Hi all. I have a panel dataset from Brazilian states with information on the number of radio stations per state (n_radio_stations).
What I need: n_radio_stations needs to be normalized by the area of the state (area_km2) to give the number of radio stations per square miles. Then this variable needs to be normalized by each state’s population (i_pop_total) to give the number of radio stations per unit of area per population.
This variable will provide an index of the intensity of the radio infrastructure in each state. I’m confused about how to do this calculation. Can someone help?
The aforementioned data follows below.
ibge_uf_code = state’s ID
What I need: n_radio_stations needs to be normalized by the area of the state (area_km2) to give the number of radio stations per square miles. Then this variable needs to be normalized by each state’s population (i_pop_total) to give the number of radio stations per unit of area per population.
This variable will provide an index of the intensity of the radio infrastructure in each state. I’m confused about how to do this calculation. Can someone help?
The aforementioned data follows below.
ibge_uf_code = state’s ID
Code:
* Example generated by -dataex-. For more info, type help dataex clear input int(year ibge_uf_code n_radio_stations) long area_km2 double i_pop_total 1970 11 2 243044 111064 1970 12 4 152589 215299 1970 13 9 1558987 955203 1970 14 1 230104 40885 1970 15 12 1227530 2166998 1970 16 4 139068 114230 1970 17 . 286944 521139 1970 21 10 324616 2992678 1970 22 9 250934 1680573 1970 23 26 146817 4361603 1970 24 12 53015 1550184 1970 25 11 56372 2382463 1970 26 26 98306 5161866 1970 27 9 27652 1588068 1970 28 6 21994 900679 1970 29 34 559921 7493437 1970 31 121 582586 11485663 1970 32 11 45597 1599324 1970 33 34 43305 8994802 1970 35 262 247320 17770975 1970 41 98 199060 6929821 1970 42 62 95495 2901660 1970 43 123 267527 6664841 1970 50 . 350548 998160 1970 51 19 881001 598849 1970 52 33 355092 2416890 1970 53 10 5771 537492 1974 11 3 243044 263048 1974 12 7 152589 249690 1974 13 12 1558987 1145333 1974 14 1 230104 56179 1974 15 13 1227530 2661598 1974 16 4 139068 138641 1974 17 . 286944 608303 1974 21 10 324616 3394184 1974 22 9 250934 1864022 1974 23 24 146817 4732333 1974 24 12 53015 1689644 1974 25 11 56372 2537616 1974 26 29 98306 5554521 1974 27 9 27652 1746007 1974 28 7 21994 996559 1974 29 32 559921 8278219 1974 31 125 582586 12243440 1974 32 10 45597 1768930 1974 33 63 43305 9913534 1974 35 240 247320 20679415 1974 41 103 199060 7209832 1974 42 63 95495 3192313 1974 43 125 267527 7108444 1974 50 . 350548 1146804 1974 51 19 881001 814877 1974 52 33 355092 2698584 1974 53 13 5771 793258 1978 11 6 243044 415033 1978 12 . 152589 284081 1978 13 14 1558987 1335463 1978 14 2 230104 71474 1978 15 14 1227530 3156198 1978 16 3 139068 163052 1978 17 . 286944 695467 1978 21 11 324616 3795691 1978 22 10 250934 2047471 1978 23 31 146817 5103064 1978 24 13 53015 1829105 1978 25 15 56372 2692769 1978 26 30 98306 5947176 1978 27 11 27652 1903946 1978 28 8 21994 1092439 1978 29 40 559921 9063001 1978 31 134 582586 13001217 1978 32 12 45597 1938535 1978 33 72 43305 10832265 1978 35 281 247320 23587854 1978 41 137 199060 7489843 1978 42 77 95495 3482966 1978 43 149 267527 7552047 1978 50 . 350548 1295447 1978 51 30 881001 1030904 1978 52 39 355092 2980278 1978 53 10 5771 1049025 1982 11 10 243044 607692 1982 12 6 152589 322447 1982 13 20 1558987 1552840 1982 14 5 230104 104296 1982 15 22 1227530 3684691 1982 16 3 139068 196011 1982 17 . 286944 771924 1982 21 16 324616 4166227 1982 22 15 250934 2219731 1982 23 49 146817 5484469 1982 24 15 53015 1992786 1982 25 21 56372 2848667 1982 26 41 98306 6322476 1982 27 10 27742 2079494 1982 28 10 21994 1204288 1982 29 52 559951 9894046 1982 31 155 582586 13809750 1982 32 18 45597 2128298 1982 33 88 43305 11567281 end
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