-
Notifications
You must be signed in to change notification settings - Fork 0
/
equity_analyses.R
233 lines (204 loc) · 25 KB
/
equity_analyses.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
## The below code was used for the analyses presented in Chapter 4 of The State
## of the Urban Forest in New York City. This workflow imports datasets from a
## PostGIS database into R and then entails preprocessing and analysis.
##
## Running this code as-is would require either setting up a comparable
## database with the appropriate data, or working with the raw datasets and adjusting
## code as appropriate. However, some of the code included here illustrates key parts
## of workflows, such as aggregating data from the Social Vulnerability Index dataset
## to the scale of Neighborhood Tabulation Areas, which can support similar analyses.
##
## Users can also download the summarized data on canopy and relative change in
## canopy, street tree stocking rates, Social Vulnerability, and Heat Vulnerability
## Index, as used in the correlation analyses from the Zenodo repository where
## supplemental data are available: https://zenodo.org/record/5210261.
## In that case, see line 99.
##
## Code for correlation analyses between urban forest and socioeconomic
## variables goes run through line 227; for code related to analysis of canopy
## around Schools & Hospitals compared to broader NTAs, see from line 229 onward.
##
## Code developed by Mike Treglia, The Nature Conservancy ([email protected])
# Load Packages
library(sf)
library(dplyr)
library(Hmisc)
library(RPostgres)
### Load RPostgres package and connect to database. Note - users may need to
### adjust or add information for the connection based on their system and settings
### (e.g., users may adjust the database name and user name, add password
### information [which should not be stored within code as good practice], and the
### port)
con <- dbConnect(RPostgres::Postgres(), user='postgres', host='localhost', dbname="nyc_urbanforest")
######### Begin code related to correlations of urban forest and socioeconomic variables #########
# Load Social Vulnerability Data
# Note - for details on loading these data into the PostGIS Database, or into R more generally
# see the section of "dataloadingexamples.R" starting at line 315.
# Note - NTA Code (and name) are integrated into the social vulnerability data loaded, as it was joined
# to the NYC Census Tracts, which has NTA information, before the data were added to the database.
sovi2018 <- st_read(con, Id(schema="socioeconomic_health", table="sovi2018_censustract2010"))
st_geometry(sovi2018) <- NULL # Drop the geometry column
# Select only necessary/desired columns
sovi2018 <- select(sovi2018, boroname, borocode, boroct2010, fips,
ntaname, ntacode, e_totpop, e_pci, ep_pov, ep_age65,ep_age17,
ep_limeng, ep_minrty, ep_crowd, ep_noveh, spl_theme1, spl_theme2, spl_theme3, spl_theme4, spl_themes)
# For the Social Vulnerability variables, aggregate the data from census tracts to neighborhood
# tabulation areas by averaging, weighted by the population estimate.
nta_sovi2018 <- group_by(sovi2018, ntacode, boroname) %>%
summarise(
pci_wtdavg = weighted.mean(e_pci, e_totpop, na.rm = TRUE),
povrate_wtdavg = weighted.mean(ep_pov, e_totpop, na.rm = TRUE),
gte65rate_wtdavg = weighted.mean(ep_age65, e_totpop, na.rm = TRUE),
lte17rate_wtdavg = weighted.mean(ep_age17, e_totpop, na.rm = TRUE),
limengrate_wtdavg = weighted.mean(ep_limeng, e_totpop, na.rm = TRUE),
minorityrate_wtdavg = weighted.mean(ep_minrty, e_totpop, na.rm = TRUE),
crowding_wtdavg = weighted.mean(ep_crowd, e_totpop, na.rm = TRUE),
noveh_wtdavg = weighted.mean(ep_noveh, e_totpop, na.rm = TRUE),
spl_theme1_wtdavg = weighted.mean(spl_theme1, e_totpop, na.rm = TRUE),
spl_theme2_wtdavg = weighted.mean(spl_theme2, e_totpop, na.rm = TRUE),
spl_theme3_wtdavg = weighted.mean(spl_theme3, e_totpop, na.rm = TRUE),
spl_theme4_wtdavg = weighted.mean(spl_theme4, e_totpop, na.rm = TRUE),
spl_themes_wtdavg = weighted.mean(spl_themes, e_totpop, na.rm=TRUE)) %>% as.data.frame()
# Load data on canopy cover and canopy change by NTA (NTAs for this analysis were buffered by 1/4 mile, clipped to borough boundary land area)
# For example of doing that analysis in SQL given canopy change data and administrative boundaries
# see Example 2 in ./SQL/CanopyCoverOverlay.sql.
nta.canopy <- st_read(con, Id(schema="results_utc_landcover", table="canopyvector_nycnta_qtr_mi_buff"))
# join the canpy data with the Social Vulnerability data based on common nta code
nta_canopy_sovi <- merge(nta.canopy, nta_sovi2018, by="ntacode")
# Create non-spatial version to reduce size and complexity.
nta_canopy_sovi.ns <- nta_canopy_sovi
st_geometry(nta_canopy_sovi.ns) <- NULL
# Load Heat Vulnerability Index. Can be exported from a NYC Department of Health
# and Mental Hygiene website at
# https://a816-dohbesp.nyc.gov/IndicatorPublic/VisualizationData.aspx?id=2411,719b87,107,Map,Score,2018
hvi_nta <- st_read(con, Id(schema="socioeconomic_health", table="hvi_rank_nta_2018"))
st_geometry(hvi_nta) <- NULL
# Load in data on Street Trees (for Street Tree Stocking Rate)
# Note - the queries used to calculate the street tree stocking rate are
# in ./SQL/StreetTrees_Queries.sql
stockingrate_nta <- st_read(con, Id(schema="results_streetrees", table="treecensus_summaries_nta_final"))
# Select the appropriate variables to keep
stockingrate_nta <- select(stockingrate_nta, ntacode, stockingrate=stockingrate_2015_living)
st_geometry(stockingrate_nta) <- NULL
# Join the multiple datasets together
nta_canopy_sovi_streettrees <- merge(nta_canopy_sovi.ns, hvi_nta, by="ntacode") %>% merge(stockingrate_nta, by="ntacode") %>% select(ntacode, boroname, unit_ft2, canopy_2017_pct, canopy_17min10_pct, canopy_17min10_div10, pci_wtdavg, povrate_wtdavg, gte65rate_wtdavg, lte17rate_wtdavg, limengrate_wtdavg, minorityrate_wtdavg, crowding_wtdavg, noveh_wtdavg, hvi_rank, spl_theme1_wtdavg, spl_theme2_wtdavg, spl_theme3_wtdavg, spl_theme4_wtdavg, spl_themes_wtdavg, stockingrate)
# Remove unpopulated areas - some have census data very strangely (w/ very small populations) like Central Park
nta_canopy_sovi_streettrees <- nta_canopy_sovi_streettrees[which(nta_canopy_sovi_streettrees$ntacode %nin% c("SI99", "QN99", "QN98", "MN99", "BX99", "BX98", "BK99")),]
############
#NOTE: Users can also load the equity data available on Zenodo and start here
# After importing that dataset the variable in that dataset called 'relativecanopychange_percent'
# should be renamed to canopy_17min10_div10. Sample R code for that:
# names(nta_canopy_sovi)[names(nta_canopy_sovi)=="relativecanopychange_percent"] <- "canopy_17min10_div10"
# Correlations between canopy % in 2017 and socioeconomic data at the scale of NTAs by borough
nta_canopy_sovi_streettrees %>%
filter(!is.na(spl_themes_wtdavg)) %>%
group_by(boroname) %>%
summarise(cor_canopy_pci=cor(canopy_2017_pct,pci_wtdavg, use="complete.obs", method = "kendall"), pval1=cor.test(canopy_2017_pct,pci_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_povrate=cor(canopy_2017_pct,povrate_wtdavg, use="complete.obs", method = "kendall"), pval2=cor.test(canopy_2017_pct,povrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_gte65=cor(canopy_2017_pct,gte65rate_wtdavg, use="complete.obs", method = "kendall"), pval3=cor.test(canopy_2017_pct,gte65rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_lte17=cor(canopy_2017_pct,lte17rate_wtdavg, use="complete.obs", method = "kendall"), pval_lte17=cor.test(canopy_2017_pct,lte17rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_limeng=cor(canopy_2017_pct,limengrate_wtdavg, use="complete.obs", method = "kendall"), pval4=cor.test(canopy_2017_pct,limengrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_minorityrate=cor(canopy_2017_pct,minorityrate_wtdavg, use="complete.obs", method = "kendall"), pval5=cor.test(canopy_2017_pct,minorityrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_crowding=cor(canopy_2017_pct,crowding_wtdavg, use="complete.obs", method = "kendall"), pval6=cor.test(canopy_2017_pct,crowding_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_noveh=cor(canopy_2017_pct,noveh_wtdavg, use="complete.obs", method = "kendall"), pval7=cor.test(canopy_2017_pct,noveh_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_hvirank=cor(canopy_2017_pct,hvi_rank, use="complete.obs", method = "kendall"), pval8=cor.test(canopy_2017_pct,hvi_rank, use="complete.obs", method="kendall")$p.value,
cor_canopy_spltheme1=cor(canopy_2017_pct,spl_theme1_wtdavg, use="complete.obs", method = "kendall"), pval9=cor.test(canopy_2017_pct,spl_theme1_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_spltheme2=cor(canopy_2017_pct,spl_theme2_wtdavg, use="complete.obs", method = "kendall"), pval10=cor.test(canopy_2017_pct,spl_theme2_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_spltheme3=cor(canopy_2017_pct,spl_theme3_wtdavg, use="complete.obs", method = "kendall"), pval11=cor.test(canopy_2017_pct,spl_theme3_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_spltheme4=cor(canopy_2017_pct,spl_theme4_wtdavg, use="complete.obs", method = "kendall"), pval12=cor.test(canopy_2017_pct,spl_theme4_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_splthemes=cor(canopy_2017_pct,spl_themes_wtdavg, use="complete.obs", method = "kendall"), pval13=cor.test(canopy_2017_pct,spl_themes_wtdavg, use="complete.obs", method="kendall")$p.value,
)
# Correlations between relative canopy change and socioeconomic data at the scale of NTAs by borough
nta_canopy_sovi_streettrees %>%
filter(!is.na(spl_themes_wtdavg)) %>%
group_by(boroname) %>%
summarise(cor_relcanchange_pci=cor(canopy_17min10_div10,pci_wtdavg, use="complete.obs", method = "kendall"), pval1=cor.test(canopy_17min10_div10,pci_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_povrate=cor(canopy_17min10_div10,povrate_wtdavg, use="complete.obs", method = "kendall"), pval2=cor.test(canopy_17min10_div10,povrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_gte65=cor(canopy_17min10_div10,gte65rate_wtdavg, use="complete.obs", method = "kendall"), pval3=cor.test(canopy_17min10_div10,gte65rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relchange_lte17=cor(canopy_17min10_div10,lte17rate_wtdavg, use="complete.obs", method = "kendall"), pval_lte17=cor.test(canopy_17min10_div10,lte17rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_limeng=cor(canopy_17min10_div10,limengrate_wtdavg, use="complete.obs", method = "kendall"), pval4=cor.test(canopy_17min10_div10,limengrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_minorityrate=cor(canopy_17min10_div10,minorityrate_wtdavg, use="complete.obs", method = "kendall"), pval5=cor.test(canopy_17min10_div10,minorityrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_crowding=cor(canopy_17min10_div10,crowding_wtdavg, use="complete.obs", method = "kendall"), pval6=cor.test(canopy_17min10_div10,crowding_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_noveh=cor(canopy_17min10_div10,noveh_wtdavg, use="complete.obs", method = "kendall"), pval7=cor.test(canopy_17min10_div10,noveh_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_hvirank=cor(canopy_17min10_div10,hvi_rank, use="complete.obs", method = "kendall"), pval8=cor.test(canopy_17min10_div10,hvi_rank, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_spltheme1=cor(canopy_17min10_div10,spl_theme1_wtdavg, use="complete.obs", method = "kendall"), pval9=cor.test(canopy_17min10_div10,spl_theme1_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_spltheme2=cor(canopy_17min10_div10,spl_theme2_wtdavg, use="complete.obs", method = "kendall"), pval10=cor.test(canopy_17min10_div10,spl_theme2_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_spltheme3=cor(canopy_17min10_div10,spl_theme3_wtdavg, use="complete.obs", method = "kendall"), pval11=cor.test(canopy_17min10_div10,spl_theme3_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_spltheme4=cor(canopy_17min10_div10,spl_theme4_wtdavg, use="complete.obs", method = "kendall"), pval12=cor.test(canopy_17min10_div10,spl_theme4_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_splthemes=cor(canopy_17min10_div10,spl_themes_wtdavg, use="complete.obs", method = "kendall"), pval13=cor.test(canopy_17min10_div10,spl_themes_wtdavg, use="complete.obs", method="kendall")$p.value
)
# Correlations between street tree stocking rate and socioeconomic data at the scale of NTAs by borough
nta_canopy_sovi_streettrees %>%
filter(!is.na(spl_themes_wtdavg)) %>%
group_by(boroname) %>%
summarise(cor_stkngrate_pci=cor(stockingrate,pci_wtdavg, use="complete.obs", method = "kendall"), pval1=cor.test(stockingrate,pci_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_povrate=cor(stockingrate,povrate_wtdavg, use="complete.obs", method = "kendall"), pval2=cor.test(stockingrate,povrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_gte65=cor(stockingrate,gte65rate_wtdavg, use="complete.obs", method = "kendall"), pval3=cor.test(stockingrate,gte65rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_lte17=cor(stockingrate,lte17rate_wtdavg, use="complete.obs", method = "kendall"), pval_lte17=cor.test(stockingrate,lte17rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_limeng=cor(stockingrate,limengrate_wtdavg, use="complete.obs", method = "kendall"), pval4=cor.test(stockingrate,limengrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_minorityrate=cor(stockingrate,minorityrate_wtdavg, use="complete.obs", method = "kendall"), pval5=cor.test(stockingrate,minorityrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_crowding=cor(stockingrate,crowding_wtdavg, use="complete.obs", method = "kendall"), pval6=cor.test(stockingrate,crowding_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_noveh=cor(stockingrate,noveh_wtdavg, use="complete.obs", method = "kendall"), pval7=cor.test(stockingrate,noveh_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_hvirank=cor(stockingrate,hvi_rank, use="complete.obs", method = "kendall"), pval8=cor.test(stockingrate,hvi_rank, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_spltheme1=cor(stockingrate,spl_theme1_wtdavg, use="complete.obs", method = "kendall"), pval9=cor.test(stockingrate,spl_theme1_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_spltheme2=cor(stockingrate,spl_theme2_wtdavg, use="complete.obs", method = "kendall"), pval10=cor.test(stockingrate,spl_theme2_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_spltheme3=cor(stockingrate,spl_theme3_wtdavg, use="complete.obs", method = "kendall"), pval11=cor.test(stockingrate,spl_theme3_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_spltheme4=cor(stockingrate,spl_theme4_wtdavg, use="complete.obs", method = "kendall"), pval12=cor.test(stockingrate,spl_theme4_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_splthemes=cor(stockingrate,spl_themes_wtdavg, use="complete.obs", method = "kendall"), pval13=cor.test(stockingrate,spl_themes_wtdavg, use="complete.obs", method="kendall")$p.value
)
# Correlations between canopy % in 2017 and socioeconomic data at the scale of NTAs, citywide
nta_canopy_sovi_streettrees %>%
filter(!is.na(spl_themes_wtdavg)) %>%
summarise(cor_canopy_pci=cor(canopy_2017_pct,pci_wtdavg, use="complete.obs", method = "kendall"), pval1=cor.test(canopy_2017_pct,pci_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_povrate=cor(canopy_2017_pct,povrate_wtdavg, use="complete.obs", method = "kendall"), pval2=cor.test(canopy_2017_pct,povrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_gte65=cor(canopy_2017_pct,gte65rate_wtdavg, use="complete.obs", method = "kendall"), pval3=cor.test(canopy_2017_pct,gte65rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_lte17=cor(canopy_2017_pct,lte17rate_wtdavg, use="complete.obs", method = "kendall"), pval_lte17=cor.test(canopy_2017_pct,lte17rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_limeng=cor(canopy_2017_pct,limengrate_wtdavg, use="complete.obs", method = "kendall"), pval4=cor.test(canopy_2017_pct,limengrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_minorityrate=cor(canopy_2017_pct,minorityrate_wtdavg, use="complete.obs", method = "kendall"), pval5=cor.test(canopy_2017_pct,minorityrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_crowding=cor(canopy_2017_pct,crowding_wtdavg, use="complete.obs", method = "kendall"), pval6=cor.test(canopy_2017_pct,crowding_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_noveh=cor(canopy_2017_pct,noveh_wtdavg, use="complete.obs", method = "kendall"), pval7=cor.test(canopy_2017_pct,noveh_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_hvirank=cor(canopy_2017_pct,hvi_rank, use="complete.obs", method = "kendall"), pval8=cor.test(canopy_2017_pct,hvi_rank, use="complete.obs", method="kendall")$p.value,
cor_canopy_spltheme1=cor(canopy_2017_pct,spl_theme1_wtdavg, use="complete.obs", method = "kendall"), pval9=cor.test(canopy_2017_pct,spl_theme1_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_spltheme2=cor(canopy_2017_pct,spl_theme2_wtdavg, use="complete.obs", method = "kendall"), pval10=cor.test(canopy_2017_pct,spl_theme2_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_spltheme3=cor(canopy_2017_pct,spl_theme3_wtdavg, use="complete.obs", method = "kendall"), pval11=cor.test(canopy_2017_pct,spl_theme3_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_spltheme4=cor(canopy_2017_pct,spl_theme4_wtdavg, use="complete.obs", method = "kendall"), pval12=cor.test(canopy_2017_pct,spl_theme4_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_canopy_splthemes=cor(canopy_2017_pct,spl_themes_wtdavg, use="complete.obs", method = "kendall"), pval13=cor.test(canopy_2017_pct,spl_themes_wtdavg, use="complete.obs", method="kendall")$p.value,
)
# Correlations between relative canopy change and socioeconomic data at the scale of NTAs, citywide
nta_canopy_sovi_streettrees %>%
filter(!is.na(spl_themes_wtdavg)) %>%
summarise(cor_relcanchange_pci=cor(canopy_17min10_div10,pci_wtdavg, use="complete.obs", method = "kendall"), pval1=cor.test(canopy_17min10_div10,pci_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_povrate=cor(canopy_17min10_div10,povrate_wtdavg, use="complete.obs", method = "kendall"), pval2=cor.test(canopy_17min10_div10,povrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_gte65=cor(canopy_17min10_div10,gte65rate_wtdavg, use="complete.obs", method = "kendall"), pval3=cor.test(canopy_17min10_div10,gte65rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relchange_lte17=cor(canopy_17min10_div10,lte17rate_wtdavg, use="complete.obs", method = "kendall"), pval_lte17=cor.test(canopy_17min10_div10,lte17rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_limeng=cor(canopy_17min10_div10,limengrate_wtdavg, use="complete.obs", method = "kendall"), pval4=cor.test(canopy_17min10_div10,limengrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_minorityrate=cor(canopy_17min10_div10,minorityrate_wtdavg, use="complete.obs", method = "kendall"), pval5=cor.test(canopy_17min10_div10,minorityrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_crowding=cor(canopy_17min10_div10,crowding_wtdavg, use="complete.obs", method = "kendall"), pval6=cor.test(canopy_17min10_div10,crowding_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_noveh=cor(canopy_17min10_div10,noveh_wtdavg, use="complete.obs", method = "kendall"), pval7=cor.test(canopy_17min10_div10,noveh_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_hvirank=cor(canopy_17min10_div10,hvi_rank, use="complete.obs", method = "kendall"), pval8=cor.test(canopy_17min10_div10,hvi_rank, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_spltheme1=cor(canopy_17min10_div10,spl_theme1_wtdavg, use="complete.obs", method = "kendall"), pval9=cor.test(canopy_17min10_div10,spl_theme1_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_spltheme2=cor(canopy_17min10_div10,spl_theme2_wtdavg, use="complete.obs", method = "kendall"), pval10=cor.test(canopy_17min10_div10,spl_theme2_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_spltheme3=cor(canopy_17min10_div10,spl_theme3_wtdavg, use="complete.obs", method = "kendall"), pval11=cor.test(canopy_17min10_div10,spl_theme3_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_spltheme4=cor(canopy_17min10_div10,spl_theme4_wtdavg, use="complete.obs", method = "kendall"), pval12=cor.test(canopy_17min10_div10,spl_theme4_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_relcanchange_splthemes=cor(canopy_17min10_div10,spl_themes_wtdavg, use="complete.obs", method = "kendall"), pval13=cor.test(canopy_17min10_div10,spl_themes_wtdavg, use="complete.obs", method="kendall")$p.value
)
# Correlations between street tree stocking rate and socioeconomic data at the scale of NTAs, citywide
nta_canopy_sovi_streettrees %>%
filter(!is.na(spl_themes_wtdavg)) %>%
summarise(cor_stkngrate_pci=cor(stockingrate,pci_wtdavg, use="complete.obs", method = "kendall"), pval1=cor.test(stockingrate,pci_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_povrate=cor(stockingrate,povrate_wtdavg, use="complete.obs", method = "kendall"), pval2=cor.test(stockingrate,povrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_gte65=cor(stockingrate,gte65rate_wtdavg, use="complete.obs", method = "kendall"), pval3=cor.test(stockingrate,gte65rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_lte17=cor(stockingrate,lte17rate_wtdavg, use="complete.obs", method = "kendall"), pval_lte17=cor.test(stockingrate,lte17rate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_limeng=cor(stockingrate,limengrate_wtdavg, use="complete.obs", method = "kendall"), pval4=cor.test(stockingrate,limengrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_minorityrate=cor(stockingrate,minorityrate_wtdavg, use="complete.obs", method = "kendall"), pval5=cor.test(stockingrate,minorityrate_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_crowding=cor(stockingrate,crowding_wtdavg, use="complete.obs", method = "kendall"), pval6=cor.test(stockingrate,crowding_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_noveh=cor(stockingrate,noveh_wtdavg, use="complete.obs", method = "kendall"), pval7=cor.test(stockingrate,noveh_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_hvirank=cor(stockingrate,hvi_rank, use="complete.obs", method = "kendall"), pval8=cor.test(stockingrate,hvi_rank, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_spltheme1=cor(stockingrate,spl_theme1_wtdavg, use="complete.obs", method = "kendall"), pval9=cor.test(stockingrate,spl_theme1_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_spltheme2=cor(stockingrate,spl_theme2_wtdavg, use="complete.obs", method = "kendall"), pval10=cor.test(stockingrate,spl_theme2_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_spltheme3=cor(stockingrate,spl_theme3_wtdavg, use="complete.obs", method = "kendall"), pval11=cor.test(stockingrate,spl_theme3_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_spltheme4=cor(stockingrate,spl_theme4_wtdavg, use="complete.obs", method = "kendall"), pval12=cor.test(stockingrate,spl_theme4_wtdavg, use="complete.obs", method="kendall")$p.value,
cor_stkngrate_splthemes=cor(stockingrate,spl_themes_wtdavg, use="complete.obs", method = "kendall"), pval13=cor.test(stockingrate,spl_themes_wtdavg, use="complete.obs", method="kendall")$p.value
)
###### End Code for Correlations between Urban Forest and Socioeconomic Data #######
###### Begin code for analysis of canopy around schools & hospitals #######