Occupy Wall Street Mapping, and Others

In the process of gathering data about the Tea Party, I also collected data about Occupy Wall Street protests in the U.S. For the OWS map below, I used 3 sources, one event per city, and mapped the events by congressional district (2012). The first OWS map is by the number of cities in each district with a registered protest. The second OWS map is by the largest estimated number of protesters per district (all city protests per district summed). Following these two OWS maps, I have several other maps from data that have been collecting, all of which is mapped at the congressional district level. Map 3 is a Partisan Voter Index, similar to that found in the Cook Report, but calculated by a non-Cook-related individual, estimating voter party preference for that district based on the last 2 major elections. Map 4 is Tea Party related, in that it is shows the FreedomWorks rating for each district representative as of 2013 (FreedomWorks is a major Tea Party donor). Finally, map 5 is a political rating score from a researcher at Stanford, Bonica, based on candidate criteria (the CF score).

The city-mapped data by congressional district is estimated. In other words, I could not find a good database that lists U.S. cities by congressional district, so I situated listed cities into districts by a brief Google search. If any of you discovers an error (and there may be several), please let me know.

Occupy Wall Street Sources:

1. http://en.wikipedia.org/wiki/List_of_Occupy_movement_protest_locations_in_the_United_States

2. http://www.theguardian.com/news/datablog/2011/oct/17/occupy-protests-world-list-map

3. Occupy Research Google Docs personal communication

PVI Source:

http://wheresthepartydoc.blogspot.com/2012/12/2012-cook-pvi-calculations.html

Bonica

http://data.stanford.edu/dime/

Original Data for Occupy Posted Below

Occupy Protest Inclusion--Decision Criteria

1. Many cities were listed in all three sources, with various amounts of information, or contradictory information
2. One event per city
3. Most reasonable information (i.e., occupiers listed higher than NYC were rejected)
4. Most information
5. If a range was given, they were averaged (i.e., 100-200=150)
6. If a number of protesters "+" was given, just the number was used (i.e., 40+ = 40)
7. If "hundreds" was given, then 200 was used
8. Highest population listed
9. If there was no reported population, or was less than 5, a value of 5 was used
10. If a city was in multilple districts, the total protesters were divided equally into the districts
11. No effort was made to independently verify the existence of protests in a given city, or the number of protesters, other than the above criteria
12. There may be errors in how I identified cities with congressional districts

---

Table 1-final data, Table 2-original raw data

Table 1: Final Data used for Mapping

Dist1	Protests	Protesters
AK01	9	402
AL01	1	50
AL02	1	5
AL03	1	25
AL05	1	200
AL06	1	300
AL07	1	40
AR01	1	5
AR02	2	505
AR03	2	300
AS01	1	5
AZ01	1	5
AZ03	1	4000
AZ04	1	25
AZ05	1	5
AZ07	2	1005
CA01	4	360
CA02	9	565
CA03	3	120
CA04	1	150
CA05	2	300
CA06	3	3805
CA08	2	5005
CA09	2	3200
CA10	1	250
CA11	1	24
CA12	1	100
CA13	1	5
CA14	3	158
CA16	1	300
CA17	2	205
CA18	4	305
CA19	3	155
CA20	1	3
CA21	1	5
CA22	1	185
CA23	3	410
CA24	6	595
CA25	1	5
CA27	1	5
CA28	1	5
CA29	1	100
CA30	1	5000
CA33	2	10
CA36	2	10
CA37	1	5
CA41	2	105
CA43	1	35
CA44	2	305
CA45	1	5
CA46	3	98
CA47	2	153
CA48	1	1200
CA50	2	10
CA53	2	7000
CO01	1	3000
CO02	1	500
CO03	4	403
CO04	3	67
CO05	1	500
CO5	1	5
CT01	1	5
CT02	1	5
CT03	2	505
CT05	1	5
DC	1	5000
DE01	2	155
FL01	1	500
FL02	1	300
FL03	1	500
FL06	1	100
FL07	2	205
FL08	1	3000
FL09	1	5
FL10	1	5
FL11	2	55
FL13	2	205
FL14	2	405
FL15	3	15
FL17	1	1000
FL18	1	5
FL19	3	355
FL21	1	100
FL22	3	410
GA01	2	300
GA02	1	5
GA05	1	150
GA08	1	5
GA10	2	75
GA14	1	5
HI01	1	200
HI02	9	430
IA01	3	15
IA02	2	205
IA03	1	350
IA04	1	300
ID01	3	15
ID02	4	20
IL01	1	8000
IL11	5	150
IL12	1	100
il14	2	10
IL15	3	417
IL16	2	205
IL17	2	100
IL18	1	300
IL19	1	5
IN01	2	10
IN02	2	105
IN03	1	300
IN04	2	1050
IN05	1	5
IN06	2	25
IN07	1	1000
IN08	1	100
IN09	1	200
KS02	4	370
KS03	1	5
KS04	1	300
KY01	1	5
KY02	2	10
KY03	1	300
KY04	1	5
KY06	2	105
LA02	1	400
LA04	1	100
LA06	1	150
LA07	2	17
MA01	4	51
MA02	3	105
MA03	2	115
MA04	1	5
MA06	3	125
MA07	1	5
MA08	2	10
MA09	4	1047
MA10	1	100
MD02	1	70
MD03	1	5
MD06	3	15
MD07	1	200
ME01	4	130
ME02	2	55
MI01	1	80
MI02	1	5
MI03	1	400
MI05	2	380
MI06	1	500
MI07	1	5
MI08	3	510
MI13	1	1000
MI15	4	215
MN01	1	150
MN05	2	4100
MN07	4	60
MN08	1	100
MO01	1	1000
MO05	1	500
MO06	2	10
MO07	2	205
MO08	1	5
MO09	1	5
MS02	1	50
MS04	1	5
MT01	7	810
NC02	1	1200
NC04	1	100
NC06	1	1000
NC07	2	10
NC09	1	600
NC11	3	15
NC12	1	600
ND01	4	220
NE01	1	500
NE02	1	1000
NH01	2	105
NH02	5	75
NJ02	1	150
NJ07	1	28
NJ08	1	3
NJ09	1	3
NJ10	2	10
NJ12	2	29
NJ13	2	30
NM01	1	500
NM02	4	75
NM03	2	1010
NV01	1	1000
NV02	2	350
NV04	1	5
NY01	2	320
NY07	1	7500
NY10	2	7650
NY12	1	7500
NY13	1	7500
NY14	1	5
NY20	2	10
NY21	1	5
NY22	7	620
NY23	2	10
NY24	3	505
NY25	1	200
NY27	2	205
NY28	1	5
OH01	1	1200
OH03	1	300
OH05	3	60
OH06	1	50
OH09	1	250
OH11	1	150
OH12	1	90
OH13	3	325
OH16	1	150
OK01	2	305
OK02	1	25
OK04	1	5
OK05	2	300
OR01	2	10040
OR02	6	680
OR04	6	2115
OR05	2	328
PA01	1	5000
PA03	1	5
PA05	2	45
PA06	1	5
PA08	1	5
PA10	1	5
PA11	2	205
PA12	1	5
PA15	3	145
PA16	1	300
PA17	3	210
PA18	1	3000
PA19	1	5
R02	1	500
RI01	1	500
SC01	2	320
SC02	1	12
SC03	1	5
SC04	3	210
SC06	2	155
SD01	4	150
TN01	1	130
TN02	1	500
TN03	1	50
TN05	1	300
TN06	1	5
TN07	2	155
TX01	1	5
TX05	1	250
TX07	1	300
TX11	1	5
TX12	1	200
TX13	1	125
TX14	1	5
TX15	1	5
TX16	1	50
TX17	2	10
TX19	2	55
TX20	1	250
TX23	1	5
TX25	1	2000
TX26	2	10
TX27	3	60
TX31	1	5
TX35	1	100
UT01	2	10
UT02	3	12
UT03	2	7
UT04	1	2
VA01	1	5
VA02	2	170
VA03	1	200
VA05	2	125
VA06	2	10
VA07	1	200
VA08	1	5
VA09	1	5
VT01	6	1455
WA01	3	15
WA02	3	755
WA03	4	910
WA04	5	455
WA05	3	2055
WA06	3	1405
WA07	1	5000
WA08	1	5
WA09	3	15
WA23	1	5
WI01	1	20
WI02	1	200
WI03	1	120
WI04	1	1000
WI07	1	5
WI08	2	240
WV01	4	20
WV02	3	110
WV03	2	105
WY01	3	125

Table 2: Original Data from 3 sources

Variables:

OrigMaxEst-Original maximum estimate from the largest event in a given city

RevMaxEst-Revised maximum estimate (using 5 for any lower, or missing values)

Dist1 - Dist4: Any congressional district where the listed city appears (may contain significant errors--please let me know when you find them)

ProtMultDist: Protesters per district--reflects having divided the total protesters per city equally into various districts where the city intersects.

State	Town/city	Date of first event	OrigMaxEst	RevMaxEst	Dist1	Dist2	Dist3	Dist4	ProtMultDist
NY	New York City	Sept. 17, 2011	30,000	30,000	NY07	NY10	NY12	NY13	7500
AK	Kenai			5	AK01				5
AK	Bethel	Oct. 15, 2011	1	5	AK01				5
AK	Unalaska	Oct. 16, 2011	10	10	AK01				10
AK	Cordova	10/16/2011	30	30	AK01				30
AK	Skagway	10/16/2011	30	30	AK01				30
AK	Juneau	10/15/2011	50	50	AK01				50
AK	Homer	10/15/2011	67	67	AK01				67
AK	Anchorage	8-Oct-11	80	80	AK01				80
AK	Fairbanks	10/15/2011	125	125	AK01				125
AL	Mobile	Oct. 8, 2011	50	50	AL01				50
AL	Montgomery	Oct. 22, 2011		5	AL02				5
AL	Auburn	Oct. 15, 2011	25	25	AL03				25
AL	Huntsville	7/10/11	200	200	AL05				200
AL	Birmingham	10/14/2011	300	300	AL06				300
AL	Tuscaloosa	Oct. 8, 2011	40	40	AL07				40
AR	Jonesboro	Oct. 15, 2011		5	AR01				5
AR	Conway	Oct. 26, 2011		5	AR02				5
AR	Little Rock	10/15/2011	500	500	AR02				500
AR	Bentonville	10/19/2011	50	50	AR03				50
AR	Fayetteville	10/15/2011	250	250	AR03				250
AZ	Sedona			5	AS01				5
AZ	Flagstaff	Oct. 15, 2011		5	AZ01				5
AZ	Phoenix	Oct. 14, 2011	4,000	4,000	AZ03				4000
AZ	Prescott	Oct. 6, 2011	25	25	AZ04				25
AZ	Tempe	Oct. 15, 2011		5	AZ05				5
AZ	Yuma			5	AZ07				5
AZ	Tucson	10/15/2011	1000	1,000	AZ07				1000
CA	Grass Valley	10/16/2011		5	CA01				5
CA	Nevada City			5	CA01				5
CA	Mount Shasta	10/15/2011	150	150	CA01				150
CA	Redding	10/15/2011	200	200	CA01				200
CA	Eureka	Oct. 13, 2011		5	CA02				5
CA	Monte Rio			5	CA02				5
CA	Petaluma	Oct. 29, 2011		5	CA02				5
CA	Sebastopol			5	CA02				5
CA	Ukiah			5	CA02				5
CA	Point Reyes Station	9/10/11	40	40	CA02				40
CA	arcata	1/10/11	100	100	CA02				100
CA	Chico	8/10/11	200	200	CA02				200
CA	Humboldt	1/10/11	200	200	CA02				200
CA	Amador County			5	CA03				5
CA	Clearlake	10/15/2011	40	40	CA03				40
CA	Marysville	10/16/2011	75	75	CA03				75
CA	Auburn	Nov. 17, 2011	150	150	CA04				150
CA	Lakeport	10/15/2011	50	50	CA05				50
CA	Sacramento	1/10/11	250	250	CA05				250
CA	San Rafael			5	CA06				5
CA	Sonoma	10/14/2011	300	300	CA06				300
CA	santa rosa	oct 15th	3500	3,500	CA06				3500
CA	Victorville			5	CA08				5
CA	San Francisco	10/13/11	5000	5,000	CA08				5000
CA	Berkeley	8/10/11	200	200	CA09				200
CA	Oakland	10/10/11	3000	3,000	CA09				3000
CA	Walnut Creek	12/10/11	250	250	CA10				250
CA	san ramon		24	24	CA11				24
CA	Santa Cruz	Sep-11	100	100	CA12				100
CA	Alameda			5	CA13				5
CA	Half Moon Bay	Oct. 4, 2011		5	CA14				5
CA	Redwood City	Oct. 28, 2011		5	CA14	CA18			2.5
CA	Palo Alto	12/10/11	150	150	CA14				150
CA	San Jose	10/16/2011	300	300	CA16				300
CA	Salinas	Oct. 15, 2011		5	CA17				5
CA	Monterey	10/15/2011	200	200	CA17				200
CA	Modesto			5	CA18	CA19			2.5
CA	Stockton	12/10/11	100	100	CA18				100
CA	Merced	10/15/2011	200	200	CA18				200
CA	Gilroy			5	CA19	CA20			2.5
CA	Fresno	8/10/11	150	150	CA19				150
CA	Visalia			5	CA21				5
CA	Bakersfield	10/15/2011	185	185	CA22				185
CA	Oxnard	Oct. 15, 2011		5	CA23				5
CA	Santa Maria	Oct. 15, 2011		5	CA23				5
CA	Santa Barbara	6/10/11	400	400	CA23				400
CA	Atascadero			5	CA24				5
CA	Camarillo	Oct. 5, 2011		5	CA24				5
CA	Ojai			5	CA24				5
CA	Lompoc	10/15/2011	30	30	CA24				30
CA	Ventura	10/15-16/11	250	250	CA24				250
CA	San Luis Obispo		300	300	CA24				300
CA	Santa Clarita			5	CA25				5
CA	San Marino	Oct. 5, 2011		5	CA27				5
CA	Van Nuys	Oct. 28, 2011		5	CA28				5
CA	Pasadena	12/10/11	100	100	CA29				100
CA	Los Angeles	10/15/2011	5000	5,000	CA30				5000
CA	Culver City			5	CA33				5
CA	Santa Monica College			5	CA33				5
CA	Coachella Valley	Oct. 11, 2011		5	CA36				5
CA	Venice	Oct. 9, 2011		5	CA36				5
CA	Torrance	Oct. 15, 2011		5	CA37				5
CA	Yucca			5	CA41				5
CA	Redlands	8/10/11	100	100	CA41				100
CA	Fontana	12/10/11	35	35	CA43				35
CA	Riverside	Oct. 15, 2011		5	CA44				5
CA	Riverside	10/15/2011	300	300	CA44				300
CA	Temecula	Oct. 15, 2011		5	CA45				5
CA	Huntington Beach	Mar. 9, 2012	20	20	CA46				20
CA	Anaheim	10/10/11	75	75	CA46				75
CA	Santa Ana	Oct. 22, 2011		5	CA47	CA46			2.5
CA	Long Beach	10/16/2011	150	150	CA47				150
CA	Irvine	Oct. 15, 2011	1200	1,200	CA48				1200
CA	Encinitas	Oct. 15, 2011		5	CA50				5
CA	Escondido	Nov. 5, 2011		5	CA50				5
CA	San Diego	7/10/11	2000	2,000	CA53				2000
CA	Davis		5,000	5,000	CA53				5000
CO	Denver	9/24/2011	3000	3,000	CO01				3000
CO	Boulder	10/15/2011	500	500	CO02				500
CO	Aspen	8/10/11	53	53	CO03				53
CO	Durango	10/14/2011	100	100	CO03				100
CO	Pueblo	October 7th	100	100	CO03				100
CO	Grand Junction	10/15/2011	150	150	CO03				150
CO	Greeley			5	CO04				5
CO	Longmont	Oct. 10, 2011	12	12	CO04				12
CO	Fort Collins	10/10/11	50	50	CO04				50
CO	Colorado Springs	4-Oct-11	500	500	CO05				500
CO	Canon City			5	CO5				5
CT	Hartford	Oct. 7, 2011		5	CT01				5
CT	New London			5	CT02				5
CT	Branford	Oct. 6, 2011		5	CT03				5
CT	New Haven	October 15th, 2011	500	500	CT03				500
CT	Danbury			5	CT05				5
DC	Washington DC	10/15/2011	5000	5,000	DC				5000
DE	Delmar			5	DE01				5
DE	Wilmington	Oct. 15, 2011	150	150	DE01				150
FL	Pensacola	8/10/11	500	500	FL01				500
FL	Tallahassee	10/14/2011	300	300	FL02				300
FL	jacksonville	oct 8th	500	500	FL03				500
FL	Gainesville	11/10/11	100	100	FL06				100
FL	St. Augustine			5	FL07				5
FL	Daytona	October 15th 2011	200	200	FL07				200
FL	Orlando	10/15/2011	3000	3,000	FL08				3000
FL	New Port Richey			5	FL09				5
FL	St. Petersburg	10/15/2011		5	FL10				5
FL	Tampa			5	FL11				5
FL	University of South Florida	3/11/11	50	50	FL11				50
FL	Bradenton			5	FL13				5
FL	Sarasota	10/15/2011	200	200	FL13				200
FL	fort myers			5	FL14				5
FL	Naples	10/15/2011	400	400	FL14				400
FL	Cocoa			5	FL15				5
FL	Melbourne			5	FL15				5
FL	Vero Beach			5	FL15				5
FL	Miami	10/15/2011	1000	1,000	FL17				1000
FL	Key West			5	FL18				5
FL	Lehigh Acres			5	FL19				5
FL	Boca Raton	10/13/2011	100	100	FL19				100
FL	Lake Worth	8/10/11	250	250	FL19				250
FL	Delray Beach	10/15/2011	100	100	FL21				100
FL	Palm Beach			5	FL22				5
FL	West Palm Beach			5	FL22				5
FL	Ft. Lauderdale	10/15/2011	400	400	FL22				400
GA	Savannah	9/10/11	100	100	GA01				100
GA	Valdosta	8/10/11	200	200	GA01				200
GA	Fort Benning			5	GA02				5
GA	Atlanta	10/10/11	150	150	GA05				150
GA	Macon			5	GA08				5
GA	Athens			5	GA10				5
GA	Augusta	12/10/11	70	70	GA10				70
GA	Dalton			5	GA14				5
HI	Honolulu	10/15/2011	200	200	HI01				200
HI	Kauaʻi			5	HI02				5
HI	Kona			5	HI02				5
HI	Oahu			5	HI02				5
HI	Waikiki			5	HI02				5
HI	Hilo	6/10/11	50	50	HI02				50
HI	Kahului	10/26/2011	50	50	HI02				50
HI	Kailua-Kona	10/26/2011	60	60	HI02				60
HI	Maui	10/15/2011	100	100	HI02				100
HI	Wailuku	7/10/11	150	150	HI02				150
IA	Cedar Valley			5	IA01				5
IA	Decorah	Nov. 5th, 2011		5	IA01				5
IA	Dubuque			5	IA01				5
IA	Cedar Rapids	Oct. 22, 2011		5	IA02				5
IA	Iowa City	10/10/11	200	200	IA02				200
IA	Des Moines	9/10/11	350	350	IA03				350
IA	Ames	10/13/2011	300	300	IA04				300
ID	Boise			5	ID01				5
ID	Coeur d'Alene	10/15/2011		5	ID01				5
ID	Sandpoint			5	ID01				5
ID	Idaho Falls			5	ID02				5
ID	Moscow			5	ID02				5
ID	Pocatello	7/10/11		5	ID02				5
ID	Salmon			5	ID02				5
IL	Chicago		8,000	8,000	IL01				8000
IL	Kankakee			5	IL11				5
IL	Normal			5	IL11				5
IL	Streator	Nov. 30, 2011		5	IL11				5
IL	Normal and Bloomington	Octorber 5	65	65	IL11				65
IL	Naperville	10/23/2011	70	70	IL11				70
IL	CarbonDale	10/15/2011	100	100	IL12				100
IL	Aurora			5	il14				5
IL	DeKalb			5	IL14				5
IL	Bloomington	Oct. 5, 2011		5	IL15				5
IL	Charleston	10/17/2011	12	12	IL15				12
IL	Champaign	10/15/2011	400	400	IL15				400
IL	Ottawa			5	IL16				5
IL	Rockford	4/10/11	200	200	IL16				200
IL	Galesburg	Oct. 17, 2011	40	40	IL17				40
IL	Macomb	Oct. 21, 2011	60	60	IL17				60
IL	Peoria	10/15/2011	300	300	IL18				300
IL	Springfield			5	IL19				5
IN	Portage	Oct. 22, 2011		5	IN01				5
IN	Valparaiso			5	IN01				5
IN	Elkhart	12/10/11		5	IN02				5
IN	South Bend	8/10/11	100	100	IN02				100
IN	Fort Wayne	10/15/2011	300	300	IN03				300
IN	West Lafayette	Dec. 10, 2011	50	50	IN04				50
IN	Lafayette	10/29/2011	1000	1,000	IN04				1000
IN	Kokomo			5	IN05				5
IN	Anderson			5	IN06				5
IN	Muncie	Oct. 19, 2011	20	20	IN06				20
IN	Indianapolis	Oct. 8, 2011	1,000	1,000	IN07				1000
IN	Evansville	10/15/2011	100	100	IN08				100
IN	Bloomington	9/10/11	200	200	IN09				200
KS	Topeka			5	KS02				5
KS	Pittsburg	October 12th	15	15	KS02				15
KS	Lawrence	October 8th, 201	150	150	KS02				150
KS	Manhattan	10/15/2011	200	200	KS02				200
KS	Kansas City			5	KS03				5
KS	Wichita	2/10/11	300	300	KS04				300
KY	Paducah			5	KY01				5
KY	Bowling Green			5	KY02				5
KY	Owensboro			5	KY02				5
KY	Louisville	Oct. 04, 2011	300	300	KY03				300
KY	Ashland			5	KY04				5
KY	Berea			5	KY06				5
KY	Lexington	Sept. 31, 2011	100	100	KY06				100
LA	New Orleans	6/10/11	400	400	LA02				400
LA	Shreveport	10/15/2011	100	100	LA04				100
LA	Baton Rouge	October 22nd, 2011	150	150	LA06				150
LA	Lake Charles			5	LA07				5
LA	Lafayette	Nov. 17, 2011	12	12	LA07				12
MA	Berkshires	Oct. 10, 2011		5	MA01				5
MA	Lenox			5	MA01				5
MA	Williamstown			5	MA01				5
MA	Amherst	Oct. 5, 2011	36	36	MA01				36
MA	Springfield	Oct. 10, 2011		5	MA02				5
MA	Greenfield	12/10/11	50	50	MA02				50
MA	Northampton	Oct. 6, 2011	50	50	MA02				50
MA	Marlborough	10/15/2011		5	MA03				5
MA	Worcester	October 16th 2011	110	110	MA03				110
MA	Newton			5	MA04				5
MA	Salem	Oct. 22, 2011		5	MA06				5
MA	Reading		20	20	MA06				20
MA	New Bedford	10/15/2011	100	100	MA06				100
MA	Jamaica Plain	Nov. 13, 2011		5	MA07				5
MA	Cambridge	Nov. 9, 2011		5	MA08				5
MA	Somerville			5	MA08				5
MA	Needham			5	MA09				5
MA	Provincetown			5	MA09				5
MA	Falmouth	8/10/11	37	37	MA09				37
MA	Boston	Sept. 30	1000	1,000	MA09				1000
MA	Hyannis	1014/11	100	100	MA10				100
ME	Bar Harbor	Oct. 15, 2011	70	70	MD02				70
MD	Annapolis			5	MD03				5
MD	Frederick	Nov. 11, 2011		5	MD06				5
MD	Hagerstown			5	MD06				5
MD	Cumberland	Oct. 8, 2011	3	5	MD06				5
MD	Baltimore	4/10/11	200	200	MD07				200
ME	Brunswick			5	ME01				5
ME	Portland			5	ME01				5
ME	South Portland			5	ME01				5
ME	Augusta	10/15/2011	115	115	ME01				115
ME	Presque Isle			5	ME02				5
ME	Bangor	12/10/11	50	50	ME02				50
MI	Traverse City	10/15/2011	80	80	MI01				80
MI	Muskegon			5	MI02				5
MI	Grand Rapids	8/10/11	400	400	MI03				400
MI	Saginaw			5	MI05				5
MI	Flint	Oct. 14th	375	375	MI05				375
MI	Kalamazoo	12/10/11	500	500	MI06				500
MI	Saline			5	MI07				5
MI	Brighton			5	MI08				5
MI	East Lansing			5	MI08				5
MI	Lansing	10/15/2011	500	500	MI08				500
MI	Detroit	10/15/2011	1000	1,000	MI13				1000
MI	Marquette			5	MI15				5
MI	Niles			5	MI15				5
MI	Ypsilanti			5	MI15				5
MI	Ann Arbor	6/10/11	200	200	MI15				200
MN	Rochester		150	150	MN01				150
MN	Minneapolis	October 7th, 2011	600	600	MN05				600
MO	arnold	10/23/2011	3500	3,500	MN05				3500
MN	Alexandria			5	MN07				5
MN	Moorhead			5	MN07				5
MN	Marshall	10/14/2011	20	20	MN07				20
MN	Bemidji	Oct 15th	30	30	MN07				30
MN	Duluth	9/10/11	100	100	MN08				100
MO	St. Louis	1/10/11	1000	1,000	MO01				1000
MO	Kansas City	3/10/11	500	500	MO05				500
MO	Kirksville			5	MO06				5
MO	St. Joseph	Oct. 5, 2011		5	MO06				5
MO	Joplin			5	MO07				5
MO	Springfield	10/15/2011	200	200	MO07				200
MO	Cape Girardeau	Nov. 5, 2011		5	MO08				5
MO	Columbia			5	MO09				5
MS	Jackson	Oct. 15, 2011	50	50	MS02				50
MS	Biloxi	Oct. 15, 2011		5	MS04				5
MT	Butte			5	MT01				5
MT	Great Falls			5	MT01				5
MT	Billings	10/15/2011	50	50	MT01				50
MT	kalispell	10/17/2011	50	50	MT01				50
MT	Bozeman	10/14/2011	200	200	MT01				200
MT	Helena	October 15th	200	200	MT01				200
MT	Missoula	8/10/11	300	300	MT01				300
NC	Raleigh	10/15/2011	1200	1,200	NC02				1200
NC	Durham	10/15/2011	100	100	NC04				100
NC	Greensboro	10/15/2011	1000	1,000	NC06				1000
NC	Fayetteville			5	NC07				5
NC	Wilmington			5	NC07				5
SC	Charlotte	8/10/11	600	600	NC09				600
NC	Asheville			5	NC11				5
NC	Hendersonville			5	NC11				5
NC	Sylva			5	NC11				5
NC	Winston-Salem		600	600	NC12				600
ND	Grand Forks	Oct. 15, 2011		5	ND01				5
ND	Minot			5	ND01				5
ND	Bismarck	10/15/2011	60	60	ND01				60
ND	Fargo-Moorhead	10/15/2011	150	150	ND01				150
NE	Lincoln	3/15/2011	500	500	NE01				500
NE	Omaha	10/15/2011	1000	1,000	NE02				1000
NH	Exeter			5	NH01				5
NH	Manchester		100	100	NH01				100
NH	Concord			5	NH02				5
NH	Conway			5	NH02				5
NH	Nashua			5	NH02				5
NH	Hanover	Oct. 13, 2011	20	20	NH02				20
NH	Keene	OCT 15th	40	40	NH02				40
NJ	Newton	10/15/2011	150	150	NJ02				150
NJ	Mount Olive	Oct. 10, 2011	28	28	NJ07				28
NJ	Kearney			5	NJ08	NJ09			2.5
NJ	Newark			5	NJ10				5
NJ	Toms River			5	NJ10				5
NJ	Princeton			5	NJ12				5
NJ	Trenton	6th October, 2011	24	24	NJ12				24
NJ	Jersey City			5	NJ13				5
NJ	Atlantic City	Nov. 5, 2011	25	25	NJ13				25
NM	Albuquerque	October 6th, 2011	500	500	NM01				500
NM	Carlsbad			5	NM02				5
NM	Los Lunas			5	NM02				5
NM	Roswell			5	NM02				5
NM	Las Cruces	10/15/2011	60	60	NM02				60
NM	Taos	6/10/11	10	10	NM03				10
NM	Santa Fe	10/15/2011	1000	1,000	NM03				1000
NV	Las Vegas	10/15/2011	1000	1,000	NV01				1000
NV	Carson City	10/15/2011	75	75	NV02				75
NV	reno		275	275	NV02				275
NV	Tahoe			5	NV04				5
NY	Sag Harbor	10/15/2011	120	120	NY01				120
NY	The Hamptons	10/15/2011	200	200	NY01				200
NY	Brooklyn	10/15/2011	150	150	NY10				150
NY	Astoria	10/28/2011		5	NY14				5
NY	Glens Falls			5	NY20				5
NY	Saratoga Springs			5	NY20				5
NY	Albany	Oct. 21, 2011		5	NY21				5
NY	Kingston			5	NY22				5
NY	Long Island			5	NY22				5
NY	New Paltz			5	NY22				5
NY	Paltz			5	NY22				5
NY	Binghamton	10/15/2011	100	100	NY22				100
NY	Poughkeepsie	Oct. 15, 2011	200	200	NY22				200
NY	Ithaca	5/10/11	300	300	NY22				300
NY	Plattsburgh			5	NY23				5
NY	Saranac Lake			5	NY23				5
NY	Cortland			5	NY24				5
NY	Oneonta	10/15/2011	100	100	NY24				100
NY	Utica	10/13/2011	400	400	NY24				400
NY	Syracuse	2/10/11	200	200	NY25				200
NY	Fredonia			5	NY27				5
NY	Buffalo	8/10/11	200	200	NY27				200
NY	Rochester			5	NY28				5
OH	Cincinnati	8/10/11	1200	1,200	OH01				1200
OH	Dayton	10/10/11	300	300	OH03				300
OH	Defiance			5	OH05				5
OH	Findlay			5	OH05				5
OH	Bowling Green	10/19/2011	50	50	OH05				50
OH	Athens	10/16/2011	50	50	OH06				50
OH	Toledo	10-Oct-11	250	250	OH09				250
OH	Cleveland	7/10/11	150	150	OH11				150
OH	Columbus	10/10/11	90	90	OH12				90
OH	Kent			5	OH13				5
OH	Akron	10/15/2011	20	20	OH13				20
OH	Youngstown	10/15/2011	300	300	OH13				300
OH	Canton	Oct. 15, 2011	150	150	OH16				150
OK	Bartlesville			5	OK01				5
OK	Tulsa	10/15/2011	300	300	OK01				300
OK	Tahlequah	8-Oct-11	25	25	OK02				25
OK	Norman			5	OK04				5
OK	Shawnee	Oct. 11, 2011	50	50	OK05				50
OK	Oklahoma City	7/10/11	250	250	OK05				250
OR	Seaside	8/10/11	40	40	OR01				40
OR	Portland	6/10/11	10000	10,000	OR01				10000
OR	Klamath Falls			5	OR02				5
OR	Mosier	Nov. 5, 2011	20	20	OR02				20
OR	Eureka		30	30	OR02				30
OR	Bend	October 15,1011	150	150	OR02				150
OR	Medford	10/15/2011	200	200	OR02				200
OR	Ashland	6/10/11	275	275	OR02				275
OR	Cottage Grove			5	OR04				5
OR	Port Orford			5	OR04				5
OR	Roseburg			5	OR04				5
OR	Coos Bay	October 15,1011	40	40	OR04				40
OR	Corvallis	6/10/11	60	60	OR04				60
OR	Eugene	10/15/2011	2000	2,000	OR04				2000
OR	Tillamook	10/15/2011	28	28	OR05				28
OR	Salem	10/10/11	300	300	OR05				300
PA	Philadelphia	6/10/11	5000	5,000	PA01				5000
PA	Erie			5	PA03				5
PA	University Park			5	PA05				5
PA	State College	10/17/2011	40	40	PA05				40
PA	Temple	11/11/11		5	PA06				5
PA	Doylestown			5	PA08				5
PA	Williamsport			5	PA10				5
PA	Stroudsburg			5	PA11				5
PA	Scranton	10/15/2011	200	200	PA11				200
PA	Ambridge			5	PA12				5
PA	Bethlehem/Lehigh Valley			5	PA15				5
PA	Bethlehem	Oct. 24, 2011	20	20	PA15				20
PA	Allentown	3/10/11	120	120	PA15				120
PA	Lancaster	10/15/2011	300	300	PA16				300
PA	Easton	Nov. 17, 2011		5	PA17				5
PA	Pottsville			5	PA17				5
PA	Harrisburg	10/15/2011	200	200	PA17				200
PA	Pittsburgh	10/15/11	3000	3,000	PA18				3000
PA	York			5	PA19				5
RI	Providence	10/15/2011	1000	1,000	RI01	R02			500
NC	Charleston	10/15/2011	150	150	SC01				150
SC	Charleston	10/15/2011	170	170	SC01				170
SC	Hilton Head	Dec. 29, 2011	12	12	SC02				12
SC	Simpsonville			5	SC03				5
SC	Greenville			5	SC04				5
SC	Greer			5	SC04				5
SC	spartanburg	10/13/2011	200	200	SC04				200
SC	Florence			5	SC06				5
SC	Columbia	10/15/2011	150	150	SC06				150
SD	Spearfish			5	SD01				5
SD	Vermillion			5	SD01				5
SD	Sioux Falls	Oct. 15, 2011	50	50	SD01				50
SD	Rapid City	Oct. 15th	90	90	SD01				90
TN	johnson city	oct.15, 20011	130	130	TN01				130
TN	Knoxville	7/10/11	500	500	TN02				500
TN	Chattanooga	10/15/2011	50	50	TN03				50
TN	Nashville	10/15/2011	300	300	TN05				300
TN	Murfreesboro			5	TN06				5
TN	Clarksville			5	TN07				5
TN	memphis	10/15/2011	150	150	TN07				150
TX	Texarkana			5	TX01				5
TX	Dallas		250	250	TX05				250
TX	Houston	7/10/11	300	300	TX07				300
TX	San Angelo			5	TX11				5
TX	Ft. Worth	Oct. 10, 2011	200	200	TX12				200
TX	Amarillo	10/15/2011	125	125	TX13				125
TX	Galveston			5	TX14				5
TX	McAllen			5	TX15				5
TX	El Paso	10/17/2011	50	50	TX16				50
TX	Bryan			5	TX17				5
TX	College Station			5	TX17				5
TX	Abilene			5	TX19				5
TX	Lubbock	10/15/2011	50	50	TX19				50
TX	San Antonio	6/10/11	250	250	TX20				250
TX	Marfa			5	TX23				5
TX	Austin	6/10/11	2000	2,000	TX25				2000
TX	Denton	Oct 15th, 2011		5	TX26				5
TX	Lewisville			5	TX26				5
TX	Brownsville			5	TX27				5
TX	Port Aransas	october 15th	1	5	TX27				5
TX	Corpus Christi	October 15th	50	50	TX27				50
TX	Temple	11/11/11		5	TX31				5
TX	San Marcos	5/10/11	100	100	TX35				100
UT	Ogden	Nov. 6, 2011		5	UT01				5
UT	Park City	Oct. 31, 2011	1	5	UT01				5
UT	Moab			5	UT02				5
UT	Salt Lake City	Oct. 7, 2011		5	UT02	UT04	UT03		1.666666667
UT	St. George	Oct. 7, 2011		5	UT02				5
UT	Provo	Oct. 29, 2011		5	UT03				5
VA	Williamsburg			5	VA01				5
VA	Virginia Beach	10/15/2011	50	50	VA02				50
VA	Norfolk	6/10/11	120	120	VA02				120
VA	Martinsville			5	VA05				5
VA	Charlottesville	Oct. 15, 2011	120	120	VA05				120
VA	Harrisonburg			5	VA06				5
VA	Roanoke			5	VA06				5
VA	Richmond	10/15/2011	400	400	VA07	VA03			200
VA	Arlington			5	VA08				5
VA	Blacksburg			5	VA09				5
VT	Bennington	Nov. 19, 2011	10	10	VT01				10
VT	Upper Valley	Dec. 9, 2011	20	20	VT01				20
VT	Rutland	Nov. 9, 2011	25	25	VT01				25
VT	Brattleboro		100	100	VT01				100
VT	Central Vermont	Oct. 9, 2011	300	300	VT01				300
VT	Burlington	Oct. 9, 2011	1,000	1,000	VT01				1000
WA	Bainbridge Island			5	WA01				5
WA	Marysville			5	WA01				5
WA	Mt. Vernon			5	WA01				5
WA	Stanwood			5	WA02				5
WA	Everett	25-Oct-11	150	150	WA02				150
WA	Bellingham	October 7th	600	600	WA02				600
WA	Centralia			5	WA03				5
WA	Longview			5	WA03				5
WA	Olympia	10/18/11	200	200	WA03				200
WA	Vancouver	10/15/2011	700	700	WA03				700
WA	Cle Elum			5	WA04				5
WA	Leavenworth			5	WA04				5
WA	Wenatchee	7/10/11	45	45	WA04				45
WA	Yakima	10/15/2011	100	100	WA04				100
WA	Richland	10/15/2011	300	300	WA04				300
WA	Colville			5	WA05				5
WA	Walla Walla	10/16/2011	50	50	WA05				50
WA	Spokane	9/28/2011	2000	2,000	WA05				2000
WA	Bremerton			5	WA06				5
WA	Port Angeles	10/15/2011	200	200	WA06				200
WA	Tacoma	10/15/2011	1200	1,200	WA06				1200
WA	Seattle	10/15/11	5,000	5,000	WA07				5000
WA	Ellensburg			5	WA08				5
WA	Bellevue			5	WA09				5
WA	Federal Way			5	WA09				5
WA	Puyallup			5	WA09				5
WA	Port Townsend			5	WA23				5
WI	Janesville	Oct. 11, 2011	20	20	WI01				20
WI	Madison	12/10/11	200	200	WI02				200
WI	La Crosse	10/15/2011	120	120	WI03				120
WI	Milwaukee	Oct. 15, 2011	1,000	1,000	WI04				1000
WI	Saint Croix Falls			5	WI07				5
WI	Green Bay	October 16th	40	40	WI08				40
WI	Appleton	10/15/2011	200	200	WI08				200
WV	Buckhannon	10/29/2011		5	WV01				5
WV	Fairmont	Oct. 15, 2011		5	WV01				5
WV	Morgantown	Oct. 15, 2011		5	WV01				5
WV	Parkersburg			5	WV01				5
WV	Charleston	Oct. 15, 2011		5	WV02				5
WV	Davis	Oct. 15, 2011		5	WV02				5
WV	Martinsburg	Oct. 15, 2011	100	100	WV02				100
WV	Oak Hill, Fayetteville	Oct. 15, 2011		5	WV03				5
WV	Huntington	7/10/11	100	100	WV03				100
WY	Jackson Hole			5	WY01				5
WY	Casper	Oct. 8, 2011	50	50	WY01				50
WY	Cheyenne	10/15/2011	70	70	WY01				70

Tea Party Map, 2013

Several sources have generated a "Tea Party Map" based on various metrics. For example, the New Yorker generated a map called the "GOP Suicide Caucus" based on who signed the letter by Congressman Mark Meadows back in August, stating that the GOP should shut down the government before allowing the Affordable Care Act to be funded.

More recently, the LA Times has generated a map of the 144 Congress people who voted to keep the government shutdown going--i.e., who refused to vote "Yes" on the clean continuing resolution sent down by the Senate that eventually passed Boehner's House.

I generated my own map based on these two same data sources, but included a third, members of the House Tea Party Caucaus as of 2013 (from Wikipedia, don't tell my students). This allowed me to create a rating of each of the House members from 0-3, 0 meaning they have no documented affiliation with the Tea Party by any of these three metrics, and 3, meaning that each of these 3 sources show an affiliation. So for that these metrics are worth, below is my map. Over the next few days I will be updating some demographic information for these districts and generating correlations--updated from a similar post I wrote on the topic from Aug 2011.

US Militarization and GDP

I am currently reading McNally's Global Slump: The Economics and Politics of Crisis and Resistance. The author makes the claim that "When the war commenced in 1939, the American economy was half the size of Europe, Japan and the Soviet Union together. But after its wartime boom, as factories churned out steel, aircraft, tanks, electrical goods, bombs and more, it emerged by war's end as larger than all these others combined. American-based manufacturing accounted for fully half of world output at the end of WWII."

I hadn't realized that WWII had been so great for the US economy, so I set out to verify McNally's claims. I found a spreadsheet of historical nominal GDP by country estimated back to year "0" at the University of Gronigen's Maddison Project. Presuming the validity of the data, it indeed appears that the vast US militarization for WWII pushed the United States far ahead of other national competitors. The three lines I plotted were: United States, non-US Allies (UK, France, Soviet Union, China, Belgium, Netherlands), and the Axis Powers (Japan, Germany, Italy). No wonder General Eisenhower felt the need to warn the future US about the growth of the Military-Industrial Complex--he recognized the economic value and feared we would ourselves become another Fascist state, the same as what we had just defeated.

Deficits as a Percent of GDP--Historical

Quick data note. What do your national (federal) deficits look like charted since 1929, when compared to our GDP? Are they getting better or worse in the last several years? Are they far worse than we have ever seen? I will let the chart speak for itself. The red bars above the 0 line are surpluses--notice there aren't many of these since WWI, so it doesn't seem this has ever been a concern in our history.

How Much do the Poor Spend on What? How about the Wealthy?

A recent Yahoo! Finance article has some nice graphs based on the newest BLS Consumption spending data by quintiles. Being a sociologist who studies poverty, the graphs looked sketchy to me. For example, Thompson's representation that the bottom 20% of earners only spend 15% of their income on food, while the top 20% of earners spend over 10% on food?!?! If the top quintile mean income is $160k, that's about $1,500/month--on "average"!

However, a closer examination of the numbers reveal a different picture. First, Thompson isn't graphing a percent of income, but a percent of expenditures. The top quintile spend less than 60% of their income, presumably the rest going into savings. So the 12% Thompson shows for food expenditure, is only of total expenditures, coming to about $940/month--still a lot, but a more reasonable, which is likely why he graphed it that way. However, one of the things that a graph comparing the bottom and top quintile's expenditures, is that the bottom quintile is saving nothing. Neither is the 2nd bottom quintile. So for the bottom 40% of earners, nothing is going into savings--there is nothing left after buying the essentials.

Another feature of Thompson's graph that I dispute (which is a reasonably arguable point--my disagreement is that his graph seems to "hide" a lot), is that he is graphing "post-aid" expenditure. What this means, is that the 16% that the poor are spending on food, isn't really just 16% of their income. Actually it's a whopping 34.4% of their income!!! However, once you factor in food stamps, the average income of the bottom 20% gets effectively doubled, giving Thompson his final graphed data. What this hides is the fact that the bottom 20% of workers have to have charity and government subsidies literally double their income in order for them to survive--the mean income for this group is $10,171, or just below the poverty line. This is slightly less than a worker makes full time at minimum wage. Keep in mind also that this is "household/family" data, not individuals. So this bottom quintile income of $10k may be feeding several children, an elderly parent, or a disabled spouse.

Here I have corrected what I see as oversight in this Yahoo! Finance graphing of data. This is post-tax, pre-aid data. Therefore, the numbers for the bottom two quintiles (0-20% and 20-40% will add up to more than 100% of their income). Below the chart are the percentages used for the graphing. The original data is available from the BLS link above. If the graph is too small for you to see, it should enlarge if you click on it.

Table 1: Percent of their income each quintile spends on the category of goods/services

Post-Tax, Pre-Aid Income	Lowest Quintile (0-20%)	Second Lowest Quintile (20-40%)	Highest Quintile 80-100%)
Apparel and services	7.5%	4.1%	2.0%
Utilities, fuels, and public services	21.4%	10.7%	3.2%
Entertainment	9.7%	5.8%	3.3%
Health care	16.5%	10.0%	3.5%
Food	34.4%	16.3%	6.8%
Transportation	33.9%	19.7%	9.8%
Housing	86.9%	44.2%	17.8%
Savings	0.0%	0.0%	40.5%
PostTax Income (Mean)	$10,171	$27,743	$158,024

Vaccine Deniers vs. Scientific Data

Since part of what I do is "medical sociology," where we look at issues of social factors of health and illness, including epidemiological data, I occasionally get into discussions with vaccine-deniers. Since it happens with regularity, I decided to post some of the data here that I end up spending hours trying to "re-find."

In 1977, husband and wife team, the McKinlays, published a controversial article in respected health policy journal, Milbank Quarterly, titled, "The Questionable Contribution of Medical Measures to the Decline of Mortality in the US in the Twentieth Century." If there is any peer-reviewed "data" that I have repeatedly seen cited, it is a series of ambiguous graphs, shown here, from pg. 442-43. The larger context of this article is the question of the relative contribution of "medical" interventions to disease vs. "public health" interventions and technology, such as clean water, sewage systems, hand washing, better nutrition, etc.

First, the graphs are very poorly done. They would receive an F from me if a student turned these in to me. They are poorly labeled, the scales on each of them are different thus confusing if not outright misleading, and there are no data source citations.

Second, the information the authors seem to want to convey is not represented by the data they use. For example, take the measles graph. As with the other diseases, they show that there was a precipitous drop in the early 1900s, then they have an exciting arrow pointing to the 1960s labeled "Vaccine" implying that the disease was eradicated long before the measles vaccine, the same as all of the other diseases+interventions they graph. However, if you look at the very bottom of the page in fine print, these are death rates, not disease incidence. The difference is that "incidence" is how common these diseases are in the population, and "death rates" are telling us how many people died after being infected with these diseases. The primary reason these charts are incredibly misleading, is that, while the measles graph implies that the vaccine was irrelevant to measles, what the graphs actually show is that in the early 1900s we developed ways to keep measles patients alive until their bodies could fight off the disease. The McKinlays are partially correct, that some of this had to do with sanitation and nutrition, but there were also medical contributions as well (Orenstein, 2004, "Clinical Significance of Measles", J Inf Dis, Suppl 1). They do a very poor job explaining the details of these processes--a far more convincing argument would have been if they spent a paper-length treatment of each of these diseases. Their current strategy, arguably, seems to overwhelm the reader with a whole bunch of poorly constructed graphs, and let the imagination do the rest.

Third, given the poor presentation of data by the McKinlays in these graphs, it is relevant to actually look at the correct data. Sticking with the measles question, three sources give us a far better representation of measles history in the 1900s US. The first (Hinman, 2004, "Evolution of Measles Elimination Strategies," J Inf Dis, Suppl 1) presents the following US history of measles incidence and deaths:

As you can see from the chart, the incidence of measles itself remained unchanged throughout this period, until the implementation of the first vaccine attempt in 1963. The article describes the history of the development of the vaccine, which went through several changes even through the 1990s, which improved methods producing better immunity until very few endemic US cases exist, although outbreaks have become more common now that the vaccine-denier movement is convincing greater numbers of people to refuse vaccination.

The second source is the CDC. It basically shows the same thing at a closer time-scale. Look specifically at the section "Secular Trends in the US" and the graph "Measles-United States, 1950-2009." Here you can also see the 1989-1991 measles outbreak, which primarily occurred in an area where a large population of children were not being vaccinated. CNN recently reported (9-13-2013) about the newest resurgence, again, geographically related to areas of low vaccination rates. Similarly, NPR reported (9-1-2013) about the 2013, identifying the epicenter around a popular anti-vaccine church in Texas.

The third source of data on measles incidence, again, basically reports the same pattern, this time from Journal of the American Medical Association, (Roush, 2007, "Historical Comparisons of Morbidity and Mortality for Vaccine-Preventable Diseases in the US").

This chart shows not only measles, but many of the infectious diseases: rates of incidence pre-vaccine, rates of death pre-vaccine, and the same for 2006 (incidence) and 2004 (deaths). For measles, the approximate pre-vaccine incidence of disease prior to 1963 was 530k/yr, and 440 deaths/yr (1953-1962). There were no measles deaths in 2004, and 55 cases in 2006.

There have been several specific slides I have used in my medical sociology course when we talk about the importance of vaccines.

Here are two of them. The first is the number of reported cases of smallpox in Shanghai in 1951 vs the vaccination rates. 1951 was a big year for the global smallpox vaccination push, and Shanghai cracked down incredibly hard--everyone had to be vaccinated, including tourists (which is how you get greater than 100% vaccination rates in the chart). This graph, as the previous data I posted, shows a clear linear relationship during this 12-month period of vaccination rates of 100% vs. smallpox cases down to 0 (Bulletin of the World Health Organization, 1981, Xui and Yutu).

The second photo is similarly smallpox, visualizing different rates of smallpox in the US based statewide vaccine policy (New England Journal of Medicine, 1933). The graph shows a clear relationship between high incidence of smallpox in states where compulsory vaccination was prohibited, vs. states where vaccination was compulsory, where you see very little smallpox incidence as of 1933--a 17x higher rates in the former.

---

Finally, some random other sources of data about vaccine effectiveness:

Influenza B: Public Health Agency of Canada, 1979-2004

Polio incidence and death rates, 1932-1990? (unsourced, poliosurvivorsnetwork.org.uk)

Mumps in Croatia, 1976-2006 (eurosurveillance.org)

Mumps in the United States, 1966-2010 (unsourced)

Pertussis in Great Britain vs. vaccination rates (1970-2009, unsourced, Gideon Informatics)

NOAA Temperature Anomaly Data

Quick Data Note: Temperature Anomaly Data from the NOAA
The National Oceanic and Atmospheric Administration has collected a tremendous amount of data on climate, temperature, etc, and has estimated historical data as well. Here, I post data that is an average of 10 different studies of historical temperature anomalies. I have simplified the data, averaging by decade, and only going from 500-2010. It clearly shows the recent nature of the "global warming" trend. The original data is freely downloadable from the NOAA site. First, I present a graph of the data, then the averaged data by decade. One might ask why a sociologist has, or cares about this data. I use it in my medical sociology class, when we look at the historical spread of epidemics, since disease and climate have a strong relationship.

500	-0.323497852
510	-0.315000706
520	-0.32227912
530	-0.422895574
540	-0.505192024
550	-0.386910009
560	-0.360068409
570	-0.358621848
580	-0.262656822
590	-0.227102938
600	-0.29378561
610	-0.38650493
620	-0.425825308
630	-0.447376616
640	-0.466410148
650	-0.396960008
660	-0.345318205
670	-0.331185634
680	-0.357119685
690	-0.295640133
700	-0.29667654
710	-0.396699437
720	-0.356770655
730	-0.340114869
740	-0.398125726
750	-0.338450729
760	-0.318274015
770	-0.394940009
780	-0.326433687
790	-0.347188911
800	-0.469880972
810	-0.449717432
820	-0.389584614
830	-0.378674166
840	-0.320943431
850	-0.453708852
860	-0.49090818
870	-0.348880378
880	-0.273697926
890	-0.247197849
900	-0.47859
910	-0.502708508
920	-0.389622358
930	-0.305419967
940	-0.370014959
950	-0.36120756
960	-0.227905615
970	-0.194287504
980	-0.113387492
990	-0.12676191
1000	-0.245891338
1010	-0.210755664
1020	-0.164585192
1030	-0.195697153
1040	-0.232424769
1050	-0.263088831
1060	-0.278985044
1070	-0.256468199
1080	-0.172363353
1090	-0.201308953
1100	-0.238236349
1110	-0.311217341
1120	-0.402008447
1130	-0.408862065
1140	-0.401110076
1150	-0.374842471
1160	-0.307971222
1170	-0.32342515
1180	-0.353795722
1190	-0.421050427
1200	-0.47059786
1210	-0.437767266
1220	-0.367162036
1230	-0.416708993
1240	-0.406199133
1250	-0.413133424
1260	-0.448386957
1270	-0.422380371
1280	-0.48277461
1290	-0.541081815
1300	-0.448283075
1310	-0.379419505
1320	-0.366690214
1330	-0.456611529
1340	-0.530941583
1350	-0.45882268
1360	-0.369221586
1370	-0.376896598
1380	-0.351813767
1390	-0.340287461
1400	-0.309642479
1410	-0.293746862
1420	-0.296922982
1430	-0.28152729
1440	-0.353793596
1450	-0.489541859
1460	-0.508193195
1470	-0.449154347
1480	-0.402012949
1490	-0.400616442
1500	-0.40761362
1510	-0.420729453
1520	-0.469170986
1530	-0.45752636
1540	-0.444190487
1550	-0.389448643
1560	-0.379599174
1570	-0.481176139
1580	-0.552806561
1590	-0.575415593
1600	-0.586685208
1610	-0.560531527
1620	-0.554669254
1630	-0.550633976
1640	-0.573575503
1650	-0.49735177
1660	-0.496265165
1670	-0.554600928
1680	-0.530783262
1690	-0.546579516
1700	-0.551013194
1710	-0.470755751
1720	-0.423584598
1730	-0.456005153
1740	-0.452800478
1750	-0.39068468
1760	-0.353957784
1770	-0.363295204
1780	-0.384086656
1790	-0.364324891
1800	-0.402277801
1810	-0.533674475
1820	-0.489271066
1830	-0.495577731
1840	-0.479862513
1850	-0.410817711
1860	-0.384946148
1870	-0.366527608
1880	-0.372989761
1890	-0.339186542
1900	-0.327383008
1910	-0.284888394
1920	-0.172514302
1930	-0.058052136
1940	-0.00798066
1950	-0.030761379
1960	-0.052368285
1970	-0.06999941
1980	0.022378998
1990	0.20256274
2000	0.484414716

Economic data by sector by state for 2012

Data note: For a Facebook conversation I'm having, we were looking for % industry contribution to GDP by state. It wasn't easy to find in the format I wanted, but for 2012 I got raw current dollar data from BEA (Gross Domestic Product by State). We are Hoosiers (people from Indiana) in this particular discussion, so I have sorted the data horizontally from largest to smallest percent according to Indiana GDP, just for the top 6 sectors (I originally tried to include all 20 sectors, but "blogger" mangled the table), and vertically I have sorted it by largest % contribution of manufacturing. In 2012 Indiana held the top slot.

State	Total State GDP (current $)	Manufacturing	Government	Real estate and rental and leasing	Health care and social assistance	Finance and insurance	Retail trade
Indiana	$298,625	28.2%	9.6%	9.3%	7.8%	6.2%	5.9%
Oregon	$198,702	27.8%	11.3%	11.6%	7.4%	4.4%	4.7%
Louisiana	$243,264	22.6%	10.6%	9.0%	6.1%	3.4%	5.9%
North Carolina	$455,973	19.4%	14.4%	10.1%	6.5%	11.1%	5.4%
Wisconsin	$261,548	19.1%	10.4%	11.9%	9.0%	8.2%	5.9%
Great Lakes	$2,165,308	17.5%	10.5%	11.1%	8.2%	8.0%	6.2%
Kentucky	$173,466	17.1%	16.3%	9.0%	8.5%	5.4%	6.3%
Ohio	$509,393	17.1%	11.1%	10.1%	9.1%	7.8%	6.5%
Iowa	$152,436	16.7%	11.2%	9.9%	6.8%	12.7%	5.9%
Michigan	$400,504	16.5%	11.4%	11.2%	8.9%	6.4%	6.8%
Alabama	$183,547	16.3%	16.5%	9.8%	7.3%	6.1%	7.2%
South Carolina	$176,217	16.3%	17.0%	11.2%	6.5%	5.1%	7.3%
Texas	$1,397,369	15.1%	10.6%	8.7%	5.9%	6.6%	5.8%
Mississippi	$101,490	15.0%	18.1%	9.3%	7.5%	4.9%	8.0%
Tennessee	$277,036	14.9%	11.6%	10.1%	10.3%	6.6%	7.4%
Kansas	$138,953	14.8%	14.8%	8.8%	7.5%	6.9%	6.6%
Utah	$130,486	14.7%	12.6%	11.0%	5.4%	11.0%	6.9%
Arkansas	$109,557	14.2%	14.1%	10.5%	8.0%	4.7%	7.4%
Minnesota	$294,729	13.7%	9.7%	12.4%	8.8%	9.8%	5.5%
Illinois	$695,238	13.3%	10.0%	12.3%	7.0%	9.7%	5.8%
Idaho	$58,243	13.0%	13.7%	11.7%	7.8%	5.1%	7.8%
Nebraska	$99,557	12.5%	13.1%	9.0%	7.3%	8.8%	5.7%
Missouri	$258,832	12.5%	12.5%	10.3%	9.0%	6.6%	6.5%
Washington	$375,730	12.4%	14.3%	13.0%	6.6%	4.7%	7.2%
New Hampshire	$64,697	11.8%	10.3%	13.8%	9.3%	9.1%	7.8%
Pennsylvania	$600,897	11.8%	9.6%	12.0%	10.1%	7.7%	5.7%
Vermont	$27,296	11.5%	13.9%	13.5%	10.4%	5.4%	8.0%
Georgia	$433,569	11.2%	13.8%	11.1%	6.6%	6.8%	6.2%
Oklahoma	$160,953	10.9%	16.8%	10.3%	7.2%	5.0%	6.6%
California	$2,003,479	10.6%	11.2%	15.4%	6.5%	5.7%	6.2%
Connecticut	$229,317	10.5%	9.2%	14.6%	8.2%	16.1%	5.3%
Massachusetts	$403,823	10.3%	9.4%	14.1%	9.8%	9.2%	4.5%
Maine	$53,656	10.2%	13.8%	13.7%	11.5%	7.6%	8.5%
South Dakota	$42,464	9.4%	12.3%	8.1%	8.8%	15.9%	7.0%
Virginia	$445,876	9.0%	18.4%	12.8%	6.0%	6.5%	5.3%
West Virginia	$69,380	9.0%	17.6%	9.1%	9.2%	3.5%	7.3%
Arizona	$266,891	8.2%	12.8%	14.0%	8.3%	9.0%	7.5%
Rhode Island	$50,956	7.7%	13.2%	15.0%	10.2%	11.0%	5.4%
New Jersey	$508,003	7.5%	11.0%	16.8%	7.7%	8.0%	6.1%
Colorado	$274,048	7.3%	12.7%	12.6%	6.2%	6.2%	5.8%
New Mexico	$80,600	7.2%	19.3%	12.5%	7.6%	3.5%	6.4%
Montana	$40,422	7.1%	15.7%	11.9%	9.2%	5.2%	6.7%
Delaware	$65,984	6.7%	9.2%	10.6%	6.2%	37.3%	3.9%
North Dakota	$46,016	6.6%	11.3%	10.3%	7.1%	6.3%	6.0%
Wyoming	$38,422	5.9%	14.0%	9.6%	3.9%	2.3%	5.3%
Maryland	$317,678	5.9%	18.4%	16.0%	7.8%	5.8%	5.6%
New York	$1,205,930	5.2%	10.5%	13.7%	7.5%	16.1%	5.3%
Florida	$777,164	4.8%	12.3%	15.9%	8.7%	7.0%	7.7%
Nevada	$133,584	4.1%	10.4%	12.5%	5.4%	11.5%	6.9%
Alaska	$51,859	3.2%	19.0%	8.4%	6.1%	3.2%	4.0%
Hawaii	$72,424	1.8%	24.3%	17.7%	6.2%	3.8%	6.6%
District of Columbia	$109,793	0.2%	34.3%	8.6%	4.5%	4.3%	1.1%

"Read More" copy/paste hijacking, and NotScript nanny managing

I hate being nanny managed, especially on my own computer. Microsoft does it, Google does it, all the big corporations do it in order to find ways to extract money from us as users.

Since 2010, copy/past hijacking has been one of these ways. For example, take a recent article on obesity and mortality from the well respected "American Journal of Public Health." As a medical sociologist, I believe this article is misguided, but that is neither here nor there in relation to the topic for this page. I was trying to copy/paste the title to see if anybody on the Web has been discussing this article, and when I pasted it into the Google search space, the URL also was inserted, messing up my search. There is a javascript coding that hijacks the reader's attempt to copy/paste that not only inserts this text, but it also tracks what you are doing. I hate being both tracked and nanny managed--I can copy my own citation when I need it. I've done the same thing with several Facebook posts, when I want to copy paste certain sentences into the news article I'm sharing, and each time the URL appears, so I have to go back in and delete all of that crap.

I couldn't find any recent solutions--when the process started back in 2010, a company called Tynt started doing it, and all of the ways to disable this "Read more at this URL" copy/paste hijacking were only in relation to browser extensions specific to Tynt. However, other companies are now doing it on their own, like the above medical journal. I tried many ways to get Ad Block Plus to filter it out, with no effect. I finally added a javascript blocker, which does the trick. Once you load in the extension, a small pyramid appears in the URL box, it blocks all javascript on the page, and if you want to enable specific javascript domains, you have to click on the pyramid, and it will list the domains you can activate, permanently or temporarily. I have Chrome, so I used Notscripts (Mozilla/Firefox has NoScript).

Unfortunately, NotScripts has problems of its own. Chrome, being an evil Google product, tries to make it difficult to do many things. For example, if you Google something and click on a link, you may notice that you don't actually get directly connected to that link, but first you get connected to a "Google redirect" to track you, that eventually sends you to the link of your choice. It's primarily noticeable when your connection is slow, or Google is slow, so you get stuck at the weird Google redirect link for several seconds. Anyway, NotScripts requires you to tinker with the Chrome Extension File to add a password to "protect your whitelist." I don't give a crap about, and it's obnoxious that you can't opt-out of it, or that the programmer wasn't clever enough to be able to do it automatically. It's an older extension, so he gives you some instructions for Windows 7 and prior, but I have Windows 8, so I was out of luck--I had to find the files on my own. Here's the address, for those of you who want to enter a password for Windows 8, then follow the instructions from NotScripts:

C:\Users\(username)\AppData\Local\Google\Chrome\User Data\Default\Extensions\odjhifogjcknibkahlpidmdajjpkkcfn\0.9.6_0

It may not be exactly that, but somewhere thereabouts. Of course "(username)" is whatever username you are logging in with. You can't bypass any of this. Until you find this hidden file and enter a password, every time you open Chrome an unwanted tab will open demanding that you enter a password, and until you do, the program doesn't work, nor can you get rid of the nag screen.

On the other hand, as stated above, I hate being nanny managed. You can't just enter in any old password, it is some ridiculous, medieval torture device password that you'll never remember, and you'll have to re-enter every time you update the stupid program. As an alternative, just disable it. You'll still have to find the appropriate extension folder listed above. However, instead of entering the password, trick NotScript into believing that you have. I tried simply deleting several random files, but it just disables NotScript. Eventually what worked was going to the following folder and modifying the file "common.js" by opening it in notepad--don't forget to save it.

C:\Users\jtownsle\AppData\Local\Google\Chrome\User Data\Default\Extensions\odjhifogjcknibkahlpidmdajjpkkcfn\0.9.6_0\common

Go down to the line "const PASSWORD_STATUS = {" and you will see 6 lines of ways you somehow messed up the password you were supposed to enter, numbering from 0 to 6. Notice that #5, is labeled as "okay"--change all of the other signifier numbers to "5" (no quotes). Conversely, you could change all of the labels to the word "okay" (in quotes). That tricks the program into believing that your password is fine. You may have to redo this every time the program updates, I have only had it for an hour, so I don't know its update procedures.

Additional anti-nanny-tip: pay-wall avoidance

Whatever site you are trying to use, go into your cookies and delete every reference to that domain. For example, I'm using Chrome--they don't make it easy--I go to "Customize" (the three parallel lines in the top right of the browser), then "Settings" then "Show Advanced Settings..." (at the bottom in tiny text) then "Privacy" then "Content Settings" then "Cookies" then "Manage Exceptions"---PSHEW!! They likely change this every update to foil instructions on how to change your cookie exceptions. Do a search for all relevant domains. So if there is a paywall blocking "slugline.com" then search for "slugline" (no quotes), and simply delete every reference. If the newspaper you are trying to access gives customers a couple of free articles a day or permanently, then shuts you out, this removes the block. You will have to keep doing this every time you reach your limit, then restart your browser.

Clinton-Era Welfare Reform

Quick data note. The Personal Responsibility and Work Opportunity Reconciliation Act (PRWORA) was passed in the House in 1996, and implemented mid-1997. Did this Clinton-era welfare reform succeed? It depends on what you mean by "succeed." The blue-dog Democrat and Republican goal of welfare reform was to cut the welfare budget and cut the number of people on welfare. This goal is based on the theory that welfare creates dependency on government hand-outs, creating a generation of lazy people--in Paul Ryan's language, "more takers than makers." This theory persists, despite the fact that no social science data supports this theory, and on the contrary, the opposite is actually supported by the data (lack of welfare support statistically ensures that people will not climb out of poverty), it has become the governing party policy since Reagan, as well as the national assumption of voters from center to right, including the "socially liberal but economically fiscal" voters, often self-labeling as "blue-dogs."

If your metric of welfare reform "success" is cutting the welfare budget, then that was somewhat successful, as the table below shows. Likely not nearly as much as supporters of reform would have liked, a mere 28% decrease in the SNAP budget from 1995-2000 (in 2002 $). Considering that the total welfare budget (housing, SNAP, TANF, etc), is less than 3% of the total federal budget, a 28% decrease in this one program is negligible, despite the tidal wave of rhetorical use this reform has generated by the right and blue-dogs regarding the "success" of this bipartisan welfare reform legislation.

If your metric of success is a decrease in the number of people on TANF (Temporary Aid to Needy Families) and SNAP (food stamps), then millions of families were removed, thus representing "success." On the other hand, if your metric of welfare success is the well-being of children, then the program was a resounding failure. This result is unsurprising if one recognizes that SNAP feeds children, and the average time that people are on welfare (those who have received aid), is 6-12 months, far from the narrative of "life-time dependence" described by the right and center. As the table shows, in 1995, 95% of children in poverty were receiving food stamps, and 62% were receiving temporary aid. However, in 2000, 75% of impoverished children were receiving food stamps, and 38% were receiving temporary aid. This means that despite measurable food insecurity of these children and family poverty status, over 5 million impoverished children were dropped from the food aid program, and the number of children in poor families who received TANF support was more than cut in half. Personally, I would not count malnourished children as a successful program reform. (The data was taken from a Congressional report in 2004, "Indicators of Welfare Dependence.").

Fiscal Year	Food stamp child participants (in thousands)	Food stamp child participants as a % of children in poverty	TANF child recipients (in thousands)	TANF child recipients as a % of children in poverty	Total SNAP program cost (in millions of 2002 $)
1994	14,391	94.1	9,448	61.8	$26,133
1995	13,860	94.5	9,013	61.5	$26,368
1996	13,189	91.2	8,355	57.8	$26,169
1997	11,847	83.9	7,077	50.1	$23,391
1998	10,524	78.1	5,781	42.9	$20,881
1999	9,332	76	4,836	39.4	$19,572
2000	8,743	75.5	4,406	38	$19,029
2001	8,819	75.2	4,138	35.3	$19,970

Health Care Satisfaction and Finance Source

This is not a full post, just a quick data note for a Facebook conversation I am having, regarding the issues around universal payer health care systems vs. health care satisfaction rankings. The data comes from two sources: health care financing comes from OECD data, and satisfaction comes from Commonwealth Fund. The former has data from 34 OECD (basically high income) countries, while the latter has survey results from 11 of those countries from 2010. The satisfaction measure asked how much change their health care system needs, ranging from very little change, a moderate level of change, to "rebuild completely."

For the financing data, I looked at 4 different programs: Individual health programs, Individual health + all medical goods, and Total health expenditure, averaging the US$ PPP per capita expenditurefor 2003-2012 for each country. The correlations with satisfaction were almost identical for all three measures--I present the results for Total Expenditure. The financing number I show in the table represents "public financing/private financing." Therefore the higher the number, the greater the proportion of public funding vs. private financing. Public financing represents federal and local sources, and private financing includes private insurance and out of pocket expenses. In this case, Australia has a value of 2.2, meaning that, on average, the government spends just over 2x per person what the individual spends on health care, while the 0.9 value for the U.S. indicates that the government spends less than individuals do for health care. The country that spend the most, Norway, has government expenditure more than 5x what individuals spend privately on health care.

For the countries surveyed, individuals with greater government contribution were strongly more likely to be satisfied with their health care system, with a correlation of 0.61. Typically, correlations above 0.3 indicate moderate relationship, and above .6 indicate a strong relationship. In the table below, the data is ordered based on financing agent, with the lowest public financing at the top, and the greatest public financing at the bottom.

Country	Total Health Care Expenditure, Public vs. Private in US$PPP	Rebuild health care system completely, % of those surveyed
United States	0.9	27
Switzerland	1.6	8
Australia	2.2	20
Canada	2.3	10
Germany	3.4	14
France	3.5	11
United Kingdom	4.1	3
Netherlands	4.3	7
Sweden	4.5	8
New Zealand	4.5	11
Norway	5.3	12