I finished my crosswalk of 2016 to 2020 precincts over the holidays (apologies for posting a Xmas-inspired but not colorblind-safe version initially). A few things jumped out & led me down a rabbit hole of demographics & light plagiarism. Let's go! #ElectionTwitter 🧵1/?
When I initially did the municipal-level shifts map several weeks ago, some cities in the Eastern half of the state had a conspicuous red shift (Lebanon, Reading, Lancaster, etc.), & I was looking forward to diving deeper into those +Trump shifts... 2/?
These happen to all be cities with large Latino populations. Then @dawncai624 & @fordfess wrote a
great piece in the NYT looking at these trends in large cities across the US, so I'm going to look at our mid-size cities the same way... 3/?
nytimes.com/interactive/20…
One disclaimer before I start posting maps: This data is not suggesting that Latino voters failed to 'come though' for Biden or any nonsense like that. Mainly because it isn't the responsibility of any demographic group to come through for any candidate or party... 4/?
...and second because even with this shift towards Trump, Latino voters still backed Biden in a big way. The NYT piece linked in this thread does a good job of addressing this as well. Ultimately, Dems need to do a much better job in this arena... 5/?
I'm going to start with #Lancaster. The legend in the map should explain all of the symbology, and the divide here is striking. A significant red shift in the more heavily Latino parts of the city, while the north side saw modest shifts away from Trump... 6/?
Going up Rte222, and arriving in #Reading next, you see the same pattern, strong blue shifts in the first ring suburbs, but red shift in the city, with the largest shifts towards Trump in areas where at least 40% of the population is Latino... 7/?
Next is #Allentown, & we get #Bethlehem as a bonus in this map extent. This one is super-interesting bc we had remarked weeks ago it was curious Bethlehem had not shifted red at the municipal-level. Yet we still see that same red shift in wards w/large Latino populations... 8/?
Jumping back west to #Lebanon, the red shift is in almost all precincts, but it is a much larger shift in the precincts on the north side that are home to larger Latino populations. Suburbs are almost all shifting (modestly) blue... 9/?
And a quick look at #Hazelton as well, in Luzerne County. Again an unmistakable and consistent pattern, showing that the trends that @dawncai624 & @fordfess discussed in the NYT piece are present in these smaller PA cities with significant Latino populations as well... 10/?
Finally, even though the NYT already did Philly, I'll include it in my format here as well for comparison. While there were red shifts in much of the city, the largest shifts were concentrated in wards with large Latino populations... 11/?
In addition to matching national trends re: election results in larger cities, this also matches a lot of polling in 2020 that indicated Trump was making gains with Latino voters, so this isn't terribly surprising, but the precision of the correlation is still striking... 12/?
And it was important to look at in PA specifically, bc Latino voters are not a monolith & the demographics of Latinos in PA are quite different from many other states (predominantly of Puerto Rican and Dominican descent, relatively few of Central/South Am. ancestry)... 13/?
Finally a few data footnotes. Precincts from 2016 & 2020 were compared and I merged precincts/data if necessary to arrive at common 2016/2020 geographies. Statewide I only had to merge precincts in 80 instances, and in 57 of those it was just merging 2 precincts... 14/?
Demographics shaded in yellow is from the 2019 5-year ACS data at the Census Tract level. This doesn't perfectly align with the precincts (which is why you see some precincts that are only partially yellow), but it's the most accurate way to present the overlays... 15/?
I will also point out that not all of the 2020 precinct data is final/certified. I compiled this from the 67 county election offices, so it's a mix of initial and final data, but the ultimate discrepancies aren't likely to have any real impact on the analysis here... 16/?
I'm going to leave the thread here for now, but may come back and present some other demographic overlays later on, and will keep it going on this same thread if I do. 17/? 🧵

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