Hello,
I have data on air pollution (the "d" variable) for different latitude and longitude coordinates for different months. This data contains missing values (represented as "99999"). How can I fill in or interpolate these missing values based on the nearest location and time?
Thank you.
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I have data on air pollution (the "d" variable) for different latitude and longitude coordinates for different months. This data contains missing values (represented as "99999"). How can I fill in or interpolate these missing values based on the nearest location and time?
Thank you.
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Code:
* Example generated by -dataex-. For more info, type help dataex
clear
input float(latnum longnum d) int month byte year
0 0 .535 2021 2
0 0 1 2021 3
0 0 .22 2021 4
0 0 .201 2021 5
24.23531 68.578545 .24 2021 2
24.23531 68.578545 .346 2021 3
24.23531 68.578545 .358 2021 4
24.23531 68.578545 .264 2021 5
24.29183 67.68441 .315 2021 2
24.29183 67.68441 .606 2021 3
24.29183 67.68441 .354 2021 4
24.29183 67.68441 .472 2021 5
24.3051 67.62449 .398 2021 2
24.3051 67.62449 .484 2021 3
24.3051 67.62449 .449 2021 4
24.3051 67.62449 .531 2021 5
24.333544 68.25871 .339 2021 2
24.333544 68.25871 .358 2021 3
24.333544 68.25871 .346 2021 4
24.333544 68.25871 .362 2021 5
24.4073 68.0002 .346 2021 2
24.4073 68.0002 .472 2021 3
24.4073 68.0002 .425 2021 4
24.4073 68.0002 .461 2021 5
24.58326 68.84695 .433 2021 2
24.58326 68.84695 .508 2021 3
24.58326 68.84695 .476 2021 4
24.58326 68.84695 .61 2021 5
24.664936 68.82957 .437 2021 2
24.664936 68.82957 .5 2021 3
24.664936 68.82957 .461 2021 4
24.664936 68.82957 .417 2021 5
24.69325 69.24865 .354 2021 2
24.69325 69.24865 .441 2021 3
24.69325 69.24865 .433 2021 4
24.69325 69.24865 .386 2021 5
24.73327 67.94638 .177 2021 2
24.73327 67.94638 .248 2021 3
24.73327 67.94638 .205 2021 4
24.73327 67.94638 .394 2021 5
24.82097 67.0228 0 2021 2
24.82097 67.0228 0 2021 3
24.82097 67.0228 0 2021 4
24.82097 67.0228 0 2021 5
24.82809 67.13196 0 2021 2
24.82809 67.13196 0 2021 3
24.82809 67.13196 0 2021 4
24.82809 67.13196 0 2021 5
24.83239 66.999664 0 2021 2
24.83239 66.999664 0 2021 3
24.83239 66.999664 0 2021 4
24.83239 66.999664 0 2021 5
24.83339 67.016266 0 2021 2
24.83339 67.016266 0 2021 3
24.83339 67.016266 0 2021 4
24.83339 67.016266 0 2021 5
24.833817 67.16148 0 2021 2
24.833817 67.16148 0 2021 3
24.833817 67.16148 0 2021 4
24.833817 67.16148 0 2021 5
24.837254 67.01205 0 2021 2
24.837254 67.01205 0 2021 3
24.837254 67.01205 0 2021 4
24.837254 67.01205 0 2021 5
24.8378 67.1263 0 2021 2
24.8378 67.1263 0 2021 3
24.8378 67.1263 0 2021 4
24.8378 67.1263 0 2021 5
24.84752 67.1944 0 2021 2
24.84752 67.1944 0 2021 3
24.84752 67.1944 0 2021 4
24.84752 67.1944 0 2021 5
24.86844 67.388504 .236 2021 2
24.86844 67.388504 .268 2021 3
24.86844 67.388504 .374 2021 4
24.86844 67.388504 .508 2021 5
24.86852 67.05905 0 2021 2
24.86852 67.05905 0 2021 3
24.86852 67.05905 0 2021 4
24.86852 67.05905 0 2021 5
24.868704 67.00315 99999 2021 2
24.868704 67.00315 .327 2021 3
24.868704 67.00315 .315 2021 4
24.868704 67.00315 99999 2021 5
24.869104 66.98569 99999 2021 2
24.869104 66.98569 99999 2021 3
24.869104 66.98569 .283 2021 4
24.869104 66.98569 99999 2021 5
24.87051 67.048805 0 2021 2
24.87051 67.048805 0 2021 3
24.87051 67.048805 0 2021 4
24.87051 67.048805 0 2021 5
24.870966 67.0646 0 2021 2
24.870966 67.0646 0 2021 3
24.870966 67.0646 0 2021 4
24.870966 67.0646 0 2021 5
24.874966 67.053955 0 2021 2
24.874966 67.053955 0 2021 3
24.874966 67.053955 0 2021 4
24.874966 67.053955 0 2021 5
end
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