Stata IC is more limited than SE or MP, so you should try it out on one of these depending on how large your dataset is.
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would there be other solutions such as reducing number of observations generated by increasing distance (from 1 mile to 50 miles) or reducing number of newid variable (i.e. instead of grouping gvkey year longitude latitude patent number) ?
Maybe the question would be quite stupid, but should we always group by (gvkey year longitude latitude patent number) ? Can we just group them as (gvkey longitude latitude patent number) ?
Or would there by other method to identify clusters instead of geonear (such as space-time K-function or Ripley's spatial K-function) ?
Sorry if I have too much questions but I am using limited resources (Stata IC) so I think I should find other solutions..Comment
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would there be other solutions such as reducing number of observations generated by increasing distance (from 1 mile to 50 miles) or reducing number of newid variable (i.e. instead of grouping gvkey year longitude latitude patent number) ?
Maybe the question would be quite stupid, but should we always group by (gvkey year longitude latitude patent number) ? Can we just group them as (gvkey longitude latitude patent number) ?
In the code in #14, I have firms identified by
I do not have your full dataset, but in your translation you included more variables. I am sorry that I do not have the time to figure out how to optimize the code taking into account your computing limitations, but it should be possible to implement the code in several steps on subsets of your data. Personally, I would find it easier to attempt and run the code in Stata SE or MP.egen newid= group(gvkey lat lon)
Or would there by other method to identify clusters instead of geonear (such as space-time K-function or Ripley's spatial K-function) ?
Sorry if I have too much questions but I am using limited resources (Stata IC) so I think I should find other solutions..
I am not familiar with these. Maybe you can start a new thread referencing these methods in the title and someone familiar with them can give you guidance.Comment
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Thank you so much for your help !
I would try to implement the code in the subset of my dataset and also find some other methods on Ripley's.
Thanks again !Comment
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Andrew Musau I have just one last question, does the number of observations increase with distance ?
For instance, if I set the distance (1 mile) to 10 miles, does the number of observations increase ?
I wanted to make less observations possible for the output.Comment
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No, that does not affect the number of observations.Comment
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I said that I have one last question, but I still have some questions left.. sorry.
1) Is there any option that I could modify to reduce the number of observations ?
2) Is there a way to know how much observations the code would create ?Last edited by Anne-Claire Jo; 24 Jul 2022, 03:13.Comment
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Only the joinby will expand the data, so it depends on how many pairwise combinations need to be formed.
I wonder why you need to use patent number to identify a firm. There are several firms which share the same gvkey but are in different locations (lat lon). This made me think that you have branches of the same firm, so in my code, I haveegen newid= group(gvkey patent_num lat lon)
as a branch identifier. How does patent number affect location? The same firm can obtain several patents, but will be located in the same place.egen newid= group(gvkey lat lon)
Comment
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Ah okay it works well with the code that you have provided ! Thank you !
But I see that group number (or identifier #) is different between newid2 and newid.
Can I ask you about the difference ?
Actually, I don't feel like I have understood the result generated as in #14...Last edited by Anne-Claire Jo; 25 Jul 2022, 21:23.Comment
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The following implies that firms with newid=14, 21, and 25 are within one mile of firm with newid=5.
newid2 latitu~2 longitu~2 city2 newid latitude longitude city
5 41.65819 -87.67947 Chicago 14 41.65819 -87.67947 Schaumburg
5 41.65819 -87.67947 Chicago 21 41.65819 -87.67947 Hoffman Estates
5 41.65819 -87.67947 Chicago 25 41.65819 -87.67947 Chicago
5 41.65819 -87.67947 Chicago 21 41.65819 -87.67947 Hoffman Estates
Res.:Code:* Example generated by -dataex-. For more info, type help dataex clear input int gvkey double year long patent_num str100 city str8 state_code str33 county float(latitude longitude tag) 0 1950 123456 "Charlotte" "NC" "Mecklenburg" 35.410557 -80.84306 1 0 1950 123456 "Charlotte" "NC" "Mecklenburg" 35.410557 -80.84306 1 0 1950 123456 "Charlotte" "NC" "Mecklenburg" 35.410557 -80.84306 1 0 1950 123456 "Charlotte" "NC" "Mecklenburg" 35.410557 -80.84306 1 0 1950 123456 "Charlotte" "NC" "Mecklenburg" 35.410557 -80.84306 1 1 1950 123456 "Maumee" "OH" "Lucas" 41.70798 -83.70683 1 1 1950 123456 "Naperville" "IL" "DuPage" 41.79007 -88.20559 1 1 1950 123456 "Maumee" "OH" "Lucas" 41.70798 -83.70683 1 1 1950 123456 "Naperville" "IL" "DuPage" 41.79007 -88.20559 1 1 1950 123456 "Naperville" "IL" "DuPage" 41.79007 -88.20559 1 2 1950 123456 "Waukegan" "IL" "Lake" 42.16139 -88.13834 1 2 1950 123456 "Chicago" "IL" "Cook" 41.65819 -87.67947 1 2 1950 123456 "Naugatuck" "CT" "New Haven" 41.28052 -72.874146 1 2 1950 123456 "Waukegan" "IL" "Lake" 42.16139 -88.13834 1 2 1950 123456 "Downers Grove" "IL" "DuPage" 41.81107 -88.02453 1 3 1950 123456 "Irving" "TX" "Dallas" 32.9027 -96.5636 1 3 1950 123456 "Irving" "TX" "Dallas" 32.9027 -96.5636 1 3 1950 123456 "Irving" "TX" "Dallas" 32.9027 -96.5636 1 3 1950 123456 "Irving" "TX" "Dallas" 32.9027 -96.5636 1 3 1950 123456 "Irving" "TX" "Dallas" 32.9027 -96.5636 1 4 1950 123456 "Memphis" "TN" "Shelby" 35.0337 -89.9343 1 4 1950 123456 "Blue Bell" "PA" "Montgomery" 40.3128 -75.32134 1 4 1950 123456 "Blue Bell" "PA" "Montgomery" 40.3128 -75.32134 1 4 1950 123456 "Blue Bell" "PA" "Montgomery" 40.3128 -75.32134 1 4 1950 123456 "Memphis" "TN" "Shelby" 35.0337 -89.9343 1 5 1950 123456 "Dayton" "OH" "Montgomery" 39.84139 -84.41647 1 5 1950 123456 "New York" "NY" "New York" 40.74838 -73.996704 1 5 1950 123456 "Schaumburg" "IL" "Cook" 41.65819 -87.67947 1 5 1950 123456 "Chicago" "IL" "Cook" 41.65819 -87.67947 1 5 1950 123456 "La Jolla" "CA" "San Diego" 33.02935 -116.85355 1 6 1950 123456 "Stamford" "CT" "Fairfield" 41.25555 -73.43528 1 6 1950 123456 "Stamford" "CT" "Fairfield" 41.25555 -73.43528 1 6 1950 123456 "Stamford" "CT" "Fairfield" 41.25555 -73.43528 1 6 1950 123456 "Palo Alto" "CA" "Santa Clara" 37.444324 -122.1497 1 6 1950 123456 "Racine" "WI" "Racine" 42.69673 -87.90308 1 7 1950 123456 "Hoffman Estates" "IL" "Cook" 41.65819 -87.67947 1 7 1950 123456 "Cleveland" "OH" "Cuyahoga" 41.45346 -81.92177 1 7 1950 123456 "Pittsburgh" "PA" "Allegheny" 40.59417 -79.97028 1 7 1950 123456 "Denver" "CO" "Denver" 39.7507 -104.989 1 7 1951 123456 "Hoffman Estates" "IL" "Cook" 41.65819 -87.67947 1 8 1950 123456 "Culver City" "CA" "Los Angeles" 34.26187 -118.45866 1 8 1950 123456 "Chicago" "IL" "Cook" 41.65819 -87.67947 1 8 1950 123456 "Harrisburg" "PA" "Dauphin" 40.2782 -76.70937 1 8 1950 123456 "Houston" "TX" "Harris" 30.00409 -95.28248 1 8 1950 123456 "Chicago" "IL" "Cook" 41.65819 -87.67947 1 9 1950 123456 "Houston" "TX" "Harris" 30.00409 -95.28248 1 9 1950 123456 "Austin" "MN" "Freeborn" 43.75284 -93.16799 1 9 1950 123456 "Houston" "TX" "Harris" 30.00409 -95.28248 1 9 1950 123456 "Houston" "TX" "Harris" 30.00409 -95.28248 1 9 1950 123456 "Houston" "TX" "Harris" 30.00409 -95.28248 1 10 1950 123456 "Philadelphia" "PA" "Philadelphia" 40.1162 -75.0141 1 10 1950 123456 "Philadelphia" "PA" "Philadelphia" 40.1162 -75.0141 1 10 1950 123456 "Philadelphia" "PA" "Philadelphia" 40.1162 -75.0141 1 10 1950 123456 "Philadelphia" "PA" "Philadelphia" 40.1162 -75.0141 1 10 1950 123456 "Philadelphia" "PA" "Philadelphia" 40.1162 -75.0141 1 11 1950 123456 "" "TX" "" . . 1 11 1950 123456 "Downers Grove" "IL" "DuPage" 41.81107 -88.02453 1 11 1950 123456 "Buffalo" "NY" "Erie" 42.834 -78.63425 1 11 1950 123456 "Oak Brook" "IL" "DuPage" 41.8364 -87.95317 1 11 1950 123456 "White Pine" "MI" "Ontonagon" 46.73806 -89.17944 1 12 1950 123456 "Brentwood" "TN" "Davidson" 36.260387 -86.70456 1 12 1950 123456 "Pittsburgh" "PA" "Allegheny" 40.59417 -79.97028 1 12 1951 123456 "Brentwood" "TN" "Davidson" 36.260387 -86.70456 1 12 1951 123456 "Pittsburgh" "PA" "Allegheny" 40.59417 -79.97028 1 12 1952 123456 "Pittsburgh" "PA" "Allegheny" 40.59417 -79.97028 1 13 1950 123456 "Parsippany" "NJ" "Morris" 40.8819 -74.62099 1 13 1950 123456 "Saint Paul" "MN" "Ramsey" 44.96996 -93.08317 1 13 1950 123456 "Clarks Summit" "PA" "Lackawanna" 41.34319 -75.530136 1 13 1950 123456 "" "" "" . . 1 13 1951 123456 "Clarks Summit" "PA" "Lackawanna" 41.3731 -75.6841 1 14 1950 123456 "Richmond" "VA" "Richmond city" . . 1 14 1950 123456 "Atlanta" "GA" "DeKalb" 33.888504 -84.28954 1 14 1950 123456 "Atlanta" "GA" "Fulton" 34.040833 -84.3859 1 14 1950 123456 "Carteret" "NJ" "Middlesex" 40.33243 -74.56883 1 14 1950 123456 "Richmond" "VA" "Richmond city" . . 1 15 1950 123456 "" "" "" . . 1 15 1950 123456 "" "" "" . . 1 15 1950 123456 "" "" "" . . 1 15 1950 123456 "Dayton" "OH" "Warren" 39.37145 -84.21078 1 15 1950 123456 "" "" "" . . 1 16 1950 123456 "New Britain" "CT" "Hartford" 41.92608 -72.64576 1 16 1950 123456 "New York" "NY" "New York" 40.74838 -73.996704 1 16 1950 123456 "Columbus" "OH" "Franklin" 40.03219 -83.13834 1 16 1950 123456 "New York" "NY" "New York" 40.74838 -73.996704 1 16 1950 123456 "New York" "NY" "New York" 40.74838 -73.996704 1 17 1950 123456 "Houston" "TX" "Harris" 30.00409 -95.28248 1 17 1950 123456 "Houston" "TX" "Harris" 30.00409 -95.28248 1 17 1950 123456 "Philadelphia" "PA" "Philadelphia" 40.1162 -75.0141 1 17 1950 123456 "Houston" "TX" "Harris" 30.00409 -95.28248 1 17 1950 123456 "Philadelphia" "PA" "Philadelphia" 40.1162 -75.0141 1 18 1950 123456 "Upland" "IN" "Grant" 40.5112 -85.82655 1 18 1950 123456 "El Segundo" "CA" "Los Angeles" 34.26187 -118.45866 1 18 1950 123456 "El Segundo" "CA" "Los Angeles" 34.26187 -118.45866 1 18 1950 123456 "Mckeesport" "PA" "Allegheny" 40.59417 -79.97028 1 18 1950 123456 "Oakland" "CA" "" . . 1 19 1950 123456 "Yardley" "PA" "Bucks" 40.57824 -75.21906 1 19 1950 123456 "Yardley" "PA" "Bucks" 40.57824 -75.21906 1 19 1950 123456 "Yardley" "PA" "Bucks" 40.57824 -75.21906 1 19 1950 123456 "Yardley" "PA" "Bucks" 40.57824 -75.21906 1 19 1950 123456 "Cupertino" "CA" "Santa Clara" 37.444324 -122.1497 1 end drop if missing(latitude)| missing(longitude) duplicates drop gvkey year lat lon, force egen newid= group(gvkey lat lon) sort newid l, sepby(newid)
Now you need to find group_id from SSC to create the clusters, then merge the results with the full dataset. Search the forum for mentions of group_id for examples.Code:. l, sepby(newid) +----------------------------------------------------------------------------------------------------------+ | gvkey year patent~m city state_~e county latitude longitude tag newid | |----------------------------------------------------------------------------------------------------------| 1. | 0 1950 123456 Charlotte NC Mecklenburg 35.41056 -80.84306 1 1 | |----------------------------------------------------------------------------------------------------------| 2. | 1 1950 123456 Maumee OH Lucas 41.70798 -83.70683 1 2 | |----------------------------------------------------------------------------------------------------------| 3. | 1 1950 123456 Naperville IL DuPage 41.79007 -88.20559 1 3 | |----------------------------------------------------------------------------------------------------------| 4. | 2 1950 123456 Naugatuck CT New Haven 41.28052 -72.87415 1 4 | |----------------------------------------------------------------------------------------------------------| 5. | 2 1950 123456 Chicago IL Cook 41.65819 -87.67947 1 5 | |----------------------------------------------------------------------------------------------------------| 6. | 2 1950 123456 Downers Grove IL DuPage 41.81107 -88.02453 1 6 | |----------------------------------------------------------------------------------------------------------| 7. | 2 1950 123456 Waukegan IL Lake 42.16139 -88.13834 1 7 | |----------------------------------------------------------------------------------------------------------| 8. | 3 1950 123456 Irving TX Dallas 32.9027 -96.5636 1 8 | |----------------------------------------------------------------------------------------------------------| 9. | 4 1950 123456 Memphis TN Shelby 35.0337 -89.9343 1 9 | |----------------------------------------------------------------------------------------------------------| 10. | 4 1950 123456 Blue Bell PA Montgomery 40.3128 -75.32134 1 10 | |----------------------------------------------------------------------------------------------------------| 11. | 5 1950 123456 La Jolla CA San Diego 33.02935 -116.8536 1 11 | |----------------------------------------------------------------------------------------------------------| 12. | 5 1950 123456 Dayton OH Montgomery 39.84139 -84.41647 1 12 | |----------------------------------------------------------------------------------------------------------| 13. | 5 1950 123456 New York NY New York 40.74838 -73.9967 1 13 | |----------------------------------------------------------------------------------------------------------| 14. | 5 1950 123456 Schaumburg IL Cook 41.65819 -87.67947 1 14 | |----------------------------------------------------------------------------------------------------------| 15. | 6 1950 123456 Palo Alto CA Santa Clara 37.44432 -122.1497 1 15 | |----------------------------------------------------------------------------------------------------------| 16. | 6 1950 123456 Stamford CT Fairfield 41.25555 -73.43528 1 16 | |----------------------------------------------------------------------------------------------------------| 17. | 6 1950 123456 Racine WI Racine 42.69673 -87.90308 1 17 | |----------------------------------------------------------------------------------------------------------| 18. | 7 1950 123456 Denver CO Denver 39.7507 -104.989 1 18 | |----------------------------------------------------------------------------------------------------------| 19. | 7 1950 123456 Pittsburgh PA Allegheny 40.59417 -79.97028 1 19 | |----------------------------------------------------------------------------------------------------------| 20. | 7 1950 123456 Cleveland OH Cuyahoga 41.45346 -81.92177 1 20 | |----------------------------------------------------------------------------------------------------------| 21. | 7 1951 123456 Hoffman Estates IL Cook 41.65819 -87.67947 1 21 | 22. | 7 1950 123456 Hoffman Estates IL Cook 41.65819 -87.67947 1 21 | |----------------------------------------------------------------------------------------------------------| 23. | 8 1950 123456 Houston TX Harris 30.00409 -95.28248 1 22 | |----------------------------------------------------------------------------------------------------------| 24. | 8 1950 123456 Culver City CA Los Angeles 34.26187 -118.4587 1 23 | |----------------------------------------------------------------------------------------------------------| 25. | 8 1950 123456 Harrisburg PA Dauphin 40.2782 -76.70937 1 24 | |----------------------------------------------------------------------------------------------------------| 26. | 8 1950 123456 Chicago IL Cook 41.65819 -87.67947 1 25 | |----------------------------------------------------------------------------------------------------------| 27. | 9 1950 123456 Houston TX Harris 30.00409 -95.28248 1 26 | |----------------------------------------------------------------------------------------------------------| 28. | 9 1950 123456 Austin MN Freeborn 43.75284 -93.16799 1 27 | |----------------------------------------------------------------------------------------------------------| 29. | 10 1950 123456 Philadelphia PA Philadelphia 40.1162 -75.0141 1 28 | |----------------------------------------------------------------------------------------------------------| 30. | 11 1950 123456 Downers Grove IL DuPage 41.81107 -88.02453 1 29 | |----------------------------------------------------------------------------------------------------------| 31. | 11 1950 123456 Oak Brook IL DuPage 41.8364 -87.95317 1 30 | |----------------------------------------------------------------------------------------------------------| 32. | 11 1950 123456 Buffalo NY Erie 42.834 -78.63425 1 31 | |----------------------------------------------------------------------------------------------------------| 33. | 11 1950 123456 White Pine MI Ontonagon 46.73806 -89.17944 1 32 | |----------------------------------------------------------------------------------------------------------| 34. | 12 1950 123456 Brentwood TN Davidson 36.26039 -86.70456 1 33 | 35. | 12 1951 123456 Brentwood TN Davidson 36.26039 -86.70456 1 33 | |----------------------------------------------------------------------------------------------------------| 36. | 12 1952 123456 Pittsburgh PA Allegheny 40.59417 -79.97028 1 34 | 37. | 12 1951 123456 Pittsburgh PA Allegheny 40.59417 -79.97028 1 34 | 38. | 12 1950 123456 Pittsburgh PA Allegheny 40.59417 -79.97028 1 34 | |----------------------------------------------------------------------------------------------------------| 39. | 13 1950 123456 Parsippany NJ Morris 40.8819 -74.62099 1 35 | |----------------------------------------------------------------------------------------------------------| 40. | 13 1950 123456 Clarks Summit PA Lackawanna 41.34319 -75.53014 1 36 | |----------------------------------------------------------------------------------------------------------| 41. | 13 1951 123456 Clarks Summit PA Lackawanna 41.3731 -75.6841 1 37 | |----------------------------------------------------------------------------------------------------------| 42. | 13 1950 123456 Saint Paul MN Ramsey 44.96996 -93.08317 1 38 | |----------------------------------------------------------------------------------------------------------| 43. | 14 1950 123456 Atlanta GA DeKalb 33.8885 -84.28954 1 39 | |----------------------------------------------------------------------------------------------------------| 44. | 14 1950 123456 Atlanta GA Fulton 34.04083 -84.3859 1 40 | |----------------------------------------------------------------------------------------------------------| 45. | 14 1950 123456 Carteret NJ Middlesex 40.33243 -74.56883 1 41 | |----------------------------------------------------------------------------------------------------------| 46. | 15 1950 123456 Dayton OH Warren 39.37145 -84.21078 1 42 | |----------------------------------------------------------------------------------------------------------| 47. | 16 1950 123456 Columbus OH Franklin 40.03219 -83.13834 1 43 | |----------------------------------------------------------------------------------------------------------| 48. | 16 1950 123456 New York NY New York 40.74838 -73.9967 1 44 | |----------------------------------------------------------------------------------------------------------| 49. | 16 1950 123456 New Britain CT Hartford 41.92608 -72.64576 1 45 | |----------------------------------------------------------------------------------------------------------| 50. | 17 1950 123456 Houston TX Harris 30.00409 -95.28248 1 46 | |----------------------------------------------------------------------------------------------------------| 51. | 17 1950 123456 Philadelphia PA Philadelphia 40.1162 -75.0141 1 47 | |----------------------------------------------------------------------------------------------------------| 52. | 18 1950 123456 El Segundo CA Los Angeles 34.26187 -118.4587 1 48 | |----------------------------------------------------------------------------------------------------------| 53. | 18 1950 123456 Upland IN Grant 40.5112 -85.82655 1 49 | |----------------------------------------------------------------------------------------------------------| 54. | 18 1950 123456 Mckeesport PA Allegheny 40.59417 -79.97028 1 50 | |----------------------------------------------------------------------------------------------------------| 55. | 19 1950 123456 Cupertino CA Santa Clara 37.44432 -122.1497 1 51 | |----------------------------------------------------------------------------------------------------------| 56. | 19 1950 123456 Yardley PA Bucks 40.57824 -75.21906 1 52 | +----------------------------------------------------------------------------------------------------------+ .Comment
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Thank you I got it !Comment
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Andrew Musau Hi again, can I ask you other question for the geonear command if it doesn't bother you ?
I have posted other question, however, I haven't yet solved the problem..
By using this same firm-level panel data, I would like to reference university (org_id) that is in same sector (bea_code) for generating firm clusters (below is the example of university patent data).
In other words, this time, firms are clustered with respect to a given university (reference) with same sector within 1 mile, and firms should be clustered in only one group.
I have tried to think of Spatial k-function or k-density clustering methods, however, I always face problems..
With geonear command, would it be possible to realize the study ?
I am SO sorry to ask for your help..
Code:* 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
Last edited by Anne-Claire Jo; 29 Jul 2022, 05:30.Comment
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