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Which Party will Win the Redistricting Battle?

My suggestion is for a grid system screw the political boundaries I don't care if its 5 miles wide by 5 miles wide or 10 X 10 but its the most fair for voting, you might have a couple of blocks voting in one place but each square is measured not drawn by a 3 year old with a crayon.
I not saying do away from towns and cities for government but just for voting
 
In addition to redistricting the democrats will loose 12 -15 seats in the 2030 census and they will go to GOP states. Thats why they opened the border and created sanctuary cities to attract them to their states to replace lost population. What they got were thousands of rapists, child molesters, drug dealers, mental patients, prisoners, drunk truck drivers, and added their crimes to the defund police no bail disaster. Crime exploded and what did they do. Cover it up.
 
tommu56 said:
My suggestion is for a grid system screw the political boundaries I don't care if its 5 miles wide by 5 miles wide or 10 X 10 but its the most fair for voting, you might have a couple of blocks voting in one place but each square is measured not drawn by a 3 year old with a crayon.
I not saying do away from towns and cities for government but just for voting
Click to expand...
The problem with that idea is that each congressional district must contain approximately 747,000 people, so that each congress member of each district represents about the same number of people as every other congress member. If we were to draw all congressional districts the same size then some congress members would represent many more people than others, effectively giving those who represent a lot less people more power, since they still each get just one vote in congress.
For example, a 50 x 50 mile wide district in western Texas might contain 400 people, whereas the same sized district covering Houston might contain 3 million people. Yet, if each of those districts had one congress member representing them, then each person in the western Texas district, in this example, would have about 7 thousand times the influence per person via their congress member's one vote than each person in the Houston district.
Thus, districts must be drawn differently from each other to capture about the same number of people within them (747,000), which inevitably lends itself to, let us say, creativity in the shape of the districts.
 
Patriot said:
The problem with that idea is that each congressional district must contain approximately 747,000 people, so that each congress member of each district represents about the same number of people as every other congress member. If we were to draw all congressional districts the same size then some congress members would represent many more people than others, effectively giving those who represent a lot less people more power, since they still each get just one vote in congress.
For example, a 50 x 50 mile wide district in western Texas might contain 400 people, whereas the same sized district covering Houston might contain 3 million people. Yet, if each of those districts had one congress member representing them, then each person in the western Texas district, in this example, would have about 7 thousand times the influence per person via their congress member's one vote than each person in the Houston district.
Thus, districts must be drawn differently from each other to capture about the same number of people within them (747,000), which inevitably lends itself to, let us say, creativity in the shape of the districts.
Click to expand...
How about we use zip codes
 
Melensdad said:
But some urban zip codes have way too many people!
Click to expand...
A quick search on the web if you can believe that.


Largest ZIPs by Population​


#1
08701 (Lakewood, NJ)
Previously 134,008
136,784

#2
92336 (Fontana, CA)
Previously #4 at 100,571
103,393

#3
90650 (Norwalk, CA)
Previously #2 at 101,983
100,808

#4
94565 (Pittsburg, CA)
Previously #3 at 100,826
99,933

#5
77573 (League City, TX)
Previously #7 at 95,704
97,871

#6
78130 (New Braunfels, TX)
Previously #12 at 91,275
95,787

#7
90250 (Hawthorne, CA)
Previously #6 at 96,200
94,991

#8
90805 (Long Beach, CA)
Previously #5 at 96,515
94,631

#9
90201 (Bell Gardens, CA)
Previously #8 at 95,134
93,783

#10
78641 (Leander, TX)
Previously #20 at 87,106
93,399

#11
77479 (Sugar Land, TX)
Previously 91,618
92,355

#12
90280 (South Gate, CA)
Previously #10 at 92,262
91,719

#13
77584 (Pearland, TX)
Previously #14 at 90,364
90,979

#14
92683 (Westminster, CA)
Previously #13 at 90,630
90,140

#15
78613 (Cedar Park, TX)
Previously #18 at 88,189
89,162

#16
11230 (Brooklyn, NY)
Previously #15 at 90,245
88,252

#17
92503 (Riverside, CA)
Previously 88,617
88,242

#18
92345 (Hesperia, CA)
Previously #19 at 87,476
87,906

#19
78521 (Brownsville, TX)
Previously #21 at 87,040
87,860

#20
11234 (Brooklyn, NY)
Previously #16 at 89,976
87,778

#21
78666 (San Marcos, TX)
Previously #22 at 86,530
87,775

#22
85142 (Queen Creek, AZ)
Previously #33 at 82,360
87,201

#23
73099 (Yukon, OK)
Previously #29 at 83,451
86,749

#24
99301 (Pasco, WA)
Previously #25 at 85,210
86,467

#25
75035 (Frisco, TX)
Previously #27 at 83,930
86,135

#26
60647 (Chicago, IL)
Previously #24 at 85,685
85,589

#27
37042 (Clarksville, TN)
Previously #30 at 83,409
85,169

#28
30024 (Suwanee, GA)
Previously unranked at 85,442
85,106

#29
92376 (Rialto, CA)
Previously #23 at 85,727
84,161

#30
22193 (Woodbridge, VA)
Previously unranked at 83,796
83,798

#31
93727 (Fresno, CA)
Previously unranked at 82,176
83,630

#32
60804 (Cicero, IL)
Previously #26 at 84,189
83,223

#33
11233 (Brooklyn, NY)
Previously unranked at 83,125
82,711

#34
34953 (Port Saint Lucie, FL)
Previously unranked at 78,558
82,469

#35
95076 (Watsonville, CA)
Previously #28 at 83,716
82,409

#36
11235 (Brooklyn, NY)
Previously #31 at 83,069
81,958

#37
27587 (Wake Forest, NC)
Previously #50 at 78,666
81,902

#38
84043 (Lehi, UT)
Previously #58 at 77,551
81,839

#39
76063 (Mansfield, TX)
Previously #43 at 80,101
81,758

#40
92592 (Temecula, CA)
Previously #34 at 81,636
80,933

#41
93033 (Oxnard, CA)
Previously #32 at 82,517
80,482

#42
93535 (Lancaster, CA)
Previously #38 at 80,905
80,478

#43
92704 (Santa Ana, CA)
Previously #40 at 80,767
80,408

#44
28269 (Charlotte, NC)
Previously unranked at 78,540
80,396

#45
91710 (Chino, CA)
Previously #36 at 81,466
80,186

#46
92509 (Jurupa Valley, CA)
Previously #41 at 80,325
80,149

#47
94112 (San Francisco, CA)
Previously #39 at 80,880
80,133

#48
93550 (Palmdale, CA)
Previously #35 at 81,501
79,929

#49
78577 (Pharr, TX)
Previously #44 at 79,496
79,903

#50
11229 (Brooklyn, NY)
Previously #37 at 81,109
79,802
* The maximum margin of error for ranking is 3,000



 
Not sure I believe that, my daughter's zip code has a population of roughly 46,000 per square mile.
 
tommu56 said:
yes but you could add up zip# to get to your 3/4 million people give or take.
Click to expand...
That might work for some zip combos, but most that are adjacent probably don't add up right. And even if that did work, states would just manipulate the boundaries of zip code areas, and we're back to the same situation.
 
Klein1014 said:
We could double them up make them even. I dont think there will be an answer that makes everyone happy
Click to expand...
Sure, but remember that you need to end up with about 747,000 people in each district. It's unlikely you'd get that number in most places by simply combining adjacent zip code areas.
 
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