Where Racial Groups are Growing Fastest in Franklin County




The US Census recently released updated estimates for 2016 for smaller-area designations like tracts and blocks. Looking at them, I wanted to see where individual racial groups were growing the fastest at that level.
The first map is based on the % change from 2010 to 2016.

What’s interesting about this map is that it is such a hodgepodge. No single part of the county is dominated by growth in any specific racial group. However, a few things can be generally determined. For example, almost all of the tracts where the White population is growing the fastest are within I-270, and the majority of those within the eastern half of the Columbus in what have long been dominated by Black majority populations. These areas include parts of Linden, the Near South and Near East sides. That said, the White population was growing the fastest in just 30 census tracts by % change. This compared to 53 for the Black population, 83 for the Asian population and 107 for the Hispanic population.

The next map takes a slightly different approach, measuring the TOTAL change in population, rather than by %.

Again, a hodgepodge, but much less so than before. Instead of being the fastest-growing in just 30 tracts, the White population rockets up to 108 tracts. This shows that, while Asian and Hispanic populations have respectable % growth, this is largely based on comparatively small population bases. Still, non-White populations are clearly making inroads throughout Franklin County.

For more information on demographics, go to: Columbus Demographics
And for Franklin County racial and economic maps, go to: Census Tract and Zip Code Maps




Historic Halloween Weather

1981-2010 Halloween Averages
High: 61
Low: 41
Mean: 51
Precipitation: 0.09″
Snowfall: 0.0″

Top 20 Warmest Halloween Highs
1. 1950: 83
2. 1974: 80
3. 1900: 79
4. 1927, 1933: 78
5. 1979, 2003: 75
6. 1882, 1901, 1982, 1999: 74
7. 1909, 1990: 73
8. 1888, 1935, 1944, 1987: 72
9. 1915, 1971: 71
10. 1919: 70
11. 1891, 1986, 2001, 2008: 69
12. 1929, 1956, 1978, 1981, 1985, 2005, 2007: 68
13. 1903, 1943, 1946, 1952, 1991, 2013: 67
14. 1902, 1916, 1953, 1965, 1984: 66
15. 1942, 1958, 1961, 1968, 1969, 2009: 65
16. 1896, 1897, 1912, 1921, 1922, 1940, 1945, 1964, 1997, 2004: 64
17. 1914, 1941, 1970, 2000, 2006: 63
18. 1886, 1924, 1934, 1959, 1977, 1983, 1998: 62
19. 1892, 1938, 1989, 1995: 61
20. 1963, 1966, 1994: 60

Top 20 Coldest Halloween Highs
1. 1906: 38
2. 1993: 39
4. 1878, 1895: 40
5. 1913, 1923, 1954: 41
6. 1885, 1917, 2012: 42
7. 1890: 43
8. 1908, 1925, 1926: 44
9. 1898, 1905: 45
10. 1930, 1976: 46
11. 1879, 1931, 2002: 47
12. 1894, 1918, 1939: 48
13. 1955, 1962, 1996, 2014: 49
14. 1880, 1972: 50
15. 1973, 2010, 2011: 52
16. 1907, 1951, 1988: 53
17. 1887, 1893, 1932, 2015: 54
18. 1883, 1928, 1949, 1975: 55
19. 1899, 1967: 56
20. 1884, 1911, 1937, 1957, 1980, 1992: 57

Top 20 Warmest Halloween Lows
1. 1919: 61
2. 1882: 60
3. 2003: 59
4. 1927, 1929: 58
5. 1900, 1956, 2013: 57
6. 1921, 1941, 1982: 56
7. 1950: 55
8. 1959, 1979: 54
9. 1971: 53
10. 1881, 1891, 1933, 1946, 1974, 1991: 52
11. 1901, 1984, 1998: 51
12. 1947: 50
13. 1961, 2001: 49
14. 1935, 1948, 1960, 1967, 1985, 1995, 1999: 48
15. 1889, 1896, 1909, 1943, 1977: 47
16. 1924, 1945, 2004, 2016: 46
17. 1899, 1911, 1916: 45
18. 1903, 1940, 2015: 44
19. 1965, 1970, 1973, 1981, 1989, 1990, 1992, 1997, 2009: 43
20. 1888, 1942, 1957, 1994, 2006: 42

Top 20 Coldest Halloween Lows
1. 1887: 20
2. 1962, 1988: 25
3. 1923: 27
4. 1908, 1925: 28
5. 1885, 1893, 1913, 1917, 1953, 1975: 29
6. 1904, 1906: 30
7. 1878, 1938, 1954, 1968: 31
8. 1928, 1934, 1949, 1958, 1964, 1976, 1980, 2000: 32
9. 1879, 1926, 1930: 33
10. 1890, 1936, 1951, 1966, 1969, 1993, 1996, 2002, 2008, 2010: 34
11. 1892, 1920: 35
12. 1894, 1895, 1932, 1955, 1978: 36
13. 1910, 1983: 37
14. 1886, 1898, 1914, 1939, 1944, 1963, 2007, 2012, 2014: 38
15. 1884, 1905, 1918, 1937, 2005, 2011: 39
16. 1880, 1883, 1907, 1952, 1972, 1986, 1987: 40
17. 1897, 1902, 1912, 1915, 1922, 1931: 41
18. 1888, 1942, 1957, 1994, 2006: 42
19. 1965, 1970, 1973, 1981, 1989, 1990, 1992, 1997, 2009: 43
20. 1903, 1940, 2015: 44

Top 20 Wettest Halloweens
1. 1932: 1.44″
2. 2009: 1.21″
3. 2013: 0.98″
4. 1941: 0.97″
5. 1919: 0.91″
6. 1942: 0.51″
7. 1960: 0.45″
8. 1905, 1973: 0.43″
9. 2006: 0.36″
10. 1989: 0.34″
11. 1976: 0.32″
12. 1993: 0.31″
13. 1972: 0.29″
14. 1994: 0.26″
15. 1895: 0.23″
16. 1959: 0.20″
17. 1948: 0.18″
18. 1889: 0.17″
19. 1921, 1951: 0.15″
20. 1963, 1967, 2012, 2014: 0.11″

Snowiest Halloweens
1. 1993: 1.0″
2. 1954: 0.2″
3. 1906, 1917, 1926, 1930, 1951, 2012: Trace

Greatest Snow Depth
1. 1954: Trace

Fall Weather Correlation to Winter Severity?




As we go into the winter season, it’s time to talk about how this one might end up. There’s a belief that fall weather is a good sign of how cold or warm winter will be. How true is that for Columbus? Also, what might any correlation mean for the winter of 2017-2018?

First, let’s just look at October temperatures.
The October normal mean temperature for Columbus is 55 degrees.

Between 1878 and 2016, there have been 47 Octobers that featured a mean temperature of 53.9 degrees or lower, what we’re considering a Cold October for the purposes of this comparison.
Of those 47 Octobers, 27 of the 47 had following winters that were colder than normal, or 57.4%, 13 had average temperature winters, or 27.7%, and the remaining 7 were warmer than normal, or 14.9%.
Interestingly, this category contains both the warmest winter on record- 1889-1890 and the coldest on record- 1976-1977- as shown by the chart below.

Next, we look at Normal Octobers, which are +/- 1 degree of the 1981-2010 Average of 55 degrees.
Between 1878 and 2016, there were 45 normal Octobers. Of those, 21 had colder than normal following winters, or 46.7%. 11 were followed by normal winters, or 24.4%, and 13 had warmer than normal winters, or 28.9%.

Finally, let’s look at warm Octobers, which are those with means of 56.1 degrees or higher. There were 46 Octobers with warmer than normal means since 1878. Of those, 18 featured following winters that were colder than normal, or 39.1%. Another 18, or 39.1%, were followed by average winters. The final 10 winters were warmer than normal. Here’s the graph.

So just based on the October mean temperature, Octobers that are colder than normal have a 47% higher chance of having a colder than normal winter than warmer than normal Octobers do. But is October a better indicator than November, a month that is closer to actual winter?

Colder than normal Novembers- 43.3 degrees or lower- included 78 Novembers since 1878. Of those, 38 or 48.7% had colder than normal winters. 21 (26.9%) had normal winters and 19 (24.4) had warmer than normal winters.

With the 38 normal Novembers, 43.4 to 45.4 degrees, there were 18 that had colder than normal winters, or 47.4%, with 11 normal winters (28.9%) and 9 warmer than normal winters (23.7%).

Finally, there were 24 warmer than normal Novembers since 1878- 45.5 degrees or higher. Only 6, or 25%, were followed by cold winters. An additional 9 (37.5%) were normal, while the last 9 (37.5%) were warmer than normal.

To reiterate, here are the ranked percentages of cold winters by the preceding October or November.
1. Cold Octobers: 57.4%
2. Cold Novembers: 48.7%
3. Normal Novembers: 47.4%
4. Normal Octobers: 46.7%
5. Warm Octobers: 39.1%
6. Warm Novembers: 25.0%

It should be no surprise that cold Octobers and Novembers have a stronger correlation to the following winters also being colder, with colder winters becoming increasingly unlikely as those months warm. Cold Octobers have a higher correlation than Cold Novembers, as well as Warm Octobers, but Normal Novembers have a slight advantage over Normal Octobers. Based on this, October actually has a stronger correlation to the following winter’s temperature mean than does November.

Going further, though, what about bi-monthly combinations?

Rank of Bi-Monthly Combinations and the percentage of colder than normal following winters, along with total years in sample:
Normal October/Normal November: 87.5% 8 Years
Cold October/Warm November: 57.1% 7 Years
Cold October/Cold November: 53.8% 26 Years
Normal October/Cold November: 48.1% 27 Years
Warm October/Cold November: 44.0% 25 Years
Cold October/Normal November: 38.5% 13 Years
Warm October/Warm November: 28.6% 7 Years
Warm October/Normal November: 26.7% 15 Years
Normal October/Warm November: 0.0% 8 Years

So a normal fall is clearly the best, but the sample size is not particularly high. Normal to Warm is unanimously warm, but again, it has a small sample size.

October 2017 has been overwhelmingly warm. While this wouldn’t normally bode well for a cold winter, each year is influenced by a multitude of factors.

For more general Columbus weather records, go here: Columbus All-Time Weather




The Midwest Beat the South in Regional Domestic Migration in 2016

For years, if not decades, we’ve been hearing a familiar tale- that anyone and everyone is moving from the Midwest and Northeast to the South and West. This trend began during and after the collapse of Northern manufacturing, and as higher cost of living began to make the lower-cost South more attractive in particular. However, a lot of the South’s growth over the years- indeed a majority- never had anything to do with region-to-region migration. Instead, it was due largely to natural growth (births vs. deaths) and international migration, particularly from Central America. What received all the attention, though, was the belief that people were packing up and moving to the South from places like Ohio and other struggling Northern states. While that may have been true for a while, that is increasingly looking like it is no longer the case.

The Midwest, especially, has been derided as the region no one wants to live in. Despite its growing population approaching 66 million people, the common refrain was that its colder winters, flailing economies and questionable demographic future meant that it was simply a region being left behind by the booming Southern states.

Recently, the US Census released estimates for 2015-2016 geographic mobility, and they tell a very different story altogether.

First, let’s look at the total domestic migration moving to the Midwest from other regions.
South to Midwest: +309,000
West to Midwest: +72,000
Northeast to Midwest: +61,000
Total to Midwest: +442,000

And then compare that to the total that the Midwest sends to other regions.
Midwest to South: -254,000
Midwest to West: -224,000
Midwest to Northeast: -34,000
Total from Midwest: -512,000

Net difference by region.
Midwest vs. South: +55,000
Midwest vs. West: -152,000
Midwest vs. Northeast: +27,000
Total Net: -70,000

So while the Midwest is seeing and overall net domestic migration loss, it is entirely to the Western states.

This could just be an off year, as almost all recent years showed losses to the South, but then again, maybe not. The South has been in a boom for several decades now, and in that time, the region still lags the other 3 in almost every quality of life metric used. All booms end eventually, and the South’s 2 biggest perceived advantages, low cost of living and business-friendly climate, have been gradually eroding over time. As Census surveys show, people don’t actually move for a change in weather, so it’s the economic factors that are going to make the biggest impacts long-term. The Midwest now has many cities and several states that are doing well economically, including Columbus, and perhaps they are becoming more attractive than they have in many years. Time will tell, but last year, the narrative of an unattractive Midwest vs. South was at least temporarily shelved.

Columbus Foreign-Born Population and Comparison to Peers

The Census just came out with 2016 demographic numbers for cities. Given that more than half the decade is over, it’s a good point to look at where Columbus stands relative to its national/Midwest peers.

First up, let’s take a look at foreign-born populations. I have looked at this topic some in the past, but I have never done a full-scale comparison for this topic.

Total Foreign-Born Population Rank by City 2000, 2010 and 2016
2000—————————————-2010———————————-2016
1. Chicago, IL: 628,903———–1. Chicago: 557,674—————1. Chicago: 559,623
2. San Jose, CA: 329,750——–2. San Jose: 366,194————-2. San Jose: 402,776
3. San Antonio, TX: 133,675—-3. San Antonio: 192,741———-3. San Antonio: 219,520
4. Austin, TX: 109,006————4. Austin: 148,431——————4. Austin: 166,877
5. Las Vegas, NV: 90,656——-5. Las Vegas: 130,503————-5. Charlotte: 138,097
6. Sacramento, CA: 82,616—–6. Chalotte: 106,047—————6. Las Vegas: 137,583
7. Portland, OR: 68,976———7. Sacramento: 96,105————-7. Sacramento: 112,901
8. Charlotte, NC: 59,849——–8. Columbus: 86,663—————-8. Columbus: 101,300
9. Minneapolis, MN: 55,475—–9. Portland: 83,026—————–9. Portland: 87,599
10. Columbus: 47,713———–10. Indianapolis: 74,407———–10. Nashville: 82,505
11. Milwaukee, WI: 46,122—–11. Nashville: 73,327—————11. Indianapolis: 82,207
12. Detroit, MI: 45,541———–12. Minneapolis: 57,846———–12. Orlando: 64,369
13. Providence, RI: 43,947—–13. Milwaukee: 57,222————-13. Minneapolis: 63,585
14. St. Paul, MN: 41,138——-14. Providence: 52,920————14. St. Paul: 60,909
15. Nashville, TN: 38,936——-15. St. Paul: 50,366—————-15. Milwaukee: 58,300
16. Indianapolis, IN: 36,067—-16. Orlando: 43,747—————-16. Providence: 51,290
17. Virginia Beach, VA: 28,276–17. Virginia Beach: 40,756—–17. Omaha: 47,566
18. Orlando, FL: 26,741———18. Omaha: 39,288—————18. Virginia Beach: 45,650
19. Omaha, NE: 25,687———19. Kansas City: 35,532———19. Detroit: 39,555
20. Kansas City, MO: 25,632—20. Detroit: 34,307—————-20. Kansas City: 38,564
21. Cleveland: 21,372————21. St. Louis: 23,011————–21. Pittsburgh: 26,604
22. Grand Rapids, MI: 20,814–22. Pittsburgh: 18,698————22. Cleveland: 21,336
23. St Louis, MO: 19,542——-23. Cleveland: 17,739————-23. Grand Rapids: 20,270
24. Pittsburgh, PA: 18,874—–24. Grand Rapids: 16,615——–24. St. Louis: 19,245
25. Cincinnati: 12,461———–25. Cincinnati: 16,531————-25. Cincinnati: 15,625
26. Toledo: 9,475—————–26. Toledo: 11,559—————–26. Akron: 14,441
27. Akron: 6,911——————27. Akron: 8,524——————–27. Toledo: 8,830
28. Dayton: 3,245—————-28. Dayton: 5,102——————-28. Dayton: 7,058
29. Youngstown: 1,605———29. Youngstown: 3,695————29. Youngstown: 1,125

Here’s the 2000-2016 total change.

And the 2000-2016 change by %.

So Columbus has an above average total and growth compared to its peers nationally.