Data Center Public Sentiment

show code

demogs = readRDS('verasightDemogs.rds')

sentiment_study1 = merge(read.csv('dataCenterSentiment_01.csv'), readRDS('verasightDemogs.rds'), by='vsid') %>% 
  filter(dataCenterSentiment != "") %>% 
  mutate(dataCenterSentiment = factor(dataCenterSentiment, labels=c('Strongly oppose', 'Somewhat oppose', 'Have no opinion', 'Somewhat favor', 'Strongly favor')),
         dataCenterDensity = factor(dataCenterDensity, labels=c('None','1-2','More than 2', "I'm not sure"))) %>% 
  mutate(dataCenterDensity_collapsed = factor(ifelse(dataCenterDensity == "I'm not sure", 1, dataCenterDensity), labels=c('None','1-2', 'More than 2')),
         priceForSupport_withoutStrongOpposerZeroes = ifelse(dataCenterSentiment == "Strongly oppose" & priceForSupport <1, NA, priceForSupport)) %>% 
  mutate(priceForSupport_withSupporters = ifelse(dataCenterSentiment == "Somewhat favor" | dataCenterSentiment =="Strongly favor", 0, priceForSupport),
         priceForSupport_withSupporters = ifelse(dataCenterSentiment == "Strongly oppose" & priceForSupport <1, NA, priceForSupport_withSupporters),
         priceForSupport_binned = factor(ifelse(priceForSupport_withoutStrongOpposerZeroes <1, "$0", 
                                         ifelse(priceForSupport_withoutStrongOpposerZeroes > 0 & priceForSupport_withoutStrongOpposerZeroes < 1001, "$1K or less",
                                                ifelse(priceForSupport_withoutStrongOpposerZeroes >1000 & priceForSupport_withoutStrongOpposerZeroes <10001, "More than $1K",
                                                       ifelse(priceForSupport_withoutStrongOpposerZeroes >10000 & priceForSupport_withoutStrongOpposerZeroes <50001, "More than $10K",
                                                              ifelse(priceForSupport_withoutStrongOpposerZeroes >50000 & priceForSupport_withoutStrongOpposerZeroes <100001, "More than $50K", 
                                                              ifelse(priceForSupport_withoutStrongOpposerZeroes >100000 & priceForSupport_withoutStrongOpposerZeroes <1000001, 'More than $100K',
                                                                     ifelse(priceForSupport_withoutStrongOpposerZeroes >1000000 & priceForSupport_withoutStrongOpposerZeroes <1000000001, 'More than $1M', "More than $1B"))))))), levels=c(NA, '$0','$1K or less','More than $1K', 'More than $10K','More than $50K','More than $100K','More than $1M','More than $1B')),
         reasonsTally = rowSums(pick("support_qualityOfLife", "support_localEconomy", "support_AIdev", "support_environment", "support_naturalResources") > 1, na.rm = TRUE)) %>% 
         mutate(reasonsTally_fct = factor(ifelse(reasonsTally <2, "1 or less", reasonsTally)))

We collected a total of 3234 responses. Note that responding to the “price for support” option was encouraged but not required, so some were left NA.

Overall sentiment to a local data center build

show code

nice_table(sentiment_study1 %>% group_by(dataCenterSentiment) %>% tally() %>% mutate(percent = n/sum(n)*100))

dataCenterSentiment	n	percent
Strongly oppose	1,448	44.77
Somewhat oppose	681	21.06
Have no opinion	513	15.86
Somewhat favor	463	14.32
Strongly favor	129	3.99

show code

overallSentiment_plot = sentiment_study1 %>% group_by(dataCenterSentiment) %>% tally() %>% mutate(percent = n/sum(n)*100) %>% mutate(dataCenterSentiment = fct_rev(dataCenterSentiment)) %>% 
  ggplot(aes(x = .5, y=percent, fill = dataCenterSentiment))+
  geom_bar(stat = "identity", color='white') +
  theme_void(base_size=15)+
  labs(fill="", x='') +
  theme(legend.position='top')+
  scale_fill_manual(values=c('#232D4B','#C8CBD2','#FDDA24','#F9DCBF','#E57200'))+
  coord_flip()

overallSentiment_plot

show code

ggsave(overallSentiment_plot, filename='overallSentiment_plot.png', dpi = 300)

Saving 7 x 5 in image

Price for support

Participants who did not support a local data center build were asked to register their required price to gain their support. In the following analyses, participants who supported a local data center build are logged as “$0” for this variable.

In addition, there were a decent amount of participants who reported strong opposition to a local data center build, but registered “$0” as the price for their support. I interpreted these to mean “there is no amount of money that would buy my support.” Accordingly, I set those data points to NA

Raw results

Raw results can’t be plotted bc the distribution is too big:

mean = 3.4662045^{304}
min = 0
max = 10^{308}
median = 5000

Somebody registered “$-1” and they strongly opposed a local data center build. Probably should be interpreted as “nothing will buy my support”? Set to NA

Cutoff: billion

174 participants registered price tags higher than $1 billion. Excluding them yields 2711 data points.

show code

nice_table(sentiment_study1 %>% filter(priceForSupport_withSupporters<1000000001) %>% summarise(mean = mean(priceForSupport_withSupporters/1000), sd = sd(priceForSupport_withSupporters)/1000, median=median(priceForSupport_withSupporters)/1000, min = min(priceForSupport_withSupporters), max = max(priceForSupport_withSupporters)/1000), title = "Payment required for support, in thousands. (minimum reported in raw)")
ggplot(subset(sentiment_study1,priceForSupport_withSupporters<1000000001 &!is.na(priceForSupport_withSupporters)), aes(x=priceForSupport_withSupporters/1000))+
  geom_density(fill="#00bfc4", alpha=0.4)+
  geom_vline(aes(xintercept=mean(priceForSupport_withSupporters)/1000),color="#00bfc4", linetype="dashed", linewidth=0.5)+
  labs(x="Payment required for support, in thousands.", title="Cutoff: $1 billion")+
  theme_classic()

Payment required for support, in thousands. (minimum reported in raw)
mean	sd	median	min	max
23,966.79	144,119.32	5.00	0.00	1,000,000.00

Cutoff: million

401 participants registered price tags higher than $1 million. Excluding them yields 2484 data points.

show code

nice_table(sentiment_study1 %>% filter(priceForSupport_withSupporters<1000001) %>% summarise(mean = mean(priceForSupport_withSupporters/1000), sd = sd(priceForSupport_withSupporters)/1000, median=median(priceForSupport_withSupporters)/1000, min = min(priceForSupport_withSupporters), max = max(priceForSupport_withSupporters)/1000), title = "Payment required for support, in thousands. (minimum reported in raw)")
ggplot(subset(sentiment_study1,priceForSupport_withSupporters<1000001 &!is.na(priceForSupport_withSupporters)), aes(x=priceForSupport_withSupporters/1000))+
  geom_density(fill="#00bfc4", alpha=0.4)+
  geom_vline(aes(xintercept=mean(priceForSupport_withSupporters)/1000),color="#00bfc4", linetype="dashed", linewidth=0.5)+
  labs(x="Payment required for support, in thousands.", title="Cutoff: $1 million")+
  theme_classic()

Payment required for support, in thousands. (minimum reported in raw)
mean	sd	median	min	max
109.96	281.67	3.00	0.00	1,000.00

further visualizations

Among non-supporters (opposers and no opinions):

show code

nice_table(sentiment_study1 %>% filter(dataCenterSentiment != "Somewhat favor" & dataCenterSentiment != "Strongly favor" &!is.na(priceForSupport_binned)) %>% group_by(priceForSupport_binned) %>% tally() %>% mutate(percent = n/sum(n)*100))
horizontalDotPlot = ggplot(sentiment_study1, aes(x = 1, y=priceForSupport_withoutStrongOpposerZeroes))+
  geom_count(position=position_jitter(), alpha=0.65, color='#E57200')+
  scale_y_continuous(breaks = c(10000, 50000, 100000, 250000, 500000, 1000000), limits=c(0,1000001))+
  coord_flip()+
  theme_classic(base_size=13)+
  theme(axis.title.y=element_blank(),
        axis.text.y=element_blank(),
        axis.ticks.y=element_blank(),
        axis.line.y = element_blank())

ggsave(horizontalDotPlot, filename='horizontalDotPlot.png', dpi = 300, width=8, height=2)

horizontalDotPlot
percentBinsTable = data.frame("price" = c('$1K or less', '$10K or less', '$50K or less', '$100K or less', '$1M or less'),
                                "percentage" = c(22.72, 50.63, 62.14, 69.28, 82.41)) %>% 
  mutate(price = factor(price, levels=c('$1M or less','$100K or less', '$50K or less', '$10K or less', '$1K or less')))

percentBins_plot =ggplot(percentBinsTable, aes(x = price, y=percentage, fill = price))+
  geom_bar(stat = "identity", position=position_dodge(), color='white') +
  scale_y_continuous(breaks=c(0,50,100), limits=c(0,100))+
  theme_classic(base_size=15)+
  labs(fill="", x='') +
  theme(legend.position='none')+
  scale_fill_manual(values=c('#E57200','#ec9049','#f1ac79','#f7c7a7','#F9DCBF'))+
  coord_flip()

ggsave(percentBins_plot, filename='percentBinsPlot.png', dpi = 300, width = 8, height = 6)

percentBins_plot

priceForSupport_binned	n	percent
$0	161	7.02
$1K or less	360	15.70
More than $1K	640	27.91
More than $10K	264	11.51
More than $50K	166	7.24
More than $100K	301	13.13
More than $1M	227	9.90
More than $1B	174	7.59

Sentiment by demographics

current local data center density

show code

nice_table(sentiment_study1 %>% group_by(dataCenterDensity_collapsed, dataCenterSentiment) %>% tally() %>% mutate(percent = n/sum(n)*100))

dataCenterDensity_collapsed	dataCenterSentiment	n	percent
None	Strongly oppose	1,311	45.44
None	Somewhat oppose	599	20.76
None	Have no opinion	472	16.36
None	Somewhat favor	399	13.83
None	Strongly favor	104	3.60
1-2	Strongly oppose	93	37.50
1-2	Somewhat oppose	61	24.60
1-2	Have no opinion	29	11.69
1-2	Somewhat favor	47	18.95
1-2	Strongly favor	18	7.26
More than 2	Strongly oppose	43	43.88
More than 2	Somewhat oppose	21	21.43
More than 2	Have no opinion	11	11.22
More than 2	Somewhat favor	16	16.33
More than 2	Strongly favor	7	7.14
	Strongly oppose	1	33.33
	Have no opinion	1	33.33
	Somewhat favor	1	33.33

show code

sentiment_study1 %>% group_by(dataCenterDensity_collapsed, dataCenterSentiment) %>% tally() %>% mutate(percent = n/sum(n)*100) %>% filter(!is.na(dataCenterDensity_collapsed)) %>% mutate(dataCenterSentiment = fct_rev(dataCenterSentiment)) %>% 
  ggplot(aes(x = .5, y=percent, fill = dataCenterSentiment))+
  geom_bar(stat = "identity", color='white') +
  theme_void(base_size=15)+
  labs(fill="", x='') +
  theme(legend.position='top')+
  scale_fill_manual(values=c('#232D4B','#C8CBD2','#FDDA24','#F9DCBF','#E57200'))+
  coord_flip()+
  facet_wrap(~dataCenterDensity_collapsed, ncol = 1)

political orientation

show code

nice_table(sentiment_study1 %>% group_by(pid_base, dataCenterSentiment) %>% tally() %>% mutate(percent = n/sum(n)*100))

pid_base	dataCenterSentiment	n	percent
Democrat	Strongly oppose	675	52.61
Democrat	Somewhat oppose	261	20.34
Democrat	Have no opinion	170	13.25
Democrat	Somewhat favor	143	11.15
Democrat	Strongly favor	34	2.65
Republican	Strongly oppose	299	34.25
Republican	Somewhat oppose	197	22.57
Republican	Have no opinion	145	16.61
Republican	Somewhat favor	179	20.50
Republican	Strongly favor	53	6.07
Independent	Strongly oppose	371	44.01
Independent	Somewhat oppose	188	22.30
Independent	Have no opinion	142	16.84
Independent	Somewhat favor	113	13.40
Independent	Strongly favor	29	3.44
Other or none	Strongly oppose	99	45.00
Other or none	Somewhat oppose	34	15.45
Other or none	Have no opinion	52	23.64
Other or none	Somewhat favor	23	10.45
Other or none	Strongly favor	12	5.45
	Strongly oppose	4	26.67
	Somewhat oppose	1	6.67
	Have no opinion	4	26.67
	Somewhat favor	5	33.33
	Strongly favor	1	6.67

show code

sentimentByPoliticalID_plot = sentiment_study1 %>% group_by(pid_base, dataCenterSentiment) %>% tally() %>% mutate(percent = n/sum(n)*100) %>% filter(pid_base %in% c('Democrat', 'Republican','Independent')) %>% mutate(dataCenterSentiment = fct_rev(dataCenterSentiment)) %>% 
  ggplot(aes(x = .5, y=percent, fill = dataCenterSentiment))+
  geom_bar(stat = "identity", color='white') +
  theme_void(base_size=15)+
  labs(fill="", x='') +
  theme(legend.position='top')+
  scale_fill_manual(values=c('#232D4B','#C8CBD2','#FDDA24','#F9DCBF','#E57200'))+
  coord_flip()+
  facet_wrap(~pid_base, ncol = 1)

ggsave(sentimentByPoliticalID_plot, filename='sentimentByPoliticalID_plot.png', dpi = 300, width=12, height = 12)
  
sentimentByPoliticalID_plot

income

show code

nice_table(sentiment_study1 %>% group_by(income, dataCenterSentiment) %>% tally() %>% mutate(percent = n/sum(n)*100))

income	dataCenterSentiment	n	percent
Less than $15,000	Strongly oppose	138	43.40
Less than $15,000	Somewhat oppose	60	18.87
Less than $15,000	Have no opinion	76	23.90
Less than $15,000	Somewhat favor	29	9.12
Less than $15,000	Strongly favor	15	4.72
$15,000 to under $50,000	Strongly oppose	401	44.07
$15,000 to under $50,000	Somewhat oppose	183	20.11
$15,000 to under $50,000	Have no opinion	164	18.02
$15,000 to under $50,000	Somewhat favor	130	14.29
$15,000 to under $50,000	Strongly favor	32	3.52
$50,000 to under $75,000	Strongly oppose	271	44.57
$50,000 to under $75,000	Somewhat oppose	124	20.39
$50,000 to under $75,000	Have no opinion	91	14.97
$50,000 to under $75,000	Somewhat favor	99	16.28
$50,000 to under $75,000	Strongly favor	23	3.78
$75,000 to under $100,000	Strongly oppose	241	48.20
$75,000 to under $100,000	Somewhat oppose	106	21.20
$75,000 to under $100,000	Have no opinion	61	12.20
$75,000 to under $100,000	Somewhat favor	74	14.80
$75,000 to under $100,000	Strongly favor	18	3.60
$100,000 to under $150,000	Strongly oppose	213	46.20
$100,000 to under $150,000	Somewhat oppose	113	24.51
$100,000 to under $150,000	Have no opinion	54	11.71
$100,000 to under $150,000	Somewhat favor	65	14.10
$100,000 to under $150,000	Strongly favor	16	3.47
$150,000 to under $200,000	Strongly oppose	107	46.72
$150,000 to under $200,000	Somewhat oppose	49	21.40
$150,000 to under $200,000	Have no opinion	34	14.85
$150,000 to under $200,000	Somewhat favor	31	13.54
$150,000 to under $200,000	Strongly favor	8	3.49
More than $200,000	Strongly oppose	75	39.06
More than $200,000	Somewhat oppose	44	22.92
More than $200,000	Have no opinion	27	14.06
More than $200,000	Somewhat favor	30	15.62
More than $200,000	Strongly favor	16	8.33
	Strongly oppose	2	12.50
	Somewhat oppose	2	12.50
	Have no opinion	6	37.50
	Somewhat favor	5	31.25
	Strongly favor	1	6.25

show code

sentiment_study1 %>% filter(!is.na(income)) %>%  group_by(income, dataCenterSentiment) %>% tally() %>% mutate(percent = n/sum(n)*100) %>% mutate(dataCenterSentiment = fct_rev(dataCenterSentiment)) %>% 
  ggplot(aes(x = .5, y=percent, fill = dataCenterSentiment))+
  geom_bar(stat = "identity", color='white') +
  theme_void(base_size=15)+
  labs(fill="", x='') +
  theme(legend.position='top')+
  scale_fill_manual(values=c('#232D4B','#C8CBD2','#FDDA24','#F9DCBF','#E57200'))+
  coord_flip()+
  facet_wrap(~income)

region

show code

sentiment_study1 %>% filter(!is.na(region)) %>%  group_by(region, dataCenterSentiment) %>% tally() %>% mutate(percent = n/sum(n)*100) %>% mutate(dataCenterSentiment = fct_rev(dataCenterSentiment)) %>% 
  ggplot(aes(x = .5, y=percent, fill = dataCenterSentiment))+
  geom_bar(stat = "identity", color='white') +
  theme_void(base_size=15)+
  labs(fill="", x='') +
  theme(legend.position='top')+
  scale_fill_manual(values=c('#232D4B','#C8CBD2','#FDDA24','#F9DCBF','#E57200'))+
  coord_flip()+
  facet_wrap(~region)

Reasons for support/opposition

show code

nice_table(sentiment_study1 %>% filter(dataCenterSentiment=='Strongly oppose' | dataCenterSentiment=='Somewhat oppose') %>% 
             group_by(reasonsTally_fct) %>% tally() %>% mutate(percent = n/sum(n)*100), title='opposers')

opposers
reasonsTally_fct	n	percent
1 or less	50	2.35
2	14	0.66
3	83	3.90
4	352	16.53
5	1,630	76.56

show code

opposers_reasons_plot = sentiment_study1 %>% filter(dataCenterSentiment=='Strongly oppose' | dataCenterSentiment=='Somewhat oppose') %>% 
  group_by(reasonsTally_fct) %>% 
  tally() %>% mutate(percent = n/sum(n)*100) %>% 
  ggplot(aes(x = .5, y=percent, fill = reasonsTally_fct))+
  geom_bar(stat = "identity", color='white') +
  theme_void(base_size=15)+
  labs(fill="", x='') +
  theme(legend.position='top')+
  scale_fill_manual(values=c('#009fdf','#33b2e5','#66c5ec','#99d9f2','#ccecf9'))+
  coord_flip()

opposers_reasons_plot

show code

ggsave(opposers_reasons_plot, filename='opposers_reasons_plot.png', dpi = 300, width=12, height = 6)

nice_table(sentiment_study1 %>% filter(dataCenterSentiment=='Strongly favor' | dataCenterSentiment=='Somewhat favor') %>% 
             group_by(reasonsTally_fct) %>% tally() %>% mutate(percent = n/sum(n)*100), title='supporters')

supporters
reasonsTally_fct	n	percent
1 or less	9	1.52
2	23	3.89
3	41	6.93
4	72	12.16
5	447	75.51

show code

supporters_reasons_plot = sentiment_study1 %>% filter(dataCenterSentiment=='Strongly favor' | dataCenterSentiment=='Somewhat favor') %>% 
  group_by(reasonsTally_fct) %>% 
  tally() %>% mutate(percent = n/sum(n)*100) %>% 
  ggplot(aes(x = .5, y=percent, fill = reasonsTally_fct))+
  geom_bar(stat = "identity", color='white') +
  theme_void(base_size=15)+
  labs(fill="", x='') +
  theme(legend.position='top')+
  scale_fill_manual(values=c('#62bb46','#81c96b','#a1d690','#c0e4b5','#e0f1da'))+
  coord_flip()

supporters_reasons_plot

show code

ggsave(supporters_reasons_plot, filename='supporters_reasons_plot.png', dpi = 300, width=12, height = 6)

supporters_reasonsLollipop = sentiment_study1 %>% filter(dataCenterSentiment=='Strongly favor' | dataCenterSentiment=='Somewhat favor') %>% 
  group_by(reasonsTally_fct) %>% 
  tally() %>% mutate(percent = n/sum(n)*100) %>% 
  ggplot(aes(x=reasonsTally_fct, y=percent)) +
  geom_segment(
    aes(x=reasonsTally_fct, xend=reasonsTally_fct, y=0, yend=percent), linewidth=0.75) +
  geom_point(
    color="#009fdf", size= 4)+
  theme_classic() +
  coord_flip() +
  ylim(c(0,80))

supporters_reasonsLollipop

show code

ggsave(supporters_reasonsLollipop, filename='supporters_reasonsLollipop.png', dpi = 500, width=4, height = 3)

opposer_reasonsLollipop = sentiment_study1 %>% filter(dataCenterSentiment=='Strongly oppose' | dataCenterSentiment=='Somewhat oppose') %>% 
  group_by(reasonsTally_fct) %>% 
  tally() %>% mutate(percent = n/sum(n)*100) %>% 
  ggplot(aes(x=reasonsTally_fct, y=percent)) +
  geom_segment(
    aes(x=reasonsTally_fct, xend=reasonsTally_fct, y=0, yend=percent), linewidth=0.75) +
  geom_point(
    color="#62bb46", size= 4)+
  theme_classic() +
  coord_flip() +
  ylim(c(0,80))

opposer_reasonsLollipop

show code

ggsave(opposer_reasonsLollipop, filename='opposer_reasonsLollipop.png', dpi = 500, width=4, height = 3)

free responses

show code

library('ggwordcloud'); library('stringr'); library('tidytext')

freeResponse_wordCounts_supporters = sentiment_study1 %>%
  filter(dataCenterSentiment == 'Strongly favor' | dataCenterSentiment =='Somewhat favor') %>% 
  select(support_freeResponse) %>% 
  unnest_tokens(word, support_freeResponse) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE)

Joining with `by = join_by(word)`

show code

head(freeResponse_wordCounts_supporters)

       word   n
1      data 199
2      jobs 162
3        ai 131
4   centers 109
5    center  83
6 community  71

show code

# data, jobs, ai, centers, center, community, local, future, bring, people, water

freeResponse_wordCounts_opposers = sentiment_study1 %>%
  filter(dataCenterSentiment == 'Strongly oppose' | dataCenterSentiment =='Somewhat oppose') %>% 
  select(support_freeResponse) %>% 
  unnest_tokens(word, support_freeResponse) %>%
  anti_join(stop_words) %>%
  count(word, sort = TRUE)

Joining with `by = join_by(word)`

show code

head(freeResponse_wordCounts_opposers)

         word   n
1        data 783
2       water 767
3     centers 633
4          ai 376
5   resources 275
6 environment 260

show code

# data water centers ai resources environment energy electricity center people power community jobs

heatmap of the US

show code

library('sf')

Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

show code

library('tigris')

To enable caching of data, set `options(tigris_use_cache = TRUE)`
in your R script or .Rprofile.

show code

library('leaflet')

options(tigris_use_cache = TRUE)

# --- 1. Load county shapefile
counties_sf <- counties(cb = TRUE, resolution = "20m", year = 2020) %>%
  st_transform(4326)

# --- 2. Convert your respondents to sf object
respondents_sf <- sentiment_study1 %>%
  st_as_sf(coords = c("longitude", "latitude"), crs = 4326)

# --- 3. Spatial join: assign each respondent to a county
joined <- st_join(respondents_sf, counties_sf, join = st_within)

# --- 4. Aggregate sentiment to county mean (with n for transparency)
county_sentiment <- joined %>%
  st_drop_geometry() %>%
  group_by(GEOID) %>%
  summarise(
    mean_sentiment = mean(as.numeric(dataCenterSentiment), na.rm = TRUE),
    n = n()
  ) %>%
  filter(n >= 5)  # drop counties with too few respondents

# --- 5. Join back to shapefile
counties_mapped <- counties_sf %>%
  left_join(county_sentiment, by = "GEOID")

# --- 6. Color palette
pal <- colorNumeric(
  palette = c("#e74c3c", "#f5f5f5", "#2ecc71"),
  domain = counties_mapped$mean_sentiment,
  na.color = "#d0d0d0"
)

# --- 7. Interactive map
leaflet(counties_mapped) %>%
  addProviderTiles(providers$CartoDB.Positron) %>%
  addPolygons(
    fillColor = ~pal(mean_sentiment),
    fillOpacity = 0.75,
    color = "white",
    weight = 0.5,
    label = ~paste0(NAME, ": ", round(mean_sentiment, 2), " (n=", n, ")"),
    highlightOptions = highlightOptions(
      weight = 2,
      color = "#333",
      fillOpacity = 0.9,
      bringToFront = TRUE
    )
  ) %>%
  addLegend(
    pal = pal,
    values = ~mean_sentiment,
    title = "Mean sentiment",
    position = "bottomright"
  )

Claude simulated 300 responses to the following question:

And included demographic information for respondents.

show code

simulatedData = read.csv('datacenter_rebate_simulation.csv')

nice_table(simulatedData %>% group_by(ethnicity) %>% count())
nice_table(simulatedData %>% group_by(age_bracket) %>% count())
nice_table(simulatedData %>% group_by(gender) %>% count())
nice_table(simulatedData %>% group_by(income_bracket) %>% count())

ethnicity	n
Asian or Asian American	21
Black or African American	43
Hispanic or Latino	50
Other/Multiracial	17
White (non-Hispanic)	169

age_bracket	n
18-29	57
30-44	71
45-59	66
60-74	72
75+	34

gender	n
Man	155
Non-binary/other	3
Woman	142

income_bracket	n
$100k–$150k	43
$30k–$60k	67
$60k–$100k	96
Over $150k	54
Under $30k	40

Claude simulated prior approval/opposition to a data center being built in the local area.

show code

simulatedData %>% group_by(prior_attitude_numeric) %>% tally() %>% mutate(percent = n/sum(n)*100) %>% 
  ggplot(aes(x = prior_attitude_numeric, y=percent, fill = as.factor(prior_attitude_numeric)))+
  geom_bar(stat = "identity", position = "dodge") +
  theme_minimal()+
  labs(fill="")

The simulated data very much missed the mark here, likely because public sentiment about data centers is so quickly changing.

And finally, the simulated requested tax rebate amounts:

show code

gghistogram(subset(simulatedData, rebate_condition== 'one-time'), x = 'wta_amount')+labs(title='One Time Payment')
gghistogram(subset(simulatedData, rebate_condition== 'yearly'), x = 'wta_amount')+labs(title='Yearly Payments')

Note that Claude implemented a randomly assigned condition to either report a yearly or a one-time payment requirement.

Claude noted that the following likely issues:

Respondents will report $0 or refuse to answer if there is no amount of money that is sufficient
Respondents will anchor to the $1200 stimulus payment checks