SlideShare a Scribd company logo
1 of 4
Download to read offline
Sara Welge
Lab 5
GEOG48224
5/23/2014
1. The offsets refer to the viewpoints relationship to the ground, how high or low it is. The vertical
angle refers to How high above and below the horizon a viewer can see. The azimuth defines
the direction of view, east to west, and north to south.
2. This analysis is only capturing things at the ten square meter level, and doesn’t account for
things like trees that may cause visibility to differ significantly in real life from the analysis.
3. Raster resampling takes the inputs and converts all of them to the same measurement system
and the same scale. Bi linear resampling uses all surrounding cells an averages them, this
method is good for continuous data like temperature or elevation. Nearest Neighbor resampling
relates the center of the original cell to the nearest cell center of the second layer. This method
is best for discrete data sets. Since the data is in terms of elevation and slope (continuous data)
it would be best to use Bi-linear. However there is also an aspect (discrete data) layer which may
be better suited to nearest neighbor.
^_
^_
^_ Viewpoints
McCully Trail
Trail Visibility
Value
High : 105
Low : 0
^_
^_
McCully Trail
^_ Viewpoints
contourmap
Base Map Visibility From The Trail
¯
0 0.65 1.3 1.95 2.60.325
Miles
¯
0 0.85 1.7 2.55 3.40.425
Miles
Projection: Lambert_Conformal_Conic
False_Easting: 1312335.958005248
False_Northing: 0.0
Central_Meridian: -120.5
Standard_Parallel_1: 43.0
Standard_Parallel_2: 45.5
Latitude_Of_Origin: 41.75
Linear Unit: Foot (0.3048)
^_
^_
#*
#*
View Shed From Both Look Outs
View Shed From East Look Out
View Shed From West Look Out
^_ Viewpoints
McCully Trail
Visibility
Value
Visible From One Point
Visible From Both Points
#* West View Point
McCully Trail
Visibility
Value
Not Visible
Visible
#* East View Point
McCully Trail
Visibilility
Value
Not Visible
Visible
0 2 41 Miles
0 1 20.5 Miles0 1.5 30.75 Miles
¯ ¯
¯
East View Point
Aspect 264.3619
Hill Shade 212.6734
Slope 29.16117
West View Point
Aspect 129.2697
Hill Shade 60.77104
Slope 31.27201
View points
McCully Trail
Visiblity
Visible From East view point
Visible From West View Point
Visible From Both Points
Figure A
Fig. A shows a 3D rendering of the McCully trail,
viewpoints, and the Basin. The visible areas from the east
view point are shown in blue, the visibility from the from
the west is in yellow and the visible area from both is
shown in green.

More Related Content

What's hot

Well Directional Survey Processing in FME
Well Directional Survey Processing in FMEWell Directional Survey Processing in FME
Well Directional Survey Processing in FMESafe Software
 
When to use the mean (2)
When to use the mean (2)When to use the mean (2)
When to use the mean (2)Ken Plummer
 
When to use the mean
When to use the meanWhen to use the mean
When to use the meanKen Plummer
 
Types of north surveing
Types of north surveingTypes of north surveing
Types of north surveingDiana Dian
 
When to use standard deviation
When to use standard deviationWhen to use standard deviation
When to use standard deviationKen Plummer
 
Merge sort lab mannual
Merge sort lab mannualMerge sort lab mannual
Merge sort lab mannualmaamir farooq
 
A glimpse into integration
A glimpse into integrationA glimpse into integration
A glimpse into integrationCaitie Brown
 

What's hot (8)

balogh_ams
balogh_amsbalogh_ams
balogh_ams
 
Well Directional Survey Processing in FME
Well Directional Survey Processing in FMEWell Directional Survey Processing in FME
Well Directional Survey Processing in FME
 
When to use the mean (2)
When to use the mean (2)When to use the mean (2)
When to use the mean (2)
 
When to use the mean
When to use the meanWhen to use the mean
When to use the mean
 
Types of north surveing
Types of north surveingTypes of north surveing
Types of north surveing
 
When to use standard deviation
When to use standard deviationWhen to use standard deviation
When to use standard deviation
 
Merge sort lab mannual
Merge sort lab mannualMerge sort lab mannual
Merge sort lab mannual
 
A glimpse into integration
A glimpse into integrationA glimpse into integration
A glimpse into integration
 

Similar to Lab5

Estimation of global solar radiation by using machine learning methods
Estimation of global solar radiation by using machine learning methodsEstimation of global solar radiation by using machine learning methods
Estimation of global solar radiation by using machine learning methodsmehmet şahin
 
Summer 2012 Project Report
Summer 2012 Project ReportSummer 2012 Project Report
Summer 2012 Project ReportLalit Pradhan
 
Site surveying report 1
Site surveying report 1Site surveying report 1
Site surveying report 1Doreen Yeo
 
An approach for quality check
An approach for quality checkAn approach for quality check
An approach for quality checkECRD IN
 
WisconsinBreastCancerDiagnosticClassificationusingKNNandRandomForest
WisconsinBreastCancerDiagnosticClassificationusingKNNandRandomForestWisconsinBreastCancerDiagnosticClassificationusingKNNandRandomForest
WisconsinBreastCancerDiagnosticClassificationusingKNNandRandomForestSheing Jing Ng
 
Site survey Fieldwork1 (levelling)
Site survey Fieldwork1 (levelling)Site survey Fieldwork1 (levelling)
Site survey Fieldwork1 (levelling)Sheng Zhe
 
Automated Clustering Project - 12th CONTECSI 34th WCARS
Automated Clustering Project - 12th CONTECSI 34th WCARS Automated Clustering Project - 12th CONTECSI 34th WCARS
Automated Clustering Project - 12th CONTECSI 34th WCARS TECSI FEA USP
 
Density based Clustering Algorithms(DB SCAN, Mean shift )
Density based Clustering Algorithms(DB SCAN, Mean shift )Density based Clustering Algorithms(DB SCAN, Mean shift )
Density based Clustering Algorithms(DB SCAN, Mean shift )Utkarsh Sharma
 
Assessing the compactness and isolation of individual clusters
Assessing the compactness and isolation of individual clustersAssessing the compactness and isolation of individual clusters
Assessing the compactness and isolation of individual clustersperfj
 
Measures of Dispersion
Measures of DispersionMeasures of Dispersion
Measures of DispersionKainatIqbal7
 
3Data summarization.pptx
3Data summarization.pptx3Data summarization.pptx
3Data summarization.pptxAmanuelMerga
 
Survey on Unsupervised Learning in Datamining
Survey on Unsupervised Learning in DataminingSurvey on Unsupervised Learning in Datamining
Survey on Unsupervised Learning in DataminingIOSR Journals
 
Modelling process quality
Modelling process qualityModelling process quality
Modelling process qualityZenblade 93
 

Similar to Lab5 (20)

Construction surveys
Construction surveysConstruction surveys
Construction surveys
 
level set method
level set methodlevel set method
level set method
 
Estimation of global solar radiation by using machine learning methods
Estimation of global solar radiation by using machine learning methodsEstimation of global solar radiation by using machine learning methods
Estimation of global solar radiation by using machine learning methods
 
Summer 2012 Project Report
Summer 2012 Project ReportSummer 2012 Project Report
Summer 2012 Project Report
 
Site surveying report 1
Site surveying report 1Site surveying report 1
Site surveying report 1
 
Fixed sensors
Fixed sensorsFixed sensors
Fixed sensors
 
An approach for quality check
An approach for quality checkAn approach for quality check
An approach for quality check
 
WisconsinBreastCancerDiagnosticClassificationusingKNNandRandomForest
WisconsinBreastCancerDiagnosticClassificationusingKNNandRandomForestWisconsinBreastCancerDiagnosticClassificationusingKNNandRandomForest
WisconsinBreastCancerDiagnosticClassificationusingKNNandRandomForest
 
Site survey Fieldwork1 (levelling)
Site survey Fieldwork1 (levelling)Site survey Fieldwork1 (levelling)
Site survey Fieldwork1 (levelling)
 
Automated Clustering Project - 12th CONTECSI 34th WCARS
Automated Clustering Project - 12th CONTECSI 34th WCARS Automated Clustering Project - 12th CONTECSI 34th WCARS
Automated Clustering Project - 12th CONTECSI 34th WCARS
 
Density based Clustering Algorithms(DB SCAN, Mean shift )
Density based Clustering Algorithms(DB SCAN, Mean shift )Density based Clustering Algorithms(DB SCAN, Mean shift )
Density based Clustering Algorithms(DB SCAN, Mean shift )
 
50134 10
50134 1050134 10
50134 10
 
Assessing the compactness and isolation of individual clusters
Assessing the compactness and isolation of individual clustersAssessing the compactness and isolation of individual clusters
Assessing the compactness and isolation of individual clusters
 
Ss report 2
Ss report 2Ss report 2
Ss report 2
 
Measures of Dispersion
Measures of DispersionMeasures of Dispersion
Measures of Dispersion
 
3Data summarization.pptx
3Data summarization.pptx3Data summarization.pptx
3Data summarization.pptx
 
Automation Controller
Automation ControllerAutomation Controller
Automation Controller
 
Survey on Unsupervised Learning in Datamining
Survey on Unsupervised Learning in DataminingSurvey on Unsupervised Learning in Datamining
Survey on Unsupervised Learning in Datamining
 
Modelling process quality
Modelling process qualityModelling process quality
Modelling process quality
 
Cluster analysis
Cluster analysisCluster analysis
Cluster analysis
 

Lab5

  • 1. Sara Welge Lab 5 GEOG48224 5/23/2014 1. The offsets refer to the viewpoints relationship to the ground, how high or low it is. The vertical angle refers to How high above and below the horizon a viewer can see. The azimuth defines the direction of view, east to west, and north to south. 2. This analysis is only capturing things at the ten square meter level, and doesn’t account for things like trees that may cause visibility to differ significantly in real life from the analysis. 3. Raster resampling takes the inputs and converts all of them to the same measurement system and the same scale. Bi linear resampling uses all surrounding cells an averages them, this method is good for continuous data like temperature or elevation. Nearest Neighbor resampling relates the center of the original cell to the nearest cell center of the second layer. This method is best for discrete data sets. Since the data is in terms of elevation and slope (continuous data) it would be best to use Bi-linear. However there is also an aspect (discrete data) layer which may be better suited to nearest neighbor.
  • 2. ^_ ^_ ^_ Viewpoints McCully Trail Trail Visibility Value High : 105 Low : 0 ^_ ^_ McCully Trail ^_ Viewpoints contourmap Base Map Visibility From The Trail ¯ 0 0.65 1.3 1.95 2.60.325 Miles ¯ 0 0.85 1.7 2.55 3.40.425 Miles Projection: Lambert_Conformal_Conic False_Easting: 1312335.958005248 False_Northing: 0.0 Central_Meridian: -120.5 Standard_Parallel_1: 43.0 Standard_Parallel_2: 45.5 Latitude_Of_Origin: 41.75 Linear Unit: Foot (0.3048)
  • 3. ^_ ^_ #* #* View Shed From Both Look Outs View Shed From East Look Out View Shed From West Look Out ^_ Viewpoints McCully Trail Visibility Value Visible From One Point Visible From Both Points #* West View Point McCully Trail Visibility Value Not Visible Visible #* East View Point McCully Trail Visibilility Value Not Visible Visible 0 2 41 Miles 0 1 20.5 Miles0 1.5 30.75 Miles ¯ ¯ ¯ East View Point Aspect 264.3619 Hill Shade 212.6734 Slope 29.16117 West View Point Aspect 129.2697 Hill Shade 60.77104 Slope 31.27201
  • 4. View points McCully Trail Visiblity Visible From East view point Visible From West View Point Visible From Both Points Figure A Fig. A shows a 3D rendering of the McCully trail, viewpoints, and the Basin. The visible areas from the east view point are shown in blue, the visibility from the from the west is in yellow and the visible area from both is shown in green.