Areas and Surfaces: Spatial Analysis in R
 
Finding Fallopia: detection of Japanese Knotweed s.l. taxa in the UK using re...
Finding Fallopia: detection of Japanese Knotweed s.l. taxa in the UK using re...Finding Fallopia: detection of Japanese Knotweed s.l. taxa in the UK using re...
Finding Fallopia: detection of Japanese Knotweed s.l. taxa in the UK using re...
 
Big Data + Big Sim: Query Processing over Unstructured CFD Models
Big Data + Big Sim: Query Processing over Unstructured CFD ModelsBig Data + Big Sim: Query Processing over Unstructured CFD Models
Big Data + Big Sim: Query Processing over Unstructured CFD Models
 
Gis fandamentals -1
Gis fandamentals -1Gis fandamentals -1
Gis fandamentals -1
 
IEEE SIGHT Bombay section webinar talk on GIS & Remote Sensing-Introduction t...
IEEE SIGHT Bombay section webinar talk on GIS & Remote Sensing-Introduction t...IEEE SIGHT Bombay section webinar talk on GIS & Remote Sensing-Introduction t...
IEEE SIGHT Bombay section webinar talk on GIS & Remote Sensing-Introduction t...
 
The Other HPC: High Productivity Computing
The Other HPC: High Productivity ComputingThe Other HPC: High Productivity Computing
The Other HPC: High Productivity Computing
 
MACHINE LEARNING FOR SATELLITE-GUIDED WATER QUALITY MONITORING
MACHINE LEARNING FOR SATELLITE-GUIDED WATER QUALITY MONITORINGMACHINE LEARNING FOR SATELLITE-GUIDED WATER QUALITY MONITORING
MACHINE LEARNING FOR SATELLITE-GUIDED WATER QUALITY MONITORING
 

Plus de Hawaii Geographic Information Coordinating Council

Plus de Hawaii Geographic Information Coordinating Council (20)

Taking 3D to the next Level with 3D Streaming Maps
Taking 3D to the next Level with 3D Streaming MapsTaking 3D to the next Level with 3D Streaming Maps
Taking 3D to the next Level with 3D Streaming Maps
 
US Army Real Estate Holdings in Hawaii
US Army Real Estate Holdings in HawaiiUS Army Real Estate Holdings in Hawaii
US Army Real Estate Holdings in Hawaii
 
NOAA's Coastal Change Analysis Program
NOAA's Coastal Change Analysis ProgramNOAA's Coastal Change Analysis Program
NOAA's Coastal Change Analysis Program
 
Hawaii and US Pacific Basin Orthoimagery Update
Hawaii and US Pacific Basin Orthoimagery UpdateHawaii and US Pacific Basin Orthoimagery Update
Hawaii and US Pacific Basin Orthoimagery Update
 
The ArcGIS Platform: Appyling Geography Everywhere
The ArcGIS Platform: Appyling Geography EverywhereThe ArcGIS Platform: Appyling Geography Everywhere
The ArcGIS Platform: Appyling Geography Everywhere
 
Web based Data and Tools for Coastal Management
Web based Data and Tools for Coastal ManagementWeb based Data and Tools for Coastal Management
Web based Data and Tools for Coastal Management
 
Ecosystem Vulnerability and Cumulative Impacts on th eOceans of hawaii
Ecosystem Vulnerability and Cumulative Impacts on th eOceans of hawaiiEcosystem Vulnerability and Cumulative Impacts on th eOceans of hawaii
Ecosystem Vulnerability and Cumulative Impacts on th eOceans of hawaii
 
Using GIS to Connect Communities
Using GIS to Connect CommunitiesUsing GIS to Connect Communities
Using GIS to Connect Communities
 
Assessing Reef Health Using a Low Altitude Sensing Platform
Assessing Reef Health Using a Low Altitude Sensing PlatformAssessing Reef Health Using a Low Altitude Sensing Platform
Assessing Reef Health Using a Low Altitude Sensing Platform
 
Hawaii DOT Monitoring Stations Versus National Performance Measurement Resear...
Hawaii DOT Monitoring Stations Versus National Performance Measurement Resear...Hawaii DOT Monitoring Stations Versus National Performance Measurement Resear...
Hawaii DOT Monitoring Stations Versus National Performance Measurement Resear...
 
Use of GIS Technology to Inform Planning Efforts Through Visualization of Com...
Use of GIS Technology to Inform Planning Efforts Through Visualization of Com...Use of GIS Technology to Inform Planning Efforts Through Visualization of Com...
Use of GIS Technology to Inform Planning Efforts Through Visualization of Com...
 
Expanding GIS Access to Technical and Non-Technical Users to Enhance Project ...
Expanding GIS Access to Technical and Non-Technical Users to Enhance Project ...Expanding GIS Access to Technical and Non-Technical Users to Enhance Project ...
Expanding GIS Access to Technical and Non-Technical Users to Enhance Project ...
 
STEMworks: K12 Education in Hawaii in Science Technology Engineering and Math
STEMworks: K12 Education in Hawaii in Science Technology Engineering and MathSTEMworks: K12 Education in Hawaii in Science Technology Engineering and Math
STEMworks: K12 Education in Hawaii in Science Technology Engineering and Math
 
Planning for Technological Change
Planning for Technological ChangePlanning for Technological Change
Planning for Technological Change
 
Now & the Future of geodesy in Hawaii for the GIS Users
Now & the Future of geodesy in Hawaii for the GIS UsersNow & the Future of geodesy in Hawaii for the GIS Users
Now & the Future of geodesy in Hawaii for the GIS Users
 
314 woods- uav mapping history
314   woods- uav mapping history314   woods- uav mapping history
314 woods- uav mapping history
 
314 smith 2015 higicc-final
314  smith 2015 higicc-final314  smith 2015 higicc-final
314 smith 2015 higicc-final
 
Real Time Corrections for GNSS Receivers
Real Time Corrections for GNSS ReceiversReal Time Corrections for GNSS Receivers
Real Time Corrections for GNSS Receivers
 
Honolulu Board of Water Supply: Enterprise GIS
Honolulu Board of Water Supply: Enterprise GISHonolulu Board of Water Supply: Enterprise GIS
Honolulu Board of Water Supply: Enterprise GIS
 
State of Hawaii Digital Leveling Project
State of Hawaii Digital Leveling ProjectState of Hawaii Digital Leveling Project
State of Hawaii Digital Leveling Project
 

Dernier

UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UbiTrack UK
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopBachir Benyammi
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfJamie (Taka) Wang
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesDavid Newbury
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URLRuncy Oommen
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7DianaGray10
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarPrecisely
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdfPedro Manuel
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfDaniel Santiago Silva Capera
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8DianaGray10
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxUdaiappa Ramachandran
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1DianaGray10
 
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostMatt Ray
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxMatsuo Lab
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Commit University
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Brian Pichman
 

Dernier (20)

UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
 
NIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 WorkshopNIST Cybersecurity Framework (CSF) 2.0 Workshop
NIST Cybersecurity Framework (CSF) 2.0 Workshop
 
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
activity_diagram_combine_v4_20190827.pdfactivity_diagram_combine_v4_20190827.pdf
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond Ontologies
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URL
 
20150722 - AGV
20150722 - AGV20150722 - AGV
20150722 - AGV
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
 
UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7UiPath Studio Web workshop series - Day 7
UiPath Studio Web workshop series - Day 7
 
AI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity WebinarAI You Can Trust - Ensuring Success with Data Integrity Webinar
AI You Can Trust - Ensuring Success with Data Integrity Webinar
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdf
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdfIaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
IaC & GitOps in a Nutshell - a FridayInANuthshell Episode.pdf
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8
 
Building AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptxBuilding AI-Driven Apps Using Semantic Kernel.pptx
Building AI-Driven Apps Using Semantic Kernel.pptx
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
 
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCostKubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
KubeConEU24-Monitoring Kubernetes and Cloud Spend with OpenCost
 
Introduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptxIntroduction to Matsuo Laboratory (ENG).pptx
Introduction to Matsuo Laboratory (ENG).pptx
 
Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)Crea il tuo assistente AI con lo Stregatto (open source python framework)
Crea il tuo assistente AI con lo Stregatto (open source python framework)
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )Building Your Own AI Instance (TBLC AI )
Building Your Own AI Instance (TBLC AI )
 

Hawaii Pacific GIS Conference 2012: 3D GIS - Creating Bathymetry Maps with Coarse Data - Bayesian Kriging Using Open Source Tools

  • 1. Creating Bathymetry Maps With Coarse Data - Bayesian Kriging Using Open Source Tools Hal Koike University of Hawaii, Hawaii Fisheries Cooperative Research Unit
  • 2. Why do we need a Bathymetry Map?  Marine resource management is pushed toward ecosystem based management (e.g. linking with land development, marine protected area)  You need spatial data to fully understand the ecosystem of your interest  Species distribution for marine organisms is known to be influenced by depth
  • 3. Outside the United States… Most countries do not have a spatial data repository where bathymetry data, land cover data, etc. is readily available to be used for analysis.
  • 4. If $$ is Limited, What are the Options?  Stick with what you have  Create a pseudo-bathymetry map Some budget friendly data covering the world (bathymetry case)  Navigational chart (low cost)  MODIS (free)  Hyperion (free)
  • 7. Solution Create a pseudo bathymetry map using Bayesian Kriging option in GeoR (Rilbeiro jr., P.J. and Diggle, P.J. 2001)
  • 8. What is GeoR?  Created by Paulo J. Ribeiro Jr. and Peter J. Diggle.  One of the many packages available through R-CRAN project  Operated on R
  • 10. Step 2. Enter the Depth Data
  • 11. What it looks like after entering all the points
  • 12. Step 3. Import the Point Data to Geo R
  • 13. Step 4. Find your Range
  • 14. Step 5. Run the Bayesian Kriging Simulation x <- seq(241472,403019,2000) y <- seq(9449003,9559751,2000) d1 <- expand.grid(x=x,y=y) ex.bayes <- krige.bayes(YourData,loc=d1,model=model.control( cov.m="matern",kappa=0.5),prior=prior.control(phi. discrete=seq(0,80000,l=10),phi.prior="reciprocal"))
  • 18. Error of Predicted Values (Estimation Variance)
  • 19. Error of Predicted Values (Estimation Variance)
  • 21. Accuracy Comparison Bathymetry Map Standard Deviation SRTM 30 (1km grid) 76.89 Bayesian Kriging (2km grid) 9.00 Conventional Kriging (2km grid) 8.30 Statistically simulated bathymetry map had less deviation then remotely sensed data