automating regressions and yhats for unique obs in a var

[raniyah bakr]

Hello, I would like to run a regression and predict yhat for each city's enrollment. I regress population onto enrollment but have many cities in my variable

city

I want to automate this process and generate new vars named 'cityname_enroll' with missing values for each city that is not the city of interest. I used this code for the city Aleknagik:

 reg enroll_place population if city=="Aleknagik"
predict _aleknagik if city=="Aleknagik"

Can anyone help with this loop? Would I use something like forreach `i' in city...

Thanks in advance, below is an example of my data. I am using Stata 17

* Example generated by -dataex-. For more info, type help dataex
clear
input int(id year population) str12 _fips str14 city long fips int enroll_place float(yhat _aleknagik)
 19 2008  250 "02-070-01420" "Aleknagik"      207001420  33  55.84832  34.44153
 42 2009  229 "02-070-01420" "Aleknagik"      207001420  33  51.47802  30.52791
 49 2010  219 "02-070-01420" "Aleknagik"      207001420  31  49.39693  28.66428
 78 2011  232 "02-070-01420" "Aleknagik"      207001420  33  52.10235    31.087
108 2012  205 "02-070-01420" "Aleknagik"      207001420  27   46.4834   26.0552
128 2013  212 "02-070-01420" "Aleknagik"      207001420  24  47.94017 27.359743
153 2014  198 "02-070-01420" "Aleknagik"      207001420  26  45.02664 24.750664
170 2015  215 "02-070-01420" "Aleknagik"      207001420  27   48.5645  27.91883
189 2016  220 "02-070-01420" "Aleknagik"      207001420  26  49.60504 28.850645
209 2017  209 "02-070-01420" "Aleknagik"      207001420  24  47.31584 26.800653
239 2018  201 "02-070-01420" "Aleknagik"      207001420  23  45.65097  25.30975
266 2019  208 "02-070-01420" "Aleknagik"      207001420  30  47.10773  26.61429
293 2020  211 "02-070-01420" "Aleknagik"      207001420  28  47.73206  27.17338
309 2021  191 "02-070-01420" "Aleknagik"      207001420  24  43.56987 23.446123
341 2022  201 "02-070-01420" "Aleknagik"      207001420   .  45.65097  25.30975
352 2023  202 "02-070-01420" "Aleknagik"      207001420   .  45.85907 25.496115
381 2024  202 "02-070-01420" "Aleknagik"      207001420   .  45.85907 25.496115
404 2025  202 "02-070-01420" "Aleknagik"      207001420   .  45.85907 25.496115
429 2026  203 "02-070-01420" "Aleknagik"      207001420   .  46.06718  25.68248
441 2027  203 "02-070-01420" "Aleknagik"      207001420   .  46.06718  25.68248
461 2028  203 "02-070-01420" "Aleknagik"      207001420   .  46.06718  25.68248
490 2029  203 "02-070-01420" "Aleknagik"      207001420   .  46.06718  25.68248
519 2030  204 "02-070-01420" "Aleknagik"      207001420   .   46.2753  25.86884
  4 2008   71 "02-164-13670" "Chignik Lagoon" 216413670  17 18.596771         .
 30 2009   73 "02-164-13670" "Chignik Lagoon" 216413670  17 19.012989         .
 65 2010   78 "02-164-13670" "Chignik Lagoon" 216413670  18 20.053535         .
 79 2011   79 "02-164-13670" "Chignik Lagoon" 216413670  12 20.261644         .
115 2012   83 "02-164-13670" "Chignik Lagoon" 216413670  13  21.09408         .
127 2013   78 "02-164-13670" "Chignik Lagoon" 216413670  13 20.053535         .
143 2014   73 "02-164-13670" "Chignik Lagoon" 216413670  10 19.012989         .
173 2015   78 "02-164-13670" "Chignik Lagoon" 216413670  11 20.053535         .
204 2016   85 "02-164-13670" "Chignik Lagoon" 216413670  10   21.5103         .
223 2017   85 "02-164-13670" "Chignik Lagoon" 216413670  10   21.5103         .
238 2018   83 "02-164-13670" "Chignik Lagoon" 216413670  11  21.09408         .
273 2019   81 "02-164-13670" "Chignik Lagoon" 216413670  12 20.677864         .
283 2020   72 "02-164-13670" "Chignik Lagoon" 216413670  13  18.80488         .
314 2021   72 "02-164-13670" "Chignik Lagoon" 216413670  15  18.80488         .
325 2022   71 "02-164-13670" "Chignik Lagoon" 216413670   . 18.596771         .
350 2023   70 "02-164-13670" "Chignik Lagoon" 216413670   . 18.388662         .
382 2024   70 "02-164-13670" "Chignik Lagoon" 216413670   . 18.388662         .
397 2025   69 "02-164-13670" "Chignik Lagoon" 216413670   . 18.180553         .
420 2026   69 "02-164-13670" "Chignik Lagoon" 216413670   . 18.180553         .
443 2027   68 "02-164-13670" "Chignik Lagoon" 216413670   . 17.972445         .
480 2028   68 "02-164-13670" "Chignik Lagoon" 216413670   . 17.972445         .
488 2029   67 "02-164-13670" "Chignik Lagoon" 216413670   . 17.764334         .
514 2030   67 "02-164-13670" "Chignik Lagoon" 216413670   . 17.764334         .
  3 2008  104 "02-164-13780" "Chignik Lake"   216413780  25 25.464375         .
 38 2009  105 "02-164-13780" "Chignik Lake"   216413780  17 25.672483         .
 59 2010   73 "02-164-13780" "Chignik Lake"   216413780  22 19.012989         .
 72 2011   69 "02-164-13780" "Chignik Lake"   216413780  25 18.180553         .
104 2012   70 "02-164-13780" "Chignik Lake"   216413780  17 18.388662         .

[raniyah bakr]

Here is my intuition

foreach `i' in city {
    reg enroll_place population if city=="`i'"
    predict `i'_enroll if city=="`i'"
}

[Carlo Lazzaro]

Raniyah:
why not:

. bysort city: reg enroll_place population

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Aleknagik

      Source |       SS           df       MS      Number of obs   =        14
-------------+----------------------------------   F(1, 12)        =     19.14
       Model |  104.709256         1  104.709256   Prob > F        =    0.0009
    Residual |  65.6478867        12  5.47065722   R-squared       =    0.6146
-------------+----------------------------------   Adj R-squared   =    0.5825
       Total |  170.357143        13  13.1043956   Root MSE        =    2.3389

------------------------------------------------------------------------------
enroll_place | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  population |   .1863628   .0425977     4.37   0.001     .0935503    .2791752
       _cons |  -12.14917   9.149463    -1.33   0.209    -32.08413    7.785801
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Chignik Lagoon

      Source |       SS           df       MS      Number of obs   =        14
-------------+----------------------------------   F(1, 12)        =      5.56
       Model |  31.0128122         1  31.0128122   Prob > F        =    0.0362
    Residual |  66.9871878        12  5.58226565   R-squared       =    0.3165
-------------+----------------------------------   Adj R-squared   =    0.2595
       Total |          98        13  7.53846154   Root MSE        =    2.3627

------------------------------------------------------------------------------
enroll_place | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  population |  -.3070575    .130273    -2.36   0.036    -.5908981    -.023217
       _cons |   36.92856   10.17161     3.63   0.003     14.76652    59.09059
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Chignik Lake

      Source |       SS           df       MS      Number of obs   =         5
-------------+----------------------------------   F(1, 3)         =      0.01
       Model |  .266589528         1  .266589528   Prob > F        =    0.9184
    Residual |  64.5334105         3  21.5111368   R-squared       =    0.0041
-------------+----------------------------------   Adj R-squared   =   -0.3278
       Total |        64.8         4        16.2   Root MSE        =     4.638

------------------------------------------------------------------------------
enroll_place | Coefficient  Std. err.      t    P>|t|     [95% conf. interval]
-------------+----------------------------------------------------------------
  population |  -.0138849   .1247246    -0.11   0.918    -.4108141    .3830444
       _cons |   22.36911   10.70468     2.09   0.128    -11.69797    56.43618
------------------------------------------------------------------------------


. predict fitted, xb

. bysort city: list enroll_place population fitted if _n==1

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Aleknagik

     +--------------------------------+
     | enroll~e   popula~n     fitted |
     |--------------------------------|
  1. |       33        250   18.89789 |
     +--------------------------------+

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Chignik Lagoon

     +--------------------------------+
     | enroll~e   popula~n     fitted |
     |--------------------------------|
  1. |       17         71   21.38328 |
     +--------------------------------+

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Chignik Lake

     +--------------------------------+
     | enroll~e   popula~n     fitted |
     |--------------------------------|
  1. |       25        104   20.92508 |
     +--------------------------------+


.

[Nick Cox]

Why a separate variable for each city? Here I use rangestat from SSC.

* Example generated by -dataex-. For more info, type help dataex
clear
input int(id year population) str12 _fips str14 city long fips int enroll_place float(yhat _aleknagik)
 19 2008  250 "02-070-01420" "Aleknagik"      207001420  33  55.84832  34.44153
 42 2009  229 "02-070-01420" "Aleknagik"      207001420  33  51.47802  30.52791
 49 2010  219 "02-070-01420" "Aleknagik"      207001420  31  49.39693  28.66428
 78 2011  232 "02-070-01420" "Aleknagik"      207001420  33  52.10235    31.087
108 2012  205 "02-070-01420" "Aleknagik"      207001420  27   46.4834   26.0552
128 2013  212 "02-070-01420" "Aleknagik"      207001420  24  47.94017 27.359743
153 2014  198 "02-070-01420" "Aleknagik"      207001420  26  45.02664 24.750664
170 2015  215 "02-070-01420" "Aleknagik"      207001420  27   48.5645  27.91883
189 2016  220 "02-070-01420" "Aleknagik"      207001420  26  49.60504 28.850645
209 2017  209 "02-070-01420" "Aleknagik"      207001420  24  47.31584 26.800653
239 2018  201 "02-070-01420" "Aleknagik"      207001420  23  45.65097  25.30975
266 2019  208 "02-070-01420" "Aleknagik"      207001420  30  47.10773  26.61429
293 2020  211 "02-070-01420" "Aleknagik"      207001420  28  47.73206  27.17338
309 2021  191 "02-070-01420" "Aleknagik"      207001420  24  43.56987 23.446123
341 2022  201 "02-070-01420" "Aleknagik"      207001420   .  45.65097  25.30975
352 2023  202 "02-070-01420" "Aleknagik"      207001420   .  45.85907 25.496115
381 2024  202 "02-070-01420" "Aleknagik"      207001420   .  45.85907 25.496115
404 2025  202 "02-070-01420" "Aleknagik"      207001420   .  45.85907 25.496115
429 2026  203 "02-070-01420" "Aleknagik"      207001420   .  46.06718  25.68248
441 2027  203 "02-070-01420" "Aleknagik"      207001420   .  46.06718  25.68248
461 2028  203 "02-070-01420" "Aleknagik"      207001420   .  46.06718  25.68248
490 2029  203 "02-070-01420" "Aleknagik"      207001420   .  46.06718  25.68248
519 2030  204 "02-070-01420" "Aleknagik"      207001420   .   46.2753  25.86884
  4 2008   71 "02-164-13670" "Chignik Lagoon" 216413670  17 18.596771         .
 30 2009   73 "02-164-13670" "Chignik Lagoon" 216413670  17 19.012989         .
 65 2010   78 "02-164-13670" "Chignik Lagoon" 216413670  18 20.053535         .
 79 2011   79 "02-164-13670" "Chignik Lagoon" 216413670  12 20.261644         .
115 2012   83 "02-164-13670" "Chignik Lagoon" 216413670  13  21.09408         .
127 2013   78 "02-164-13670" "Chignik Lagoon" 216413670  13 20.053535         .
143 2014   73 "02-164-13670" "Chignik Lagoon" 216413670  10 19.012989         .
173 2015   78 "02-164-13670" "Chignik Lagoon" 216413670  11 20.053535         .
204 2016   85 "02-164-13670" "Chignik Lagoon" 216413670  10   21.5103         .
223 2017   85 "02-164-13670" "Chignik Lagoon" 216413670  10   21.5103         .
238 2018   83 "02-164-13670" "Chignik Lagoon" 216413670  11  21.09408         .
273 2019   81 "02-164-13670" "Chignik Lagoon" 216413670  12 20.677864         .
283 2020   72 "02-164-13670" "Chignik Lagoon" 216413670  13  18.80488         .
314 2021   72 "02-164-13670" "Chignik Lagoon" 216413670  15  18.80488         .
325 2022   71 "02-164-13670" "Chignik Lagoon" 216413670   . 18.596771         .
350 2023   70 "02-164-13670" "Chignik Lagoon" 216413670   . 18.388662         .
382 2024   70 "02-164-13670" "Chignik Lagoon" 216413670   . 18.388662         .
397 2025   69 "02-164-13670" "Chignik Lagoon" 216413670   . 18.180553         .
420 2026   69 "02-164-13670" "Chignik Lagoon" 216413670   . 18.180553         .
443 2027   68 "02-164-13670" "Chignik Lagoon" 216413670   . 17.972445         .
480 2028   68 "02-164-13670" "Chignik Lagoon" 216413670   . 17.972445         .
488 2029   67 "02-164-13670" "Chignik Lagoon" 216413670   . 17.764334         .
514 2030   67 "02-164-13670" "Chignik Lagoon" 216413670   . 17.764334         .
  3 2008  104 "02-164-13780" "Chignik Lake"   216413780  25 25.464375         .
 38 2009  105 "02-164-13780" "Chignik Lake"   216413780  17 25.672483         .
 59 2010   73 "02-164-13780" "Chignik Lake"   216413780  22 19.012989         .
 72 2011   69 "02-164-13780" "Chignik Lake"   216413780  25 18.180553         .
104 2012   70 "02-164-13780" "Chignik Lake"   216413780  17 18.388662         . 
end 

rangestat (reg) enroll_place population, int(fips 0 0)

gen predicted = b_cons + b_population * population 

list city year enroll population predicted, sepby(city)

     +--------------------------------------------------------+
     |           city   year   enroll~e   popula~n   predic~d |
     |--------------------------------------------------------|
  1. |      Aleknagik   2008         33        250   34.44153 |
  2. |      Aleknagik   2009         33        229   30.52791 |
  3. |      Aleknagik   2010         31        219   28.66428 |
  4. |      Aleknagik   2011         33        232     31.087 |
  5. |      Aleknagik   2012         27        205    26.0552 |
  6. |      Aleknagik   2013         24        212   27.35974 |
  7. |      Aleknagik   2014         26        198   24.75066 |
  8. |      Aleknagik   2015         27        215   27.91883 |
  9. |      Aleknagik   2016         26        220   28.85065 |
 10. |      Aleknagik   2017         24        209   26.80065 |
 11. |      Aleknagik   2018         23        201   25.30975 |
 12. |      Aleknagik   2019         30        208   26.61429 |
 13. |      Aleknagik   2020         28        211   27.17338 |
 14. |      Aleknagik   2021         24        191   23.44612 |
 15. |      Aleknagik   2022          .        201   25.30975 |
 16. |      Aleknagik   2023          .        202   25.49611 |
 17. |      Aleknagik   2024          .        202   25.49611 |
 18. |      Aleknagik   2025          .        202   25.49611 |
 19. |      Aleknagik   2026          .        203   25.68248 |
 20. |      Aleknagik   2027          .        203   25.68248 |
 21. |      Aleknagik   2028          .        203   25.68248 |
 22. |      Aleknagik   2029          .        203   25.68248 |
 23. |      Aleknagik   2030          .        204   25.86884 |
     |--------------------------------------------------------|
 24. | Chignik Lagoon   2008         17         71   15.12747 |
 25. | Chignik Lagoon   2009         17         73   14.51336 |
 26. | Chignik Lagoon   2010         18         78   12.97807 |
 27. | Chignik Lagoon   2011         12         79   12.67101 |
 28. | Chignik Lagoon   2012         13         83   11.44278 |
 29. | Chignik Lagoon   2013         13         78   12.97807 |
 30. | Chignik Lagoon   2014         10         73   14.51336 |
 31. | Chignik Lagoon   2015         11         78   12.97807 |
 32. | Chignik Lagoon   2016         10         85   10.82866 |
 33. | Chignik Lagoon   2017         10         85   10.82866 |
 34. | Chignik Lagoon   2018         11         83   11.44278 |
 35. | Chignik Lagoon   2019         12         81   12.05689 |
 36. | Chignik Lagoon   2020         13         72   14.82041 |
 37. | Chignik Lagoon   2021         15         72   14.82041 |
 38. | Chignik Lagoon   2022          .         71   15.12747 |
 39. | Chignik Lagoon   2023          .         70   15.43453 |
 40. | Chignik Lagoon   2024          .         70   15.43453 |
 41. | Chignik Lagoon   2025          .         69   15.74158 |
 42. | Chignik Lagoon   2026          .         69   15.74158 |
 43. | Chignik Lagoon   2027          .         68   16.04864 |
 44. | Chignik Lagoon   2028          .         68   16.04864 |
 45. | Chignik Lagoon   2029          .         67    16.3557 |
 46. | Chignik Lagoon   2030          .         67    16.3557 |
     |--------------------------------------------------------|
 47. |   Chignik Lake   2008         25        104   20.92508 |
 48. |   Chignik Lake   2009         17        105   20.91119 |
 49. |   Chignik Lake   2010         22         73   21.35551 |
 50. |   Chignik Lake   2011         25         69   21.41105 |
 51. |   Chignik Lake   2012         17         70   21.39717 |
     +--------------------------------------------------------+

[raniyah bakr]

Raniyah:
why not:

. bysort city: reg enroll_place population

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Aleknagik

Source | SS df MS Number of obs = 14
-------------+---------------------------------- F(1, 12) = 19.14
Model | 104.709256 1 104.709256 Prob > F = 0.0009
Residual | 65.6478867 12 5.47065722 R-squared = 0.6146
-------------+---------------------------------- Adj R-squared = 0.5825
Total | 170.357143 13 13.1043956 Root MSE = 2.3389

------------------------------------------------------------------------------
enroll_place | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
population | .1863628 .0425977 4.37 0.001 .0935503 .2791752
_cons | -12.14917 9.149463 -1.33 0.209 -32.08413 7.785801
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Chignik Lagoon

Source | SS df MS Number of obs = 14
-------------+---------------------------------- F(1, 12) = 5.56
Model | 31.0128122 1 31.0128122 Prob > F = 0.0362
Residual | 66.9871878 12 5.58226565 R-squared = 0.3165
-------------+---------------------------------- Adj R-squared = 0.2595
Total | 98 13 7.53846154 Root MSE = 2.3627

------------------------------------------------------------------------------
enroll_place | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
population | -.3070575 .130273 -2.36 0.036 -.5908981 -.023217
_cons | 36.92856 10.17161 3.63 0.003 14.76652 59.09059
------------------------------------------------------------------------------

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Chignik Lake

Source | SS df MS Number of obs = 5
-------------+---------------------------------- F(1, 3) = 0.01
Model | .266589528 1 .266589528 Prob > F = 0.9184
Residual | 64.5334105 3 21.5111368 R-squared = 0.0041
-------------+---------------------------------- Adj R-squared = -0.3278
Total | 64.8 4 16.2 Root MSE = 4.638

------------------------------------------------------------------------------
enroll_place | Coefficient Std. err. t P>|t| [95% conf. interval]
-------------+----------------------------------------------------------------
population | -.0138849 .1247246 -0.11 0.918 -.4108141 .3830444
_cons | 22.36911 10.70468 2.09 0.128 -11.69797 56.43618
------------------------------------------------------------------------------


. predict fitted, xb

. bysort city: list enroll_place population fitted if _n==1

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Aleknagik

+--------------------------------+
| enroll~e popula~n fitted |
|--------------------------------|
1. | 33 250 18.89789 |
+--------------------------------+

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Chignik Lagoon

+--------------------------------+
| enroll~e popula~n fitted |
|--------------------------------|
1. | 17 71 21.38328 |
+--------------------------------+

---------------------------------------------------------------------------------------------------------------------------------------------
-> city = Chignik Lake

+--------------------------------+
| enroll~e popula~n fitted |
|--------------------------------|
1. | 25 104 20.92508 |
+--------------------------------+


.

Carlo this is awesome. I did not know about bysort or how it works. I will preserve my data and try it out. I am wanting to predict yhat to 2030, I assume predict fitted,xb will predict based on the city name which is exactly what I need.

[Carlo Lazzaro]

Raniyah:
Nick's solution is far more elegant.
However, I beg to point out that the main issue here is that you have panel data.
Therefore:
1) you would be better off with -xtreg- as your first choice;
2) if (for whatever reason that is not clear to me) go -regress-, you should invoke -vce(cluster panelid)- standard errors. Otherwise, you are implicitly stating that the observations belonging to the same panel are independent, which is not true exactly because they belong to the same panel.

[raniyah bakr]

Carlo,

Nick's code was quite seamless you are right. Thank you for presenting the bysort command though, I can see its value. I will explore xtreg because you are right about the panel data

Nick,
rangestat worked great and the simple expression to create predicted is perfect.


+---------------------------------------------------------+
| city year enroll~e popula~n predicted |
|---------------------------------------------------------|
1. | Aleknagik 2008 33 250 34.44153 |
2. | Aleknagik 2009 33 229 30.52791 |
3. | Aleknagik 2010 31 219 28.66428 |
4. | Aleknagik 2011 33 232 31.087 |
5. | Aleknagik 2012 27 205 26.0552 |
6. | Aleknagik 2013 24 212 27.35974 |
7. | Aleknagik 2014 26 198 24.75066 |
8. | Aleknagik 2015 27 215 27.91883 |
9. | Aleknagik 2016 26 220 28.85065 |
10. | Aleknagik 2017 24 209 26.80065 |
11. | Aleknagik 2018 23 201 25.30975 |
12. | Aleknagik 2019 30 208 26.61429 |
...
new var generated in example below
[CODE]
* Example generated by -dataex-. For more info, type help dataex
clear
input int id str12 _fips long fips int year str25 subregion str14 city int(population enroll_place) float predicted double(reg_nobs reg_r2 reg_adj_r2 b_population b_cons se_population se_cons)
19 "02-070-01420" 207001420 2008 "Dillingham (On Road)" "Aleknagik" 250 33 34.44153 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
42 "02-070-01420" 207001420 2009 "Dillingham (On Road)" "Aleknagik" 229 33 30.52791 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
49 "02-070-01420" 207001420 2010 "Dillingham (On Road)" "Aleknagik" 219 31 28.66428 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
78 "02-070-01420" 207001420 2011 "Dillingham (On Road)" "Aleknagik" 232 33 31.087 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
108 "02-070-01420" 207001420 2012 "Dillingham (On Road)" "Aleknagik" 205 27 26.0552 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
128 "02-070-01420" 207001420 2013 "Dillingham (On Road)" "Aleknagik" 212 24 27.359743 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
153 "02-070-01420" 207001420 2014 "Dillingham (On Road)" "Aleknagik" 198 26 24.750664 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
170 "02-070-01420" 207001420 2015 "Dillingham (On Road)" "Aleknagik" 215 27 27.91883 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
189 "02-070-01420" 207001420 2016 "Dillingham (On Road)" "Aleknagik" 220 26 28.850645 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
209 "02-070-01420" 207001420 2017 "Dillingham (On Road)" "Aleknagik" 209 24 26.800653 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
239 "02-070-01420" 207001420 2018 "Dillingham (On Road)" "Aleknagik" 201 23 25.30975 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
266 "02-070-01420" 207001420 2019 "Dillingham (On Road)" "Aleknagik" 208 30 26.61429 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
293 "02-070-01420" 207001420 2020 "Dillingham (On Road)" "Aleknagik" 211 28 27.17338 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
309 "02-070-01420" 207001420 2021 "Dillingham (On Road)" "Aleknagik" 191 24 23.446123 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
341 "02-070-01420" 207001420 2022 "Dillingham (On Road)" "Aleknagik" 201 . 25.30975 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
352 "02-070-01420" 207001420 2023 "Dillingham (On Road)" "Aleknagik" 202 . 25.496115 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
381 "02-070-01420" 207001420 2024 "Dillingham (On Road)" "Aleknagik" 202 . 25.496115 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
404 "02-070-01420" 207001420 2025 "Dillingham (On Road)" "Aleknagik" 202 . 25.496115 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
429 "02-070-01420" 207001420 2026 "Dillingham (On Road)" "Aleknagik" 203 . 25.68248 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
441 "02-070-01420" 207001420 2027 "Dillingham (On Road)" "Aleknagik" 203 . 25.68248 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
461 "02-070-01420" 207001420 2028 "Dillingham (On Road)" "Aleknagik" 203 . 25.68248 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
490 "02-070-01420" 207001420 2029 "Dillingham (On Road)" "Aleknagik" 203 . 25.68248 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
519 "02-070-01420" 207001420 2030 "Dillingham (On Road)" "Aleknagik" 204 . 25.86884 14 .6146455290600332 .5825326564817027 .18636277482941566 -12.149166034875634 .04259772276900661 9.149462755234124
4 "02-164-13670" 216413670 2008 "Gulf Communities" "Chignik Lagoon" 71 17 15.12747 14 .3164572669458801 .25949537252470345 -.30705754614550074 36.92855591748139 .13027301086819132 10.171608858116873
30 "02-164-13670" 216413670 2009 "Gulf Communities" "Chignik Lagoon" 73 17 14.513355 14 .3164572669458801 .25949537252470345 -.30705754614550074 36.92855591748139 .13027301086819132 10.171608858116873
65 "02-164-13670" 216413670 2010 "Gulf Communities" "Chignik Lagoon" 78 18 12.978067 14 .3164572669458801 .25949537252470345 -.30705754614550074 36.92855591748139 .13027301086819132 10.171608858116873
79 "02-164-13670" 216413670 2011 "Gulf Communities" "Chignik Lagoon" 79 12 12.67101 14 .3164572669458801 .25949537252470345 -.30705754614550074 36.92855591748139 .13027301086819132 10.171608858116873
115 "02-164-13670" 216413670 2012 "Gulf Communities" "Chignik Lagoon" 83 13 11.44278 14 .3164572669458801 .25949537252470345 -.30705754614550074 36.92855591748139 .13027301086819132 10.171608858116873
127 "02-164-13670" 216413670 2013 "Gulf Communities" "Chignik Lagoon" 78 13 12.978067 14 .3164572669458801 .25949537252470345 -.30705754614550074 36.92855591748139 .13027301086819132 10.171608858116873
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