Difference between revisions of "Kim Chang Heon Ex2 Criterion Scores"
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| − | [[File:Normalised Criterion Maps.png|thumb|900px|center|'''Map Layout View of | + | [[File:Normalised Criterion Maps.png|thumb|900px|center|'''Map Layout View of Criterion Scores''']] |
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{| align=center width = 100% | {| align=center width = 100% | ||
| − | ! style="text-align: center; width:20em; height: 1em; border:5px ridge Blue; font-weight:bold; font-size:25px; font-family:Times New Roman !important;; background-color:White; color: Black;"| | + | ! style="text-align: center; width:20em; height: 1em; border:5px ridge Blue; font-weight:bold; font-size:25px; font-family:Times New Roman !important;; background-color:White; color: Black;"| Health Risk Factor |
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{| align=center width = 100% | {| align=center width = 100% | ||
| − | | style="text-align:justify; width:20em; height: 1em; font-size: | + | | style="text-align:justify; width:20em; height: 1em; font-size:15px; font-family: Times New Roman !important; background-color:White; color: black;"| To calculate criterion scores of the health risk factor, the proximity map of buildings were used. |
| + | From the proximity map, when the "Properties" then "INFORMATION" is checked out, it provides us with information of the maximum, mean, minimum, standard deviation and the percentage of valid points. By using the mean and the standard deviation, I used this to calculate the Z-scores ((X-Mean)/STDDEV) of each point on the raster layer, such that the map would have a normalised output which could be used to compare with other factors on the same scale. I achieved this by using the Raster Calculator, which produced another Raster layer that looked exactly like the proximity map, but with normalised values. I then used symobology to illustrate how a common scale and colour scheme was used to illustrate the different factors. | ||
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<!-- Roads Heading --> | <!-- Roads Heading --> | ||
{| align=center width = 100% | {| align=center width = 100% | ||
| − | ! style="text-align: center; width:20em; height: 1em; border:5px ridge blue; font-weight:bold; font-size:25px; font-family:Times New Roman !important;; background-color:White ; color: Black;"| | + | ! style="text-align: center; width:20em; height: 1em; border:5px ridge blue; font-weight:bold; font-size:25px; font-family:Times New Roman !important;; background-color:White ; color: Black;"| Accessibility Factor |
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{| align=center width = 100% | {| align=center width = 100% | ||
| − | | style="text-align:justify; width:20em; height: 1em; font-size:15px; font-family: Times New Roman !important; background-color:White; color: black;"| | + | | style="text-align:justify; width:20em; height: 1em; font-size:15px; font-family: Times New Roman !important; background-color:White; color: black;"| To calculate criterion scores of the accessibility factor, the proximity map of roads were used. |
| + | From the proximity map, when the "Properties" then "INFORMATION" is checked out, it provides us with information of the maximum, mean, minimum, standard deviation and the percentage of valid points. By using the mean and the standard deviation, I used this to calculate the Z-scores ((X-Mean)/STDDEV) of each point on the raster layer, such that the map would have a normalised output which could be used to compare with other factors on the same scale. I achieved this by using the Raster Calculator, which produced another Raster layer that looked exactly like the proximity map, but with normalised values. I then used symobology to illustrate how a common scale and colour scheme was used to illustrate the different factors. | ||
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{| align=center width = 100% | {| align=center width = 100% | ||
| − | ! style="text-align: center; width:20em; height: 1em; border:5px ridge blue; font-weight:bold; font-size:25px; font-family:Times New Roman !important;; background-color:White; color: Black;"| | + | ! style="text-align: center; width:20em; height: 1em; border:5px ridge blue; font-weight:bold; font-size:25px; font-family:Times New Roman !important;; background-color:White; color: Black;"| Natural Conservation Factor |
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{| align=center width = 100% | {| align=center width = 100% | ||
| − | | style="text-align:justify; width:20em; height: 1em; font-size: | + | | style="text-align:justify; width:20em; height: 1em; font-size:15px; font-family: Times New Roman !important; background-color:white; color: black;"| To calculate criterion scores of the natural conservation factor, the proximity map of natural features were used. |
| + | From the proximity map, when the "Properties" then "INFORMATION" is checked out, it provides us with information of the maximum, mean, minimum, standard deviation and the percentage of valid points. By using the mean and the standard deviation, I used this to calculate the Z-scores ((X-Mean)/STDDEV) of each point on the raster layer, such that the map would have a normalised output which could be used to compare with other factors on the same scale. I achieved this by using the Raster Calculator, which produced another Raster layer that looked exactly like the proximity map, but with normalised values. I then used symobology to illustrate how a common scale and colour scheme was used to illustrate the different factors. | ||
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{| align=center width = 100% | {| align=center width = 100% | ||
| − | ! style="text-align: center; width:20em; height: 1em; border:5px ridge blue; font-weight:bold; font-size:25px; font-family:Times New Roman !important;; background-color:white; color: black;"| | + | ! style="text-align: center; width:20em; height: 1em; border:5px ridge blue; font-weight:bold; font-size:25px; font-family:Times New Roman !important;; background-color:white; color: black;"| Economic Factor |
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{| align=center width = 100% | {| align=center width = 100% | ||
| − | | style="text-align:justify; width:20em; height: 1em; font-size: | + | | style="text-align:justify; width:20em; height: 1em; font-size:15px; font-family: Times New Roman !important; background-color:white; color: black;"| To calculate criterion scores of the economic factor, the slope analysis map of the DEM Raster layer was used. |
| + | From the slope analysis map, when the "Properties" then "INFORMATION" is checked out, it provides us with information of the maximum, mean, minimum, standard deviation and the percentage of valid points. By using the mean and the standard deviation, I used this to calculate the Z-scores ((X-Mean)/STDDEV) of each point on the raster layer, such that the map would have a normalised output which could be used to compare with other factors on the same scale. I achieved this by using the Raster Calculator, which produced another Raster layer that looked exactly like the proximity map, but with normalised values. I then used symobology to illustrate how a common scale and colour scheme was used to illustrate the different factors. | ||
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Latest revision as of 17:14, 9 November 2019
| OVERVIEW | PROXIMITY MAPS | CRITERION SCORES | AHP | RECOMMENDATION |
|---|
| Health Risk Factor |
|---|
| To calculate criterion scores of the health risk factor, the proximity map of buildings were used.
From the proximity map, when the "Properties" then "INFORMATION" is checked out, it provides us with information of the maximum, mean, minimum, standard deviation and the percentage of valid points. By using the mean and the standard deviation, I used this to calculate the Z-scores ((X-Mean)/STDDEV) of each point on the raster layer, such that the map would have a normalised output which could be used to compare with other factors on the same scale. I achieved this by using the Raster Calculator, which produced another Raster layer that looked exactly like the proximity map, but with normalised values. I then used symobology to illustrate how a common scale and colour scheme was used to illustrate the different factors. |
| Accessibility Factor |
|---|
| To calculate criterion scores of the accessibility factor, the proximity map of roads were used.
From the proximity map, when the "Properties" then "INFORMATION" is checked out, it provides us with information of the maximum, mean, minimum, standard deviation and the percentage of valid points. By using the mean and the standard deviation, I used this to calculate the Z-scores ((X-Mean)/STDDEV) of each point on the raster layer, such that the map would have a normalised output which could be used to compare with other factors on the same scale. I achieved this by using the Raster Calculator, which produced another Raster layer that looked exactly like the proximity map, but with normalised values. I then used symobology to illustrate how a common scale and colour scheme was used to illustrate the different factors. |
| Natural Conservation Factor |
|---|
| To calculate criterion scores of the natural conservation factor, the proximity map of natural features were used.
From the proximity map, when the "Properties" then "INFORMATION" is checked out, it provides us with information of the maximum, mean, minimum, standard deviation and the percentage of valid points. By using the mean and the standard deviation, I used this to calculate the Z-scores ((X-Mean)/STDDEV) of each point on the raster layer, such that the map would have a normalised output which could be used to compare with other factors on the same scale. I achieved this by using the Raster Calculator, which produced another Raster layer that looked exactly like the proximity map, but with normalised values. I then used symobology to illustrate how a common scale and colour scheme was used to illustrate the different factors. |
| Economic Factor |
|---|
| To calculate criterion scores of the economic factor, the slope analysis map of the DEM Raster layer was used.
From the slope analysis map, when the "Properties" then "INFORMATION" is checked out, it provides us with information of the maximum, mean, minimum, standard deviation and the percentage of valid points. By using the mean and the standard deviation, I used this to calculate the Z-scores ((X-Mean)/STDDEV) of each point on the raster layer, such that the map would have a normalised output which could be used to compare with other factors on the same scale. I achieved this by using the Raster Calculator, which produced another Raster layer that looked exactly like the proximity map, but with normalised values. I then used symobology to illustrate how a common scale and colour scheme was used to illustrate the different factors. |
