Finding nearest neighbor using geonear

[Anne-Claire Jo]

Dear stata users,

I am using - geonear - command to generate firm-level clusters with respect to reference university within 1 mile.
However, firms are included in several clusters that are near universities.
What I would like to do is to make firms included in only one cluster which is nearest to the given reference and in same sector (naics6).
For more information, my master dataset is firm-level panel data merged with patent data and another one is university patent-level data.
In more detail, below is the previous question that I posted containing the example of my datasets (both firm-level data in #12 and university patent-level data in #27 which is identical below).
https://www.statalist.org/forums/for...analysis/page2

* Example generated by -dataex-. For more info, type help dataex
clear
input long patent_num str10 org_id str70 city str7 state_code str30 bea_code str22 county float(latitude longitude)
6575965 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
4595014 "UN_208905" "Westport" "CT" "118" "Fairfield" 41.14343 -73.34958
4916505 "UN_208752" "Honolulu" "HI" "074" "Honolulu" 21.3095 -157.863
4282965 "UN_209262" "Binghamton" "NY" "162" "Broome" 42.14631 -75.88652
5157702 "UN_1786" "Madison" "WI" "101" "Dane" 43.073 -89.3817
5034515 "UN_1786" "Madison" "WI" "101" "Dane" 43.073 -89.3817
5049280 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
6185469 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
7355081 "UN_209181" "Rochester" "NY" "139" "Monroe" 43.1683 -77.6026
5196396 "UN_208904" "Cambridge" "MA" "022" "Middlesex" 42.37704 -71.12561
4824359 "UN_208853" "Ann Arbor" "MI" "047" "Washtenaw" 42.27938 -83.784
4581315 "UN_208905" "Norwalk" "CT" "118" "Fairfield" 41.12222 -73.43583
4946773 "UN_208904" "Cambridge" "MA" "022" "Middlesex" 42.37704 -71.12561
5754575 "UN_208940" "Scottsdale" "AZ" "128" "Maricopa" 33.521767 -111.90492
6502576 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
7084407 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
6130254 "UN_1647" "Ann Arbor" "MI" "047" "Washtenaw" 42.27938 -83.784
4038143 "UN_1647" "Ann Arbor" "MI" "047" "Washtenaw" 42.27938 -83.784
6166295 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
3948213 "UN_200429" "Des Plaines" "IL" "032" "Cook" 42.04673 -87.8859
7378498 "UN_211119" "Baltimore" "MD" "174" "Baltimore City" 39.29463 -76.62521
5888473 "UN_1542" "Los Angeles" "CA" "097" "Los Angeles" 33.97309 -118.2479
5037746 "UN_208905" "Westport" "CT" "118" "Fairfield" 41.14343 -73.34958
4816125 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
5925818 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
4974113 "UN_208904" "Cambridge" "MA" "022" "Middlesex" 42.37704 -71.12561
7219249 "UN_1380" "Albany" "NY" "004" "Albany" 42.6525 -73.7566
7560287 "UN_209183" "Eugene" "OR" "053" "Lane" 44.07368 -123.07876
4684891 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
7298820 "UN_1786" "Madison" "WI" "101" "Dane" 43.073 -89.3817
4164214 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
6251322 "UN_209442" "Clemson" "SC" "068" "Pickens" 34.68306 -82.825
5951881 "UN_208904" "Cambridge" "MA" "022" "Middlesex" 42.37704 -71.12561
7609220 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
4654598 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
7638300 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
5395521 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
4043934 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
4666847 "UN_208986" "Bedford" "MA" "022" "Middlesex" 42.48429 -71.276794
6625135 "UN_999999" "Pittsburgh" "PA" "129" "Allegheny" 40.47454 -79.95252
5452824 "UN_209262" "Binghamton" "NY" "162" "Broome" 42.14631 -75.88652
7188559 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
5416115 "UN_999999" "College Park" "MD" "174" "Prince Georges" 38.9963 -76.92989
5888789 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
5719784 "UN_211213" "Seattle" "WA" "152" "King" 47.61143 -122.33046
5912466 "UN_1542" "Los Alamos" "NM" "147" "Los Alamos" 35.86632 -106.26762
5820678 "UN_1542" "Los Alamos" "NM" "147" "Los Alamos" 35.86632 -106.26762
5448513 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
4769326 "UN_1542" "" "CA" "OTHER_USA" "" . .
6605202 "UN_1542" "Los Alamos" "NM" "147" "Los Alamos" 35.86632 -106.26762
6448012 "UN_1786" "Madison" "WI" "101" "Dane" 43.073 -89.3817
5625137 "UN_1786" "Madison" "WI" "101" "Dane" 43.073 -89.3817
5362622 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
5385541 "UN_209140" "Loma Linda" "CA" "097" "San Bernardino" 34.0524 -117.2618
5032519 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
6254865 "UN_208823" "Boulder" "CO" "045" "Boulder" 40.04973 -105.21426
5595761 "UN_1698" "Norman" "OK" "119" "Cleveland" 35.2212 -97.4448
6664137 "UN_207741" "Ewing" "NJ" "118" "Mercer" 40.23769 -74.78206
4409910 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
5530309 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
5335598 "UN_209025" "Phoenix" "AZ" "128" "Maricopa" 33.451 -112.0685
5302609 "UN_1647" "Ann Arbor" "MI" "047" "Washtenaw" 42.27938 -83.784
5459235 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
4550187 "UN_1380" "Albany" "NY" "004" "Albany" 42.6525 -73.7566
5142559 "UN_1380" "Albany" "NY" "004" "Albany" 42.6525 -73.7566
5212072 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
5272429 "UN_1786" "Madison" "WI" "101" "Dane" 43.073 -89.3817
4897444 "UN_1380" "Albany" "NY" "004" "Albany" 42.6525 -73.7566
4933639 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
6318146 "UN_1786" "Madison" "WI" "101" "Dane" 43.073 -89.3817
7094205 "UN_999999" "Los Angeles" "CA" "097" "Los Angeles" 33.97309 -118.2479
4431263 "UN_208905" "Norwalk" "CT" "118" "Fairfield" 41.12222 -73.43583
5141851 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
6562619 "UN_211119" "Baltimore" "MD" "174" "Baltimore City" 39.29463 -76.62521
5306447 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
7476384 "UN_1647" "Ann Arbor" "MI" "047" "Washtenaw" 42.27938 -83.784
4427306 "UN_208905" "Norwalk" "CT" "118" "Fairfield" 41.12222 -73.43583
5490169 "UN_209046" "Huntsville" "AL" "076" "Madison" 34.726868 -86.56732
6730686 "UN_209142" "Kansas City" "KS" "084" "Wyandotte" 39.11573 -94.62714
4352864 "UN_209254" "Saugus" "CA" "097" "" . .
4701953 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
5416115 "UN_999999" "College Park" "MD" "174" "Prince Georges" 38.9963 -76.92989
5849719 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
6046925 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
6222209 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
5856252 "UN_1542" "Oakland" "CA" "146" "Alameda" 37.780594 -122.21658
5747469 "UN_1768" "Austin" "TX" "013" "Williamson" 30.50597 -97.74718
5486636 "UN_1786" "Madison" "WI" "101" "Dane" 43.073 -89.3817
5238711 "UN_208904" "Cambridge" "MA" "022" "Middlesex" 42.37704 -71.12561
4043934 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
4871252 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
4076579 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
4874746 "UN_208904" "Cambridge" "MA" "022" "Middlesex" 42.37704 -71.12561
6689192 "UN_1542" "Los Alamos" "NM" "147" "Los Alamos" 35.86632 -106.26762
5049673 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
5757839 "UN_1647" "Ann Arbor" "MI" "047" "Washtenaw" 42.27938 -83.784
6677444 "UN_209122" "Birmingham" "AL" "019" "Shelby" 33.401558 -86.70551
4689399 "UN_209184" "Palo Alto" "CA" "146" "Santa Clara" 37.444324 -122.1497
5650392 "UN_1698" "Norman" "OK" "119" "Cleveland" 35.2212 -97.4448
4275270 "UN_1542" "Berkeley" "CA" "146" "Alameda" 37.8691 -122.2696
end

Could someone please help me with this issue ?
Thank you very much in advance !

Best,
AC

[Andrew Musau]

As you are asking for coding help, I recommend that you spend time creating a reproducible example. Assuming that you have already created the clusters following the advice in previous posts, start with:

However, firms are included in several clusters that are near universities.

Present a data example that shows this.

What I would like to do is to make firms included in only one cluster which is nearest to the given reference and in same sector (naics6).

Based on the data example, show what you mean by "nearest to the given reference", e.g., by showing how you want the final result to look like. Otherwise, it is difficult for anyone to follow what you want.

[Anne-Claire Jo]

* Example generated by -dataex-. For more info, type help dataex
clear
input float(newid uni_id) long gvkey double year float(latitude longitude) double mi_to_uni_id
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 673      .    .        .        . .
   . 577      .    .        .        . .
   . 577      .    .        .        . .
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
7197 527 180466 2009 42.04673 -87.8859 0
end


. l newid uni_id gvkey year latitude longitude in 20300/20400, sepby(newid)

       +-----------------------------------------------------+
       | newid   uni_id   gvkey   year   latitude   longit~e |
       |-----------------------------------------------------|
20300. |   181      177    1478   1950   40.74838   -73.9967 |
20301. |   181      921    1478   1950   40.74838   -73.9967 |
20302. |   181      177    1478   1950   40.74838   -73.9967 |
20303. |   181      177    1478   1950   40.74838   -73.9967 |
20304. |   181      177    1478   1950   40.74838   -73.9967 |
20305. |   181      177    1478   1950   40.74838   -73.9967 |
20306. |   181      177    1478   1950   40.74838   -73.9967 |
20307. |   181      177    1478   1950   40.74838   -73.9967 |
20308. |   181      929    1478   1950   40.74838   -73.9967 |
20309. |   181      177    1478   1950   40.74838   -73.9967 |
20310. |   181      177    1478   1950   40.74838   -73.9967 |
20311. |   181      177    1478   1950   40.74838   -73.9967 |
20312. |   181      177    1478   1950   40.74838   -73.9967 |
20313. |   181      516    1478   1950   40.74838   -73.9967 |
20314. |   181      177    1478   1950   40.74838   -73.9967 |
20315. |   181      177    1478   1950   40.74838   -73.9967 |
20316. |   181      714    1478   1950   40.74838   -73.9967 |
20317. |   181     1076    1478   1950   40.74838   -73.9967 |
20318. |   181      522    1478   1950   40.74838   -73.9967 |
20319. |   181      177    1478   1950   40.74838   -73.9967 |
20320. |   181      516    1478   1950   40.74838   -73.9967 |
20321. |   181      177    1478   1950   40.74838   -73.9967 |
20322. |   181      177    1478   1950   40.74838   -73.9967 |
20323. |   181      915    1478   1950   40.74838   -73.9967 |
20324. |   181      177    1478   1950   40.74838   -73.9967 |
20325. |   181      177    1478   1950   40.74838   -73.9967 |
20326. |   181      177    1478   1950   40.74838   -73.9967 |
20327. |   181       66    1478   1950   40.74838   -73.9967 |
20328. |   181     1199    1478   1950   40.74838   -73.9967 |
20329. |   181      177    1478   1950   40.74838   -73.9967 |
20330. |   181      714    1478   1950   40.74838   -73.9967 |
20331. |   181       54    1478   1950   40.74838   -73.9967 |
20332. |   181      177    1478   1950   40.74838   -73.9967 |
20333. |   181      177    1478   1950   40.74838   -73.9967 |
20334. |   181      177    1478   1950   40.74838   -73.9967 |
20335. |   181      177    1478   1950   40.74838   -73.9967 |
20336. |   181      177    1478   1950   40.74838   -73.9967 |
20337. |   181      177    1478   1950   40.74838   -73.9967 |
20338. |   181       54    1478   1950   40.74838   -73.9967 |
20339. |   181      522    1478   1950   40.74838   -73.9967 |
20340. |   181       66    1478   1950   40.74838   -73.9967 |
20341. |   181      915    1478   1950   40.74838   -73.9967 |
20342. |   181      177    1478   1950   40.74838   -73.9967 |
20343. |   181      987    1478   1950   40.74838   -73.9967 |
20344. |   181      177    1478   1950   40.74838   -73.9967 |
20345. |   181      177    1478   1950   40.74838   -73.9967 |
20346. |   181      177    1478   1950   40.74838   -73.9967 |
20347. |   181      177    1478   1950   40.74838   -73.9967 |
20348. |   181     1076    1478   1950   40.74838   -73.9967 |
20349. |   181      412    1478   1950   40.74838   -73.9967 |
20350. |   181      177    1478   1950   40.74838   -73.9967 |
20351. |   181      177    1478   1950   40.74838   -73.9967 |
20352. |   181      177    1478   1950   40.74838   -73.9967 |
20353. |   181      177    1478   1950   40.74838   -73.9967 |
20354. |   181      516    1478   1950   40.74838   -73.9967 |
20355. |   181      921    1478   1950   40.74838   -73.9967 |
20356. |   181       66    1478   1950   40.74838   -73.9967 |
20357. |   181       66    1478   1950   40.74838   -73.9967 |
20358. |   181      177    1478   1950   40.74838   -73.9967 |
20359. |   181      177    1478   1950   40.74838   -73.9967 |
20360. |   181      177    1478   1950   40.74838   -73.9967 |
20361. |   181      921    1478   1950   40.74838   -73.9967 |
20362. |   181      714    1478   1950   40.74838   -73.9967 |
20363. |   181      113    1478   1950   40.74838   -73.9967 |
20364. |   181      177    1478   1950   40.74838   -73.9967 |
20365. |   181      177    1478   1950   40.74838   -73.9967 |
20366. |   181      177    1478   1950   40.74838   -73.9967 |
20367. |   181      144    1478   1950   40.74838   -73.9967 |
20368. |   181      161    1478   1950   40.74838   -73.9967 |
20369. |   181      177    1478   1950   40.74838   -73.9967 |
20370. |   181      177    1478   1950   40.74838   -73.9967 |
20371. |   181      177    1478   1950   40.74838   -73.9967 |
20372. |   181     1076    1478   1950   40.74838   -73.9967 |
20373. |   181      177    1478   1950   40.74838   -73.9967 |
20374. |   181     1076    1478   1950   40.74838   -73.9967 |
20375. |   181       54    1478   1950   40.74838   -73.9967 |
20376. |   181      177    1478   1950   40.74838   -73.9967 |
20377. |   181      177    1478   1950   40.74838   -73.9967 |
20378. |   181      161    1478   1950   40.74838   -73.9967 |
20379. |   181      177    1478   1950   40.74838   -73.9967 |
20380. |   181      177    1478   1950   40.74838   -73.9967 |
20381. |   181     1199    1478   1950   40.74838   -73.9967 |
20382. |   181     1073    1478   1950   40.74838   -73.9967 |
20383. |   181      177    1478   1950   40.74838   -73.9967 |
20384. |   181      177    1478   1950   40.74838   -73.9967 |
20385. |   181      522    1478   1950   40.74838   -73.9967 |
20386. |   181     1076    1478   1950   40.74838   -73.9967 |
20387. |   181      177    1478   1950   40.74838   -73.9967 |
20388. |   181      177    1478   1950   40.74838   -73.9967 |
20389. |   181      915    1478   1950   40.74838   -73.9967 |
20390. |   181      177    1478   1950   40.74838   -73.9967 |
20391. |   181      177    1478   1950   40.74838   -73.9967 |
20392. |   181      877    1478   1950   40.74838   -73.9967 |
20393. |   181      177    1478   1950   40.74838   -73.9967 |
20394. |   181      177    1478   1950   40.74838   -73.9967 |
20395. |   181      177    1478   1950   40.74838   -73.9967 |
20396. |   181      177    1478   1950   40.74838   -73.9967 |
20397. |   181      177    1478   1950   40.74838   -73.9967 |
20398. |   181      177    1478   1950   40.74838   -73.9967 |
20399. |   181      177    1478   1950   40.74838   -73.9967 |
20400. |   181      915    1478   1950   40.74838   -73.9967 |
       +-----------------------------------------------------+

As you might see above, newid is group of (gvkey latitude longitude) and uni_id is group of (university_id latitude longitude).
I made clusters of firms (newid) depending on the reference of uni_id.
However, there are many firm observation (181) that are included in several clusters (uni_id).
What I would like to do is to make firm allocated in only one cluster.
But the problem is that with this results, I do not know which university is nearest to each firms.
In this case, would there be any solution to make firms allocated in one cluster (not just keeping one cluster)?

[Andrew Musau]

Assuming that latitude and longitude in the dataset relate to

newid is group of (gvkey latitude longitude)

[,] the following

uni_id is group of (university_id latitude longitude)

implies that each uni_id is associated with a latitude and longitude pair. Call them latitude2 and longitude2 and include them in the dataset, you can use

ssc install geodist
help geodist

to calculate the geodetic distance between the two sets of coordinates, sort within clusters and pick out the smallest distance (uni_id) for that particular cluster.

[Anne-Claire Jo]

Got it ! Thank you very much !