The Holy (Midwestern) Trinity: Culvers, Kwik Trip, and Menards

Published April 16, 2022 on Chandler Swift's Blog Source

If you live in the Upper Midwest, you know what I mean: There’s no place like Culvers. And Kwik Trip. And Menards. (All Wisconsin-based, for that lovely hometown charm.) But the worst part of visiting all three is the period of time between stepping out of one and arriving at another. Where is the best location we can minimize this pain?

First, obligatory memes:
The holy trinity: Culver's, Kwik Trip, and Menards Spongebob meme: Culvers is the Krusty Krab; Dairy Queen is the Chum Bucket 'Me explaining to my mom' meme: 'Me explaining why Kwik Trip isn't just an ordinary gas station'/'my friend from out of state'

I grew up near Hutchinson, MN. Hutchinson gained a Menards in 20011, a Culvers in 20052, and a Kwik Trip in 20133. They’re all within a few blocks of each other—a good start for minimizing the total trip time of a Culver’s–Kwik Trip–Menards lap.

But is there a location that has a closer clustering? Here we brute force every Culver’s/Kwik Trip/Menards combination, checking the sum of their distances and finding the top ten results. (We use the sum of distances as a rough estimate of the stores’ total distance apart; one could also find the “center of mass”—the point where the sum of distances to each of the other points is minimal—and find the sum of distances to the other points, or any number of other scoring methods.)

import json
import math

# Should take ~20 seconds to run

def dist(store1, store2):
    lat1 = store1['lat'] * math.pi/180
    lon1 = store1['long'] * math.pi/180
    lat2 = store2['lat'] * math.pi/180
    lon2 = store2['long'] * math.pi/180
    distance_in_radians = 2*math.asin(math.sqrt((math.sin((lat1-lat2)/2))**2 + 
    earth_radius = 3959 # freedom units (miles)
    return distance_in_radians * earth_radius

def total_dist(store1, store2, store3):
    return dist(store1, store2) + dist(store2, store3) + dist(store3, store1)

# Format an address
def a(x):
    return  f"[{x['address']}, {x['city']}, {x['state']} {x['zip']}]({x['website']})"

if __name__ == '__main__':
    # A naïve approach might be to check for each city how close the Culver's,
    # Kwik Trip, and Menards are (if a city has all three). However, this would
    # break if there happen to be close clusterings across city lines. Instead,
    # we make a list of the locations of each chain, and process that. (While
    # that does make a difference for some results, none of them make it into
    # the top 10.)
    with open("stores.json", 'r') as f:
        stores = json.loads(

    culverses = list(filter(lambda s: s['chain'] == "Culver's", stores))
    kwiktrips = list(filter(lambda s: s['chain'] == "Kwik Trip", stores))
    menardses = list(filter(lambda s: s['chain'] == "Menards", stores))

    top_matches = []

    for culvers in culverses:
        for kwiktrip in kwiktrips:
            # This is a hack to speed the program up; without this the program
            # takes hours to run. With this optimization, it takes <1 minute.
            if abs(kwiktrip['lat'] - culvers['lat']) + abs(kwiktrip['long'] - culvers['long']) > 2: # More than ~50 miles apart
            for menards in menardses:
                if abs(kwiktrip['lat'] - menards['lat']) + abs(kwiktrip['long'] - menards['long']) > 2:
                    "culvers": culvers,
                    "kwiktrip": kwiktrip,
                    "menards": menards,
                    "dist": total_dist(culvers, kwiktrip, menards),
                top_matches.sort(key=lambda s: s['dist'])
                top_matches = top_matches[:10]

    print("| Rank | Culver's | Kwik Trip | Menards | Total distance |")
    for rank, top_match in enumerate(top_matches, start=1):
        print(f"| {rank} | {a(top_match['culvers'])} | {a(top_match['kwiktrip'])} | {a(top_match['menards'])} | {top_match['dist']:.2f} miles |")
Rank Culver’s Kwik Trip Menards Total distance
1 1101 2nd St S, Waite Park, MN 56387 106 10Th Ave S, Waite Park, MN 56387 251 10Th Ave S, Waite Park, MN 56387 0.39 miles
2 3604 University Dr, Muscatine, IA 52761 3605 University Dr, Muscatine, IA 52761 3408 N Highway 61, Muscatine, IA 52761 0.43 miles
3 525 Village Walk Ln, Johnson Creek, WI 53038 465 Village Walk Ln, Johnson Creek, WI 53038 440 Wright Rd, Johnson Creek, WI 53038 0.53 miles
4 W6606 State Rd 23, Fond du Lac, WI 54937 1123 W Johnson St, Fond Du Lac, WI 54937 1200 Rickmeyer Dr, Fond Du Lac, WI 54937 0.58 miles
5 2270 Westowne Ave, Oshkosh, WI 54904 1090 N Washburn St, Oshkosh, WI 54904 2351 Westowne Ave, Oshkosh, WI 54904 0.74 miles
6 1510 Montreal St, Hutchinson, MN 55350 10 Denver Ave Se, Hutchinson, MN 55350 1525 Montreal St Se, Hutchinson, MN 55350 0.74 miles
7 1499 Lawrence Dr, De Pere, WI 54115 1620 Lawrence Dr, De Pere, WI 54115 1313 Lawrence Dr, De Pere, WI 54115 0.86 miles
8 1601 E 1st St, Grimes, IA 50111 2351 E 1St St, Grimes, IA 50111 300 Ne Destination Dr, Grimes, IA 50111 0.89 miles
9 3048 5th Ave S, Fort Dodge, IA 50501 3121 5Th Ave S, Fort Dodge, IA 50501 3319 5Th Ave S, Fort Dodge, IA 50501 0.93 miles
10 2520 Folsom St, Eau Claire, WI 54703 2327 N Clairemont Ave, Eau Claire, WI 54703 3210 N Clairemont Ave, Eau Claire, WI 54703 1.18 miles

Waite Park, MN comes out on top:

with Muscatine, IA a close second:

And Hutchinson does make the list! The 0.74 miles for a Culvers-Menards-Kwik Trip lap puts it in 6th place.

While we have all this fun data, let’s check out a few more things! I’ve noticed that Menardses and Culver’ses seem to have a habit of being located right next to each other though I have yet to see a Culver’s in a Menards, the way you might see a Subway in a Walmart, or something). This was true in Hutchinson, Eden Prairie4, Burnsville/Savage, Richfield/Bloomington – every Menards I’ve ever been to, I believe, except Apple Valley.

It’s very possible that this is just confirmation bias, though – it’s not true in Apple Valley, at least. When else isn’t this the case? That is, what cities have the biggest distances between their Culver’s and Menards?

import json
import math
from find_closest_trio import dist, a

if __name__ == '__main__':
    most_widely_spaced_pairs = []
    with open("stores.json", 'r') as f:
        stores = json.loads(

    culverses = list(filter(lambda s: s['chain'] == "Culver's", stores))
    menardses = list(filter(lambda s: s['chain'] == "Menards", stores))

    for culvers in culverses:
        for menards in menardses:
            if culvers['city'] == menards['city'] and culvers['state'] == menards['state']:
                most_widely_spaced_pairs.append((culvers, menards))
                most_widely_spaced_pairs.sort(key=lambda x: dist(x[0], x[1]), reverse=True)
                most_widely_spaced_pairs = most_widely_spaced_pairs[:10]

    print("| Rank | Culver's | Menards | Distance |")
    for rank, pair in enumerate(most_widely_spaced_pairs):
        print(f"| {rank+1} | {a(pair[0])} | {a(pair[1])} | {dist(pair[0], pair[1]):.2f} miles |")
Rank Culver’s Menards Distance
1 4701 Kentucky Ave, Indianapolis 46221 7145 E 96Th St, Indianapolis 46250 19.41 miles
2 7953 State Line Rd, Kansas City 64114 3701 Nw 90Th St, Kansas City 64154 18.84 miles
3 7105 E 96th St, Indianapolis 46250 7140 S Emerson Ave, Indianapolis 46237 18.28 miles
4 8232 Country Village Dr, Indianapolis 46214 7145 E 96Th St, Indianapolis 46250 17.85 miles
5 5020 W 71st St, Indianapolis 46268 7140 S Emerson Ave, Indianapolis 46237 17.36 miles
6 1444 Rentra Dr, Columbus 43228 6800 E Broad St, Columbus 43213 17.14 miles
7 11050 S. Doty Avenue W, Chicago 60628 2601 N Clybourn Ave, Chicago 60614 16.82 miles
8 11050 S. Doty Avenue W, Chicago 60628 4501 W North Ave, Chicago 60639 16.38 miles
9 575 W Layton Ave, Milwaukee 53207 8110 W Brown Deer Rd, Milwaukee 53223 16.07 miles
10 5115 Shear Avenue, Indianapolis 46203 7145 E 96Th St, Indianapolis 46250 15.77 miles

Unsurprisingly, it’s just a bunch of really big cities with multiple stores across their respective metro areas. (With some cities with widely spread locations highly overrepresented, too!) Here’s Indianapolis:

Finally, since I have all this neat data, here’s a map of every Culvers, Kwik Trip, and Menards.

Here's how I retrieved the data I used:

import csv
import requests
import json # TODO: remove

stores = []

# Culvers
print("Searching for Culver's locations")
response = requests.get('').json()

state_names = {}
for f in response['states']['features']:
    state_names[f['properties']['name']] = f['properties']['regiondesc']

for s in response['labels']['features']:
    if s['properties']['num_stores'] > 0:
        state = s['properties']['name']
        print(f"Searching {state}...", end="")
        json_data = {
            'request': {
                'appkey': '1099682E-D719-11E6-A0C4-347BDEB8F1E5',
                'formdata': {
                    'geolocs': {
                        'geoloc': [
                                'addressline': state,
                                'state': state_names[state],
                    'stateonly': 1,

        response ='', json=json_data)
        count = 0
        for store in response.json()['response']['collection']:
            # I tried a bunch of other things and this is the only one that matched :facepalm:
            if "coming soon" in store['name'].lower(): # store['comingsoondate']: # not store['dine_in'] and not store['takeout']: # not store['opendate']: # store['comingsoondate']:
                # print(f"{store['name']} not yet open")
                'chain': "Culver's",
                'lat':  float(store['latitude']),
                'long': float(store['longitude']),
                'address': store['address1'],
                'city': store['city'],
                'state': store['state'],
                'zip': store['postalcode'],
                'website': store['url'],
            count += 1
        if not count == s['properties']['num_stores']:
            print(f"Inequal for {state}: {count} != {s['properties']['num_stores']}")
print(f"""{len(stores)} locations found""")

# Kwik Trip
# Export to CSV from
print("Searching for Kwik Trip locations")
kwiktrip_count = 0
with open('stores.csv') as f:
    reader = csv.DictReader(f)
    for row in reader:
        kwiktrip_count += 1
            'chain': "Kwik Trip",
            'lat':  float(row['Latitude']),
            'long': float(row['Longitude']),
            'address': row['Address'].title(),
            'city': row['City'].title(),
            'state': row['State'],
            'zip': row['Zip'],
            'website': f"{row['Store Number']}",
print(f"{kwiktrip_count} locations found")

# # Menards
print("Searching for Menards locations")

# Visit; view source; find a
# value for `data-initial-stores`; copy its value into data here. Incapsula, the
# DDoS mitigation platform that Menards uses, makes this reeeeaaaally hard to do
# with `requests`. Those lines should look something like this:
# 125 | <meta
# 126 |     id="initialStores"
# 127 |     data-initial-stores="[{&quot;number&quot;:3132,..."
# 128 |   >
# 129 | </head>
# (line 127 is the one you want here)

menardses = json.loads(data.replace("&quot;", '"'))
for menards in menardses:
        'chain': "Menards",
        'lat':  float(menards['latitude']),
        'long': float(menards['longitude']),
        'address': menards['street'].title(),
        'city': menards['city'].title(),
        'state': menards['state'],
        'zip': menards['zip'],
        'website': f"{menards['number']}",
print(f"{len(menardses)} locations found")

with open('stores.json', 'w') as f:

  1. Source: Eric says so. (The Menards API used to expose this information, but has since stopped.) ↩︎

  2. p.36-38 ↩︎

  3. ↩︎

  4. The Culver’s here moved across the street a few years ago; it’s now on the north side of 212. However, the Culver’s restaurant, while updated to the correct address, still has the wrong coordinates: ↩︎

I don't have a formal commenting system set up. If you have questions or comments about anything I've written, send me an email and I'd be delighted to hear what you have to say!