Translate

Thursday, March 29, 2018

Cross County Active Voter Migrations: GE2016 to GE2017


 42,890 active voters moved counties between 11.2016 and 12.2017.   Click to enlarge the chart above to show what the top 20 migration flows looked liked. Thanks to consultant Kyle Walker for the clear instructions on using the networkD3 library.

GE2016 to GE2017 Active Voter Migrations

Portrayed are cross county migrations of active voters. These migrations don't involve changes to or migrations from intra-county precincts. The top 4 migration paths:

   oldCounty newCounty    N
1         KI        SN 4415
2         KI        PI 3846
3         SN        KI 3047
4         PI        KI 2211

I see voters moving to King County to find or to be closer to work and also voters moving away from King County to find affordable housing. After that, your guess is a good as mine:  college students, retirees, new jobs, ex-urban families, moving in with relatives,etc.  Besides the top migrations, I  see paired county migrations to and from adjacent rural counties.  The top 40 cross county migration paths are listed in the table 'Top 40 County Migrations: GE2016 - GE2017'

These migrations are not the dispositions of cancelled or (temporarily) inactive voters. There are a total 42,890 voters who were active in one county in November 2016 and then active in another county as of November 2017.  These migrations represents about 1% of 2017 active voter database. All "PrecinctCode" changes/migrations total (374,400) for the same period. These "Precinct Code" changes/migrations are nearly 9% of the 2017 active voter database. That number includes all reorganized/renamed precincts. In such cases, the voter did not move, just their precinct name was changed.

In the left chart below, I plot linear and kernel density per precinct 2016 to 2017 migrations of between 500 - 800 per migration (e.g. oldPrecinct to newPrecinct) on the top row and 499 - 250 per migration on the bottom row. Many of these are probably Precinct reorganization and renaming. The right chart below plots linear and kernel density per precinct 2016 to 2017 migrations of between 249 - 10 per migration (e.g. oldPrecinct to newPrecinct) on the top row and less than 10 per migration on the bottom row. 




The top 40 cross county migration paths are listed in the table 'Top 40 County Migrations: GE2016 - GE2017'.  Far below is 'Top 40 Precinct Migrations: GE2016 - GE2017' . I use a CountyCode prefix on the precincts to avoid confusion in that table.  The top 40 precincts migrations are probably the result of precinct reorganization. 

Top 40 County Migrations: GE2016 - GE2017

   oldCounty newCounty    N
1         KI        SN 4415
2         KI        PI 3846
3         SN        KI 3047
4         PI        KI 2211
5         KI        KP  805
6         KI        WM  761
7         PI        TH  725
8         KI        TH  562
9         WM        KI  540
10        TH        PI  521
11        KI        SP  502
12        TH        KI  448
13        KP        KI  442
14        SP        KI  419
15        CR        CZ  391
16        PI        KP  391
17        KP        PI  370
18        KI        IS  342
19        BE        FR  337
20        SN        PI  335
21        FR        BE  322
22        SN        SK  305
23        KI        KS  292
24        SN        WM  289
25        KI        SK  279
26        CR        KI  265
27        SN        IS  260
28        KI        CR  254
29        KI        CH  242
30        PI        SN  227
31        WM        SK  218
32        WM        SN  217
33        SN        SP  210
34        CZ        CR  203
35        KI        CM  200
36        SP        ST  196
37        IS        SN  188
38        KI        MA  185
39        TH        LE  184
40        KI        GY  183

Top 40 Precinct Migrations: GE2016 - GE2017

     N   oldPrecinct newPrecinct
1  847       YA_133       YA_131
2  819      YA_3101      YA_3100
3  797       CR_692       CR_715
4  790       YA_188       YA_180
5  789       CR_360       CR_365
6  787       CR_447       CR_479
7  742      PI_2131      PI_2164
8  730      SP_6017      SP_6038
9  726      SP_7018      SP_7039
10 722       CR_120       CR_125
11 711       CR_200       CR_173
12 710      PI_2116      PI_2163
13 696       CR_500       CR_501
14 692       YA_184       YA_133
15 691      SP_6224      SP_6228
16 688      SP_7005      SP_7037
17 686        TH_30        TH_27
18 685       CR_180       CR_185
19 676      SP_4438      SP_4460
20 671       CR_456       CR_458
21 671         FR_1         FR_2
22 655       CH_560       CH_400
23 647       SK_321       SK_320
24 646       TH_704       TH_705
25 629       CR_697       CR_684
26 621     PI_29676     PI_29684
27 621       YA_186       YA_192
28 606     PI_26304     PI_26307
29 603      SP_4403      SP_4456
30 583      SP_6303      SP_6319
31 582       CR_573       CR_564
32 568      SP_4430      SP_4459
33 566      SP_4012      SP_4036
34 555      KI_3289      KI_3764
35 540      SP_4423      SP_4458
36 537       YA_180       YA_188
37 535       GY_801       GY_810
38 528       GY_802       GY_820
39 523       CR_550       CR_484
40 517       CR_610       CR_611

No comments: