Schelling Out
Whether or not it’s windier than average, my adopted hometown of Chicago is definitely not known for its balmy winters. And even though I thought I was prepared for the worst when I moved here nearly a decade(!) ago, the frigid isolation over the past two pandemic years has pushed me over the edge: I want out.
Not entirely out, of course. I still love the city, and maintain that it’s one of the finest places to spend the summer in the continental US. Not only does the city take its warm weather activities seriously (having dreamed about them all winter), but unbeknownst to many nonresidents, Chicago is a beach town, with 22 miles of sandy parks and trails along Lake Michigan, and within walking distance of nearly anywhere in the city!
There’s also the inertia: I like my Chicago apartment and my Chicago friends, and I’m not quite ready to reboot my life for a third time in as many decades. But I am ready to GTFO next winter. The only question is: where to?
Four factors
We’ve already talked about weather, which is the primary reason for this (partial) move. But weather itself is not a sufficient criterion for evaluating destinations: I also want to participate in enjoyable activities, find (or import?) a like-minded community, and not pay through the nose for the opportunity.
And so, being an engineer, I went in search of some data that to analyze, and eventually landed on the following four factors: (winter) weather, bike-ability, vegan-friendliness, and housing costs. While these don’t result in a perfect approximation of a location’s desirability, they at least serve as a rough estimate, and may even reveal some hidden gems that would otherwise get lost in the shuffle.
Winter weather
I admit the following section is, in part, an excuse to promote a personal hobbyhorse: Universal Thermal Climate Index (UTCI). See, there’s something fundamentally broken about the way meteorologists attempt to convey weather conditions to the public. A typical forecast will include a bevy of information: temperature, humidity, precipitation/cloudiness, wind speed, pressure, etc. It may even present niche derivatives like heat index and wind chill (which, perplexingly, are typically reported in the same units as temperature).
What they do not provide (and therefore expect the end-user to infer for themselves via multi-dimensional analysis) is the answer to the question that I care about most: will I be comfortable outside?
As an example, take the following two real forecasts for May 15th, 2022:
- Seattle, WA: High of 61F, 89% humidity, 100% chance of rain, and winds from 5-10 mph
- Phoneix, AZ: High of 106F, 5% humidity, clear skies, and winds from 10-15 mph
Imagine you have to choose to spend the day in either Seattle or Phoenix–which should you prefer? Despite providing tens of bytes worth of information, the standard forecasts fail to explicitly provide the single bit that would actually answer that question.
So why hasn’t a metric like UTCI caught on? I’m not sure, but it might have something to do with the reference implementation, an extremely opaque ~200 term regression model (written in Fortran), or the variance in individual preferences, which could make it impossible to capture something “universal” along a single dimension.
But small details like that won’t stop me from using UTCI in my analysis. What almost did stop me was the near impossibility of finding an easily accessible source of average climate data by city, on which I could run the computation. Thankfully I eventually located a convenient Python library and data source (both of which seem to be used primarily for building HVAC design).
UTCI is also reported on a faux temperature scale (why??), but at least specifies a set of color-coded ranges and associated thermal stress levels:
For the purposes of my analysis, I computed the seasonal (winter and summer) percentage of “waking” hours (from 8am to midnight) during which the weather in each city was “comfortable” (e.g. no thermal stress), yielding the following top 10 list (sorted by winter):
winter summer
City State
Berkeley California 0.768750 0.724864
Carlsbad California 0.764583 0.513587
Costa Mesa California 0.761111 0.496603
Santa Ana California 0.740278 0.470109
Orange California 0.740278 0.470109
Oceanside California 0.732639 0.451766
Inglewood California 0.725000 0.477582
Norwalk California 0.711111 0.403533
Fullerton California 0.711111 0.403533
San Diego California 0.702083 0.454484
Turns out California has nice weather, go figure
An example interpretation of the above: during the winter, it is “comfortable” (no thermal stress) 72.5% of waking hours in Inglewood, California. Also note that, while you could use the summer scores if you already live somewhere hot and are looking for a “summering” destination, you should skip all that and just come to Chicago instead.
Bike-ability
One of my favorite things about Chicago is how bike-able it is. After moving here with a car (essential for my previous, interminable commute in DC), I quickly realized that biking was not only great for exploration and exercise, but also the fastest way to get from point A to B. Two years after my move, I ditched my car in favor of a second bike, and haven’t looked back since (except to check for traffic).
As a side-note, my biking habits have changed dramatically in just the last few years, with the advent of cheap and widely available e-bike components. I don’t think the extent of the e-bike revolution has fully percolated through our collective consciousness, and Chicago (with its oblong shape and correspondingly lengthy commutes) represents a perfect use case for the benefits of this newly widespread tech.
My DIY e-bike, a State Wulf(?) with a TSDZ2 mid-drive retrofit kit (running open-source firmware!)
So bike-ability is important to me, but how best to approximate it with available data? I considered a few options, including using existing bike rankings, or even computing a metric for bike lane coverage via something like OpenStreetMap. But actual usage data is a better indicator than mere infrastructure, and so I opted to for American Community Survey’s B08006: SEX OF WORKERS BY MEANS OF TRANSPORTATION TO WORK table (collated by Place), which includes a estimate of bicycle commuters. Dividing this by the total population (S0101: AGE AND SEX) gives the result per 100K:
biking
City State
Boulder Colorado 4911.883946
Cambridge Massachusetts 4077.337000
Berkeley California 3330.760167
Portland Oregon 3017.927981
Fort Collins Colorado 2665.157254
Eugene Oregon 2607.109124
Madison Wisconsin 2329.253849
Washington District of Columbia 2280.996162
San Francisco California 2140.528405
Chico California 1930.423680
One thing to note here and elsewhere: college towns (with their manicured campuses and perennially youthful populations) score well on some metrics in a way that may skew overall results. For instance, if a disproportionate number of city bicycle lanes are on-campus (which could be the case in e.g. Cambridge, Massachusetts), a high score may not actually be representative of the “adult” lived experience.
Vegan-friendliness
As a third factor, I wanted to find a metric for the type of community I’m looking to join/build in my future wintering destination. After considering a few options (e.g. existing EA and adjacent communities), and not finding enough data on smaller cities, I ended up choosing vegan-friendliness (and specifically the number of vegan restaurants per-capita) as a reasonable proxy.
On its surface this is a bit of an odd fit because, despite having access to some excellent nearby vegan restaurants, I don’t eat out very often. And even though I think nearly everyone can (and should) be plant-based (see my 2018 post on the topic), I don’t have any hard restrictions when it comes to being friends, roommates, romantic partners, etc. with non-vegans. But it’s still a potential source of social friction, and knowing someone has gone vegan makes it much more likely that we’ll have other areas of political and philosophical overlap.
Getting the data on vegan restaurants was initially a bit of a pain, since I wrongly expected Happy Cow (the vegan equivalent of Yelp) to be easily scrape-able. After toying with the idea of leveraging Mechanical Turk for the task, I eventually learned that Yelp itself has a public API with an excellent free tier. A few GraphQL queries later, and we get the following top 10 list of vegan restaurants per 100K:
vegan
City State
Portland Oregon 18.084208
Berkeley California 16.891756
Hollywood Florida 15.026100
Burbank California 13.974678
Salt Lake City Utah 9.513176
Honolulu Hawaii 9.117744
Costa Mesa California 8.935113
Atlanta Georgia 8.822674
Inglewood California 8.351738
Oakland California 8.169823
There’s still a bit of a California slant here, but also a few predictable (Portland) and surprising (Atlanta) entries from other states.
Housing costs
It’s all fine and good to calculate the positive qualities of potential destination cities, but “Econ 101” gives us reason to suspect that nicer things generally cost more [citation needed]. And being notoriously cheap (for a good cause), it’s easier for me to imagine being happy getting a “good deal” on a less desirable location than the opposite.
I initially toyed with the idea of using existing cost-of-living metrics, but opted against these sources for a few reasons. First, as I mentioned above, unless you’re crunching the numbers yourself, it’s hard to find a dataset that includes the smallish cities on my list. And even if you could, the existing data I found was not entirely applicable: most sources seem to adjust for earnings potential, but as a remote consultant, my income is location-independent.
So instead I combined my need for a metric with my fledgling desire to become a real estate baron, and simply looked at housing costs. Zillow kindly provides a bunch of free data collected from their listings (just don’t use it for real estate speculation!), and so I borrowed their median figures. Here’s the corresponding top 10 list showing the least expensive locations:
housing
City State
Shreveport Louisiana 39439.0
Macon Georgia 47197.0
Jackson Mississippi 48537.0
Peoria Illinois 53939.0
Detroit Michigan 54475.0
Birmingham Alabama 60394.0
Columbus Georgia 64872.0
Dayton Ohio 70471.0
Montgomery Alabama 71741.0
Wichita Falls Texas 73433.0
As you can see there’s… not much overlap with any of our “positive qualities” lists above. No matter; we’ll just have to somehow aggregate our four metrics to get a list of candidate cities that perform well across the board.
Aggregation
A problem with combining the raw data from the four factors is that the scales are wildly different. And so we’ll need to normalize each source, while taking care to ensure that the resulting score is still intuitively useful. I opted to do this by first dividing each column by the raw data from the Chicago, Illinois row:
biking housing vegan winter summer
City State
Chicago Illinois 754.626952 297669.0 2.475979 0.05625 0.516984
I then took the log2() of each column, meaning that an additional “point” represents a doubling/halving of the underlying quantity (with Chicago pegged at 0.0 on all scales). The intuition behind this is the same as diminishing marginal utility: if my hometown has one vegan restaurant and gets a second it’s a big event, but when a new one opens in e.g. Portland it’s a drop in the bucket.
Finally, to actually aggregate the data into a single overall total, I just took the average for the four factors. This is clearly overly simplistic, but does have a few nice properties. First is that, if a hypothetical city scores 1.0 on all metrics, we’d only expect it to be twice as good as Chicago overall (i.e. points shouldn’t compound). Another is that, if a different city’s scores are [1.0, -1.0, 1.0, -1.0], its total should be 0.0 (a higher-variance equivalent of Chicago).
Conclusion
Finally, we’re ready for the overall results, but I’ll comment on them up here, since the full city list is quite long.
First, does the ordering make any intuitive sense? I think so! Known desirable locations like Berkeley and Portland do really well, and suspected undesirable locations like Newark and Fargo fare poorly.
Is it actionable though? I’m not sure! I suspect that it under-weights costs relative to my preferences. I already knew that Berkeley is great, and have been continuously deciding not to move there for nearly a decade, mostly because it’s expensive. This could be corrected by e.g. adjusting the aggregation weights a bit, but I’ll leave that as an exercise for the reader.
Any surprises? Yes! Gainesville ends up looking like a potential Schelling point (but note the aforementioned college town caveat), and I’m already considering an exploratory trip. Even though it’s the country’s most stereotypical wintering location, Florida still looks to be somewhat underrated, at least in terms benefits per cost.
Anything depressing predictable? Unfortunately! As expected, the housing market is already pretty efficient at pricing in desirable qualities:
In case you’re wondering, the cities on the Pareto frontier of the above plot are (from left to right):
housing mean(biking + vegan + winter)
City State
Berkeley California -2.166879 2.894951
Portland Oregon -0.720326 2.114104
Tempe Arizona -0.184675 1.999728
New Orleans Louisiana 0.053609 1.795931
Gainesville Florida 0.854543 1.760714
Tallahassee Florida 1.061017 0.864989
Columbia South Carolina 1.268380 0.799584
McAllen Texas 1.306605 0.651316
Edinburg Texas 1.570956 0.474594
Birmingham Alabama 2.301232 -0.049612
Macon Georgia 2.656942 -0.458740
Shreveport Louisiana 2.916014 -0.787479
Any other thoughts? Sure! Cold winters suck, so let’s all meet somewhere warm for a few months next year and see how it goes.
Results
biking housing vegan winter total
City State
Berkeley California 2.142016 -2.166879 2.770248 3.772590 1.303595
Gainesville Florida 1.117079 0.854543 0.780399 3.384664 1.227337
Tempe Arizona 1.329176 -0.184675 1.161235 3.508773 1.162902
Portland Oregon 1.999723 -0.720326 2.868659 1.473931 1.124397
New Orleans Louisiana 0.739080 0.053609 1.451345 3.197368 1.088280
Hollywood Florida -0.717470 -0.222100 2.601399 3.434937 1.019353
Boulder Colorado 2.702441 -1.017822 0.899562 2.222392 0.961315
Cambridge Massachusetts 2.433792 -1.559463 1.255650 2.648835 0.955763
Orlando Florida -1.171810 0.527952 1.594634 3.601198 0.910395
St. Petersburg Florida -0.573223 0.002294 1.410371 3.478732 0.863635
Washington District of Columbia 1.595828 -1.084329 1.521066 2.086415 0.823796
Oakland California 0.687321 -1.462576 1.722306 3.147990 0.819008
Honolulu Hawaii 0.229185 -1.088344 1.880678 3.039528 0.812209
Tampa Florida -0.657579 0.051231 1.139613 3.459432 0.798539
Costa Mesa California -0.198188 -1.428551 1.851487 3.758182 0.796586
Savannah Georgia -0.255070 0.678344 0.128550 3.421701 0.794705
Charleston South Carolina 0.403625 -0.202618 0.426785 3.311202 0.787799
Chico California 1.355082 -0.191401 0.255839 2.502500 0.784404
Atlanta Georgia -0.419401 -0.436258 1.833217 2.926937 0.780899
College Station Texas 0.482567 0.732154 -0.577164 3.237579 0.775027
San Francisco California 1.504131 -2.313744 1.422406 3.181750 0.758909
West Palm Beach Florida -0.932180 0.205645 1.045346 3.462666 0.756295
Davie Florida -1.678146 0.393529 1.612148 3.410019 0.747510
Columbia South Carolina -1.581587 1.268380 0.563633 3.416706 0.733427
Tallahassee Florida -0.922398 1.061017 0.041832 3.475533 0.731197
Fort Lauderdale Florida -0.962683 -0.497783 1.522490 3.564032 0.725211
Tucson Arizona 0.390097 0.272473 -0.578040 3.426679 0.702242
Sacramento California 0.116168 -0.527433 1.157821 2.715432 0.692398
Seattle Washington 1.327673 -1.402490 1.236571 2.293145 0.690980
Austin Texas -0.177935 -0.924690 1.207541 3.346651 0.690313
Richmond Virginia 0.517944 0.352283 1.096752 1.448053 0.683006
Eugene Oregon 1.788615 -0.325378 0.193002 1.758182 0.682884
Pompano Beach Florida -1.261591 0.049339 1.112872 3.488286 0.677781
Burbank California -1.096257 -1.653189 2.496744 3.527429 0.654945
McAllen Texas -2.082474 1.306605 0.768940 3.267480 0.652110
Wilmington North Carolina -1.344590 0.384498 0.806648 3.296775 0.628666
Concord California -0.926712 -0.498019 1.365348 3.161987 0.620521
Santa Clara California 0.353730 -1.812287 1.147196 3.344899 0.606708
Edinburg Texas -2.117161 1.570956 0.273462 3.267480 0.598947
Philadelphia Pennsylvania 0.269809 0.269624 0.303291 2.098942 0.588333
Inglewood California -1.393411 -1.154314 1.754077 3.688056 0.578882
Fort Collins Colorado 1.820385 -0.527097 -0.071921 1.643144 0.572902
Irvine California -0.304730 -1.437896 0.977508 3.545846 0.556146
Boston Massachusetts 0.656954 -1.287168 0.743086 2.648835 0.552341
Long Beach California -1.109554 -1.157459 1.423572 3.556482 0.542608
Huntington Beach California -0.525972 -1.327111 1.023257 3.511899 0.536415
Baton Rouge Louisiana -1.254039 1.105407 -0.493676 3.305808 0.532700
Sunnyvale California 0.189570 -2.083455 1.052259 3.436583 0.518991
San Diego California -0.787561 -1.271233 0.985638 3.641717 0.513712
Pasadena California 0.119192 -1.528185 1.389968 2.581991 0.512593
Durham North Carolina -1.467649 0.066828 0.889061 3.058894 0.509427
Norman Oklahoma -0.087962 1.151239 -0.664436 2.139930 0.507754
Asheville North Carolina -1.073773 -0.113853 0.772256 2.945552 0.506037
Los Angeles California -0.919500 -1.417222 1.121308 3.638860 0.484689
Scottsdale Arizona -1.036645 -0.738044 0.590733 3.581991 0.479607
Pittsburgh Pennsylvania 0.138689 0.767195 0.677784 0.779091 0.472552
Baltimore Maryland -0.930871 0.593406 0.999800 1.620152 0.456497
Athens Georgia -0.343891 0.557919 -0.656402 2.662965 0.444118
Denton Texas -1.287415 -0.002774 0.792887 2.696324 0.439804
Salt Lake City Utah 0.620573 -0.730072 1.941928 0.332575 0.433001
Birmingham Alabama -2.235491 2.301232 -0.728315 2.814968 0.430479
Ann Arbor Michigan 1.251728 0.042455 1.190750 -0.339850 0.429016
Clearwater Florida -1.076466 0.167151 -0.538104 3.530515 0.416619
Coral Springs Florida -2.963827 0.763381 0.850494 3.415037 0.413017
New Haven Connecticut 0.622749 0.489883 0.591448 0.360590 0.412934
Miami Florida -1.055553 -0.494680 -0.130904 3.465894 0.356951
Santa Ana California -1.062230 -0.957692 0.058676 3.718142 0.351379
Jacksonville Florida -1.778604 0.716357 -0.555336 3.357118 0.347907
Orange California -1.903617 -1.133613 1.014846 3.718142 0.339152
Jersey City New Jersey -0.942944 -0.995080 1.465744 2.143966 0.334337
Garden Grove California -1.817342 -0.969648 0.910020 3.511899 0.326986
Modesto California -1.400753 -0.137729 -0.113467 3.239466 0.317503
Norfolk Virginia -1.424107 0.586606 1.025971 1.374396 0.312573
Palm Bay Florida -1.080163 0.683348 -1.568145 3.500928 0.307193
Las Vegas Nevada -2.990623 0.093010 1.219099 3.054613 0.275220
Albuquerque New Mexico -0.693051 0.385454 -0.345692 2.004446 0.270231
Anaheim California -1.787178 -1.069736 0.897797 3.300395 0.268255
Houston Texas -1.882922 0.733733 -0.880235 3.334342 0.260984
Chattanooga Tennessee -2.238647 0.492615 0.420185 2.620152 0.258861
Macon Georgia -3.983572 2.656942 -0.376977 2.984331 0.256145
Chesapeake Virginia -3.472157 0.517334 1.373412 2.792007 0.242119
Carlsbad California -1.952946 -1.423428 0.815485 3.764749 0.240772
Santa Fe New Mexico -1.105789 -0.610279 1.492895 1.415037 0.238373
Downey California -2.237860 -1.065512 0.820409 3.556482 0.214704
Pomona California -1.291166 -0.708111 -0.324381 3.386368 0.212542
Mesa Arizona -1.274058 -0.087379 -0.834806 3.231903 0.207132
Fullerton California -1.646548 -1.147992 0.169774 3.660150 0.207077
Columbus Georgia -2.066752 2.198041 -2.357086 3.237579 0.202356
Glendale California -1.914729 -1.509920 0.887081 3.527429 0.197972
Bakersfield California -2.730272 0.433107 0.139025 3.145979 0.197568
Jackson Mississippi -3.896322 2.616552 -0.928126 3.159996 0.190420
Denver Colorado 0.612942 -0.804137 0.437967 0.682518 0.185858
Boise Idaho 0.946649 -0.514036 -0.222925 0.715432 0.185024
Santa Rosa California -0.643119 -0.953659 -0.555943 3.065291 0.182514
Virginia Beach Virginia -1.581353 0.436724 -0.186042 2.230006 0.179867
St. Louis Missouri -0.786591 0.831099 -0.900530 1.736966 0.176189
Reno Nevada -0.922409 -0.417641 0.460486 1.742299 0.172547
Clovis California -2.139177 -0.229617 0.012439 3.137908 0.156311
Salem Oregon -0.209830 -0.164851 -0.534796 1.676958 0.153496
Chandler Arizona -1.314484 -0.439685 -0.450672 2.968489 0.152730
Sandy Springs Georgia -2.918166 -0.052725 0.901829 2.830075 0.152203
Roanoke Virginia -2.261495 1.021981 0.276805 1.671377 0.141734
Roseville California -2.814082 -0.728780 0.935973 3.312995 0.141221
Temecula California -3.567511 -0.794051 1.554458 3.459432 0.130466
Fresno California -2.412054 0.285923 -0.424649 3.137908 0.117426
Augusta Georgia -2.564448 1.454645 -1.322933 3.017702 0.116993
Providence Rhode Island -0.877858 -0.032492 0.758927 0.715432 0.112802
Shreveport Louisiana -3.050156 2.916014 -2.215605 2.903324 0.110715
North Las Vegas Nevada -3.439314 -0.015230 0.884497 3.115477 0.109086
Provo Utah 0.604372 -0.269708 -1.511664 1.699069 0.104414
San Jose California -1.225528 -1.585143 0.158452 3.145979 0.098752
Pasadena Texas -1.883377 0.876247 -1.911596 3.389771 0.094209
Richardson Texas -2.696993 0.265249 0.020327 2.869603 0.091637
Riverside California -1.590180 -0.699944 -0.641414 3.344899 0.082672
Cape Coral Florida -3.188209 0.135017 0.057753 3.406664 0.082245
Hialeah Florida -2.556545 0.432047 -0.880785 3.403301 0.079604
Visalia California -2.432972 0.379370 -0.807618 3.230006 0.073757
San Mateo California -0.284479 -2.199505 -0.387442 3.214739 0.068663
Brownsville Texas -3.296097 1.389010 -1.209015 3.433289 0.063437
Torrance California -1.880537 -1.404593 0.135527 3.457812 0.061642
Ontario California -2.188497 -0.719594 -0.117537 3.311202 0.057115
Tacoma Washington -1.509216 -0.451054 0.143755 2.065291 0.049755
Cary North Carolina -2.466404 -0.132583 0.694303 2.139930 0.047049
High Point North Carolina -2.947338 1.621182 -0.497780 2.035189 0.042251
Rancho Cucamonga California -1.974131 -0.655039 -0.525875 3.311202 0.031231
El Paso Texas -3.507914 1.151681 -0.749090 3.129792 0.004894
Raleigh North Carolina -2.321711 0.016848 0.166965 2.139930 0.000406
Chicago Illinois 0.000000 -0.000000 0.000000 0.000000 0.000000
Pembroke Pines Florida -4.075066 0.418627 0.238432 3.410019 -0.001598
San Antonio Texas -2.929160 0.690643 -0.980676 3.199309 -0.003977
Cincinnati Ohio -2.171605 0.748550 0.232840 1.135883 -0.010866
Gilbert Arizona -2.161182 -0.514663 -0.407863 2.968489 -0.023044
Norwalk California -1.448528 -0.979440 -1.347461 3.660150 -0.023056
Fremont California -2.319555 -1.365222 0.072171 3.436583 -0.035205
Santa Clarita California -2.989000 -0.727647 0.306070 3.121629 -0.057790
Elizabeth New Jersey -1.949665 -0.043716 -0.180347 1.869603 -0.060825
Hayward California -1.936966 -1.244636 -0.427502 3.250737 -0.071673
Grand Rapids Michigan -0.533040 0.499135 0.699834 -1.054448 -0.077704
El Cajon California -3.196357 -0.693656 0.189976 3.311202 -0.077767
Waco Texas -2.554370 1.165976 -1.777739 2.773892 -0.078448
Nashville Tennessee -2.625033 -0.168392 0.491524 1.864721 -0.087436
Murrieta California -2.818751 -0.626664 -0.457896 3.459432 -0.088776
Lakewood Colorado -1.036303 -0.403443 0.050603 0.936274 -0.090574
Dallas Texas -3.045150 0.300784 -0.620973 2.886562 -0.095755
Newport News Virginia -1.622903 0.994432 -2.205216 2.307608 -0.105216
Escondido California -1.769008 -0.875757 -0.902911 3.019900 -0.105555
Memphis Tennessee -2.913350 1.856832 -1.970442 2.493040 -0.106784
Glendale Arizona -1.421741 0.216996 -2.620229 3.280370 -0.108921
San Bernardino California -3.258026 -0.312627 -0.459215 3.467505 -0.112472
El Monte California -1.452982 -0.909911 -1.438271 3.228106 -0.114612
Richmond California -1.230187 -1.193071 -1.527685 3.365782 -0.117032
Plano Texas -3.051121 -0.198400 -0.236497 2.869603 -0.123283
Lexington Kentucky -1.625524 0.756397 -0.412649 0.637430 -0.128869
Huntsville Alabama -3.320097 0.693400 -0.412376 2.353637 -0.137087
Irving Texas -3.270918 0.334751 -0.346065 2.590887 -0.138269
Louisville Kentucky -2.218831 0.670100 -0.648379 1.505640 -0.138294
Columbus Ohio -1.702103 0.818907 -0.027677 0.103093 -0.161556
League City Texas -3.065776 0.241047 -1.501985 3.493040 -0.166735
Vallejo California -2.979921 -0.670259 -0.642453 3.364054 -0.185716
Phoenix Arizona -1.430518 -0.111500 -2.730357 3.343145 -0.185846
Miami Gardens Florida -5.006708 0.129689 0.533147 3.403301 -0.188114
Oklahoma City Oklahoma -3.016488 1.326170 -1.075768 1.784271 -0.196363
Colorado Springs Colorado -1.580701 -0.238272 -0.108476 0.926937 -0.200102
Little Rock Arkansas -3.170569 1.011489 -1.326569 2.408343 -0.215461
Bellevue Washington -1.280472 -1.222507 -0.910684 2.314786 -0.219775
Cleveland Ohio -1.526667 1.670162 -0.883792 -0.385654 -0.225190
Murfreesboro Tennessee -1.892575 0.133884 -1.919351 2.548893 -0.225830
Tulsa Oklahoma -2.738674 1.279809 -1.769409 2.098942 -0.225867
Oceanside California -2.888869 -0.858195 -1.107650 3.703177 -0.230307
Arlington Texas -3.613098 0.552414 -0.965240 2.867164 -0.231752
Lancaster California -3.677616 -0.043397 -0.103068 2.637430 -0.237330
Centennial Colorado -1.714475 -0.635164 -0.424604 1.584963 -0.237856
Indianapolis Indiana -1.993118 1.034386 -0.873015 0.579013 -0.250547
Midland Texas -3.174656 0.739686 -1.714253 2.886562 -0.252532
Antioch California -3.177091 -0.058280 -1.513279 3.480329 -0.253664
Chula Vista California -3.049402 -0.902141 -0.769984 3.420038 -0.260298
Elk Grove California -2.869658 -0.648388 -0.539628 2.715432 -0.268448
Garland Texas -3.306552 0.528007 -1.606763 3.017702 -0.273521
Detroit Michigan -1.682213 2.450043 0.103397 -2.252387 -0.276232
Killeen Texas -3.864005 1.346818 -1.922426 3.048167 -0.278289
Westminster Colorado -1.247392 -0.498242 -1.526061 1.849975 -0.284344
Charlotte North Carolina -3.612402 0.170659 -0.436588 2.431639 -0.289338
Edison New Jersey -2.773973 -0.223740 0.171446 1.325486 -0.300156
Fort Worth Texas -3.168199 0.560468 -1.507931 2.551934 -0.312746
Omaha Nebraska -2.409727 0.733637 -1.267179 1.374396 -0.313775
Henderson Nevada -3.208685 -0.337998 -0.975256 2.908078 -0.322772
Simi Valley California -2.623598 -0.872812 -1.645493 3.524336 -0.323513
West Covina California -2.758425 -0.848004 -1.438943 3.386368 -0.331801
New York New York -0.291996 -1.338828 0.455884 -0.506960 -0.336380
Peoria Illinois -2.365068 2.464308 -1.486236 -0.295456 -0.336490
Mobile Alabama -4.398111 1.529021 -2.211354 3.372677 -0.341553
Everett Washington -1.685731 -0.627501 -0.453729 1.043854 -0.344621
Winston–Salem North Carolina -2.567010 1.203109 -2.627299 2.260063 -0.346227
Rio Rancho New Mexico -3.155081 0.575203 -1.365221 2.187627 -0.351494
Rochester Minnesota -0.689748 0.391691 -1.587708 0.052467 -0.366660
Alexandria Virginia -0.156274 -0.559591 -1.981257 0.840059 -0.371413
Peoria Arizona -2.594750 -0.382511 -2.241459 3.343145 -0.375115
Victorville California -3.129865 0.012598 -1.738927 2.968489 -0.377541
Olathe Kansas -2.847120 0.507275 -0.806659 1.199309 -0.389439
Kansas City Missouri -3.204755 1.123361 -0.193652 0.260063 -0.402997
Dayton Ohio -2.510456 2.078607 -1.768941 0.183712 -0.403416
Frisco Texas -5.665964 -0.512569 1.273296 2.869603 -0.407127
Sparks Nevada -2.069411 -0.292993 -1.424963 1.742299 -0.409014
Thousand Oaks California -3.041426 -0.887301 -1.652441 3.533594 -0.409515
Spokane Washington -1.612284 -0.071748 0.496722 -0.880418 -0.413546
Hartford Connecticut -2.173498 1.041302 -1.583650 0.602665 -0.422636
Lincoln Nebraska -0.492628 0.534241 -1.849425 -0.317482 -0.425059
Buffalo New York -1.042759 0.820627 -1.199932 -0.816288 -0.447670
Minneapolis Minnesota 1.289658 0.025243 0.172781 -3.754888 -0.453441
Ventura California -3.014507 -1.120766 -1.455475 3.318361 -0.454477
Milwaukee Wisconsin -1.295490 0.978530 -0.515197 -1.481869 -0.462805
Aurora Colorado -2.857760 -0.334724 -0.672613 1.548893 -0.463241
Kansas City Kansas -2.934879 1.304472 -0.955148 0.260063 -0.465098
Lansing Michigan -1.040589 1.619274 -0.479770 -2.432959 -0.466809
Greensboro North Carolina -2.582098 1.086706 -2.888314 2.035189 -0.469703
Salinas California -3.936015 -0.909299 -0.432698 2.903324 -0.474938
Syracuse New York -1.188475 1.239381 -0.879628 -1.695994 -0.504943
Cedar Rapids Iowa -1.441020 1.155098 -0.769633 -1.481869 -0.507485
Wichita Kansas -1.868761 1.477582 -3.299070 1.135883 -0.510873
Davenport Iowa -2.535718 1.341613 -1.332659 -0.036069 -0.512567
Stockton California -2.458920 -0.100752 -2.989691 2.872038 -0.535465
Des Moines Iowa -2.195016 1.002401 -0.406506 -1.210567 -0.561938
Arvada Colorado -3.317610 -0.733929 -0.623009 1.849975 -0.564915
Fort Wayne Indiana -1.947713 1.371172 -1.122952 -1.130397 -0.565978
Daly City California -2.431066 -1.899643 -1.377028 2.862274 -0.569092
Vancouver Washington -1.796828 -0.347369 -2.240930 1.473931 -0.582239
Meridian Idaho -2.053093 -0.530561 -1.542317 1.183712 -0.588452
Saint Paul Minnesota -0.454895 0.249745 -1.362394 -1.385654 -0.590639
Amarillo Texas -3.969056 1.581666 -2.310831 1.654503 -0.608744
Stamford Connecticut -1.944410 -0.536136 -0.745973 0.135883 -0.618127
Palmdale California -3.488912 -0.270733 -2.068859 2.731612 -0.619378
Lowell Massachusetts -2.410435 -0.003737 -0.516566 -0.273761 -0.640900
Anchorage Alaska -0.454084 0.321517 -1.850242 -1.339850 -0.664532
Dearborn Michigan -2.568487 0.987593 -0.445188 -1.481869 -0.701590
Sugar Land Texas -6.805274 0.307642 -0.458897 3.369234 -0.717459
Moreno Valley California -4.299475 -0.450072 -2.368974 3.197368 -0.784230
Madison Wisconsin 1.626032 0.151094 -0.418175 -5.339850 -0.796180
Grand Prairie Texas -5.707094 0.661151 -2.279589 3.137908 -0.837525
Bridgeport Connecticut -4.367892 0.364224 -0.879958 0.693573 -0.838010
Akron Ohio -3.707669 1.873323 -2.237555 -0.252387 -0.864858
Corona California -5.916725 -0.794766 -0.960013 3.300395 -0.874222
Overland Park Kansas -3.665807 0.380486 -2.287937 1.199309 -0.874790
Miramar Florida -8.049974 -0.045436 0.262026 3.403301 -0.886017
Allentown Pennsylvania -3.478934 0.581978 -1.639647 0.086415 -0.890038
Manchester New Hampshire -2.244972 0.059415 -1.517690 -0.754888 -0.891627
Springfield Massachusetts -3.309156 0.468587 -0.948889 -0.695994 -0.897090
Toledo Ohio -2.218760 1.950067 -2.745605 -1.754888 -0.953837
Allen Texas -5.916318 -0.287822 -1.373254 2.760812 -0.963316
Naperville Illinois -3.168425 0.298994 -1.888531 -0.073063 -0.966205
Elgin Illinois -2.564046 0.485045 -1.507084 -1.295456 -0.976308
Green Bay Wisconsin -1.698421 1.049224 -1.410926 -2.880418 -0.988108
Sterling Heights Michigan -4.105408 0.670941 -1.733953 0.199309 -0.993822
Yonkers New York -2.672196 -0.875530 -2.389128 0.789433 -1.029484
Joliet Illinois -4.503893 0.858193 -1.896439 0.346651 -1.039098
Newark New Jersey -4.425534 -0.021967 -0.362496 -0.432959 -1.048591
Warren Michigan -2.983875 1.483316 -1.787095 -2.252387 -1.108008
Paterson New Jersey -5.641049 0.003542 -0.983653 0.467505 -1.230731
Worcester Massachusetts -3.191207 0.010531 -0.769304 -2.639410 -1.317878
Sioux Falls South Dakota -2.093099 0.468681 -2.252985 -3.339850 -1.443451
Independence Missouri -8.787841 1.192736 -1.606787 1.230006 -1.594377
Fargo North Dakota -1.195830 0.738456 -1.641309 -6.339850 -1.687706
See here for the source code and data used to generate everything above.