How to use the latitude/longitude of points when using scikit-learn's Gaussian Process Regression？
I'm trying to interpolate some data from air monitoring stations.
Almost each record have an air quality value and their latitude, longitude. But there are some records lacking values.
For example, the data just like this:
116° 42° 10
117° 43° missing
120° 20° 1000
I want to use the scikit-learn's GPR (GaussianProcessRegressor) to interpolate the missing values.
I know that the 2-D data can be processed like the last answer in this problem
Python - Kriging (Gaussian Process) in scikit_learn
My problem is that: I shouldn't use the latitude and longitude to do this task directly, beacause the earth is a sphere so the latitude/longitude is not an usual flat 2-D grid.
I want to ask how to define the distance function between points when using the scikit-learn's GPR, or should I just project these lat/lon points to flat and use them? I hadn't try this because the precesion loss during projection made me sad :(
Thx for any suggestion :)
ps. The distance between two lat/lon points can be calculate by Haversine formula like Calculate distance between two latitude-longitude points? (Haversine formula)
python scikit-learn geolocation interpolation
asked Nov 16 '18 at 8:24
龚世泽龚世泽
11
add a comment |
I'm trying to interpolate some data from air monitoring stations.
Almost each record have an air quality value and their latitude, longitude. But there are some records lacking values.
For example, the data just like this:
116° 42° 10
117° 43° missing
120° 20° 1000
I want to use the scikit-learn's GPR (GaussianProcessRegressor) to interpolate the missing values.
I know that the 2-D data can be processed like the last answer in this problem
Python - Kriging (Gaussian Process) in scikit_learn
My problem is that: I shouldn't use the latitude and longitude to do this task directly, beacause the earth is a sphere so the latitude/longitude is not an usual flat 2-D grid.
I want to ask how to define the distance function between points when using the scikit-learn's GPR, or should I just project these lat/lon points to flat and use them? I hadn't try this because the precesion loss during projection made me sad :(
Thx for any suggestion :)
ps. The distance between two lat/lon points can be calculate by Haversine formula like Calculate distance between two latitude-longitude points? (Haversine formula)
python scikit-learn geolocation interpolation
asked Nov 16 '18 at 8:24
龚世泽龚世泽
11
Using this (stackoverflow.com/questions/43240915/…) post as a guide, I'd suggest that you use a geodesy package (e.g. geographiclib) to generate geodesics to measure the distance between your "unknown" point and some nearby known points. Then use inverse distance interpolation to predict a value for your unknown quantity.
– DatHydroGuy
Nov 16 '18 at 8:50
Thx for your method but I'm sry :( to say that I must use the GPR or kriging method beacause it's required. This method is just ok for simple situation.
– 龚世泽
Nov 16 '18 at 9:19
add a comment |
I'm trying to interpolate some data from air monitoring stations.
Almost each record have an air quality value and their latitude, longitude. But there are some records lacking values.
For example, the data just like this:
116° 42° 10
117° 43° missing
120° 20° 1000
I want to use the scikit-learn's GPR (GaussianProcessRegressor) to interpolate the missing values.
I know that the 2-D data can be processed like the last answer in this problem
Python - Kriging (Gaussian Process) in scikit_learn
My problem is that: I shouldn't use the latitude and longitude to do this task directly, beacause the earth is a sphere so the latitude/longitude is not an usual flat 2-D grid.
I want to ask how to define the distance function between points when using the scikit-learn's GPR, or should I just project these lat/lon points to flat and use them? I hadn't try this because the precesion loss during projection made me sad :(
Thx for any suggestion :)
ps. The distance between two lat/lon points can be calculate by Haversine formula like Calculate distance between two latitude-longitude points? (Haversine formula)
python scikit-learn geolocation interpolation
asked Nov 16 '18 at 8:24
龚世泽龚世泽
11
I'm trying to interpolate some data from air monitoring stations.
Almost each record have an air quality value and their latitude, longitude. But there are some records lacking values.
For example, the data just like this:
116° 42° 10
117° 43° missing
120° 20° 1000
I want to use the scikit-learn's GPR (GaussianProcessRegressor) to interpolate the missing values.
I know that the 2-D data can be processed like the last answer in this problem
Python - Kriging (Gaussian Process) in scikit_learn
My problem is that: I shouldn't use the latitude and longitude to do this task directly, beacause the earth is a sphere so the latitude/longitude is not an usual flat 2-D grid.
I want to ask how to define the distance function between points when using the scikit-learn's GPR, or should I just project these lat/lon points to flat and use them? I hadn't try this because the precesion loss during projection made me sad :(
Thx for any suggestion :)
ps. The distance between two lat/lon points can be calculate by Haversine formula like Calculate distance between two latitude-longitude points? (Haversine formula)
python scikit-learn geolocation interpolation
python scikit-learn geolocation interpolation
asked Nov 16 '18 at 8:24
龚世泽龚世泽
11
asked Nov 16 '18 at 8:24
龚世泽龚世泽
11
edited Nov 16 '18 at 8:41
龚世泽
asked Nov 16 '18 at 8:24
龚世泽龚世泽
11
asked Nov 16 '18 at 8:24
龚世泽龚世泽
11
asked Nov 16 '18 at 8:24
龚世泽龚世泽
11
11
Using this (stackoverflow.com/questions/43240915/…) post as a guide, I'd suggest that you use a geodesy package (e.g. geographiclib) to generate geodesics to measure the distance between your "unknown" point and some nearby known points. Then use inverse distance interpolation to predict a value for your unknown quantity.
– DatHydroGuy
Nov 16 '18 at 8:50
Thx for your method but I'm sry :( to say that I must use the GPR or kriging method beacause it's required. This method is just ok for simple situation.
– 龚世泽
Nov 16 '18 at 9:19
add a comment |
Using this (stackoverflow.com/questions/43240915/…) post as a guide, I'd suggest that you use a geodesy package (e.g. geographiclib) to generate geodesics to measure the distance between your "unknown" point and some nearby known points. Then use inverse distance interpolation to predict a value for your unknown quantity.
– DatHydroGuy
Nov 16 '18 at 8:50
Thx for your method but I'm sry :( to say that I must use the GPR or kriging method beacause it's required. This method is just ok for simple situation.
– 龚世泽
Nov 16 '18 at 9:19
Using this (stackoverflow.com/questions/43240915/…) post as a guide, I'd suggest that you use a geodesy package (e.g. geographiclib) to generate geodesics to measure the distance between your "unknown" point and some nearby known points. Then use inverse distance interpolation to predict a value for your unknown quantity.
– DatHydroGuy
Nov 16 '18 at 8:50
Using this (stackoverflow.com/questions/43240915/…) post as a guide, I'd suggest that you use a geodesy package (e.g. geographiclib) to generate geodesics to measure the distance between your "unknown" point and some nearby known points. Then use inverse distance interpolation to predict a value for your unknown quantity.
– DatHydroGuy
Nov 16 '18 at 8:50
Thx for your method but I'm sry :( to say that I must use the GPR or kriging method beacause it's required. This method is just ok for simple situation.
– 龚世泽
Nov 16 '18 at 9:19
Thx for your method but I'm sry :( to say that I must use the GPR or kriging method beacause it's required. This method is just ok for simple situation.
– 龚世泽
Nov 16 '18 at 9:19
add a comment |
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Using this (stackoverflow.com/questions/43240915/…) post as a guide, I'd suggest that you use a geodesy package (e.g. geographiclib) to generate geodesics to measure the distance between your "unknown" point and some nearby known points. Then use inverse distance interpolation to predict a value for your unknown quantity.
– DatHydroGuy
Nov 16 '18 at 8:50
Thx for your method but I'm sry :( to say that I must use the GPR or kriging method beacause it's required. This method is just ok for simple situation.
– 龚世泽
Nov 16 '18 at 9:19