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Successful Resistance Against the Regime of Burma (Myanmar) by the Karenni People

The civil war raging in Burma (Myanmar) is one of the world’s longest running conflicts, stretching back to 1948, the year of Burma’s independence from Britain. But as hostilities ebb and flow in both time and place, the current war is dated by some as only having begun in 2021, the year of the country’s most recent military coup and crackdown on civil society. But no matter how one measures it, this struggle is bloody and grim. According to the Wikipedia article on “ongoing armed conflicts,” the Burmese Civil War currently has the third highest death toll of 2023, following only the war in Ukraine and the insurgency in western Africa that stretches across more than a dozen countries. Almost 11,000 people have lost their lives this year alone, with a casualty count of perhaps more than 20,000* in 2022. But despite the ongoing and persistent carnage, this conflict rarely makes the news in the United States.

To follow the Burmese civil war, one can consult Burmese sources, available online in both Burmese and English. I especially recommend The Irrawaddy, produced by Burmese journalists in exile in Thailand. One of its most interesting recent articles highlights the importance of the country’s smallest state, Kayah (formerly Karenni), in successfully taking on the Tatmadaw, the brutal Burmese military. The article claims that resistance fighters in Kayah have killed 2,065 junta soldiers while losing only 153 of their own in the past two years. Leading the charge is the Karenni Nationalities Defense Force (KNDF), which was formed shortly after the February 2021 coup. Some of its fighters had previously been affiliated with the Burmese military as border guards but switched sides after the military take-over. As The Irrawaddy notes, the “KNDF supports federalism, or power-sharing between the Union and state governments with self-determination and self-administration for ethnic states.” According to one recent report, the Burmese government currently controls only some ten percent of Kayah state (including its capital, Loikaw), with the rest of it either contested (20 percent) or under the control of insurgents (65 percent). If this report and others like it are true, the Wikipedia map posted below is highly inaccurate, or at least out of date, as it significantly exaggerates the extent of governmental control.

Despite the success of their military resistance, the people of Kayah (Karenni) State have experienced intense suffering over the past two and a half years. (For those interested, the assaults on their state are regularly tabulated and mapped in detail by the Karenni Civil Society Network; see the map below). According to one recent report from a different agency:

At least 180,000 Karenni people have been forcibly displaced, which is more than 40 percent of the estimated total Karenni population. …. Some families have been displaced multiple times, as IDP sites come under attack by junta forces. Based on legal analysis of the data collected, the report finds that members of the Burmese military have committed the war crimes of attacking civilians, attacking protected objects, pillaging, murder, torture, cruel treatment, and displacing civilians in Karenni State.

As is often tragically the case in Burma, extremist Buddhist monks have been encouraging military assaults and worse. According to a recent United States Institute of Peace report:

Under the hot sun, a Pa-O monk spoke to the rally and characterized the Karenni people as a lower race, describing the KNDF and the Peoples’ Defense Forces broadly as worse than the Islamic State. Another Pa-O monk called for the burning of Karenni villages if the KNDF did not stop the alleged violence, declaring: “They say it is not a religious war. But our three monks have died.” … These alarming speeches carried themes of ethnic hierarchy, Buddhist nationalism and zealous hatred.

Surprisingly, the “ethnic hierarchy” and “Buddhist nationalism” evident in this monk’s speech do not come in their usual form, which is associated with the majority Burman (Burmese-speaking) population and directed against Muslims and members of the so-called hill tribes. In this case, both the Karenni and their Po-O antagonists are historically regarded as “tribal peoples,” both belonging to the larger Karen ethno-linguistic group, at least as it is sometimes reckoned. But the Pa-O people are almost entirely Buddhist and have aligned closely with the Burmese military, which has pursued a “divide and rule” strategy among the country’s minority populations. The strategy had been largely successful before 2021 but is currently failing. The Karenni, in contrast, are religiously divided, with some following Buddhism, others Christianity (of several sects), and others traditional animism/shamanism. They are also, needless to say, firm opponent of the Burmese military.

The success of little Kayah State in resisting the Burmese military probably has roots in colonial history. Kayah was never integrated in British Burma and largely escaped British rule. In the 1870s, the Kingdom of Burma, having been reduced to a rump state after losing two wars against the British, was trying to expand into upland regions. Threatened by this policy, the tiny principalities of the Karenni people sought help from Britain, leading to an 1875 treaty between the United Kingdom and Burma that recognized their independence. In 1892, however, Karenni leaders agreed to accept a stipend from the British government in return for allowing it some local oversight. But domestic policies remained under the control of local leaders. As a result, the Karenni lands were usually mapped as not falling under direct British rule, the only part of Burma generally given that distinction (see the map below). A fascinating 1931 map, however, classified Karenni State as one of four regions in Burma that were “loosely” administered by the British Raj, with two others depicted as “unadministered” (see below). (Intriguingly, Karenni state was reportedly the world’s largest producer of tungsten in the 1930s; geologists affiliated with the Oxford Burma Project currently hope that political stabilization will eventually allow the reestablishment of extensive commercial mining there and elsewhere in mineral-rich Burma.)

As Burma was preparing for independence after World-War II, it sought to incorporate the Karenni states into its coming union. Its 1947 constitution insisted on the amalgamation of these small indigenous realms into one Burmese state, but also allowed the possibility of secession after a ten-year period. But with independence the following year, as tersely noted by the Wikipedia article on the state, “the Karenni leader U Bee Htu Re was assassinated by central government militia for his opposition to the inclusion of the Karenni in the Union of Burma. An armed uprising swept the state that has continued to the present day.”

Despite its formidable power, the Burmese military (Tatmadaw) does not seem to be doing well in the current conflict. A recent report by the Council on Foreign Relations claims that it has lost half or more of its troops since the 2021 coup, due to death, desertion, or defection, and that it has retreated on several fronts. The Tatmadaw is also evidently having difficulty filling the classes at its military academy. According to one report, the government now has stable control over only around twenty percent of the country’s townships. Due to recent military reversals, the Tatmadaw is now engaging in extensive air attacks, often directed against civilian targets. Such a strategy is of little military significance and greatly intensifies animosity against the regime.

The Council on Foreign Relations report mentioned above also contends that the Burmese government is facing growing international problems:

Even China, which has backed the junta and sees Myanmar as a strategically critical investment destination, is playing both sides of the fence. Beijing has continued to plow money into the country and supplied the military with weapons, despite its pariah status, and it has provided the junta with diplomatic cover at international forums. Yet it has also maintained links with the ethnic militias and their political wings, and its backing of Naypyidaw has grown more tepid as the army continues to lose ground. As for Russia, though it too has supplied the junta with arms, Moscow is facing its own obvious problems right now and may not be able to ship weapons abroad for long.

Due to these reversals, Burma’s military government may be reconsidering its strategy. Or perhaps not. Another article from the United States Institute of Peace nicely summarizes the current situation:

Are conciliatory winds stirring among the leaders of Myanmar’s coup regime, or is the junta engaging in deception and distraction as it struggles on the battlefield against a broad range of resistance forces? The answer is almost certainly the latter. It would not be the first time the ruling generals have sought to stimulate international interest in promoting dialogue solely to enhance their legitimacy abroad.

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The End of the Republic of Artsakh (Nagorno-Karabakh) and the Continuing Reduction of Armenian-Populated Lands

Let us begin with a paradox: “On September 20, 2023, the world political map underwent a significant change, but that change is not reflected on the world political map.” This seemingly nonsensical statement makes sense with the addition two Latin terms: “On September 20, 2023, the de facto world political map underwent a significant change, but that change is not reflected on the de jure world political map.” The de facto map, which shows actual power on the ground, was transformed by the defeat of the self-declared Republic of Artsakh (Nagorno-Karabakh) and its impending annexation by Azerbaijan. But as Nagorno-Karabakh was already part of Azerbaijan according to diplomatic convention, the official de jure map of the region registered no change.

From its independence from the Soviet Union in 1991 until late September of this year, Azerbaijan did not exercise power over its full internationally recognized territory. Its southwestern corner was instead under the power of the self-declared and unrecognized state called Artsakh, better known as Nagorno-Karabakh. This Armenian-populated region functioned as a client state of the Republic of Armenia, if not as an appendage of it. In 2020, Azerbaijan defeated Armenia/Artsakh in a brief war and took control of most of the disputed territory, leaving only the core region of Nagorno-Karabakh, which was connected to Armenia proper by the narrow Lachin Corridor, patrolled by Russian troops. Earlier this year, Azerbaijan cut-off access to the corridor, putting great pressure on Artsakh. On September 19-20, Azerbaijan’s military overran the entire area, after which Artsakh’s leadership announced that their statelet would be dissolved on January 1, 2024. As a result, Azerbaijan will for the first time control its entire territorial extent as recognized by international convention. But the de jure and de facto maps remain out of alignment elsewhere in the Caucasus, as two official parts of Georgia are still under the control of two Russian client states, Abkhazia and South Ossetia.

An extended New York Time headline of September 29 nicely captures the current geopolitical situation of the losing country: “Armenia: Cast Adrift in a Tough Neighborhood. While the Caucasus nation might want to reduce its reliance on Russia for a more reliable ally, Western nations have offered moral support but little else.” After independence in 1991, Armenia turned to Russia for military support, hosting a Russian military base and joining the Moscow-led Collective Security Treaty Organization (along with Belarus, Kazakhstan, Kyrgyzstan, and Tajikistan). But Russia was of little help in its 2020 war with Azerbaijan, in which Azerbaijan’s Turkish- and Israeli-made drones outperformed Armenia’s Russian-made armaments. Armenia then began edging away from Russia and toward the West, a process that accelerated after Russia’s invasion of Ukraine in 2022. Just before Azerbaijan conquered the rest of Artsakh in late September 2023, U.S. military personnel were helping train Armenian troops in Armenia. But the U.S. offered nothing beyond vaguely smoothing words after Azerbaijan’s military assault. As reported by the New York Times, the United States “has so far resisted placing sanctions on Azerbaijan for a military assault that the State Department previously said it would not countenance.”

The lack of support for Armenia by the United States is not surprising. The U.S., like most countries in most circumstance, stands in favor of the official de jure world political map, and is thus reluctant to acknowledge any alternative arrangements. (Although there are certainly exceptions, such as Washington’s recognition of the independence of Kosovo, which seceded unilaterally from Serbia and is thus unrecognized by the United Nations.) Brute geopolitical realities also favor Azerbaijan, as it is much more populous and economically developed than Armenia. As a relatively secular Shia Muslim nation, moreover, Azerbaijan is also a useful counterweight against Iran (more Azeri speakers live in Iran than in Azerbaijan).

Immediately after the fall of Artsakh, ethnic Armenians began streaming out of the region, seeking refuge in Armenia proper. It is expected that by the end of the year there will be few if any Armenians left in the region. Azerbaijan claims that Armenians could remain in place as Azerbaijani citizens. Armenians, however, point to Azerbaijan’s threats and purported atrocities, arguing, with some international support, that genocide would be the more likely outcome if they were to remain. Azerbaijani apologists, for their part, point to the fact that many Azeris once lived in what is now Armenia, but were themselves victims of Armenian-led expulsions (see the map below). It also true that ethnic Kurds, who were formerly the dominant population between Armenia and Nagorno-Karabakh proper, fled or were expelled after the expansion of Armenian power following the fall of the Soviet Union (see the map below). (Other Kurdish populations from both Armenia and Azerbaijan had been deported by Soviet authorities to Kazakhstan in 1937.)

From a world historical perspective, Azerbaijan’s conquest of Artsakh and the subsequent removal of the Armenian population from the region represents one more chapter in the long history of the diminution of the Armenian territorial sphere. As the paired maps below show, Armenians once constituted either a majority or a large minority over a broad zone extending across what is now eastern Turkey and beyond (unfortunately, the Vivid Map posted here has no key). Ottoman expulsions of Armenians before and especially during World War I, recognized by most historians as an episode of genocide, vastly reduced the extent of Armenian populated land. After the downfall of the Soviet Union, Armenian communities were either expelled from or voluntarily left many former Soviet lands. With the downfall of Artsakh, the contiguous zone of Armenian-populated territory is now reduced to the small rump state of Armenia.

Understandably, many Armenian-Americans have been enraged about the lack of U.S. action on this issue. As reported in the Guardian:

Everything that is happening today is utterly predictable, and much of it could be avoided with more forceful American action,” Paul Krekorian, the first Armenian American president of the Los Angeles city council, told the Guardian.

It’s a catastrophic situation. Genocide is happening before our very eyes,” Krekorian said. “And my country is doing essentially nothing.” Memories of the 1915 Armenian genocide, when 1 million to 1.5 million Armenians died under the Ottoman Turkish empire, remain strong in the community and many of the signs held outside the Ronald Reagan library referenced it and what the protesters saw as its echoes.

Unfortunately, but unsurprisingly, few American media outlets have done much substantive reporting on this issue. At one time, something like this would have been a major news story. Over the past half-century, however, the U.S. new media have become increasingly insular, tightly focused on American politics, society, and culture, and hence little concerned with most events occurring outside the country. Economic globalization has oddly coincided with journalistic deglobalization.

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Who Are the Gagauz, Where Is Gagauzia, and Why Are They in the News?

The “Autonomous Territorial Unit of Gagauzia,” located in southern Moldova, rarely makes the news. On September 25, 2023, however, the New York Times ran a full-page article on the region under the vague title “Fugitive Oligarch Gaines Surprise Foothold in Moldova.* The article describes Gagauzia as an “enclave” within Moldova. That is not technically correct, as a geopolitical enclave is part of one country that is surrounded by the territory of another, whereas Gagauzia is merely an autonomous region of Moldova. Fear of losing that autonomy lies behind the ethnic tensions that have given this obscure region international attention.

The New York Times article focuses on the shady activities of Ilan Shor, a disgraced financier who was “convicted in 2017 for his role in ransacking Moldova’s banking system.” In the summer of 2023, a follower of Shor, Evghenia Guțul (Yevgenia Gutsal), was elected governor of Gagauzia, allowing Shor to gain considerable power in the autonomous unit. This victory was internationally significant because Guțul and Shor support Russia and oppose the E.U. The United States accused Shor in 2022 “of working with ‘Moscow-based entities’ to undermine Moldova’s efforts to join the European Union and engaging in ‘persistent malign influence campaigns on behalf of Russia.’” (Note: direct quotes in this paragraph are from the Times article.)

The description of Gagauzia in the New York Times’ article is minimal. It notes only that Gagauzia is a “Russian-speaking region wary of the largely Romanian-speaking authorities in Chisinau, Moldova’s capital,” and that “the enclave, with around 140,000 people, mostly members of the small Turkic community of Orthodox Christians, remained out of step with the rest of the country.” Although largely accurate, this depiction is not adequate for understanding the tensions in the region. One might wonder, for example, how Gagauzia can be “Russian-speaking” when its majority ethnic group, the Gagauz, are “Turkic,” indicating that they speak a Turkic language. Yet both assertions are essentially true. The Gagauz tongue, the territory’s official language, is indeed in the Turkic language family, but its use is rapidly declining, especially in cities and towns, in favor of Russian, long used as Moldova’s main language of inter-ethnic communication. While the Gagauz are turning to Russian, they are also rejecting Romanian (or “Moldovan,” as it is often locally called), their county’s official** language. Such attitudes do not augur well for Moldova’s national future.

The origin of the Gagauz people is obscure, owing in part to their combination of speaking a Turkic language and following Eastern Orthodox Christianity. As the Wikipedia article on the Gagauz notes, “In the beginning of the 20th century, a Bulgarian historian counted 19 different theories about their origin. A few decades later the Gagauz ethnologist M. N. Guboglo increased the number to 21.” The most intriguing, if highly unlikely, theory is that they are descendants of the original Balkan Bulgarians, who were a Turkic-speaking people who conquered the area now known as Bulgaria beginning in the late seventh century. The Bulgars subsequently adopted the Slavic language widely spoken in their new kingdom, which became known as Bulgarian, and also converted to Christianity under influence from the neighboring Byzantine (East Roman) Empire.

Whatever their origins, the Gagauz stress their affinity with the Bulgarians. In early times they generally called themselves “Hasli Bulgars” (True Bulgarians) or “Eski Bulgars” (Old Bulgarians), Under Russian Empire, they were usually called “Turkic-speaking Bulgars,” as the term “Gagauz” was at the time often considered offensive. Most Gagauz today live near Bulgarian-speaking settlements in southern Moldova and the adjacent Ukrainian region of Budjak, as can be seen on the map posted below. (Since I cobbled this map together from separate and questionable language maps of Moldova and Ukraine, its accuracy is probably not very high.)

It might be surprising that so many Bulgarians live in southern Moldova and southwestern Ukraine, considering how far this area is from Bulgaria. Before population exchanges in the early twentieth century, however, many Bulgarians lived in the intermediate coastal region of Romania, thus forming a nearly continuous swath of settlement in an admittedly highly mixed area (see the first map below). The language map of the Bessarabia Governorate of the Russian Empire in the late nineteenth century, posted below as well, is also revealing. Bessarabia, which included what is now Moldova, the Budjak region, and a small section of northwestern Ukraine, was highly ethnically mixed. Note the sizable German-speaking area and the prominent positions of Jews in the towns and cities (visible in the pie charts). Today there are probably fewer than 20,000 Jews in Moldova, and its German population is negligible.

The Gagauz in Moldova identify with Bulgarians and Russians rather than with ethnic Moldovans in part because they are concerned about cultural domination by Romanian-speaking people. When the Soviet Union began to fracture in 1990, Gagauz leaders declared the formation of a Gagauz Republic, which gained de facto independence when the Soviet system collapsed in the following year. A similar situation emerged in eastern Moldova, where the heavily Russian- and Ukrainian-speaking region called Transnistria also separated from the rest of the country. Unlike Transnistria, however, Gagazia was peacefully reunited with Moldova in 1995 after its people accepted limited self-rule within their own spatially reduced Autonomous Territorial Unit of Gagauzia (see the map below). Importantly, the Gagauz were promised that if Moldova were ever to unite with Romania, they would be able to opt out of the union. Unification with Romania, however, has little support in Moldova; in the country’s most recent parliamentary election, the pro-unification party AUR (Alliance for the Union of Romanians) received less than one half of one percent of the vote. In Romania, in contrast, AUR got over nine percent of the vote in the most recent election, finishing in fourth place.

But if union with Romania is unlikely, the Moldovan government has still been emphasizing the use of the Romanian (“Moldovan”) language and deemphasizing that of Russian. In protest, as noted in a Balkan Insight article, “Gagauzia adopted a regional education code that implied a greater use of the Gagauz language in school, as well as a more detailed study of Gagauz history and culture” in 2016. The Moldovan government, however, declared this new policy to be “unconstitutional and provocative.” Today, a more immediate concern of the Gagauz is Moldova’s quest to join the European Union (official candidacy was gained June 2022). If that were to happen, Gagauzia could lose its autonomous status. To guard against this possibility, Gagauz leaders have been seeking support from Moscow, a dangerous gambit indeed.

Reports on feelings of national identity in Gagauzia are mixed. One recent article cites a Gagauz informant as stating that “anyone who lives in our autonomy feels like a citizen of Moldova, because the Gagauz have no other homeland. For example, Bulgarians can go to Bulgaria, Greeks to Greece, Russians to Russia… But the Gagauz have no other homeland.” The same person also stated, however, that few Gagauz students seek higher education elsewhere in Moldova, preferring to study instead in the break-away statelet of Transnisria, where Russian is the main language of instruction. Other sources, moreover, claim that anti-Moldovan sentiments are so pronounced that most Gagauz do not even want to learn Romanian, their “national” language. In response, many Moldovan observers fear that the autonomous territory is planning outright secession, in concert with Russia.

In the Ukrainian region of Budjak, Bulgarian and Gagauz speakers have generally supported Russia-friendly candidates over their Ukrainian nationalist rivals. As can be seen on the paired maps posted below, in the first round of the 2019 election, Ukrainian-speaking areas in Budjak generally supported Volodymyr Zelensky, whereas the Bulgarian- and Gagauz-speaking areas supported Yuriy Boyko. Boyko’s party, Opposition Platform – For Life, has been banned by the Ukrainian government for its pro-Russian leanings. But as the Wikipedia article on Boyko notes, after the 2022 Russian invasion of Ukraine he reversed most of his pro-Russian stances and now supports Ukraine’s proposed ascension to the European Union. Not surprisingly, the political environment of Ukraine changed much more dramatically than that of Moldova after the 2022 invasion.

* That is the title in the print edition. In the on-line edition it isCash, Mules and Paid Protests: How a Fraudster Seized an Ethnic Enclave”

** Moldova also recognizes Belarusian, Bulgarian, Gagauz, German, Hebrew, Polish, Romani, Russian, and Ukrainian as minority languages

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Non-Metropolitan Patterns of Population Change in the United States, 2020-2022

Earlier this year Axios published a revealing map of population change in all counties in the United States from 2020 through 2022. This map, unlike the ones that I made and posted earlier this week, allows one to assess population change in non-metropolitan as well as metropolitan areas. As can be easily seen for the United States as a whole, rapid growth was concentrated in three areas: western and central Florida; the suburban and exurban fringes ringing the largest cities of Texas; and a western belt encompassing Utah, Idaho, and western Montana. Other interesting patterns can also be discerned. To clarify them, the rest of this post will examine state-and regional-level map-details extracted from this national map.  

Let us begin with Appalachia. Several recent articles (for example, this one by Aaron M. Renn) have noted that southern Appalachia is doing much better than northern Appalachia on almost every metric. It is therefore no surprise that most counties in southern Appalachia grew during this period while many if not most in the north shrank (that is, if “north” is defined as all areas north of the northern borders of North Carolina and Tennessee).

Appalachia is often placed in the same cultural and socio-economic category as the Ozark Plateau, located mostly in southern Missouri and northern Arkansas. Both areas are characterized by steep terrain, heavy forests, and a backwoods folk culture that is both widely denigrated and romanticized. In terms of recent population change, the Ozark Plateau clearly groups with southern Appalachia. But as can be seen on the paired maps below, most counties in this region lost population, or remained relatively static, during the 2010 to 2020 period. The only substantial growth then was in its two metropolitan areas, Springfield in southwestern Missouri and Fayetteville-Springdale-Rogers (home of Walmart and several other major corporations) in northwestern Arkansas. When the COVID pandemic hit, however, people began to relocate to the region’s rural counties. I was intrigued by the very rapid growth shown for Wright County. A quick Internet search, however, returned almost nothing other than a single highly misleading post from World Population Review, which claimed that the county’s population dropped during this period. But as the table and graph posted below indicate, this information was improperly extrapolated from a tiny snippet of information from an earlier period. I find it amusing that this reputable website claims that Wright County lost exactly 63 people every single year between 2011 and 2023! Such are the dangers of automated demographic interpretation.

Recent population growth in the Ozark Plateau is reflected in the expansion experienced in other lightly populated, scenic parts of the country. Most of Maine, the northern lower peninsula of Michigan, and northern Wisconsin also saw rural population growth in this period. An interesting place to examine this phenomenon is in the Dakotas. As the maps posted below show, most counties in far western South Dakota saw major population gains from 2020 to 2022 whereas most of those of western North Dakota saw significant declines. This pattern is easily explained. Western North Dakota experienced massive growth from 2010 to 2020 due to the oil boom in the Bakken Formation. That boom came and went (although it may return), and as a result the region’s population dropped sharply after the 2020 census. Western South Dakota, in contrast, contains the Black Hills, a scenic region with high amenity values. It is therefore no surprised that it saw a boom during the COVID period. It is important to note, however, that many counties in the western Dakotas have so few people that the gain or loss of a small number can make a dramatic difference on this map.

Differences between states are also apparent on the national COVID-era population-change map. Consider, for example, the neighboring states of Illinois and Indiana. Although Indiana and Illinois are politically very distinct, their non-metropolitan counties are quite similar. But recent population change at the county level differs greatly across the state border. Only five counties in Indiana had more than a one-percent population loss during this period, whereas only three counties in Illinois had more than a one-percent gain. The financial woes of Illinois are probably a significant factor here.

Idaho and western & south-central Montana show stark difference between the 2010-2020 and the 2020-2022 population-change maps. In the earlier period, quite a few primarily rural counties lost population. In the latter, only tiny Wheatland County, Montana (population 2,069 in 2020) lost more than one percent of its residents. From 2020 to 2022, many counties in this region, both metropolitan and rural, saw population gains of more than five percent.

California makes an interesting contrast with Idaho and Montana. Population loss from 2020 to 2022 was concentrated in the affluent coastal region, with San Francisco County exhibiting a drop of 7 percent, the largest in the country. But quite a few low-population, peripheral counties also experienced big drops, with Lassen declining by more than five percent. Intriguingly, some of these scenic counties with high outdoor-amenity values had experienced demographic booms in the final decades of the twentieth century. But as can be seen in the tables posted with the map below, this growth had essentially come to an end by 2010. Both Tuolumne and Mono counties, adjacent to Yosemite National Park, lost more than one percent of their population between 2020 and 2022. Evidently, state boundaries matter considerably in relocation decisions, and California is no longer a very attractive state.

 

 

 

 

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Striking Patterns of Population Change in U.S. Metropolitan Areas, 2020-2022

The 2020 to 2022 COVID period saw major population changes in the metropolitan areas of the United States, with some experiencing rapid gains and others rapid losses. Wildwood-The Villages, Florida, for example, saw a staggering 11.75 percent population increase, whereas Lake Charles, Louisiana witnessed a sobering decline of 6.01 percent. Mapping these changes reveals some interesting patterns.

The first map, showing population change in major metropolitan areas (defined here as those with more than 1.5 million people in 2002) exhibits clear regional differences. A stark north/south divide is evident in the region east of the Mississippi River. Here, every major metro area in the South saw population gains, some significant. So too did three out four in the lower Midwest (Columbus, OH, Cincinnati, OH, and Indianapolis, IN), although by smaller margins. By contrast, every major metropolitan area in the Northeast and upper Midwest lost population. In the western two-thirds of the country, population declines were restricted to the Pacific Coastal region. Here every major metropolitan area except Seattle saw a decline. Texas, in contrast, is notable for its rapid metropolitan expansion, with Dallas, Houston, Austin, and San Antonio all registering major gains in this period.

Somewhat different patterns are seen on the map of secondary metropolitan areas, defined here as those with populations between 700,000 and 1.5 million in 2022. As can be seen, fewer of these smaller metro areas lost population, indicating a shift from larger to smaller cities. Intriguingly, most of those that did decline are in or near the Mississippi River and the eastern Great Lakes, the main transportation corridor of the central part of the U.S. before the coming of railroads. New Orleans (official, the New Orleans–Metairie metropolitan statistical area) saw a drop of over 3.5 percent. I was surprised to see that New Orleans is no longer populous enough to qualify for the higher categories on this map, as its population has apparently dropped below one million. A major statistical discrepancy, however, complicates this analysis. According to the Wikipedia table that I used to make this map, New Orleans–Metairie had a population of only 972,913 in 2022, having declined from 1,007,275 in 2020. The Wikipedia article on the New Orleans–Metairie metro area, however, gives it a population of 1,271,845 in 2020. But no matter how one looks at it, New Orleans has hemorrhaged population, with the city itself dropping from 627,525 residents in 1960 to 383,997 in 2020.

The secondary metro areas that saw population growth in this period also exhibit some interesting patterns. Those in the Atlantic Northeast all saw minor population gains, presumably due to people fleeing the region’s larger and more expensive major metro areas. Much more rapid expansion, however, was experienced in the secondary metro areas of the southeast, particularly in Florida and the Carolinas. Secondary metro areas in the interior West also saw substantial growth.

Even more distinct patterns are visible on the map showing the fastest growing and fastest shrinking metro areas of all sizes during this period. (Many official metropolitan areas, it is important to note, are not large; Eagle Pass, TX, for example, has fewer than 60,000 inhabitants.) As can easily be seen, most of the fastest growing metro areas are in the southeastern coastal region, stretching from the Gulf Coast of Alabama through the Atlantic Coast of the Carolinas. Florida really stands out on this map. Several smaller metro areas in the non-coastal West also saw extremely rapid growth. St. George UT, for example, went from 180,279 to 197,680 inhabitants, a gain of almost 10 percent. After having witnessed the boomtown atmosphere of Bozeman MT, which does not even qualify for this map with a growth rate of just under 5%, I have a difficult time understanding how the infrastructure of Saint George could keep up with such rapid population expansion.

In contrast, three states stand out for the rapid population decline of many of their metropolitan areas: California, Louisiana, and West Virginia (metro area #16 on this map is Weirton–Steubenville, located in both West Virginia and Ohio). Although metropolitan growth from 2020 to 2022 was concentrated in Republican-voting states, Louisiana and West Virginia form clear exceptions.

The final map shows population loss-and-gain patterns in California’s metropolitan areas during the same 2020-2022 period. Here again the pattern is clear: all coastal metro areas,  which have equable climates but are very expensive, lost population, whereas most less-expensive metro areas in the Central Valley, a region noted for its scorching summers, gained population, as did the similarly toasty San Bernardino-Riverside metro area in Southern California, the so-called Inland Empire. The college town of Chico in Butte County in the northern Central Valley (or Sacramento Valley) however, saw a significant population drop.

Tomorrow’s post will examine the geography of population change in this period in rural counties.

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Patterns of Income Inequality in Major U.S. Metropolitan Areas and Population Change, 2020-2022

The four U.S. states with the highest levels of income inequality are, in order, New York, Connecticut, Louisiana, and Mississippi. When mapped at the county level, however, New York and Connecticut appear to have lower levels of inequality than Louisiana and Mississippi. The seeming discrepancy is easily explained by population density. In New York and Connecticut, high GINI coefficients are found in densely populated counties that are part of the greater New York metropolitan area; counties with smaller cities, in contrast, tend to have average levels of income inequality, whereas most rural counties in these states have relatively low levels. (Unfortunately, the scale of resolution on the maps that I have used does not adequately reveal this phenomenon; most of New York City, for example, is obscured by the heavy black line that is used for state boundaries.) In Louisiana and especially Mississippi, in contrast, many rural and semi-rural counties are characterized by pronounced income inequality.

But how do the high levels of income inequality in the New York area compare to those found in and around other major U.C. cities? To address this question, I extracted details from the county-level GINI map of the United States to show the situation in the vicinity of 16 major metro areas found across the country. As can be seen, in each case the central county or counties, those with the highest population densities, have higher levels of income inequality than the more suburban and peripheral counties.

Such comparisons are made difficult, however, by the incommensurable nature of the units. In some cases, inner counties are extremely small; San Francisco County, for example, is coterminous with the city of San Francisco, whereas New York City is itself divided into multiple counties. In contrast, Phoenix tends to vanish in the vast expanse of Maricopa County.

But even with these limitations in mind, there are still some intriguing lessons to be drawn from these maps. At the high end of the inequality spectrum is Miami, followed by New York and San Francisco, where almost all counties in the greater metro areas have average to high GINI coeffiecient. Seattle, Denver, Minneapolis, and Washington DC/Baltimore, in contrast, are surrounded by suburban and peripheral counties with relatively low levels of income inequality. I was surprised to see this pattern in the Washington D.C. area, which is by some measures the wealthiest part of the country. As can be seen on the small map, Baltimore and the District of Columbia are, not surprisingly, characterized by high inequality, as is, more surprisingly, rural Talbot County in eastern Maryland. Affluent Montgomery County, in contrast, falls in the middle category.

Many of the country’s major metropolitan areas saw population decreases between 2020 and 2022. Such declines tended to be steepest in areas of pronounced inequality. The New York metro area, for example, lost 2.6 percent of its population and the San Francisco metro area 3.6 percent, the steepest drop in the country. The less unequal Seattle, Denver, Minneapolis, and Phoenix metro areas, in contrast, all gained population. But exceptions are certainly found. The Washington, D.C. area, with its relatively income-equal suburban counties, lost population, although just barely (0.21 percent), while the highly unequal Miami metro area gained population, although again just barely (0.02 percent).

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Air-Conditioning Needs and Cartographic Failure at the Washington Post

The Washington Post recently ran an article entitled “Addicted to Cool: How the Dream of Air Conditioning Turned into the Dark Future of Climate Change,” which features three maps of “Summer Days Requiring AC” in the U.S. at different periods of time (1981-200, 2001-2002, and 2060). As expected, the region needing air conditioning is projected to expand. Determining how many days actually “require” air conditioning is, however, an impossible task, as different people vary significantly in their cooling desires and demands, while housing design and shade considerations make big differences as well. Understandably bypassing such complexities, the newspaper used the heat index, a measurement of temperature and humidity, as a proxy. Unsurprisingly, their maps show that a large area of the country already needs summer air conditioning, and that in the decades to come the need for cooling will geographically expand.

The maps included in the article, however, are not impressive, to put it mildly. Their problems are particularly severe regarding California. As can be seen on the map detail of Southern California posted below, the Post’s mapping accurately shows the eastern deserts and the inland western regions as needing air conditioning on most if not all summer days. It also accurately depicts the highest elevation areas as rarely requiring it. But the same map also portrays the coastal zone as AC-dependent. This is not true. Downtown San Diego, for example, has an average July high temperature of 75 degrees Fahrenheit and an average July low of 66 (over the 1991-2020 period). Further north, in Rancho Palos Verdes, similar conditions prevail. According to Weather Spark, “The hottest month of the year in Rancho Palos Verdes is August, with an average high of 76°F and low of 64°F.” The same site also notes that “Over the course of the year, the temperature typically varies from 51°F to 76°F and is rarely below 46°F or above 84°F.” This is climate that very rarely calls for cooling.

The map is equally inaccurate in its depiction of Northern California. As can be seen on the map detail posted below, it does capture the cool summers characteristic of the coastal regions in and around San Francisco and Monterey bays. It completely misses the fact, however, that other coastal regions also have mild summers. The average July high and low temperatures in Point Arena, Santa Cruz, and Carmel are, respectively 65 and 50 degrees F.; 74 and 54; and 70 and 53 (all based on the climatological data found in the Wikipedia articles on these towns). In Point Arena, one is more likely to want heating than air conditioning in June and July, yet the map indicates that cooling is needed on most summer days.

Other odd features mar the map. As can be seen, the city of San Francisco is mislocated in the bay and on its eastern shore.  The national map also features a faint white line that traces part of San Francisco Bay and would appear to indicate the actual coastline, at least in some areas.  Was one map imprecisely overlaid on another?

Although these problems are serious enough, it is the map of projected air-conditioning needs in the year 2060 that truly fails. This can be seen easily on the paired maps showing current and projected AC requirements in California. Here much of the currently cool Big Sur coastal zone is projected to have much reduced air-conditioning needs by 2060. This region of projected cooling is bizarrely shown as extending over the Santa Lucia Range into the southern Salinas Valley, an area that now experiences warm summers (King City has an average July high of 85 degrees F.). Similarly, the currently warm inland area north of Santa Barbara is shown as being expected to have much cooler summers in 2060 than it does today, while with the rest of Southern California is projected to warm.

One can only wonder whether the cartographers in question actually examined these maps before publication, or, if they did, whether they have much of an understanding of the geography of climate. It often seems that journalists use maps as mere ornaments or, alternatively, to have the appearance of spatial precision without the substance. The maps in this article do little more than make the trite point that “more of us will need air conditioning as the climate warms.” Readers deserve better, especially from a once-great paper that is owned by the third richest person in the world.

Air-Conditioning Needs and Cartographic Failure at the Washington Post Read More »

Geographical Patterns of Income Inequality in the U.S. at the State and County Levels

I have long been intrigued by the geography of income inequality in the United States. As maps of the GINI coefficient show, income inequality is highest some of the country’s richest states (New York, Connecticut) and in some of its poorest (Louisiana, Mississippi). Similarly, some of the country’s most Democratic-voting states and some of its most Republican-voting ones are characterized by pronounced income inequality. Relatively low levels of income inequality are concentrated in an area that might crudely be described as the center-north-west, with four contiguous states occupying the lowest category on the map (Utah, Idaho, Wyoming, South Dakota). Low population density characterizes states with low income inequality. All of the states in the bottom two categories on this map except Hawaii have a lower-than-average population density. Politically, these states show the same mixed pattern that characterized the most economically polarized states. Although all the states in the lowest GINI category are bright red on electoral maps, two that fall into the next lowest category (Vermont, Hawaii) are bright blue.

A county-level GINI map clarifies the geography of U.S. income inequality and reveals some interesting patterns (unfortunately, the best map that I could find on this topic is somewhat dated). As can be seen, the elevated levels of income inequality found in northeastern states is largely an urban phenomenon. In the southeast, in contrast, some counties with high GINI coefficients are metropolitan (in southeastern Florida, for example), but others are markedly rural. In Western and Great Plains states characterized by relatively low income inequality, quite a few of their rural counties have high GINI scores.

In North Dakota, South Dakota, and Montana, some rural counties characterized by high income inequality also have a high percentage of Native American residents. To illustrate this correlation, I have placed a GINI map of the Dakotas next to one of indigenous population percentage. But there are a few striking exceptions to this pattern, two of which are noted on the map. As can be seen, Divide County, North Dakota has a small Native American population and a high GINI coefficient. Pete Morris’s agricultural explanation of income inequality, outlined in his comment on yesterday’s post, is probably relevant here as well. In contrast, Buffalo County, South Dakota has a large Native American population and a low GINI coefficient. Both of these counties have very small populations. Buffalo County is noteworthy for having the least populous county seat in the United States (Gann Valley, with a population of 14).

In the south-central region of the country, most counties with high levels of income inequality have large black populations. But again, interesting exceptions can be found. As can be seen, Jefferson County, Arkansas has a high percentage of Black residents and a mid-level GINI ranking. In contrast, Marshall County, Alabama has a very low percentage of Black residents and a high level of inequality. Jefferson County, intriguingly, is known for its concentration of “correctional facilities,” mostly located in and around Pine Bluff. Marshall County, Alabama, in contrast, is part of the Huntsville-Decatur Combined Statistical Area, a region noted for its many well-paid technical workers, owing largely to that presence of NASA’s Marshall Space Flight Center, the United States Army Aviation and Missile Command, and the FBI ‘s Operational Support Headquarters. Marshall’s largest city, Albertville, is mostly noted, however, as the home of the fire-hydrant-manufacturing Mueller Company. As noted by the Wikipedia article on the city, “Albertville holds the title of “Fire Hydrant Capital of the World.” To commemorate the one millionth fire hydrant, a chrome fire hydrant was placed outside the Albertville Chamber of Commerce.”

The next GeoCurrents post will examine the geography of income  inequality in the country’s largest metropolitan areas.

Geographical Patterns of Income Inequality in the U.S. at the State and County Levels Read More »

Explaining Seeming Discrepancies on County-Level Income Maps of the United States

When working on a recent GeoCurrents post that involved maps of income in the United States, I noticed a few unusual patterns. A number of counties, for example, are mapped as having relatively high per capita personal income and relatively low median household income, whereas in others the opposite pattern obtains. In part this is a matter of household size, an explanation that works particularly well for Utah. Consider, for example Utah County, Utah which is characterized by relatively low per capita personal income, relatively high median household income, and a large number of people per household. In contrast, Grand County is characterized by relatively high per capita personal income, relatively low median household income, and a small number of people per household.

In Utah, the number of people per household correlates closely with religion. Members of the LDS church (Mormons) often have high fertility rates, leading to large households. Utah County, Utah, home of Brigham Young University, is usually considered the cultural center of the LDS faith. As can be seen on the second map below, Utah County has one of the highest fertility rates in the country. In contrast, Grand County has a relatively low fertility level (which is not shown in the map due to its small population) and the lowest LDS percentage in the state. Whereas Utah as a whole is roughly 62% Mormon, in Grand County the figure is only 26%.

These easy correlations, however, collapse when one examines North Dakota. As can be seen on the map below, Cavalier County has the highest per capita personal income in the state, which is why it is outlined with a heavy white line on the map posted here. But Cavalier County is also characterized by relatively low median household income and relatively few people per household. This seeming anomaly can be explained by taking into account the different way that the two income measurements are determined. Median household income is calculated by taking the income of all households in a county and finding the middle point; per capita personal income, on the other hand, is calculated by dividing the total income of all persons in the county by the population. If a county has a small population with a few very high-income individuals, the per capita personal income figure is inflated, whereas the median household income figure will remain low if most households have lower incomes.

If this explanation is correct, one would expect Cavalier County to have a relatively high Gini Coefficient, which measures the degree of inequality. The most recent GINI map of all U.S. counties that I was able to find (posted below) indicates that this is indeed the case. Overall, North Dakota is characterized by very wide range in GINI figures, which is probably largely an attribute of the small populations of most of its counties.

 Regardless of its income level, Cavalier has not exactly been a thriving county over the past century. It had more than 15,000 people in 1920 and fewer than 4,000 in 2020.

Explaining Seeming Discrepancies on County-Level Income Maps of the United States Read More »

How Well Can ChatGPT Analyze a Complex Map? Part 2: Rich Counties in Red States

In the 2020 U.S. presidential election, the neighboring states of Wyoming, Idaho, and Utah all strongly supported Donald Trump, giving him respectively 70, 64, and 58 percent of their votes. But in all three states several counties opted for Joe Biden, a few by considerable margins (see the map below). All of these seeming exceptions are easily explained on the basis of demographic characteristics. Latah County, Idaho and Albany County, Wyoming, for example, are the sites of their states’ main universities, and college towns and nearby communities usually support candidates in the Democratic Party. Salt Lake County, Utah, in contrast, contains a major metropolitan area, which are also generally “blue” on electoral maps. Sparsely populated Grand County, Utah falls into neither category, but its main town, Moab, is a major center of outdoor recreation. Western communities with economies based on outdoor amenities also typically vote for Democrats. Teton County, Idaho falls into this same category.

The darkest shade of blue on this map is given to Blaine County, Idaho and Teton County, Wyoming. Their deciding demographic characteristic, as of Summit County Utah, is a variant of the environmental amenities theme, as all are sites of major ski resorts and their associated residential communities. What really distinguishes these three counties from their neighbors, however, is their wealth. As the somewhat dated map below posted shows, these are among the richest counties in the United States. With the exception of those whose wealth is largely derived from natural resources and farming, wealthy counties generally support Democratic candidates.

I wanted to see if ChatGPT could find and explain the seeming anomaly of low-density, white-dominated counties located in the conservative intermontane West strongly favoring Joe Biden over Donald Trump. I therefore made the following pointed, and hinting, request:

“Write a 500-word analysis of the geographical patterns found in the 2020 United States presidential election based on a map of all U.S. counties shaded by the margin of victory, paying attention to counties that were exceptions to the general geographical rules found on this map and taking into account per capita income.”

The essay that the chatbot gave in response is reproduced below.  As can be seen, it fails to either locate or explain this interesting phenomenon.

Thinking that the chatbot would perform better if I limited its analysis to Utah, Idaho, and Wyoming, I gave it another chance. This response is also included below. Here again it failed its assignment. Although ChatGPT did notice and explain the seeming oddity of Teton County, Wyoming, it also included a major error in the same sentence: “County-level data shows that Trump won all but one county in the state, Teton County, which is known for its higher-income residents and a significant number of transplants from more liberal areas.” Perhaps ChatGPT did not notice that Biden also won Albany County, Wyoming because he took less than 50 percent of its votes. But Biden’s margin of victory here was still a reasonably solid 2.76 percent.

To sum up to this brief series, ChatGPT seems to be particularly bad at geographical analysis. Somehow, that does not surprise me.

ChatGPT Response 1:

The 2020 United States presidential election exhibited intriguing geographical patterns when analyzed using a county-level map shaded by the margin of victory. These patterns were influenced by various factors, including economic conditions such as per capita income. Let’s delve into the key observations, general trends, and notable exceptions:

General Patterns:

  1. Urban-Rural Divide: A conspicuous urban-rural divide was evident. Urban and suburban areas, particularly along the coasts and in major metropolitan regions, tended to favor the Democratic candidate, Joe Biden. Conversely, rural and less densely populated areas, especially in the Midwest and the South, leaned toward the Republican incumbent, Donald Trump.
  2. Economic Centers: Counties encompassing major economic hubs, like Manhattan (New York County), Los Angeles County, and Cook County (Chicago), predominantly supported Biden. These urban centers typically have higher per capita incomes due to their concentration of economic activity, educational institutions, and diverse populations.

Notable Exceptions:

Several counties deviated from the general patterns, and these exceptions can be partly explained by considering per capita income:

  1. Rural Prosperity: In some rural counties, particularly in states like Wyoming, North Dakota, and parts of Texas, which benefit from natural resource industries (e.g., oil, gas, and agriculture), Trump received strong support despite their rural status. These exceptions underscore the economic diversity within rural America, where high per capita incomes can translate into political allegiance to Trump.
  2. Urban Economic Variation: In urban areas, especially in Rust Belt cities like Youngstown, Ohio, and Scranton, Pennsylvania, Trump gained ground in counties with lower per capita incomes. Economic hardship and the decline of manufacturing jobs in these regions likely contributed to this shift, illustrating how economic conditions can influence political preferences.
  3. Florida’s Diversity: Florida’s diverse economic landscape and a significant retiree population resulted in a mix of counties concerning per capita income and political leanings. While Miami-Dade County, with its high per capita income derived from tourism and international trade, experienced a shift toward Trump among some Hispanic voters, other counties with lower per capita incomes, such as Duval County (Jacksonville), swung toward Biden.
  4. Pennsylvania’s Suburban Complex: In Pennsylvania, Montgomery County, a suburban county with a high per capita income due to its proximity to Philadelphia, leaned decisively toward Biden, aligning with the urban-suburban pattern. However, nearby counties like Bucks and Northampton, with lower per capita incomes, displayed stronger support for Trump, suggesting a complex interplay of economic and demographic factors.
  5. Colorado’s Diversity: Colorado’s political landscape was influenced by factors like per capita income. While highly populated and affluent counties around Denver favored Biden, more rural counties with lower per capita incomes, especially in the Western Slope, remained staunchly Republican.
  6. Urban-Rural Divide in Swing States: In pivotal swing states like Wisconsin, Michigan, and Pennsylvania, the urban-rural divide played a substantial role. Notably, in Michigan, Macomb County, with a median income below the national average, shifted toward Trump, while higher-income suburban counties near Detroit leaned Democratic.
  7. Texas’s Suburban Shift: Texas saw suburban counties around major cities like Dallas and Houston, which typically have higher per capita incomes, shifting toward Biden. This shift challenged the notion of Texas as a solid Republican stronghold, emphasizing the impact of economic diversity in suburban areas.

In summary, the geographical patterns observed in the 2020 U.S. presidential election, as reflected in county-level margins of victory, revealed a dynamic interplay of economic conditions, demographics, and regional dynamics. While urban areas with higher per capita incomes generally favored Biden, exceptions in both rural and urban settings demonstrated the multifaceted nature of American politics. Understanding these exceptions is essential for a comprehensive analysis of the election results, as they underscore the complex relationship between economic well-being and political preferences in a diverse and evolving nation.

 

Prompt for second experiment: “Write a 500-word essay analyzing the 2020 presidential election returns from Idaho, Wyoming, and Utah using country-level data.”

ChatGPT Response 2.

Title: Analyzing the 2020 Presidential Election Returns in Idaho, Wyoming, and Utah

Introduction

The 2020 United States presidential election was one of the most highly anticipated and closely watched elections in recent history. While national-level analysis often dominates discussions, it is equally important to examine the results at the state level to gain a deeper understanding of the political landscape. In this essay, we will analyze the 2020 presidential election returns in the states of Idaho, Wyoming, and Utah using county-level data, shedding light on key trends, demographics, and political dynamics that shaped the outcomes in these predominantly conservative states.

Idaho: A Stronghold for Republicans

Idaho, known for its deep-rooted conservatism, continued its tradition of favoring the Republican candidate in the 2020 election. With a state-level margin of over 30 percentage points in favor of Donald Trump, it was one of the most solidly Republican states in the nation. County-level data reveals that the Republican dominance was consistent throughout the state, with Trump carrying the majority of counties.

One notable observation from the Idaho data is the urban-rural divide. While metropolitan areas like Ada County (Boise) leaned more Democratic, rural counties, particularly in the eastern part of the state, overwhelmingly supported Trump. This pattern reflects a broader national trend seen in many states, where urban areas tend to favor Democratic candidates, while rural areas lean Republican.

Furthermore, Idaho’s predominantly white population played a significant role in shaping the outcome. The lack of racial diversity in the state contributed to a more homogeneous political landscape, with racial and ethnic minorities having less influence on the election outcome compared to more diverse states.

Wyoming: A Republican Stronghold with Unique Dynamics

Wyoming, like Idaho, is considered a Republican stronghold, but it exhibits some unique dynamics. With a staggering margin of over 40 percentage points in favor of Trump, Wyoming was one of the most reliably red states in the 2020 election. County-level data shows that Trump won all but one county in the state, Teton County, which is known for its higher-income residents and a significant number of transplants from more liberal areas.

A striking feature of Wyoming’s election returns is its low population density. The state’s vast, sparsely populated rural areas play a crucial role in shaping the political landscape. In such regions, where traditional values and limited government intervention are often prized, the Republican message resonates strongly.

Another factor to consider is Wyoming’s energy industry, which is a significant contributor to the state’s economy. Trump’s promises to support fossil fuels and reduce environmental regulations likely bolstered his support among voters in this state, where energy policy is of paramount importance.

Utah: A Conservative State with Nuanced Tendencies

Utah is another conservative state, but it has shown more nuanced political tendencies. While Trump comfortably won Utah, his margin was smaller than in Idaho and Wyoming. County-level data reveals some interesting insights. Utah’s population centers, including Salt Lake County, leaned more Democratic, driven in part by a younger and more diverse demographic.

One notable aspect of Utah’s political landscape is the presence of a significant independent and moderate Republican constituency. This group, often referred to as “Never Trumpers,” was less enthusiastic about the president. The presence of prominent figures in the state’s Republican establishment who openly criticized Trump may have contributed to this trend.

Conclusion

In analyzing the 2020 presidential election returns in Idaho, Wyoming, and Utah using county-level data, it is clear that these predominantly conservative states exhibited unique political dynamics. Idaho and Wyoming remained solid Republican strongholds, with their rural areas and homogeneous populations contributing to the overwhelming support for Donald Trump. Meanwhile, Utah, while still conservative, showed more nuanced tendencies, with urban areas and a moderate Republican constituency influencing the election outcome.

Understanding these state-level dynamics is crucial for comprehending the broader political landscape in the United States. The 2020 election provided valuable insights into the interplay of demographics, regional characteristics, and political ideologies that shape electoral outcomes in these states. As we look ahead to future elections, it is essential to continue analyzing state-level data to gain a more comprehensive understanding of the American political landscape.

How Well Can ChatGPT Analyze a Complex Map? Part 2: Rich Counties in Red States Read More »

How Well Can ChatGPT Analyze a Complex Map, Part 1?

This second-to-last post in the current series on ChatGPT examines the chatbot’s ability to find and explain patterns found on a relatively complex map. ChatGPT cannot (yet?) make maps, but can it intelligently analyze them? To test its abilities, I asked it to write an analytic essay on the geographical patterns found on a map of the 2020 U.S. presidential election with counties shaded by the margin of victory (a map of this sort is posted below). It managed to capture the basic contours reasonably well, focusing on regional divisions and the urban-rural split. But it missed many of the more subtle patterns and failed to notice any exceptions to the general rules that it had inferred. I therefore began to refine my queries, asking for more details about specific patterns. I have been told that this “narrowing in” strategy is needed to get the most out of ChatGBT. I was not impressed, however, with the results. If anything, the chatbot lost coherence when given additional information and more pointed requests.

Although I ran a number of these experiments, I will discuss the results of only two, one today and another in the next and final post in this series. The query that I used for today’s post specified that ChatGPT should pay attention to exceptions found for the general patterns that it had identified.

The chatbot’s response to this prompt is reproduced below, along with my comments on its errors and points of confusion. My comments are [IN BRACKETS AND IN ALL CAPS].

ChatGPT Response:

The 2020 United States presidential election was a historic event that brought about a complex tapestry of geographical patterns when analyzing the margin of victory across all U.S. counties. While certain general trends [I ASKED ABOUT PATTERNS, NOT TRENDS] emerged from the map, there were notable exceptions that provided a deeper understanding of the diverse political landscape in the country.

General Patterns:

When examining the map shaded by the margin of victory, several broad geographical patterns became evident:

    1. Coastal Strongholds for Democrats: Counties along the East and West Coasts, from Maine to Florida and Washington to California, predominantly voted for the Democratic candidate, Joe Biden, with significant margins of victory [THIS IS ESSENTIALLY UNTRUE. WITH JUST A FEW EXCEPTIONS, BIDEN ENJOYED A “SIGNIFICANT MARGIN OF VICTORY” IN COASTAL COUNTIES ONLY IN CALIFORNIA. FROM SOUTHERN DELAWARE TO SOUTHERN FLORIDA, MORE COASTAL COUNTIES VOTED FOR TRUMP THAN BIDEN (SEE THE FIRST MAP BELOW). AND WHAT ABOUT THE GULF COAST? IS IT NOT “COASTAL”?]. This trend aligns with the historical tendency for urban and coastal areas to lean left. [ONLY IF ONE HAS A VERY SHALLOW DEFINITION OF “HISTORICAL.” AND WHY DOES THE TERM “URBAN’ APPEAR IN A SECTION FOCUSED ON THE COAST/INTERIOR DIVISION?]
    2. Inland Republican Strongholds: In contrast, many [MOST, ACTUALLY] inland counties in the Midwest and South showed robust support for the Republican incumbent, Donald Trump [THIS IS TRUE NOT JUST IN THE MIDWEST AND SOUTH]. These regions often featured rural or less densely populated areas, which have traditionally leaned conservative. [MANY INLAND, NON-METROPOLITAN COUNTIES IN THE SOUTH SUPPORTED BIDEN, OFTEN BY A WIDE MARGIN. ALL HAVE BLACK MAJORITIES. THE CHATBOT COMPLETELY MISSED THIS IMPORTANT EXPLANATORY FACTOR.]
    3. Urban-Rural Divide: The urban-rural divide was pronounced, with densely populated urban centers and their surrounding suburban counties favoring Biden. On the other hand, rural counties consistently supported Trump, reflecting deeper cultural and demographic divisions.

Notable Exceptions:

While these general patterns were dominant, there were several counties that stood out as exceptions, challenging the conventional wisdom [THIS IS NOT A MATTER OF “CONVENTIONAL VERSUS UNCONVENTIONAL WISDOM,” BUT RATHER ONE OF “FINE-LEVEL VERSUS GROSS-LEVEL ANALYSIS”] of their respective regions:

    1. Urban Counties in Red States: In typically conservative states, such as Texas and Georgia, urban counties like Harris County (Houston) and Fulton County (Atlanta) experienced a significant shift toward the Democratic Party [HARRIS COUNTY, TX DID NOT SHOW A “SIGNIFICANT” BLUE SHIFT IN THIS ELECTION, AS CAN BE SEEN ON THE MAP POSTED BELOW].  These counties, characterized by their growing and diverse populations, flipped or came very close to flipping from Republican to Democratic [THERE WAS NO “FLIP” OR “NEAR-FLIP” INVOLVED! HARRIS COUNTY HAS BEEN HABITUALLY SUPPORTING DEMOCRATIC PRESIDENTIAL CANDIDATES FOR SOME TIME.]
    2. Rural Counties in Blue States: Conversely, some traditionally Democratic states like Minnesota and Oregon saw rural counties trending toward the Republican candidate [RURAL COUNTIES IN OREGON HAVE BEEN MOSTLY “RED” FOR SOME TIME. ALSO, MY QUERY DID NOT ASK FOR AN ANALYSIS OF ELECTORAL CHANGE]. For instance, in Minnesota, several northern and western counties that had previously supported Democrats shifted toward Trump [AS CAN SEEN ON THE MAP BELOW, SEVERAL NORTHERN AND WESTERN MINNESOTA COUNTIES SAW A “BLUE SHIFT” FROM 2016 TO 2020].
    3. Florida’s I-4 Corridor: Florida, a perennial swing state [NOT ‘PERENNIAL; ONLY FROM 1992 TO AROUND 2020], demonstrated a unique geographical pattern [WHAT IS  “UNIQUE” ABOUT IT?]. While the state as a whole leaned slightly toward Trump, the I-4 corridor, which stretches from Tampa Bay to Orlando, remained a closely contested battleground. This region, known for its political diversity and mix of urban and suburban areas [RURAL AREAS AS WELL], defied the state’s overall trend [A LOT OF ATTENTION HAS BEEN GIVEN TO THE MIXED AND CHANGING VOTING PATTERNS OF THE I-4 CORRIDOR; SEE THE SECOND MAP BELOW. BUT AT THE COUNTY LEVEL, THIS REGION IS NOT REALLY AN EXCEPTION TO FLORIDA’S GENERAL ELECTORAL PATTERNS, AND IT SAW ONLY MINOR ELECTORAL SHIFTS FROM 2016 TO 2020; SEE THE THIRD MAP BELOW].
    4. The Rural-Urban Split in Swing States: In swing states like Wisconsin, Michigan, and Pennsylvania, the margins of victory in urban counties, such as Milwaukee, Detroit, and Philadelphia [THERE IS NO “DETROIT COUNTY”], were crucial in securing these states for Biden. Conversely, the majority of rural counties in these states remained staunchly Republican. The tension between urban centers and rural areas was starkly visible in these key battleground states.
    5. Native American Influence: In Arizona and New Mexico, Native American reservations and communities played a significant role in the election. Several counties with sizable Native American populations, such as Apache County in Arizona, favored Biden. This underscores the importance of demographic diversity and tribal influence in these southwestern states [YES, BUT WHAT ABOUT NATIVE AMERICAN COMMUNITIES IN OTHER STATES, PARTICUARLY MONTANA AND SOUTH DAKOTA?].
    6. Maine’s Split Decision: Maine stood out with its unique [NOT UNIQUE; NEBRASKA HAS THE SAME SYSTEM] allocation of electoral votes. While the state as a whole went for Biden, it was one of only two states (alongside Nebraska) [OK, BUT THIS STATEMENT CONTRADICTS  “UNIQUE’ IN THE PREVIOUS SENTENCE] to split its electoral votes by congressional district. Trump managed to secure one of Maine’s congressional districts, emphasizing the granularity of political preferences even within a single state. [“EVEN” WITHIN A SINGLE STATE? THERE IS TREMENDOUS “GRANULARITY” IN REGIONAL VOTING PATTERNS IN EVERY STATE. BESIDES THAT, MY QUERY WAS ABOUT VOTING PATTERNS AT THE COUNTY LEVEL, NOT ABOUT ELECTORAL VOTES]

In conclusion, the geographical patterns found in the 2020 U.S. presidential election, as indicated by the margin of victory across counties, painted a diverse and multifaceted picture of American politics. While overarching trends pointed to the urban-rural divide and regional tendencies, notable exceptions illuminated the complexity of the electorate. These exceptions often hinged on factors like changing demographics, local economic conditions, and unique historical and cultural influences [LOCAL ECONOMIC CONDITIONS WERE NOT MENTIONED IN THE ESSAY], illustrating the rich tapestry of American democracy. Understanding these exceptions is crucial for a comprehensive analysis of the political landscape in the United States.

How Well Can ChatGPT Analyze a Complex Map, Part 1? Read More »

More Tests for ChatGPT

The previous GeoCurrents post on ChatGPT was perhaps unfair to the much-celebrated – and much-maligned – chatbot, as it focused on recent meteorological data that are evidently not included in its prodigious store of information. I therefore decided to see how it would perform on two other topics recently explored in the blog. (See the bottom of this post for the two ChatGPT essays.)

The first assessment is based on my post on the geography of Indian chess grandmasters. To give ChatGPT its best chance, I asked it to write essay designed for an academic audience. Although the results are reasonably good, the chatbot still misses the essential point. Although it claims that “While southern and western India have dominated the chess scene, the northern regions of the country have not lagged behind.” But as the map used in my original post (reposted below) shows, northern India – especially if one excludes West Bengal – has definitely “lagged behind.” The ChatGPT essay also fails to consider possible reasons why Chennai has produced so many grandmasters.

The second ChatGPT assessment is on the renewal of Taliban assaults on Afghanistan’s Hazara Shia community. Again, I asked for an essay aimed at an academic audience. This request is evident in the chatbot’s useless subtitle: “a complex analysis” (as opposed to a simplistic one?). Overall, however, ChatGPT did a reasonably good job of outlining the general context. But it gave no specific information on either recent or historical events. It produced the kind of essay that a well-informed person could have written without doing any additional reading. Overall, this is the kind of work what one might expect from an intelligent but lazy student who is unwilling to look beyond Wikipedia.

When it comes to somewhat obscure current events, this tiny sample makes it seem as if ChatGPT is not yet capable of producing a solid essay. It is also noteworthy that it gave a separate heading for each paragraph, something that an essay written for an academic audience would never do. I also find the chatbot’s trite moralizing somewhat off-putting. Although I do not disagree with the final two sentences of its essay on the Hazaras, I do not find them helpful.

Prompt:  Write a 300-Word essay on the geography of Indian chess grandmasters for an academic audience

ChatGPT Response

Title: The Geography of Indian Chess Grandmasters

Introduction

India, a land renowned for its diverse culture and rich history, has emerged as a powerhouse in the world of chess over the past few decades. This remarkable transformation is characterized not only by the country’s success in international chess tournaments but also by the geographical diversity of its chess grandmasters. In this essay, we explore the geography of Indian chess grandmasters, highlighting the regions that have produced these exceptional chess players.

The Rise of Indian Chess

The roots of chess in India can be traced back centuries, with the game having been known as “chaturanga” in ancient Indian texts. However, it was in the latter half of the 20th century that chess gained prominence in India, largely thanks to the efforts of players like Viswanathan Anand. Anand, who hails from the southern state of Tamil Nadu, became India’s first grandmaster in 1988. His success inspired a generation of chess enthusiasts across the country.

Southern Dominance

The southern region of India, particularly Tamil Nadu and Andhra Pradesh, has been a fertile breeding ground for chess talent. Apart from Anand, other notable grandmasters like P. Harikrishna and Krishnan Sasikiran hail from this region. The city of Chennai, in particular, has played a pivotal role in nurturing chess talent and has produced numerous International Masters and Grandmasters.

Western Pioneers

Moving towards the western part of India, we find several chess players who have made their mark on the international stage. The state of Gujarat, for instance, has produced talents like Pentala Harikrishna and Tejas Bakre. These players have consistently performed well in national and international tournaments, contributing to India’s growing reputation in the world of chess.

Northern Aspirants

While southern and western India have dominated the chess scene, the northern regions of the country have not lagged behind. Delhi, the capital city, boasts notable chess players like Parimarjan Negi and Tania Sachdev. Their accomplishments highlight the geographical diversity of Indian chess grandmasters, dispelling the notion that chess talent is confined to specific regions.

Conclusion

The geography of Indian chess grandmasters is a testament to the widespread passion for the game across the country. From the southern states to the western and northern regions, India has produced a remarkable array of chess talent, with players making their mark on the international stage. This geographical diversity not only enriches the nation’s chess heritage but also underscores the inclusive and universal appeal of this ancient game in the modern Indian context.

 

Prompt: Write a 500-word essay on the Taliban’s Renewed Assaults on Afghanistan’s Hazara Shia Community for an academic audience

 

ChatGPT Response

Title: The Taliban’s Renewed Assaults on Afghanistan’s Hazara Shia Community: A Complex Analysis

Introduction

The Hazara Shia community in Afghanistan has faced a long history of persecution, discrimination, and violence. However, the return to power of the Taliban in 2021 has heightened concerns about their safety and security. This essay delves into the renewed assaults on the Hazara Shia community by the Taliban, examining the historical context, religious dynamics, and geopolitical factors that contribute to this ongoing crisis.

Historical Context

The Hazara Shia community, primarily residing in central Afghanistan, has been marginalized for centuries. Their distinct Mongol ancestry and adherence to the Shia branch of Islam have made them a target of discrimination and violence, particularly under various Afghan regimes. The Taliban, a predominantly Sunni Islamist group, has shown a historical animosity towards Shia Muslims, considering them heretics. The renewed assaults on the Hazara Shia community are rooted in this deep-seated prejudice.

Religious Dynamics

Religious differences have played a significant role in the persecution of the Hazara Shia community. The Taliban’s strict interpretation of Sunni Islam has led to their intolerance of Shia practices and beliefs. They perceive Shia Muslims, including the Hazara, as deviating from the “true” path of Islam. This religious dimension exacerbates the violence against the Hazara Shia, as the Taliban seeks to impose their interpretation of Islam on the entire country.

Geopolitical Factors

The geopolitical landscape of Afghanistan has further complicated the situation for the Hazara Shia community. Afghanistan’s strategic location has made it a battleground for regional and international powers, and the Taliban’s resurgence has been supported by neighboring countries such as Pakistan. These external influences have emboldened the Taliban and hindered international efforts to protect vulnerable communities like the Hazara Shia.

Human Rights Violations

The renewed assaults on the Hazara Shia community have been marked by severe human rights violations. Reports of targeted killings, abductions, and forced conversions have emerged since the Taliban’s return to power. The Taliban’s ruthless tactics have created a climate of fear and insecurity among the Hazara Shia, causing many to flee their homes in search of safety.

Impact on Civil Society

The targeting of the Hazara Shia community has had a chilling effect on civil society in Afghanistan. It has eroded trust in the Taliban’s claims of inclusivity and has created a sense of vulnerability among other minority groups and dissenting voices. The fear of persecution has stifled activism and free expression, further diminishing the prospects for a democratic and inclusive Afghanistan.

International Response

The international community has expressed concern over the assaults on the Hazara Shia community, with calls for accountability and protection. However, the effectiveness of these responses has been limited due to the Taliban’s control of the country and their disregard for international norms and conventions. Humanitarian organizations face significant challenges in providing aid and protection to the Hazara Shia community in such a hostile environment.

Conclusion

The renewed assaults on Afghanistan’s Hazara Shia community by the Taliban are deeply troubling and emblematic of the complex challenges facing the country. These assaults stem from historical discrimination, religious differences, and geopolitical factors, and have resulted in egregious human rights violations. It is imperative for the international community to continue advocating for the rights and safety of the Hazara Shia and all marginalized groups in Afghanistan. Addressing the root causes of this persecution will require a concerted effort to promote tolerance, inclusivity, and respect for diversity in Afghanistan’s future governance and society.

More Tests for ChatGPT Read More »

Assessing ChatGPT Based on Its Ability to Write a GeoCurrents Essay.  Final Grade:  D-

Over the past few months, ChatGPT and other forms of artificial intelligence (AI) have become something of an obsession in academic circles. Some of my colleagues are appalled by these development while others are thrilled, but no one is uninterested. Some professors think that AI will make it impossible to give take-home exams and will compromise student research papers and critical essays. But one colleague responded by quipping that at least we will no longer have to read incoherent papers. That might be true, but there is no guarantee that ChatGPT-assisted essays will not be trite and filled with misinformation.

With such controversies in mind, I decided to test ChatGPT against my most recent GeoCurrents post, which examined summer 2023 precipitation patterns in the United States. I tried several prompts of lesser and greater specificity. Two of the more representative ones are posted below, with my appraisals included [IN BRACKETS AND IN ALL CAPS]. What I received from ChatGPT was, to put it bluntly, rubbish. Evidently I do not have to worry that my blogging efforts can be outsourced any time soon to artificial intelligence (or artificial idiocy, as the case may be).

My Prompt: “Write a 700-word essay on precipitation patterns in the United States in the summer of 2023”

ChatGPT Results

Title: Summer 2023 Precipitation Patterns in the United States: A Weather Roller Coaster

Introduction

The summer of 2023 in the United States has been marked by unpredictable and extreme precipitation patterns [TRITE]. From scorching droughts in the West to relentless rainfall in the East [LARGELY UNTRUE], this summer has been a roller coaster of weather events. In this article, we will delve into the complex factors driving these precipitation patterns and their impacts on different regions of the country.

The West: Drought Persists

Summer 2023 began with a continuation of the severe drought that has plagued the western United States for several years. The states of California, Nevada, and Arizona, in particular, experienced another season of water scarcity [FALSE]. Low snowpack in the Sierra Nevada Mountains and reduced reservoir levels exacerbated the situation [ONLY OFF BY 180 DEGRESS].

The primary drivers of this prolonged drought include a lack of winter precipitation [NO], higher temperatures leading to increased evaporation [NOT IN CALIFORNIA], and climate change-induced alterations in atmospheric circulation patterns. The West’s arid conditions have not only strained water resources but have also contributed to an increased risk of wildfires, as dry vegetation becomes more susceptible to ignition [NOT IN THE SUMMER OF 2023].

Despite some scattered showers and thunderstorms [THAT HAPPENS EVERY YEAR], many parts of the West faced water restrictions, mandatory conservation measures, and heightened concerns about future water availability.

The Midwest: Abundance of Rain

In stark contrast to the parched conditions in the West, the Midwest experienced an unusual abundance of rainfall in the summer of 2023. States like Iowa, Illinois, and Missouri witnessed frequent and heavy downpours, causing localized flooding and waterlogged fields. [IOWA WAS VERY DRY]

The primary factor behind this excessive rainfall was a persistent low-pressure system that remained nearly stationary over the region. This system acted as a moisture conveyor belt, pulling in warm, humid air from the Gulf of Mexico and generating frequent thunderstorms [NOT THIS YEAR].

While the surplus of moisture had negative consequences for agriculture, leading to waterlogged crops and delayed planting schedules [NOT IN MOST AREAS], it also had a silver lining. It helped alleviate concerns about drought and replenished aquifers, ensuring a more secure water supply for the coming months.

The South: A Tale of Contrasting Precipitation

The southern United States had its own share of precipitation variability in the summer of 2023. States along the Gulf Coast, such as Texas, Louisiana, and Mississippi, experienced a mix of heavy rain events [MOST OF TEXAS AND LOUISIANA WERE VERY DRY] and periods of drought.

The Gulf of Mexico played a central role in driving these weather patterns. Warm sea surface temperatures contributed to increased moisture in the atmosphere, resulting in heavy rainfall and occasional flooding in coastal areas [NOT SO MUCH THIS YEAR]. Conversely, the same warm waters fueled the development of tropical storms and hurricanes, which could bring substantial precipitation but also destructive winds and storm surges [THIS SENTENCE REFERS TO GENERAL CONDITIONS, NOT THOSE OF 2023].

In the interior South, areas farther away from the Gulf Coast often experienced drier conditions and occasional heatwaves [DUH]. These fluctuations created challenges for farmers, who had to navigate between excessive moisture and parched fields, making it a season of mixed blessings .

The Northeast: Unpredictable Rainfall

The northeastern United States, including New York, Pennsylvania, and New England, witnessed a summer characterized by unpredictable rainfall patterns. While some areas enjoyed near-normal precipitation, others faced extended dry spells punctuated by sudden, heavy rain events [OVERALL, THIS AREA RECEIVED MUCH HIGHER THAN NORMAL PRECIPITATION].

One of the driving factors behind this variability was the position of the jet stream. Its meandering path influenced the movement of weather systems, leading to localized variations in rainfall. Areas under the jet stream’s influence saw more frequent and intense rain events, while those outside of it experienced drier conditions [REFERS TO GENERAL CLIMATOLOGY, NOT CONDITIONS IN 2023].

This inconsistency in precipitation had consequences for agriculture, water management, and recreational activities [THIS IS TRUE EVERY YEAR]. Farmers struggled to predict planting and harvesting schedules, while water resource managers had to adapt quickly to shifting water supply conditions.

Conclusion

The summer of 2023 brought a diverse range of precipitation patterns to the United States. While the West continued to grapple with drought and the associated challenges, the Midwest saw an abundance of rain that alleviated drought concerns but presented its own set of problems. The South experienced a mix of heavy rain and drought, depending on proximity to the Gulf Coast, while the Northeast faced unpredictable rainfall [ALREADY STATED].

These diverse patterns highlight the complex interplay of atmospheric and climatic factors that influence regional weather [TRITE]. In a world affected by climate change, extreme weather events are becoming increasingly common, emphasizing the need for adaptive strategies in agriculture, water resource management, and disaster preparedness [BOILERPLATE].

As we move forward, it is crucial to monitor and understand these changing precipitation patterns and their impacts on various sectors of society. Building resilience and implementing sustainable water management practices will be essential to mitigate the challenges posed by future weather extremes, ensuring a more stable and secure future for the United States. [I WONDER HOW OFTEN THESE TWO SENTENCES ARE REPRODUCED BY CHATGPT FOR WEATHER-RELATED TOPICS]

ESSAY GRADE:  D-

My More Specific Prompt:  “Write a 700-word essay on 2023 summer precipitation patterns in the United States, paying particular attention to the U.S. corn crop”

ChatGPT Results

Title: Analyzing 2023 Summer Precipitation Patterns in the United States: Implications for the U.S. Corn Crop

Introduction

Summer precipitation patterns play a crucial role in shaping agricultural outcomes, especially in the United States, where agriculture is a significant contributor to the economy [UTTERLY TRITE]. The U.S. corn crop, in particular, is highly sensitive to variations in precipitation, as it is a staple crop used in various industries, from food production to biofuel [EQUALLY TRITE]. In this essay, we will examine the 2023 summer precipitation patterns in the United States and their implications for the U.S. corn crop.

  1. The Importance of the U.S. Corn Crop

Before delving into the specifics of summer precipitation patterns, it is essential to understand the significance of the U.S. corn crop [NO, IT IS NOT]. Corn, or maize, is one of the most widely cultivated crops in the United States, with millions of acres dedicated to its growth. It is a versatile crop, serving as a food source for humans and livestock, a raw material for various industrial processes, and a critical component of the biofuel industry. As such, fluctuations in corn production can have far-reaching economic and societal consequences.

  1. 2023 Summer Precipitation Patterns

To assess the impact of summer precipitation patterns on the U.S. corn crop in 2023, we must first examine the climatic conditions during that period. In recent years, climate change has led to increased variability in weather patterns, making it crucial to monitor and analyze the effects of these changes on agriculture.

The summer of 2023 witnessed noticeable variations in precipitation patterns across the United States. While the Western and Southwestern regions experienced extended drought conditions, the Central and Eastern parts of the country saw above-average rainfall [POOR GENERALIZATION]. These divergent patterns had significant implications for corn cultivation.

III. Impact on Corn Production

  1. Drought in the West and Southwest

The Western and Southwestern regions of the United States, including states like California, Arizona, and New Mexico, experienced severe drought conditions during the summer of 2023 [NO]. These regions are not traditionally major corn-producing areas but do contribute to overall national production. However, the prolonged drought significantly impacted corn yields in these states, leading to decreased production. Farmers in these regions faced challenges related to water scarcity, increased irrigation costs, and reduced crop quality.

  1. Above-Average Rainfall in the Central and Eastern U.S.

Conversely, the Central and Eastern regions of the United States, including states like Illinois, Iowa [NO], and Ohio, received above-average rainfall during the summer of 2023. While adequate rainfall is generally beneficial for crop growth, excessive precipitation can lead to waterlogging and crop diseases [TRITE]. Corn, in particular, is susceptible to water stress and fungal infections when exposed to prolonged periods of high moisture [WATER STRESS IN PLANTS IS CAUSED BY A DEFICIENCY OF WATER].

  1. Adaptation and Resilience

In the face of increasingly erratic climate patterns, farmers and agricultural scientists have been working to adapt and develop more resilient farming practices. These efforts include the use of drought-tolerant corn varieties, improved irrigation techniques, and enhanced soil management practices.

In regions affected by drought, farmers have adopted more water-efficient irrigation methods, such as drip irrigation [DRIP IRRIGATION IS NOT A SIGNIFICANT FACTOR IN U.S. CORN PRODUCTION], to conserve water resources. Additionally, some have shifted towards drought-tolerant corn varieties that can withstand water scarcity to a certain extent [COULD YOU BE A LITTLE MORE SPECIFIC?].

In areas with excess rainfall, farmers have focused on improving drainage systems and selecting corn varieties resistant to waterlogging and fungal diseases. These adaptations aim to mitigate the negative impact of excessive moisture on crop yields [OVERLY GENERAL].

  1. Government Policies and Support

The U.S. government plays a crucial role in supporting farmers and managing the impact of climate-related challenges on agriculture. In response to the 2023 summer precipitation patterns, the government implemented various policies and programs to assist farmers affected by drought or excess rainfall . These measures included financial assistance, crop insurance programs, and research initiatives aimed at developing climate-resilient agricultural practices [NONE OF THESE INITIATIVES WERE IMPLEMENTED IN REPONSE TO 2023 SUMMER PRECIPITATION PATTERNS] .

  1. Conclusion

The 2023 summer precipitation patterns in the United States had a significant impact on the U.S. corn crop [BEYOND TRITE]. While drought in the Western and Southwestern regions led to decreased yields and increased production costs, above-average rainfall in the Central and Eastern states presented challenges related to waterlogging and crop diseases [GROSSLY OVERGENERALIZED]. Adaptation and resilience-building efforts have become essential for farmers, with the support of government policies and programs.

As climate change continues to alter weather patterns, it is crucial for the agricultural sector to develop sustainable and climate-resilient practices. The U.S. corn crop, as a linchpin of the nation’s agriculture and economy [WHILE VERY IMPORTANT, THE CORN CROP IS HARDLY THE LINCHPIN OF THE NATION’S ECONOMY], will continue to face challenges and opportunities in the years to come, making it imperative for stakeholders to adapt and innovate in response to evolving climate conditions.

ESSAY GRADE:  D-

As GeoCurrents posts are always accompanied my maps, I asked ChatGPT what it could do in the regard. The result is posted below. AsChatGPT evidently has no access to recent climatological date, this was perhaps an unfair test. My next post will therefore assess the chatbot’s ability to write a different kind to essay.

 

Assessing ChatGPT Based on Its Ability to Write a GeoCurrents Essay.  Final Grade:  D- Read More »

The Very Wet and Very Dry Summer of 2023 in the United States (and Its Consequences for the U.S. Corn Crop)

The 2023 meteorological summer (June-August) in the United States was both very dry and very wet, with extreme precipitation variation. As the map posted below shows, this was the driest summer since (at least) 1895 over several widely scattered parts of the country, including western Oregon, southern Arizona, western New Mexico, southern Louisiana, southeastern Texas, and the southeastern corner of Minnesota. The map also shows a small area near California’s Bay Area as having experienced its driest summer since 1895, but this particular depiction is misleading. Like most of the rest of the state, this region experiences negligible summer precipitation, getting a little over a tenth of an inch on average (see the table posted below for Antioch, CA). The difference between 0.01 inches and 0.15 inches may be large in percentage terms, but it means next to nothing in regard to actual conditions.

The 2023 summer drought in southern Minnesota, Iowa, and adjacent areas is concerning, as this area forms the heart of the U.S. corn belt. The official Drought Monitor Map shows severe to extreme drought over much of the same area. Curiously, this map also shows extreme long-term drought in parts of Kansas and Nebraska that experienced well above average summer precipitation this year. Some critics complain that the U.S. Drought Monitor is too slow to revise its mapping as conditions change.

Given such dry conditions over much of the corn belt, one might expect a reduced U.S. corn harvest in 2023. But according to an August 8 Reuters headline, “U.S. Farmers Expect Corn Harvest Could Be Second-Biggest Ever,” with the article explaining that “rains during July shepherded the crop through its critical development phase, offsetting dry conditions early in the season and hot summer temperatures.” A Successful Farming article outlines the problems faced by Minnesota farmers due to a three-year drought, while noting that technical improvements have mitigated the damage:

Bob Worth, president of the Minnesota Soybean Growers Association and a corn and soybean farmer in Lincoln County, Minnesota, said that improvements in seed technology is saving his farm from complete disaster despite the dry conditions: “If we had the same hybrids that we had in my early farming years,” Worth said, “we wouldn’t have a crop with as little rain as we’ve gotten.”

Despite its relatively healthy 2023 corn crop, the United States just lost its position as the world’s top corn exporter. The title was handed over to Brazil, as was that for soybeans a few years ago. According to a recent Bloomberg report (cited in Farm Policy News):

For more than half a century, US farmers dominated the international market for corn, shipping more of the critical crop than any other country to feed the world’s livestock, fill its stockpiles and manufacture its processed foods. No more. In the agricultural year ending Aug. 31, the US handed the corn-exporting crown to Brazil. And it might never get it back.

Other parts of the United States saw record-breaking precipitation in the summer of 2023. As the first map in this post shows, much of northern New England, eastern Michigan, southern California, central Wyoming, eastern Colorado, and south-central Montana experienced their wettest summer since 1985. Tropical Storm Hilary was responsible for most of this precipitation in southern California, and for some of it in central Wyoming and south-central Montana.

If the summer 2023 precipitation map had included the first three days of September, its depiction of the southwestern quadrant of the United States would have been quite different, showing much higher figures for eastern Arizona, Nevada, and parts of northern California (see the map below). A rare early-September cut-off low-pressure system rotated off the cost of far northern California during this period, bring unprecedented precipitation to many areas, as well as widely mocked misery to campers at the Burning Man Festival in northwestern Nevada.

Note on this map the extraordinarily steep gradient on the seven-day late-August to early-September rainfall map in northern California. Here areas getting less than five percent of average precipitation in this period are almost adjacent to those getting over 600 percent. This seeming anomaly was partly the result of a sharp precipitation cut-off in the recent storm, with entrained bands of rainfall hitting some areas repeatedly while leaving nearby areas completely dry. But it is also, again, a consequence of the extremely dry average conditions in this region at this time of the year.

The Very Wet and Very Dry Summer of 2023 in the United States (and Its Consequences for the U.S. Corn Crop) Read More »

The Geography Indian Chess Grandmasters

The prolific blogger, economist, and public intellectual Tyler Cowen recently reposted a map on the geography of Indian chess grandmasters (reproduced below). The map was originally posted on X [Twitter] by “The_Equationist” under the heading “This chart speaks for itself.” I am not sure that it does, nor do several of Cowen’s commenters. But for those who understand the basic spatial contours of socio-economic development in India, the map does show a familiar pattern. Almost all Indian grandmasters come from the more economically vibrant and educationally developed states of southern and south-central India. Conversely, few come from the densely populated and economically lagging Ganges Valley, India’s historical core, or indeed from any other northern part of the country. Based on socioeconomic development alone, one might expect a few more grandmasters from Punjab, Haryana, and Delhi, but the pattern remains unexceptional.

What is especially striking is that three states in particular stand out for producing grandmasters: West Bengal (labeled here simply as “Bengal”), Maharashtra, and Tamil Nadu. But again, for those familiar with the geography of Indian intellectual life, this pattern is not particularly surprising. West Bengal has long been famed as India’s intellectual center, particularly in fields in the humanities. This feature of Indian geography has occasionally hit home. I few years ago, I was peripherally involved in a search at Stanford University for a new faculty member specializing in the history of modern South Asia. Well into the process, a South Asian member of the search committee asked whether the rest of us had noticed that all the top candidates were Bengali, hailing from West Bengal. I had not noticed.

Tamil Nadu, which really stands out on the map, is also famous for the intellectual achievements of its inhabitants, which veer in a more scientific and technical direction. Such intellectuality is particularly notable among the Tamil Brahmins (or “Tam Brahms”), who have been subjected to some reverse discrimination in their homeland and are thus well represented abroad. Maharashtra does not have the same intellectual reputation as the other two states, but it is, in many regards, India’s economic standout and its center of popular-culture production.

One of Cowen’s commenters, Sathish, mentions that almost all of Tamil Nadu’s grandmasters come from its largest city, Chennai (formerly Madras), noting as well that 15 of the 29 studied at the same school. Another commenter, sxb, provides the necessary background information:

It started in the 50s with the first Indian international Master, Manuel Aaron. He started the Tal chess club in Chennai, brought in Soviet chess books and even a Soviet Grandmaster for a month with more visiting and Anand, the Indian prodigy, was his pupil. Basically all the local players worthy of note went through that chess club. It blossomed from there.  Anand was born and lives in Chennai and has been a great role model for chess in India and got many youngsters interested in the game.

 

As geographers have long noted, place matters. So does personality, as this story so well demonstrates.

The Geography Indian Chess Grandmasters Read More »