US Biomedical Research Communities

So far, my research career has taken me to UCLA, Harvard, and the University of Washington. For the next step of my research career, I could end up anywhere in the US. With this came the realization that I don’t actually know how representative these research environment / community experiences are to the rest of the potential places in the US I may end up working. Should I expect something entirely different in terms of resources, community, or culture?

To begin to address this, I looked to the data in the NIH RePORTER for statistics on the annual funding amounts awarded to each institution. I grouped the data by city, but was still not content as I knew this was not grouping the entirety of each research community (eg. Boston and Cambridge should be considered part of the same community). Thus, I called latitude/longitude coordinates for each city using the Data Science Toolkit, got all pairwise distances between all 819 cities, and clustered cities (by distance) using hierarchical clustering. I cut the tree to only include groups where the maximum distances between intra-group members were 50-miles, leaving me 229 separate clusters. I consider these to be separate “Research Communities”.

Here’s the corresponding map of the US where the major city from each cluster is shown as points, with each bubble sized according to its total funding amount, and the opacity of each bubble dependent on the total number of awards (which was highly correlated with total funding amount; see below).

Click on the image to see the map in better detail.

As you can tell by the map, there is a definite coastal bias, with some of the expected big players pretty obvious (eg. Boston area, San Francisco). I actually underestimated how much NIH money was in the New York City area, though it makes a lot of sense in retrospect. Here’s a table of the results:

group city state annual funding (millions) awards 1 BOSTON MA 2647.379404 4794 2 NEW YORK NY 1856.049837 3648 3 SAN FRANCISCO CA 1613.687889 3239 4 DURHAM NC 1065.635859 1842 5 LOS ANGELES CA 1049.980965 2009 6 PHILADELPHIA PA 1005.261567 2088 7 SEATTLE WA 955.084314 1522 8 LA JOLLA CA 918.843911 1710 9 BALTIMORE MD 897.321628 1848 10 CHICAGO IL 717.191949 1608 11 PITTSBURGH PA 625.334095 1250 12 HOUSTON TX 564.113359 1271 13 ANN ARBOR MI 562.530715 1249 14 SAINT LOUIS MO 518.772469 1072 15 NEW HAVEN CT 466.434658 994 16 ATLANTA GA 447.658441 986 17 NASHVILLE TN 388.092334 849 18 CLEVELAND OH 332.186487 675 19 MADISON WI 332.030422 653 20 MINNEAPOLIS MN 318.459912 719 21 WASHINGTON DC 303.358981 579 22 BIRMINGHAM AL 293.413142 587 23 PORTLAND OR 274.705734 534 24 AURORA CO 253.082163 641 25 DALLAS TX 250.435769 583 26 BRONX NY 249.238666 498 27 DAVIS CA 240.968518 494 28 COLUMBUS OH 226.226589 549 29 ROCHESTER MN 226.088176 404 30 SALT LAKE CITY UT 209.105125 513 31 WORCESTER MA 205.521402 477 32 CINCINNATI OH 191.573358 440 33 CORAL GABLES FL 187.671298 397 34 GAINESVILLE FL 181.335999 441 35 PROVIDENCE RI 177.566364 410 36 IRVINE CA 177.126587 362 37 ROCHESTER NY 169.69009 398 38 INDIANAPOLIS IN 169.278078 355 39 IOWA CITY IA 168.922326 403 40 PISCATAWAY NJ 162.599992 382 41 CHARLOTTESVILLE VA 150.052283 394 42 BOULDER CO 145.718439 364 43 LEXINGTON KY 134.94632 310 44 CHARLESTON SC 132.368946 322 45 WINSTON-SALEM NC 130.540895 255 46 TUCSON AZ 130.011104 276 47 MILWAUKEE WI 128.291354 282 48 OMAHA NE 123.419242 266 49 SAN ANTONIO TX 119.209378 246 50 KANSAS CITY KS 114.48238 265 51 MEMPHIS TN 112.178482 231 52 AMHERST NY 109.40242 264 53 ROCKVILLE MD 106.974511 190 54 NEW ORLEANS LA 102.171044 205 55 HANOVER NH 101.658468 216 56 DETROIT MI 100.722011 232 57 TEMPE AZ 96.419453 204 58 ITHACA NY 95.211216 245 59 AUSTIN TX 89.629848 298 60 FARMINGTON CT 87.73937 225 61 TAMPA FL 85.787974 175 62 GALVESTON TX 84.170354 167 63 RICHMOND VA 83.614454 238 64 OKLAHOMA CITY OK 82.806887 177 65 ALBUQUERQUE NM 82.01337 163 66 BAR HARBOR ME 79.759078 127 67 CHAMPAIGN IL 78.912638 215 68 UNIVERSITY PARK PA 75.45165 190 69 EAST LANSING MI 73.48425 193 70 RESEARCH TRIANGLE NC 71.747136 117 71 STONY BROOK NY 70.828814 209 72 LOUISVILLE KY 68.366519 165 73 EVANSTON IL 67.453466 197 74 COLLEGE STATION TX 65.806556 203 75 ATHENS GA 65.735147 155 76 JUPITER FL 61.823641 122 77 NEWARK DE 60.758057 122 78 BURLINGTON VT 56.939476 120 79 ALBANY NY 55.686519 142 80 HERSHEY PA 53.273163 123 81 EUGENE OR 50.571811 119 82 WEST LAFAYETTE IN 50.249558 155 83 LITTLE ROCK AR 50.059408 93 84 COLUMBIA MO 49.517631 138 85 AUGUSTA GA 47.663342 108 86 PULLMAN WA 45.557797 118 87 COLUMBIA SC 43.468183 98 88 SANTA CRUZ CA 39.599484 77 89 TALLAHASSEE FL 37.72346 94 90 BLOOMINGTON IN 31.743822 103 91 RALEIGH NC 31.669777 102 92 JACKSONVILLE FL 30.683478 49 93 BLACKSBURG VA 30.636308 96 94 RIVERSIDE CA 29.929642 88 95 JACKSON MS 29.790062 64 96 BATON ROUGE LA 28.856999 61 97 ORANGEBURG NY 28.381356 64 98 MORGANTOWN WV 27.768343 63 99 SANTA BARBARA CA 26.224798 68 100 RENO NV 24.850501 47 101 SYRACUSE NY 24.29689 78 102 BOZEMAN MT 23.45062 45 103 NOVATO CA 23.43383 46 104 EL PASO TX 22.817925 45 105 NOTRE DAME IN 20.512454 52 106 PORTLAND ME 19.010488 30 107 CORVALLIS OR 18.063428 48 108 KINGSTON RI 17.589297 99 109 GRAND RAPIDS MI 16.341949 32 110 AMES IA 16.341844 51 111 CLEMSON SC 15.679785 28 112 SIOUX FALLS SD 15.427308 17 113 ORLANDO FL 15.400278 43 114 LUBBOCK TX 15.090183 44 115 MARSHFIELD WI 14.68387 5 116 MANHATTAN KS 14.310955 42 117 MISSOULA MT 14.297658 37 118 TOLEDO OH 14.231069 36 119 HUNTSVILLE AL 14.082515 13 120 LARAMIE WY 13.856071 16 121 GRAND FORKS ND 13.466647 20 122 MONROVIA CA 12.567787 23 123 SHREVEPORT LA 12.146812 29 124 MOBILE AL 12.022875 36 125 RICHLAND WA 11.708035 17 126 NORFOLK VA 11.152856 38 127 WOODS HOLE MA 10.9381 32 128 LOMA LINDA CA 10.830184 29 129 LAS VEGAS NV 10.719344 13 130 LIVERMORE CA 10.313542 24 131 KNOXVILLE TN 9.439704 27 132 DAYTON OH 9.267817 29 133 AUBURN UNIVERSITY AL 9.06897 24 134 DANVILLE PA 8.890042 16 135 FLAGSTAFF AZ 8.097707 10 136 JOHNSON CITY TN 7.71357 23 137 GREENVILLE NC 7.704072 30 138 HUNTINGTON WV 7.608947 7 139 VERMILLION SD 7.585828 18 140 FARGO ND 7.568713 15 141 MERCED CA 7.028605 22 142 SPRINGFIELD IL 6.951095 11 143 STILLWATER OK 6.437718 16 144 BETHLEHEM PA 6.414142 18 145 EDINBURG TX 6.002395 15 146 BOISE ID 5.914325 10 147 TULSA OK 5.451189 6 148 HATTIESBURG MS 5.426449 4 149 UNIVERSITY MS 5.318222 13 150 ST. PAUL MN 5.309479 9 151 MISSISSIPPI STATE MS 4.785458 10 152 FAYETTEVILLE AR 4.433915 13 153 LOS ALAMOS NM 4.367963 11 154 ATHENS OH 4.308147 11 155 TYLER TX 4.1291 13 156 DURHAM NH 3.937952 9 157 DOVER DE 3.717299 8 158 WICHITA KS 3.517968 3 159 CARBONDALE IL 3.341975 11 160 KALAMAZOO MI 3.203297 7 161 DENTON TX 2.729782 10 162 LOGAN UT 2.525725 8 163 WACO TX 2.272581 7 164 LAFAYETTE LA 2.215532 3 165 FUQUAY VARINA NC 2.149723 6 166 MOUNT PLEASANT MI 2.12931 7 167 HOUGHTON MI 1.95425 5 168 SPRINGFIELD MO 1.759795 2 169 SAN LUIS OBISPO CA 1.546307 6 170 MELBOURNE FL 1.472552 3 171 COLORADO SPRINGS CO 1.437185 5 172 NORTH WEBSTER IN 1.372833 4 173 WHITERIVER AZ 1.344707 2 174 BILLINGS MT 1.338972 2 175 SPOKANE WA 1.157481 4 176 RAPID CITY SD 1.148682 2 177 BELLINGHAM WA 1.108736 5 178 DE KALB IL 1.066215 3 179 FRESNO CA 0.973636 7 180 DAVENPORT IA 0.96552 3 181 KINGSVILLE TX 0.965045 5 182 LAC DU FLAMBEAU WI 0.944531 1 183 MACON GA 0.913288 2 184 PEMBROKE NC 0.88802 3 185 POTSDAM NY 0.843875 4 186 BOWLING GREEN KY 0.830798 2 187 WILMINGTON NC 0.829924 2 188 MIDDLEBURY VT 0.808639 2 189 UTICA NY 0.797333 1 190 EL DORADO HILLS CA 0.770035 3 191 SOCORRO NM 0.768367 2 192 TSAILE AZ 0.717571 1 193 MONROE LA 0.705226 2 194 SPRINGFIELD VA 0.637692 1 195 ALLENTOWN PA 0.59258 2 196 TEMECULA CA 0.56395 2 197 PABLO MT 0.546609 2 198 FORT SMITH AR 0.545845 2 199 BURGESS VA 0.514913 2 200 ODESSA TX 0.497717 1 201 FORT MYERS FL 0.493091 1 202 KEENE NH 0.416179 1 203 SILVERTHORNE CO 0.409 43 204 OSHKOSH WI 0.407519 1 205 GREAT FALLS MT 0.405 1 206 KIRKSVILLE MO 0.382343 1 207 DURANGO CO 0.359992 2 208 BAKERSFIELD CA 0.356689 1 209 SAVANNAH GA 0.353266 3 210 STORM LAKE IA 0.329633 1 211 KYKOTSMOVI AZ 0.307815 1 212 TERRE HAUTE IN 0.305308 1 213 SMYRNA TN 0.299244 1 214 ROLLA MO 0.280072 3 215 DOTHAN AL 0.277277 1 216 BUENA VISTA CO 0.2561 1 217 BUTTE MT 0.249307 1 218 CHATTANOOGA TN 0.236791 2 219 WINTHROP ME 0.232182 2 220 MAGNOLIA AR 0.225 1 221 SOUTH SAN FRANCISCO MI 0.224944 1 222 BUFFALO WY 0.223848 1 223 PENSACOLA FL 0.163576 3 224 BEAUMONT TX 0.1326 1 225 ELIZABETH CITY NC 0.118781 1 226 EAGLE BUTTE SD 0.097019 1 227 DAYTONA BEACH FL 0.09475 1 228 PITTSBURG KS 0.069114 1 229 SCRANTON PA 0.058594 1

Surprisingly, Seattle came in at #7 in NIH funding, and Los Angeles came in at #5. With Boston the far-and-away #1, I guess this means I do indeed have a pretty biased view of research communities. That said, I’ve looked pretty deeply into a number of places already, and I suspect anything in the top 75 or so still have communities large enough to do what I want / need to do. And with things like video-conferencing now, it’s pretty easy to converse with people further away, anyways.

CAVEAT CAVEAT CAVEAT (My usual caveat statement):

1) Yes, I realize this is only NIH-funded award. Certainly other forms of national money like NSF or private money like HHMI matter a lot (and certainly from the individual investigator perspective), and are not taken into account my this analysis. Still, NIH-awarded money drives the brunt of biomedical research, so I don’t think this exclusion will lead to any overly misleading results.

2) Obviously, don’t take these rankings to mean anything about research community quality or anything. Just a data-driven metric to semi-quantitatively assess sheer research community size.

PS. Here’s the plot of award # vs total award amounts:

As you can see, it correlates pretty nicely, with a few outliers. Silverthorne is apparently where the Keystone Symposia are based. Marshfield is apparently the base of a regional health-care system that must have a handful of decently-sized grants.

EDIT 1:

By request, here’s a plot of the top 30 places (awarded greater than $10M annually) with the highest amount of money awarded per grant. I ended up making the 10M subset since there were a lot of random, small places which completely overtook the plot otherwise. Sometime in the future, after I figure out where to get population data, I’ll perform the other request where I divide area NIH awarded amount by area population.

EDIT 2:

OK, I had a number of different people express interest in having the funding amounts divided by city population, so I pulled data from the US census and did just that. A huge caveat is that this is ONLY looking at population of the “major” city in the group / cluster, and does not take into account the entire population of the cluster (that would require a fair bit more work, which I’m unwilling to devote right now). The results:

So there’s a pretty big cloud of points. I’ve highlighted the places with the most NIH funding per individual in the city (general population). You start seeing many more “college towns” pop up. Makes sense. Now all of the people at Yale and U Michigan can feel more content now. 😉

EDIT 3:

Thought I was done, but figured I’d do one more analysis I’m assuming some people care about. I subsetted on the most populous cities by the US census, and then looked at those cities NIH funding divided by the population. San Francisco doesn’t end up in this plot b/c of it’s small population (within city limits), so I let San Jose represent the bay area. Big caveat here is that I’m only using census data that represents population within the city, rather than metro areas.