2023 Representation in NY
I had never seen this before, contrasting the different layers of government and which party the elected officials are in, so I thought I would make this map up.
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I had never seen this before, contrasting the different layers of government and which party the elected officials are in, so I thought I would make this map up.
California may require homeowners to replace their broken air conditioning units with heat pumps or more efficient HVAC systems beginning in 2026, draft rules released last month by state energy regulators show. Encouraging the adoption of heat pumps, which both cool and heat homes using electricity, is key to the state’s carbon neutrality goals. The California Energy Commission aims to quadruple the number of homes with heat pumps to 6 million by 2030. If the 600-page draft code is approved next year, California would be the first state to require broken A/C units be replaced with heat pumps or more efficient systems. Environmental groups are encouraged, saying the regulation would cut emissions and save homeowners money.
I have been experimenting with using R to calculate ADP and socialist votes for various political districts. After doing some reading up, it turns out the fastest and easier way to calculate such things is to use VTD centroids and spatially join them against the new districts.
With R, it turns out that can be done with like 10 lines of code to make some pretty nice maps and data, although I did the final map layout in QGIS. Overall, with the enacted Assembly districts, 1/3rd of them voted for Bernie Sanders, mostly upstate. This code took less then 10 seconds to run on my old laptop.
library(tidyverse)
library(tigris)
library(sf)vt20 <- read_csv('2020vote_vtd.csv')
vt20$GEOID <- as.character(vt20$GEOID)vtd <- voting_districts('ny', cb=T) %>%
inner_join(vt20, by=c('GEOID20'='GEOID')) %>%
st_transform('epsg:3857')a22 <- read_sf('/home/andy/Documents/GIS.Data/2022 Districts/NY Assembly 2022.gpkg') %>% st_transform('epsg:3857')
join <- vtd %>% st_centroid() %>%
st_join(a22)join %>% st_drop_geometry() %>%
group_by(DISTRICT) %>%
summarise(socialist = (sum(SANDERS)/sum(SANDERS,CLINTON))*100) %>%
inner_join(a22, by=c('DISTRICT')) %>%
write_sf('/tmp/socialassm.gpkg')
2022 AD | Sanders Vote |
1 | 42.8 |
2 | 47.1 |
3 | 50.4 |
4 | 47.5 |
5 | 53.1 |
6 | 37.6 |
7 | 50.2 |
8 | 46.2 |
9 | 48.6 |
10 | 37.5 |
11 | 39.2 |
12 | 44.7 |
13 | 37.6 |
14 | 42.1 |
15 | 38.4 |
16 | 32.0 |
17 | 45.7 |
18 | 28.6 |
19 | 44.9 |
20 | 39.0 |
21 | 38.3 |
22 | 35.4 |
23 | 41.9 |
24 | 35.2 |
25 | 41.8 |
26 | 40.6 |
27 | 41.0 |
28 | 43.5 |
29 | 25.9 |
30 | 47.1 |
31 | 27.6 |
32 | 25.7 |
33 | 28.7 |
34 | 47.1 |
35 | 32.3 |
36 | 49.5 |
37 | 50.1 |
38 | 45.3 |
39 | 41.3 |
40 | 38.6 |
41 | 37.3 |
42 | 35.6 |
43 | 35.6 |
44 | 45.9 |
45 | 47.6 |
46 | 48.0 |
47 | 51.9 |
48 | 40.7 |
49 | 51.9 |
50 | 57.1 |
51 | 48.5 |
52 | 37.7 |
53 | 50.3 |
54 | 37.3 |
55 | 30.4 |
56 | 42.4 |
57 | 44.5 |
58 | 22.3 |
59 | 32.9 |
60 | 27.0 |
61 | 39.2 |
62 | 53.6 |
63 | 46.0 |
64 | 52.4 |
65 | 40.9 |
66 | 35.7 |
67 | 27.1 |
68 | 35.9 |
69 | 33.6 |
70 | 38.4 |
71 | 39.4 |
72 | 35.7 |
73 | 23.4 |
74 | 37.6 |
75 | 31.9 |
76 | 28.8 |
77 | 26.7 |
78 | 32.3 |
79 | 28.6 |
80 | 35.4 |
81 | 37.0 |
82 | 34.0 |
83 | 23.0 |
84 | 30.5 |
85 | 27.8 |
86 | 26.8 |
87 | 31.3 |
88 | 28.9 |
89 | 30.0 |
90 | 36.5 |
91 | 31.0 |
92 | 33.2 |
93 | 32.2 |
94 | 45.7 |
95 | 40.6 |
96 | 40.7 |
97 | 36.0 |
98 | 47.1 |
99 | 49.6 |
100 | 49.3 |
101 | 56.0 |
102 | 60.2 |
103 | 62.3 |
104 | 47.9 |
105 | 50.3 |
106 | 52.8 |
107 | 55.5 |
108 | 55.9 |
109 | 52.6 |
110 | 49.1 |
111 | 55.2 |
112 | 54.0 |
113 | 57.9 |
114 | 64.0 |
115 | 71.4 |
116 | 54.2 |
117 | 58.4 |
118 | 58.1 |
119 | 51.9 |
120 | 55.7 |
121 | 58.2 |
122 | 56.9 |
123 | 57.3 |
124 | 53.8 |
125 | 61.8 |
126 | 48.9 |
127 | 46.6 |
128 | 42.5 |
129 | 50.8 |
130 | 52.9 |
131 | 52.5 |
132 | 57.2 |
133 | 57.4 |
134 | 50.1 |
135 | 46.0 |
136 | 47.9 |
137 | 40.2 |
138 | 53.8 |
139 | 55.2 |
140 | 53.6 |
141 | 33.6 |
142 | 54.7 |
143 | 50.4 |
144 | 55.1 |
145 | 51.3 |
146 | 46.5 |
147 | 58.3 |
148 | 58.2 |
149 | 54.2 |
150 | 53.5 |
While it's unusual to find a deep red, highly educated county, blue counties tend to range the span from highly educated like Falls Church City, Virginia (79% college graduates) to very few college graduates like Taliaferro County, Georgia (5% college graduates).
R Code: https://github.com/AndyArthur/r_maps_and_graphs/blob/main/education_vs_2020_pres.R
Somebody told me to go to hell. But with gas prices these days, who can afford such a long trip?