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).

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.