Michigan legislative district compactness

With planning for the Michigan redistricting commission underway, in class we are discussing ways of assessing compactness and partisan wasted votes along with the corresponding ‘efficiency gap’ (Stephanopoulos and McGhee 2015). A recent article by Kaufman, King, and Komisarchik (2018, http://j.mp/2jNf1Jl) on a survey based method to judge compactness includes an R package, “compactness” (https://github.com/aaronrkaufman/compactness), to calculate several traditional compactness scores along with the survey method proposed in the article. We used the package to compare compactness scores for district lines in Michigan.

Here I review the results, contrasting compactness scores across Michigan State House, Senate, and U.S. House districts. In a subsequent post I will compare these scores to the partisan symmetry of the districts.

The compactness package is available via devtools and github:

#devtools::install_github("aaronrkaufman/compactness")
library(compactness)
library(tidyverse) # for data maniplation and visualization

The package measures a series of compactness scores from district boundary line files stored in ESRI shape (.shp) files. The State’s open GIS portal contains the file (https://gis-michigan.opendata.arcgis.com/) to download to a local file path.

With the shapefile in a working directory, the compactness package makes reading in the boundary lines and measuring compactness easy:

shp<-"Michigan_U.S._Congressional_Districts_v17a.shp" # assign shapefile indicator to the .shp file

namecol<-"NAME" # specify the column containing the name of the legislative district

US_House<-read_shapefiles(shp, namecol) # function read_shapefile() which reads in the district coordinates

features_US_House <- generate_features(US_House) #generate the compactness measures

names(features_US_House) # multiple compactness measures

We copy the measures to an object ushouse_compactness, before updating the districts with the most recent legislator names, since the shape files pre-date changes in the 2018 election.

## Features contains several measures of compactness
ushouse_compactness<-features_US_House

# updating the names of representatives
ushouse_compactness<- ushouse_compactness %>%
  mutate(LEGISLATOR= fct_recode(LEGISLATOR,
    "Rashida Tlaib" = "John Conyers Jr.", 
    "Elissa Slotkin" = "Mike Bishop", 
    "Andy Levin" = "Sander Levin", 
    "Haley Stevens" = "Dave Trott"
      )) 
ushouse_compactness$LEGISLATOR

# updating incumbent party of district
ushouse_compactness<-ushouse_compactness %>%
  mutate(PARTY = case_when(
    LEGISLATOR == "Rashida Tlaib" ~ "D",
    LEGISLATOR == "Elissa Slotkin" ~ "D",
    LEGISLATOR == "Haley Stevens" ~ "D",
    TRUE ~ as.character(PARTY)))

# names(ushouse_compactness)

We focused on the compactness metrics with intuitive geometric interpretations; the GIS analytics firm Azavea has a great explanation of the metrics here https://redistricting.azavea.com/.

ushouse_compactness<-ushouse_compactness %>%
  select(NAME, LEGISLATOR, PARTY, hull, reock, polsby, schwartzberg, sym_x, sym_y)

For example, rank ordered by Polsby-Popper. Perhaps not surprisingly, the nearly rectangular 6th district represented by Fred Upton (R) (https://www.govtrack.us/congress/members/MI#map), scores the highest. Apart from the 1st district, represented by Jack Bergman (R),spanning the upper and lower peninsulas, the second least compact is the 14th, represented by Brenda Lawrence (D). It is interesting that while neighboring the 14th district, the 13th district represented by Rashida Tlaib (D) ranks as slightly more compact than 9th and 11th districts that form part of the spiral of districts over the Pontia, Farmington Hills, to Warren triangle.

ushouse_compactness %>%
  arrange(desc(polsby))

 ## # A tibble: 14 x 6

##    NAME  LEGISLATOR      PARTY  hull reock polsby
##    <chr> <chr>           <chr> <dbl> <dbl>  <dbl>
##  1 06    Fred Upton      R     0.865 0.344 0.539 
##  2 04    John Moolenaar  R     0.808 0.349 0.387 
##  3 08    Elissa Slotkin  D     0.763 0.167 0.368 
##  4 02    Bill Huizenga   R     0.833 0.382 0.338 
##  5 07    Tim Walberg     R     0.702 0.246 0.247 
##  6 03    Justin Amash    R     0.743 0.270 0.245 
##  7 12    Debbie Dingell  D     0.672 0.191 0.217 
##  8 13    Rashida Tlaib   D     0.612 0.195 0.182 
##  9 11    Haley Stevens   D     0.641 0.289 0.156 
## 10 09    Andy Levin      D     0.689 0.207 0.152 
## 11 10    Paul Mitchell   R     0.850 0.428 0.122 
## 12 05    Daniel Kildee   D     0.581 0.190 0.105 
## 13 14    Brenda Lawrence D     0.396 0.152 0.0983
## 14 01    Jack Bergman    R     0.435 0.127 0.0335

On to the Michigan State Senate (a PDF map: https://www.michigan.gov/documents/cgi/senate10statewide_371479_7.pdf), with the same sort on Polsby-Popper:

misenate_compactness %>%
  arrange(desc(polsby)) %>%
  print(n=38)

 ## # A tibble: 38 x 6

##    NAME  LEGISLATOR           PARTY  hull reock polsby
##    <chr> <chr>                <chr> <dbl> <dbl>  <dbl>
##  1 20    Sean McCann          D     0.998 0.449 0.787 
##  2 34    Jon Bumstead         R     0.955 0.462 0.739 
##  3 30    Roger Victory        R     0.882 0.413 0.632 
##  4 23    Curtis Hertel, Jr.   D     0.866 0.343 0.600 
##  5 27    Jim Ananich          D     0.861 0.324 0.552 
##  6 22    Lana Theis           R     0.931 0.397 0.545 
##  7 33    Rick Outman          R     0.822 0.413 0.543 
##  8 09    Paul Wojno           D     0.846 0.269 0.524 
##  9 18    Jeff Irwin           D     0.894 0.392 0.485 
## 10 16    Mike Shirkey         R     0.765 0.275 0.479 
## 11 15    Jim Runestad         R     0.799 0.386 0.471 
## 12 26    Aric Nesbitt         R     0.796 0.335 0.467 
## 13 21    Kim LaSata           R     0.821 0.175 0.458 
## 14 07    Dayna Polehanki      D     0.824 0.362 0.456 
## 15 24    Tom Barrett          R     0.779 0.268 0.403 
## 16 17    Dale Zorn            R     0.951 0.235 0.397 
## 17 19    Dr. John Bizon       R     0.718 0.341 0.368 
## 18 10    Michael MacDonald    R     0.764 0.289 0.341 
## 19 32    Ken Horn             R     0.681 0.301 0.327 
## 20 13    Mallory McMorrow     D     0.795 0.392 0.316 
## 21 28    Peter MacGregor      R     0.745 0.305 0.312 
## 22 29    Winnie Brinks        D     0.789 0.266 0.310 
## 23 11    Jeremy Moss          D     0.785 0.172 0.304 
## 24 12    Rosemary Bayer       D     0.701 0.342 0.250 
## 25 03    Sylvia Santana       D     0.719 0.360 0.243 
## 26 35    Curt VanderWall      R     0.636 0.238 0.238 
## 27 06    Erika Geiss          D     0.669 0.283 0.237 
## 28 14    Ruth Johnson         R     0.662 0.314 0.232 
## 29 31    Kevin Daley          R     0.654 0.196 0.179 
## 30 01    Stephanie Chang      D     0.472 0.116 0.175 
## 31 36    Jim Stamas           R     0.637 0.296 0.158 
## 32 08    Pete Lucido          R     0.580 0.261 0.153 
## 33 05    Betty Jean Alexander D     0.525 0.239 0.150 
## 34 02    Adam Hollier         D     0.507 0.148 0.134 
## 35 04    Marshall Bullock     D     0.441 0.113 0.111 
## 36 25    Dan Lauwers          R     0.749 0.273 0.0989
## 37 38    Ed McBroom           R     0.471 0.167 0.0697
## 38 37    Wayne A Schmidt      R     0.527 0.202 0.0540

And then the Michigan State House:

mihouse_compactness %>%
  arrange(desc(polsby)) %>%
  print(n=110)

## # A tibble: 110 x 6
##     NAME  LEGISLATOR         PARTY  hull reock polsby
##     <chr> <chr>              <chr> <dbl> <dbl>  <dbl>
##   1 035   Kyra Bolden        D     0.998 0.441 0.785 
##   2 037   Christine Greig    D     0.996 0.443 0.782 
##   3 082   Gary Howell        R     0.961 0.478 0.720 
##   4 054   Ronnie Peterson    D     0.994 0.435 0.691 
##   5 041   Padma Kuppa        D     0.956 0.426 0.655 
##   6 058   Eric Leutheuser    R     0.950 0.299 0.653 
##   7 019   Laurie Pohutsky    D     0.971 0.429 0.651 
##   8 080   Mary Whiteford     R     0.936 0.335 0.641 
##   9 103   Daire Rendon       R     0.907 0.298 0.629 
##  10 042   Ann Bollin         R     0.903 0.318 0.623 
##  11 057   Bronna Kahle       R     0.946 0.398 0.598 
##  12 012   Alex Garza         D     0.912 0.251 0.570 
##  13 059   Aaron Miller       R     0.949 0.264 0.567 
##  14 004   Isaac Robinson     D     0.892 0.393 0.563 
##  15 100   Scott VanSingel    R     0.840 0.380 0.555 
##  16 047   Hank Vaupel        R     0.840 0.319 0.539 
##  17 085   Ben Frederick      R     0.884 0.331 0.535 
##  18 026   Jim Ellison        D     0.828 0.288 0.519 
##  19 033   Jeff Yaroch        R     0.880 0.393 0.513 
##  20 027   Robert Wittenberg  D     0.807 0.247 0.512 
##  21 066   Beth Griffin       R     0.815 0.256 0.493 
##  22 045   Michael Webber     R     0.909 0.439 0.493 
##  23 046   John Reilly        R     0.881 0.299 0.474 
##  24 093   Graham Filler      R     0.908 0.379 0.473 
##  25 040   Mari Manoogian     D     0.877 0.287 0.469 
##  26 050   Tim Sneller        D     0.812 0.298 0.467 
##  27 090   Bradley Slagh      R     0.846 0.194 0.461 
##  28 088   Luke Meerman       R     0.842 0.400 0.458 
##  29 052   Donna Lasinski     D     0.814 0.287 0.457 
##  30 089   Jim Lilly          R     0.734 0.315 0.452 
##  31 067   Kara Hope          D     0.920 0.412 0.446 
##  32 061   Brandt Iden        R     0.899 0.386 0.432 
##  33 043   Andrea Schroeder   R     0.885 0.413 0.424 
##  34 077   Tommy Brann        R     0.843 0.406 0.422 
##  35 038   Kathy Crawford     R     0.815 0.298 0.421 
##  36 036   Douglas Wozniak    R     0.931 0.317 0.421 
##  37 016   Kevin Coleman      D     0.794 0.297 0.416 
##  38 001   Tenisha Yancey     D     0.840 0.229 0.410 
##  39 003   Wendell Byrd       D     0.823 0.148 0.408 
##  40 021   Kristy Pagan       D     0.882 0.348 0.408 
##  41 064   Julie Alexander    R     0.787 0.259 0.406 
##  42 095   Vanessa Guerra     D     0.843 0.364 0.397 
##  43 083   Shane Hernandez    R     0.784 0.268 0.392 
##  44 063   Matt Hall          R     0.810 0.240 0.387 
##  45 015   Abdullah Hammoud   D     0.778 0.265 0.381 
##  46 008   Sherry Gay-Dagnogo D     0.809 0.280 0.380 
##  47 020   Matt Koleszar      D     0.769 0.263 0.378 
##  48 048   Sheryl Kennedy     D     0.762 0.223 0.378 
##  49 023   Darrin Camilleri   D     0.727 0.267 0.368 
##  50 099   Roger Hauck        R     0.809 0.250 0.367 
##  51 072   Steven Johnson     R     0.765 0.317 0.358 
##  52 028   Lori Stone         D     0.816 0.426 0.358 
##  53 071   Angela Witwer      D     0.870 0.369 0.353 
##  54 070   James Lower        R     0.771 0.195 0.353 
##  55 096   Brian Elder        D     0.834 0.283 0.349 
##  56 031   William Sowerby    D     0.766 0.321 0.349 
##  57 069   Julie Brixie       D     0.899 0.207 0.338 
##  58 044   Matt Maddock       R     0.679 0.316 0.337 
##  59 009   Karen Whitsett     D     0.797 0.302 0.335 
##  60 092   Terry Sabo         D     0.709 0.286 0.333 
##  61 022   John Chirkun       D     0.696 0.276 0.331 
##  62 025   Nate Shannon       D     0.864 0.286 0.324 
##  63 056   Jason Sheppard     R     0.826 0.378 0.323 
##  64 007   LaTanya Garrett    D     0.711 0.224 0.322 
##  65 079   Pauline Wendzel    R     0.765 0.193 0.317 
##  66 087   Julie Calley       R     0.739 0.274 0.317 
##  67 104   Larry Inman        R     0.783 0.346 0.315 
##  68 074   Mark Huizenga      R     0.780 0.256 0.302 
##  69 062   Jim Haadsma        D     0.617 0.186 0.299 
##  70 086   Thomas Albert      R     0.676 0.251 0.298 
##  71 073   Lynn Afendoulis    R     0.728 0.279 0.295 
##  72 039   Ryan Berman        R     0.785 0.266 0.295 
##  73 078   Brad Paquette      R     0.756 0.190 0.283 
##  74 002   Joe Tate           D     0.743 0.259 0.278 
##  75 030   Diana Farrington   R     0.695 0.288 0.278 
##  76 102   Michele Hoitenga   R     0.687 0.227 0.274 
##  77 029   Brenda Carter      D     0.668 0.207 0.265 
##  78 014   Cara Clemente      D     0.660 0.243 0.264 
##  79 032   Pamela Hornberger  R     0.598 0.280 0.259 
##  80 017   Joseph Bellino     R     0.712 0.273 0.258 
##  81 053   Yousef Rabhi       D     0.790 0.319 0.242 
##  82 060   Jon Hoadley        D     0.797 0.392 0.237 
##  83 091   Greg VanWoerkom    R     0.661 0.210 0.236 
##  84 005   Cynthia Johnson    D     0.687 0.268 0.231 
##  85 018   Kevin Hertel       D     0.642 0.220 0.231 
##  86 010   Leslie Love        D     0.543 0.157 0.227 
##  87 051   Mike Mueller       R     0.566 0.244 0.224 
##  88 011   Jewell Jones       D     0.617 0.264 0.220 
##  89 097   Jason Wentworth    R     0.831 0.114 0.220 
##  90 098   Annette Glenn      R     0.680 0.252 0.216 
##  91 094   Rodney Wakeman     R     0.637 0.256 0.216 
##  92 105   Triston Cole       R     0.477 0.133 0.214 
##  93 075   David LaGrand      D     0.716 0.333 0.210 
##  94 055   Rebekah Warren     D     0.688 0.268 0.203 
##  95 068   Sarah Anthony      D     0.723 0.374 0.202 
##  96 101   Jack O'Malley      R     0.580 0.148 0.193 
##  97 049   John Cherry        D     0.615 0.299 0.186 
##  98 006   Tyrone Carter      D     0.510 0.110 0.181 
##  99 106   Sue Allor          R     0.614 0.243 0.180 
## 100 109   Sara Cambensy      D     0.596 0.146 0.175 
## 101 034   Sheldon Neeley     D     0.711 0.382 0.174 
## 102 065   Sarah Lightner     R     0.590 0.176 0.160 
## 103 024   Steve Marino       R     0.559 0.199 0.160 
## 104 084   Phil Green         R     0.774 0.288 0.133 
## 105 108   Beau LaFave        R     0.638 0.265 0.122 
## 106 013   Frank Liberati     D     0.390 0.137 0.122 
## 107 110   Gregory Markkanen  R     0.456 0.194 0.0788
## 108 076   Rachel Hood        D     0.436 0.189 0.0683
## 109 081   Gary Eisen         R     0.493 0.226 0.0679
## 110 107   Lee Chatfield      R     0.464 0.158 0.0548

Next up on this subject a review of the wasted votes and efficiency gap, as well as recent mapping packages to plot the shapefiles.