Calculate coverage percentage of an area covered by shapefiles using GDAL？ [closed]The minimum bounding circle of geometry that crosses the 180th meridianMosaicing indexed colour rasters with independent colour tablesPython, GDAL, VRT: help wth code efficiency and processing timeusing Python to calculate NDVI, output as a GeotiffCalculating Focal Statistics for Special Neighborhood?How to quantify the coverage area?How many percentage of sea area is covered with points?Can I multiply fractions in map algebra?Get field names of shapefiles using GDALCannot seem to load shapefiles using GDAL?
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Calculate coverage percentage of an area covered by shapefiles using GDAL？ [closed]
The minimum bounding circle of geometry that crosses the 180th meridianMosaicing indexed colour rasters with independent colour tablesPython, GDAL, VRT: help wth code efficiency and processing timeusing Python to calculate NDVI, output as a GeotiffCalculating Focal Statistics for Special Neighborhood?How to quantify the coverage area?How many percentage of sea area is covered with points?Can I multiply fractions in map algebra?Get field names of shapefiles using GDALCannot seem to load shapefiles using GDAL?
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I have thousands of shapefiles covering around the world. I need to calculate coverage percentage. For example, a pixel located in Hawaii was covered by 100 of all shapefiles, then this pixel value of 100 will be output.
I can convert them to rasters, but I cannot stack all rasters in a file for calculation because of their large volume.
So, how can I calculate coverage percentage by using shapefiles or separated rasters?
python gdal spatial-statistics
edited Jul 6 at 10:44
nmtoken
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asked Jul 6 at 8:40
Kevin LeeKevin Lee
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closed as off-topic by ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar Jul 8 at 13:17
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "When seeking help to debug/write/improve code always provide the desired behavior, a specific problem/error and the shortest code (as formatted text, not pictures) needed to reproduce it in the question body. Providing a clear problem statement and a code attempt helps others to help you." – ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar
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I have thousands of shapefiles covering around the world. I need to calculate coverage percentage. For example, a pixel located in Hawaii was covered by 100 of all shapefiles, then this pixel value of 100 will be output.
I can convert them to rasters, but I cannot stack all rasters in a file for calculation because of their large volume.
So, how can I calculate coverage percentage by using shapefiles or separated rasters?
python gdal spatial-statistics
edited Jul 6 at 10:44
nmtoken
8,7454 gold badges28 silver badges67 bronze badges
asked Jul 6 at 8:40
Kevin LeeKevin Lee
112 bronze badges
closed as off-topic by ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar Jul 8 at 13:17
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "When seeking help to debug/write/improve code always provide the desired behavior, a specific problem/error and the shortest code (as formatted text, not pictures) needed to reproduce it in the question body. Providing a clear problem statement and a code attempt helps others to help you." – ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar
add a comment
|
I have thousands of shapefiles covering around the world. I need to calculate coverage percentage. For example, a pixel located in Hawaii was covered by 100 of all shapefiles, then this pixel value of 100 will be output.
I can convert them to rasters, but I cannot stack all rasters in a file for calculation because of their large volume.
So, how can I calculate coverage percentage by using shapefiles or separated rasters?
python gdal spatial-statistics
edited Jul 6 at 10:44
nmtoken
8,7454 gold badges28 silver badges67 bronze badges
asked Jul 6 at 8:40
Kevin LeeKevin Lee
112 bronze badges
I have thousands of shapefiles covering around the world. I need to calculate coverage percentage. For example, a pixel located in Hawaii was covered by 100 of all shapefiles, then this pixel value of 100 will be output.
I can convert them to rasters, but I cannot stack all rasters in a file for calculation because of their large volume.
So, how can I calculate coverage percentage by using shapefiles or separated rasters?
python gdal spatial-statistics
python gdal spatial-statistics
edited Jul 6 at 10:44
nmtoken
8,7454 gold badges28 silver badges67 bronze badges
asked Jul 6 at 8:40
Kevin LeeKevin Lee
112 bronze badges
edited Jul 6 at 10:44
nmtoken
8,7454 gold badges28 silver badges67 bronze badges
asked Jul 6 at 8:40
Kevin LeeKevin Lee
112 bronze badges
edited Jul 6 at 10:44
nmtoken
8,7454 gold badges28 silver badges67 bronze badges
edited Jul 6 at 10:44
nmtoken
8,7454 gold badges28 silver badges67 bronze badges
edited Jul 6 at 10:44
nmtoken
8,7454 gold badges28 silver badges67 bronze badges
8,7454 gold badges28 silver badges67 bronze badges
asked Jul 6 at 8:40
Kevin LeeKevin Lee
112 bronze badges
asked Jul 6 at 8:40
Kevin LeeKevin Lee
112 bronze badges
asked Jul 6 at 8:40
Kevin LeeKevin Lee
112 bronze badges
112 bronze badges
closed as off-topic by ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar Jul 8 at 13:17
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "When seeking help to debug/write/improve code always provide the desired behavior, a specific problem/error and the shortest code (as formatted text, not pictures) needed to reproduce it in the question body. Providing a clear problem statement and a code attempt helps others to help you." – ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar
closed as off-topic by ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar Jul 8 at 13:17
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "When seeking help to debug/write/improve code always provide the desired behavior, a specific problem/error and the shortest code (as formatted text, not pictures) needed to reproduce it in the question body. Providing a clear problem statement and a code attempt helps others to help you." – ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar
closed as off-topic by ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar Jul 8 at 13:17
This question appears to be off-topic. The users who voted to close gave this specific reason:
- "When seeking help to debug/write/improve code always provide the desired behavior, a specific problem/error and the shortest code (as formatted text, not pictures) needed to reproduce it in the question body. Providing a clear problem statement and a code attempt helps others to help you." – ahmadhanb, Kadir Şahbaz, xunilk, LaughU, whyzar
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2 Answers
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A conceptual solution would be to reclass all your rasters with 1 (covered) and 0 (not covered) and then use raster algebra to add all your rasters together. The pixel value will be the total value of all rasters which cover the given area. Take extra care to deal with different raster resolutions.
answered Jul 6 at 9:23
vagvafvagvaf
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You wouldn't need to convert all the shapefiles to raster at once, so volume consideration is a non-issue.
Using a raster masking technique is going to result in imprecision at the pixel boundary -- Does a file which include the center of the pixel count as "covered" if not 100% of the pixel is covered? Using a vector technique could attain higher precision, at the cost of a lot of processing.
In raster space, you just need to mask each vector at a time (with consideration as to whether you should take a one pixel (or sqrt(2)*pixeldim) negative buffer before masking), sum the presence/absence flag with a cumulator, and then divide by the number of shapefiles (encoding this as byte (either 0-100 or 0-250) would speed future processing, at the cost of precision)
In vector space, you'd need to:
- Determine the resolution of your percentage raster (pixel size, type and depth)
- Create a partition vector to form the basis for subsetting the vector space which is in alignment with the raster (large enough to reduce the iterations, but small enough to save on vector overlay cost)
- Iterate over the tiles with a clip and union of each shapefile, then compute percentage and convert that to raster, encode the desired pixel value, and mask this into a final raster.
For most approaches to either technique, you're going to need a full GIS, not just GDAL and Python.
answered Jul 6 at 11:13
VinceVince
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2 Answers
2
active
oldest
votes
2 Answers
2
active
oldest
votes
active
oldest
votes
active
oldest
votes
A conceptual solution would be to reclass all your rasters with 1 (covered) and 0 (not covered) and then use raster algebra to add all your rasters together. The pixel value will be the total value of all rasters which cover the given area. Take extra care to deal with different raster resolutions.
answered Jul 6 at 9:23
vagvafvagvaf
9771 gold badge6 silver badges15 bronze badges
add a comment
|
A conceptual solution would be to reclass all your rasters with 1 (covered) and 0 (not covered) and then use raster algebra to add all your rasters together. The pixel value will be the total value of all rasters which cover the given area. Take extra care to deal with different raster resolutions.
answered Jul 6 at 9:23
vagvafvagvaf
9771 gold badge6 silver badges15 bronze badges
add a comment
|
A conceptual solution would be to reclass all your rasters with 1 (covered) and 0 (not covered) and then use raster algebra to add all your rasters together. The pixel value will be the total value of all rasters which cover the given area. Take extra care to deal with different raster resolutions.
answered Jul 6 at 9:23
vagvafvagvaf
9771 gold badge6 silver badges15 bronze badges
A conceptual solution would be to reclass all your rasters with 1 (covered) and 0 (not covered) and then use raster algebra to add all your rasters together. The pixel value will be the total value of all rasters which cover the given area. Take extra care to deal with different raster resolutions.
answered Jul 6 at 9:23
vagvafvagvaf
9771 gold badge6 silver badges15 bronze badges
answered Jul 6 at 9:23
vagvafvagvaf
9771 gold badge6 silver badges15 bronze badges
answered Jul 6 at 9:23
vagvafvagvaf
9771 gold badge6 silver badges15 bronze badges
answered Jul 6 at 9:23
vagvafvagvaf
9771 gold badge6 silver badges15 bronze badges
9771 gold badge6 silver badges15 bronze badges
add a comment
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add a comment
|
You wouldn't need to convert all the shapefiles to raster at once, so volume consideration is a non-issue.
Using a raster masking technique is going to result in imprecision at the pixel boundary -- Does a file which include the center of the pixel count as "covered" if not 100% of the pixel is covered? Using a vector technique could attain higher precision, at the cost of a lot of processing.
In raster space, you just need to mask each vector at a time (with consideration as to whether you should take a one pixel (or sqrt(2)*pixeldim) negative buffer before masking), sum the presence/absence flag with a cumulator, and then divide by the number of shapefiles (encoding this as byte (either 0-100 or 0-250) would speed future processing, at the cost of precision)
In vector space, you'd need to:
- Determine the resolution of your percentage raster (pixel size, type and depth)
- Create a partition vector to form the basis for subsetting the vector space which is in alignment with the raster (large enough to reduce the iterations, but small enough to save on vector overlay cost)
- Iterate over the tiles with a clip and union of each shapefile, then compute percentage and convert that to raster, encode the desired pixel value, and mask this into a final raster.
For most approaches to either technique, you're going to need a full GIS, not just GDAL and Python.
answered Jul 6 at 11:13
VinceVince
15.6k4 gold badges32 silver badges50 bronze badges
add a comment
|
You wouldn't need to convert all the shapefiles to raster at once, so volume consideration is a non-issue.
Using a raster masking technique is going to result in imprecision at the pixel boundary -- Does a file which include the center of the pixel count as "covered" if not 100% of the pixel is covered? Using a vector technique could attain higher precision, at the cost of a lot of processing.
In raster space, you just need to mask each vector at a time (with consideration as to whether you should take a one pixel (or sqrt(2)*pixeldim) negative buffer before masking), sum the presence/absence flag with a cumulator, and then divide by the number of shapefiles (encoding this as byte (either 0-100 or 0-250) would speed future processing, at the cost of precision)
In vector space, you'd need to:
- Determine the resolution of your percentage raster (pixel size, type and depth)
- Create a partition vector to form the basis for subsetting the vector space which is in alignment with the raster (large enough to reduce the iterations, but small enough to save on vector overlay cost)
- Iterate over the tiles with a clip and union of each shapefile, then compute percentage and convert that to raster, encode the desired pixel value, and mask this into a final raster.
For most approaches to either technique, you're going to need a full GIS, not just GDAL and Python.
answered Jul 6 at 11:13
VinceVince
15.6k4 gold badges32 silver badges50 bronze badges
add a comment
|
You wouldn't need to convert all the shapefiles to raster at once, so volume consideration is a non-issue.
Using a raster masking technique is going to result in imprecision at the pixel boundary -- Does a file which include the center of the pixel count as "covered" if not 100% of the pixel is covered? Using a vector technique could attain higher precision, at the cost of a lot of processing.
In raster space, you just need to mask each vector at a time (with consideration as to whether you should take a one pixel (or sqrt(2)*pixeldim) negative buffer before masking), sum the presence/absence flag with a cumulator, and then divide by the number of shapefiles (encoding this as byte (either 0-100 or 0-250) would speed future processing, at the cost of precision)
In vector space, you'd need to:
- Determine the resolution of your percentage raster (pixel size, type and depth)
- Create a partition vector to form the basis for subsetting the vector space which is in alignment with the raster (large enough to reduce the iterations, but small enough to save on vector overlay cost)
- Iterate over the tiles with a clip and union of each shapefile, then compute percentage and convert that to raster, encode the desired pixel value, and mask this into a final raster.
For most approaches to either technique, you're going to need a full GIS, not just GDAL and Python.
answered Jul 6 at 11:13
VinceVince
15.6k4 gold badges32 silver badges50 bronze badges
You wouldn't need to convert all the shapefiles to raster at once, so volume consideration is a non-issue.
Using a raster masking technique is going to result in imprecision at the pixel boundary -- Does a file which include the center of the pixel count as "covered" if not 100% of the pixel is covered? Using a vector technique could attain higher precision, at the cost of a lot of processing.
In raster space, you just need to mask each vector at a time (with consideration as to whether you should take a one pixel (or sqrt(2)*pixeldim) negative buffer before masking), sum the presence/absence flag with a cumulator, and then divide by the number of shapefiles (encoding this as byte (either 0-100 or 0-250) would speed future processing, at the cost of precision)
In vector space, you'd need to:
- Determine the resolution of your percentage raster (pixel size, type and depth)
- Create a partition vector to form the basis for subsetting the vector space which is in alignment with the raster (large enough to reduce the iterations, but small enough to save on vector overlay cost)
- Iterate over the tiles with a clip and union of each shapefile, then compute percentage and convert that to raster, encode the desired pixel value, and mask this into a final raster.
For most approaches to either technique, you're going to need a full GIS, not just GDAL and Python.
answered Jul 6 at 11:13
VinceVince
15.6k4 gold badges32 silver badges50 bronze badges
answered Jul 6 at 11:13
VinceVince
15.6k4 gold badges32 silver badges50 bronze badges
answered Jul 6 at 11:13
VinceVince
15.6k4 gold badges32 silver badges50 bronze badges
answered Jul 6 at 11:13
VinceVince
15.6k4 gold badges32 silver badges50 bronze badges
15.6k4 gold badges32 silver badges50 bronze badges
add a comment
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