Splitting data by each treatment
list_PA_by_treatment<-PA_malha %>%
st_drop_geometry() %>%
mutate(
across(where(is.character), as.factor)) %>%
group_by(TREATMENT) %>%
group_split()
#non-pa
summary(list_PA_by_treatment[[2]])
## ID CD_GEOCODI TIPO CD_GEOCODM
## 11759 : 2 210090705000024: 1 RURAL :20887 2304400: 3032
## 11760 : 2 210090705000025: 1 URBANO:25532 2408102: 907
## 11761 : 2 210090705000026: 1 2910800: 600
## 12902 : 2 210090705000031: 1 2504009: 488
## 12903 : 2 210090705000032: 1 2933307: 463
## 12904 : 2 210090705000033: 1 3143302: 413
## (Other):46407 (Other) :46413 (Other):40516
## NM_MUNICIP total_ct_area WDPAID
## FORTALEZA : 3032 Min. :5.880e+02 10821 : 0
## NATAL : 907 1st Qu.:1.134e+05 10842 : 0
## FEIRA DE SANTANA : 600 Median :4.529e+05 10846 : 0
## CAMPINA GRANDE : 488 Mean :2.087e+07 115666 : 0
## VITÓRIA DA CONQUISTA: 463 3rd Qu.:1.722e+07 115992 : 0
## MONTES CLAROS : 413 Max. :2.250e+09 (Other): 0
## (Other) :40516 NA's :46419
## WDPA_PID ORIG_NAME
## 10821 : 0 Anace : 0
## 10842 : 0 Area De Protecao Ambiental Bacia Do Rio De Janeiro: 0
## 10846 : 0 Area De Protecao Ambiental Bacia Do Rio Pandeiros : 0
## 115666 : 0 Area De Protecao Ambiental Baia De Todos Os Santos: 0
## 115992 : 0 Area De Protecao Ambiental Barra Do Rio Mamanguape: 0
## (Other): 0 (Other) : 0
## NA's :46419 NA's :46419
## DESIG IUCN_CAT
## Área de Proteção Ambiental : 0 Cannot_applied: 0
## Área de Relevante Interesse Ecológico: 0 Ia : 0
## Estação Ecológica : 0 II : 0
## Floresta : 0 III : 0
## Indigenous Area : 0 IV : 0
## (Other) : 0 (Other) : 0
## NA's :46419 NA's :46419
## MARINE REP_M_AREA GIS_M_AREA REP_AREA GIS_AREA
## 0 : 0 Min. : NA Min. : NA Min. : NA Min. : NA
## 1 : 0 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA 1st Qu.: NA
## 2 : 0 Median : NA Median : NA Median : NA Median : NA
## NA's:46419 Mean :NaN Mean :NaN Mean :NaN Mean :NaN
## 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA 3rd Qu.: NA
## Max. : NA Max. : NA Max. : NA Max. : NA
## NA's :46419 NA's :46419 NA's :46419 NA's :46419
## STATUS_YR OWN_TYPE GRUPO
## 0 : 0 Communal : 0 Área Indígena : 0
## 1946 : 0 Individual landowners: 0 Proteção Integral: 0
## 1950 : 0 Not Reported : 0 Uso Sustentável : 0
## 1961 : 0 NA's :46419 NA's :46419
## 1973 : 0
## (Other): 0
## NA's :46419
## TREATMENT PA_total_area intersected_PA_area area_occupied_pct
## APA : 0 Min. : NA Min. : 0.00000 Min. :0.000e+00
## Non_PA_CT:46419 1st Qu.: NA 1st Qu.: 0.00000 1st Qu.:0.000e+00
## PI : 0 Median : NA Median : 0.00000 Median :0.000e+00
## RPPN : 0 Mean :NaN Mean : 0.00566 Mean :8.200e-09
## TI : 0 3rd Qu.: NA 3rd Qu.: 0.00000 3rd Qu.:0.000e+00
## US : 0 Max. : NA Max. :262.52180 Max. :3.789e-04
## NA's :46419
## threshold id_dummy mang_plan mang_comitte
## Non_PA_CT :46418 Min. : NA Não : 0 Não : 0
## with_PA : 0 1st Qu.: NA Sem_info: 0 Sem_info: 0
## without_PA: 1 Median : NA Sim : 0 Sim : 0
## Mean :NaN TI : 0 TI : 0
## 3rd Qu.: NA NA's :46419 NA's :46419
## Max. : NA
## NA's :46419
# acho que não tem muito oq mostrar aqui
APA
summary(list_PA_by_treatment[[1]])
## ID CD_GEOCODI TIPO CD_GEOCODM
## 37368 : 2 210090705000010: 2 RURAL :371 2100907: 64
## 37369 : 2 210090705000011: 2 URBANO:158 2902708: 56
## 40187 : 2 210090705000012: 2 2910800: 54
## 40188 : 2 210090705000013: 2 2512903: 31
## 41276 : 2 210090705000014: 2 2305803: 25
## 541 : 2 210090705000015: 2 2301406: 19
## (Other):517 (Other) :517 (Other):280
## NM_MUNICIP total_ct_area WDPAID WDPA_PID
## ARAIOSES : 64 Min. : 11490 351861 : 67 351861 : 67
## BARRA : 56 1st Qu.: 476438 555682870: 55 555682870: 55
## FEIRA DE SANTANA: 54 Median : 13263975 352131 : 41 352131 : 41
## RIO TINTO : 31 Mean : 71625386 115666 : 32 115666 : 32
## IPU : 25 3rd Qu.: 82742808 115993 : 31 115993 : 31
## ARATUBA : 19 Max. :783615615 33544 : 25 33544 : 25
## (Other) :280 (Other) :278 (Other) :278
## ORIG_NAME
## Area De Protecao Ambiental Dunas E Veredas Do Baixo Medio Sao Francisco : 67
## Area De Protecao Ambiental Lago De Pedra Do Cavalo : 55
## Area De Protecao Ambiental Da Foz Do Rio Das Preguicas Pequenos Lencois Regiao Lagunar Adjacente: 41
## Area De Protecao Ambiental Delta Do Parnaiba : 32
## Area De Protecao Ambiental Barra Do Rio Mamanguape : 31
## Area De Protecao Ambiental Da Bica Do Ipu : 25
## (Other) :278
## DESIG IUCN_CAT MARINE
## Área de Proteção Ambiental :529 Cannot_applied: 0 0:403
## Área de Relevante Interesse Ecológico: 0 Ia : 0 1:113
## Estação Ecológica : 0 II : 0 2: 13
## Floresta : 0 III : 0
## Indigenous Area : 0 IV : 0
## Monumento Natural : 0 V :529
## (Other) : 0 VI : 0
## REP_M_AREA GIS_M_AREA REP_AREA GIS_AREA
## Min. : 0.0000 Min. : 0.000 Min. : 0.314 Min. : 0.316
## 1st Qu.: 0.0000 1st Qu.: 0.000 1st Qu.: 149.186 1st Qu.: 149.851
## Median : 0.0000 Median : 0.000 Median : 472.449 Median : 474.270
## Mean : 72.2872 Mean : 107.053 Mean : 2477.690 Mean : 2487.854
## 3rd Qu.: 0.1078 3rd Qu.: 1.556 3rd Qu.: 3095.901 3rd Qu.: 3109.663
## Max. :1358.4482 Max. :1357.761 Max. :16287.406 Max. :16359.349
##
## STATUS_YR OWN_TYPE GRUPO
## 1997 :138 Communal : 0 Área Indígena : 0
## 1998 : 61 Individual landowners: 0 Proteção Integral: 0
## 1999 : 51 Not Reported :529 Uso Sustentável :529
## 1991 : 45
## 1996 : 39
## 2000 : 35
## (Other):160
## TREATMENT PA_total_area intersected_PA_area area_occupied_pct
## APA :529 Min. :6.038e+03 Min. : 0 Min. : 0.0000
## Non_PA_CT: 0 1st Qu.:3.563e+07 1st Qu.: 51267 1st Qu.: 0.4577
## PI : 0 Median :9.292e+07 Median : 289448 Median : 54.0515
## RPPN : 0 Mean :8.873e+08 Mean : 23876010 Mean : 50.4246
## TI : 0 3rd Qu.:6.468e+08 3rd Qu.: 6404108 3rd Qu.: 99.9983
## US : 0 Max. :5.181e+09 Max. :482593013 Max. :100.0000
##
## threshold id_dummy mang_plan mang_comitte
## Non_PA_CT : 1 Min. : 2.00 Não : 91 Não :291
## with_PA :327 1st Qu.: 28.00 Sem_info:424 Sem_info: 96
## without_PA:201 Median : 72.00 Sim : 14 Sim :142
## Mean : 76.92 TI : 0 TI : 0
## 3rd Qu.:115.00
## Max. :200.00
##
list_PA_by_treatment[[1]] %>%
group_by(ORIG_NAME, CD_GEOCODI) %>%
dplyr::select(PA_total_area, intersected_PA_area, area_occupied_pct) %>%
paged_table()
list_PA_by_treatment[[1]] %>%
group_by(ORIG_NAME, threshold) %>%
summarise(ct_within_category=sum(length(unique(CD_GEOCODI)))) %>%
paged_table()
## Figure - CT amount by APA
prep_APA <- list_PA_by_treatment[[1]] %>%
group_by(ORIG_NAME) %>%
arrange(CD_GEOCODI) %>%
mutate(
cumulative_area = cumsum(intersected_PA_area),
x_position = row_number()
) %>%
ungroup()
unique_APA_names <- unique(prep_APA$ORIG_NAME)
for (name in unique_APA_names) {
data_filtered <- prep_APA %>% filter(ORIG_NAME == name)
p <- ggplot(data_filtered, aes(x = x_position, y = cumulative_area)) +
geom_line(aes(color = threshold, group = 1), size = 1) +
geom_point(aes(color = threshold, group = 1 ), size = 2) +
scale_color_manual(values = c("with_PA" = "green", "without_PA" = "red"))+
labs(title = name,
y="Total acumulado da intersecção da área da PA por CT",
x="Número de CT") +
theme_classic()
print(p)
}
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# perceba que a definição de tratamento é por proporção da área no CT, enquanto o nosso y só diz respeito a acumulação da área da PA
# tudo que tá em verde é pq mais de 10% do seu CT, enquanto oq tá em vermelho é pq ocupa menos que isso. E isso é medido pelo ponto em si, e não pela linha ;)
# tem APA que vai ser excluida com 10%
PI
summary(list_PA_by_treatment[[3]])
## ID CD_GEOCODI TIPO CD_GEOCODM NM_MUNICIP
## 19971 : 2 290370605000007: 2 RURAL :223 2903706: 23 BOA NOVA: 23
## 36799 : 2 290370605000008: 2 URBANO: 30 2602803: 14 BUÍQUE : 14
## 36800 : 2 290370605000009: 2 3135209: 13 JANUÁRIA: 13
## 36801 : 2 290370605000012: 2 2901304: 11 ANDARAÍ : 11
## 36804 : 2 290370605000014: 2 2304707: 10 GRANJA : 10
## 36806 : 2 290370605000019: 2 2908101: 9 COCOS : 9
## (Other):241 (Other) :241 (Other):173 (Other) :173
## total_ct_area WDPAID WDPA_PID
## Min. :8.658e+04 19447 : 20 19447 : 20
## 1st Qu.:1.501e+07 555576384: 15 555576384: 15
## Median :5.816e+07 351760 : 14 351760 : 14
## Mean :1.226e+08 478424 : 12 478424 : 12
## 3rd Qu.:1.511e+08 555576380: 11 555576380: 11
## Max. :1.982e+09 555576442: 10 555576442: 10
## (Other) :171 (Other) :171
## ORIG_NAME DESIG
## Parque Nacional Da Chapada Diamantina : 20 Parque :157
## Refugio De Vida Silvestre De Boa Nova : 15 Refúgio de Vida Silvestre : 35
## Parque Nacional Do Catimbau : 14 Estação Ecológica : 22
## Monumento Natural Do Rio Sao Francisco: 12 Monumento Natural : 22
## Parque Estadual Das Carnaubas : 11 Reserva Biológica : 17
## Parque Nacional De Boa Nova : 10 Área de Proteção Ambiental: 0
## (Other) :171 (Other) : 0
## IUCN_CAT MARINE REP_M_AREA GIS_M_AREA
## Cannot_applied: 0 0:246 Min. : 0.0000 Min. : 0.0000
## Ia : 39 1: 7 1st Qu.: 0.0000 1st Qu.: 0.0000
## II :157 2: 0 Median : 0.0000 Median : 0.0000
## III : 57 Mean : 0.3545 Mean : 0.3744
## IV : 0 3rd Qu.: 0.0000 3rd Qu.: 0.0000
## V : 0 Max. :22.3719 Max. :23.4784
## VI : 0
## REP_AREA GIS_AREA STATUS_YR
## Min. : 0.148 Min. : 0.148 2010 : 46
## 1st Qu.: 63.037 1st Qu.: 63.315 2002 : 34
## Median : 148.336 Median : 148.877 1985 : 20
## Mean : 621.802 Mean : 624.241 2006 : 19
## 3rd Qu.: 622.953 3rd Qu.: 625.562 2009 : 14
## Max. :8238.508 Max. :8272.734 1990 : 13
## (Other):107
## OWN_TYPE GRUPO TREATMENT
## Communal : 0 Área Indígena : 0 APA : 0
## Individual landowners: 0 Proteção Integral:253 Non_PA_CT: 0
## Not Reported :253 Uso Sustentável : 0 PI :253
## RPPN : 0
## TI : 0
## US : 0
##
## PA_total_area intersected_PA_area area_occupied_pct threshold
## Min. :1.550e+02 Min. : 0 Min. : 0.000 Non_PA_CT : 1
## 1st Qu.:1.171e+07 1st Qu.: 32258 1st Qu.: 0.104 with_PA : 87
## Median :7.207e+07 Median : 335128 Median : 1.626 without_PA:165
## Mean :1.559e+08 Mean : 17906939 Mean : 17.733
## 3rd Qu.:1.328e+08 3rd Qu.: 9458301 3rd Qu.: 25.807
## Max. :1.230e+09 Max. :395238155 Max. :100.000
##
## id_dummy mang_plan mang_comitte
## Min. : 5.00 Não : 17 Não :116
## 1st Qu.: 21.00 Sem_info:171 Sem_info: 72
## Median : 83.00 Sim : 65 Sim : 65
## Mean : 83.59 TI : 0 TI : 0
## 3rd Qu.:131.00
## Max. :215.00
##
list_PA_by_treatment[[3]] %>%
group_by(ORIG_NAME, CD_GEOCODI) %>%
dplyr::select(PA_total_area,intersected_PA_area, area_occupied_pct) %>%
paged_table()
list_PA_by_treatment[[3]] %>%
group_by(ORIG_NAME, threshold) %>%
summarise(ct_within_category=sum(length(unique(CD_GEOCODI)))) %>%
paged_table()
## Figure - CT amount by PI
prep_PI <- list_PA_by_treatment[[3]] %>%
group_by(ORIG_NAME) %>%
arrange(CD_GEOCODI) %>%
mutate(
cumulative_area = cumsum(intersected_PA_area),
x_position = row_number()
) %>%
ungroup()
unique_PI_names <- unique(prep_PI$ORIG_NAME)
for (name in unique_PI_names) {
data_filtered <- prep_PI %>% filter(ORIG_NAME == name)
p <- ggplot(data_filtered, aes(x = x_position, y = cumulative_area)) +
geom_line(aes(color = threshold, group = 1), size = 1) +
geom_point(aes(color = threshold, group = 1 ), size = 2) +
scale_color_manual(values = c("with_PA" = "green", "without_PA" = "red"))+
labs(title = name,
y="Total acumulado da intersecção da área da PA por CT",
x="Número de CT") +
theme_classic()
print(p)
}
RPPN
summary(list_PA_by_treatment[[4]])
## ID CD_GEOCODI TIPO CD_GEOCODM
## 47377 : 5 292020515000008: 5 RURAL :124 2310308:23
## 45340 : 4 291735905000029: 4 URBANO: 21 2920205: 8
## 23368 : 3 260160710000002: 3 2601607: 4
## 47372 : 3 292020515000003: 3 2606408: 4
## 3534 : 2 270900405000005: 2 2908101: 4
## 10152 : 1 210090705000072: 1 2917359: 4
## (Other):127 (Other) :127 (Other):98
## NM_MUNICIP total_ct_area WDPAID
## PARAMBU :23 Min. : 74678 555682596: 23
## MALHADA : 8 1st Qu.: 18381881 555600236: 4
## BELÉM DO SÃO FRANCISCO: 4 Median : 50508749 555600144: 3
## COCOS : 4 Mean :117530406 555600235: 3
## GRAVATÁ : 4 3rd Qu.:185165659 555682526: 3
## JABORANDI : 4 Max. :947190009 555682540: 3
## (Other) :98 (Other) :106
## WDPA_PID
## 555682596: 23
## 555600236: 4
## 555600144: 3
## 555600235: 3
## 555682526: 3
## 555682540: 3
## (Other) :106
## ORIG_NAME
## Reserva Particular Do Patrimonio Natural Fazenda Olho DAgua Do Urucu: 23
## Reserva Particular Do Patrimonio Natural Pico Do Barbado : 4
## Reserva Particular Do Patrimonio Natural Adilia Paraguassu Batista : 3
## Reserva Particular Do Patrimonio Natural Fazenda Almas : 3
## Reserva Particular Do Patrimonio Natural Fazenda Boa Ventura : 3
## Reserva Particular Do Patrimonio Natural Fazenda Tamandua : 3
## (Other) :106
## DESIG IUCN_CAT MARINE
## Reserva Particular do Patrimônio Natural:145 Cannot_applied: 0 0:145
## Área de Proteção Ambiental : 0 Ia : 0 1: 0
## Área de Relevante Interesse Ecológico : 0 II : 0 2: 0
## Estação Ecológica : 0 III : 0
## Floresta : 0 IV :145
## Indigenous Area : 0 V : 0
## (Other) : 0 VI : 0
## REP_M_AREA GIS_M_AREA REP_AREA GIS_AREA STATUS_YR
## Min. :0 Min. :0 Min. : 0.03476 Min. : 0.03488 1991 :23
## 1st Qu.:0 1st Qu.:0 1st Qu.: 1.31138 1st Qu.: 1.31683 2002 :17
## Median :0 Median :0 Median : 2.91187 Median : 2.92446 2000 :12
## Mean :0 Mean :0 Mean : 9.34245 Mean : 9.38210 2001 :12
## 3rd Qu.:0 3rd Qu.:0 3rd Qu.:15.24091 3rd Qu.:15.29550 2009 :11
## Max. :0 Max. :0 Max. :58.43412 Max. :58.68946 1998 :10
## (Other):60
## OWN_TYPE GRUPO TREATMENT
## Communal : 0 Área Indígena : 0 APA : 0
## Individual landowners:145 Proteção Integral: 0 Non_PA_CT: 0
## Not Reported : 0 Uso Sustentável :145 PI : 0
## RPPN :145
## TI : 0
## US : 0
##
## PA_total_area intersected_PA_area area_occupied_pct threshold
## Min. : 21582 Min. : 0 Min. : 0.0000 Non_PA_CT : 0
## 1st Qu.: 784974 1st Qu.: 68471 1st Qu.: 0.1344 with_PA : 30
## Median : 2009351 Median : 455000 Median : 1.1087 without_PA:115
## Mean : 7553129 Mean : 1860521 Mean : 14.1871
## 3rd Qu.: 9403487 3rd Qu.: 1639794 3rd Qu.: 5.1700
## Max. :30377439 Max. :21303100 Max. :100.0000
##
## id_dummy mang_plan mang_comitte
## Min. : 3.0 Não : 2 Não : 0
## 1st Qu.: 74.0 Sem_info:143 Sem_info:145
## Median :108.0 Sim : 0 Sim : 0
## Mean :113.8 TI : 0 TI : 0
## 3rd Qu.:158.0
## Max. :214.0
##
list_PA_by_treatment[[4]] %>%
group_by(ORIG_NAME, CD_GEOCODI) %>%
dplyr::select(PA_total_area, intersected_PA_area, area_occupied_pct) %>%
paged_table()
list_PA_by_treatment[[4]] %>%
group_by(ORIG_NAME, threshold) %>%
summarise(ct_within_category=sum(length(unique(CD_GEOCODI)))) %>%
paged_table()
# 27 CTs
PA_malha%>%
filter(ORIG_NAME=="Reserva Particular Do Patrimonio Natural Fazenda Olho DAgua Do Urucu") %>%
ggplot()+geom_sf()
## Figure - CT amount by RPPN
prep_RPPN <- list_PA_by_treatment[[4]] %>%
group_by(ORIG_NAME) %>%
arrange(CD_GEOCODI) %>%
mutate(
cumulative_area = cumsum(intersected_PA_area),
x_position = row_number()
) %>%
ungroup()
unique_RPPN_names <- unique(prep_RPPN$ORIG_NAME)
for (name in unique_RPPN_names) {
data_filtered <- prep_RPPN %>% filter(ORIG_NAME == name)
p <- ggplot(data_filtered, aes(x = x_position, y = cumulative_area)) +
geom_line(aes(color = threshold, group = 1), size = 1) +
geom_point(aes(color = threshold, group = 1 ), size = 2) +
scale_color_manual(values = c("with_PA" = "green", "without_PA" = "red"))+
labs(title = name,
y="Total acumulado da intersecção da área da PA por CT",
x="Número de CT") +
theme_classic()
print(p)
}
TI
summary(list_PA_by_treatment[[5]])
## ID CD_GEOCODI TIPO CD_GEOCODM
## 24383 : 3 260300905000018: 3 RURAL :349 2303709: 85
## 24419 : 3 260300905000054: 3 URBANO:158 2610905: 62
## 15852 : 2 230020005000027: 2 2603009: 44
## 15863 : 2 230020040000003: 2 2600500: 39
## 24382 : 2 250890105000034: 2 2706307: 24
## 24384 : 2 251290305000024: 2 2902658: 20
## (Other):493 (Other) :493 (Other):233
## NM_MUNICIP total_ct_area WDPAID WDPA_PID
## CAUCAIA : 85 Min. : 11481 352403 : 80 352403 : 80
## PESQUEIRA : 62 1st Qu.: 195795 352583 : 64 352583 : 64
## CABROBÓ : 44 Median : 2726414 352595 : 41 352595 : 41
## ÁGUAS BELAS : 39 Mean : 23514607 352407 : 27 352407 : 27
## PALMEIRA DOS ÍNDIOS: 24 3rd Qu.: 27540666 352598 : 24 352598 : 24
## BANZAÊ : 20 Max. :327060847 352442 : 23 352442 : 23
## (Other) :233 (Other):248 (Other):248
## ORIG_NAME DESIG
## Tapeba : 80 Terra Indígena :452
## Xukuru : 64 Reserva Indígena : 51
## Fulnio : 41 Indigenous Area : 4
## Truka_352407: 27 Área de Proteção Ambiental : 0
## XukuruKariri: 24 Área de Relevante Interesse Ecológico: 0
## Kiriri : 23 Estação Ecológica : 0
## (Other) :248 (Other) : 0
## IUCN_CAT MARINE REP_M_AREA GIS_M_AREA REP_AREA
## Cannot_applied:507 0:481 Min. :0 Min. : 0.0000 Min. : 0.00
## Ia : 0 1: 26 1st Qu.:0 1st Qu.: 0.0000 1st Qu.: 17.31
## II : 0 2: 0 Median :0 Median : 0.0000 Median : 57.69
## III : 0 Mean :0 Mean : 0.7098 Mean : 99.58
## IV : 0 3rd Qu.:0 3rd Qu.: 0.0000 3rd Qu.:123.00
## V : 0 Max. :0 Max. :12.0202 Max. :464.16
## VI : 0
## GIS_AREA STATUS_YR OWN_TYPE
## Min. : 2.493 2009 :103 Communal : 4
## 1st Qu.: 48.041 2006 : 86 Individual landowners: 0
## Median :103.500 2008 : 82 Not Reported :503
## Mean :119.250 1996 : 65
## 3rd Qu.:123.871 2002 : 51
## Max. :464.911 2010 : 27
## (Other): 93
## GRUPO TREATMENT PA_total_area
## Área Indígena :507 APA : 0 Min. : 741
## Proteção Integral: 0 Non_PA_CT: 0 1st Qu.: 44560238
## Uso Sustentável : 0 PI : 0 Median : 58983979
## RPPN : 0 Mean : 85663630
## TI :507 3rd Qu.:107139206
## US : 0 Max. :275818109
##
## intersected_PA_area area_occupied_pct threshold id_dummy
## Min. : 0 Min. : 0.0000 Non_PA_CT : 2 Min. : 4.0
## 1st Qu.: 23415 1st Qu.: 0.3807 with_PA :300 1st Qu.: 80.0
## Median : 111883 Median : 40.2314 without_PA:205 Median :155.0
## Mean : 4335981 Mean : 48.7349 Mean :129.2
## 3rd Qu.: 1654140 3rd Qu.: 99.9650 3rd Qu.:178.0
## Max. :102318015 Max. :100.0000 Max. :216.0
##
## mang_plan mang_comitte
## Não : 0 Não : 0
## Sem_info: 0 Sem_info: 0
## Sim : 0 Sim : 0
## TI :507 TI :507
##
##
##
list_PA_by_treatment[[5]] %>%
group_by(ORIG_NAME, CD_GEOCODI) %>%
dplyr::select(PA_total_area,intersected_PA_area, area_occupied_pct) %>%
paged_table()
list_PA_by_treatment[[5]] %>%
group_by(ORIG_NAME, threshold) %>%
summarise(ct_within_category=sum(length(unique(CD_GEOCODI)))) %>%
paged_table()
## Figure - CT amount by TI
prep_TI <- list_PA_by_treatment[[5]] %>%
group_by(ORIG_NAME) %>%
arrange(CD_GEOCODI) %>%
mutate(
cumulative_area = cumsum(intersected_PA_area),
x_position = row_number()
) %>%
ungroup()
unique_TI_names <- unique(prep_TI$ORIG_NAME)
for (name in unique_TI_names) {
data_filtered <- prep_TI %>% filter(ORIG_NAME == name)
p <- ggplot(data_filtered, aes(x = x_position, y = cumulative_area)) +
geom_line(aes(color = threshold, group = 1), size = 1) +
geom_point(aes(color = threshold, group = 1 ), size = 2) +
scale_color_manual(values = c("with_PA" = "green", "without_PA" = "red"))+
labs(title = name,
y="Total acumulado da intersecção da área da PA por CT",
x="Número de CT") +
theme_classic()
print(p)
}
US
summary(list_PA_by_treatment[[6]])
## ID CD_GEOCODI TIPO CD_GEOCODM
## 10233 : 1 210090705000010: 1 RURAL :64 2100907:18
## 10237 : 1 210090705000011: 1 URBANO:18 2301901: 7
## 10238 : 1 210090705000012: 1 2301000: 6
## 10241 : 1 210090705000013: 1 2304400: 5
## 11137 : 1 210090705000014: 1 2400208: 5
## 11146 : 1 210090705000015: 1 2908804: 5
## (Other):76 (Other) :76 (Other):36
## NM_MUNICIP total_ct_area WDPAID WDPA_PID
## ARAIOSES :18 Min. : 29537 351775 :19 351775 :19
## BARBALHA : 7 1st Qu.: 2376728 351788 :11 351788 :11
## AQUIRAZ : 6 Median : 30967424 351770 : 8 351770 : 8
## AÇU : 5 Mean : 55382559 220239 : 6 220239 : 6
## CONTENDAS DO SINCORÁ: 5 3rd Qu.: 73491451 351765 : 5 351765 : 5
## FORTALEZA : 5 Max. :345999951 555576297: 5 555576297: 5
## (Other) :36 (Other) :28 (Other) :28
## ORIG_NAME
## Reserva Extrativista Marinha Do Delta Do Parnaiba :19
## Floresta Nacional Do AraripeApodi :11
## Reserva Extrativista Do Batoque : 8
## Floresta Nacional De Contendas Do Sincora : 6
## Area De Relevante Interesse Ecologico Do Sitio Curio: 5
## Floresta Nacional De Acu : 5
## (Other) :28
## DESIG IUCN_CAT MARINE
## Reserva Extrativista :32 Cannot_applied: 0 0:75
## Floresta :30 Ia : 0 1: 3
## Área de Relevante Interesse Ecológico :16 II : 0 2: 4
## Reserva de Desenvolvimento Sustentável: 4 III : 0
## Área de Proteção Ambiental : 0 IV :16
## Estação Ecológica : 0 V : 0
## (Other) : 0 VI :66
## REP_M_AREA GIS_M_AREA REP_AREA GIS_AREA
## Min. : 0.0000 Min. : 0.00 Min. : 0.574 Min. : 0.5766
## 1st Qu.: 0.0000 1st Qu.: 0.00 1st Qu.: 6.014 1st Qu.: 6.0410
## Median : 0.0000 Median : 0.00 Median :112.160 Median :112.5746
## Mean : 15.7322 Mean : 21.89 Mean :160.583 Mean :161.2663
## 3rd Qu.: 0.9788 3rd Qu.: 26.28 3rd Qu.:270.221 3rd Qu.:271.4224
## Max. :291.7755 Max. :292.50 Max. :591.153 Max. :593.2217
##
## STATUS_YR OWN_TYPE GRUPO TREATMENT
## 2000 :19 Communal : 0 Área Indígena : 0 APA : 0
## 2003 :12 Individual landowners: 0 Proteção Integral: 0 Non_PA_CT: 0
## 1946 :11 Not Reported :82 Uso Sustentável :82 PI : 0
## 2001 :10 RPPN : 0
## 1999 : 6 TI : 0
## 2006 : 6 US :82
## (Other):18
## PA_total_area intersected_PA_area area_occupied_pct threshold
## Min. : 6360 Min. : 0 Min. : 0.0000 Non_PA_CT : 0
## 1st Qu.: 3203299 1st Qu.: 11166 1st Qu.: 0.1269 with_PA :32
## Median : 17735211 Median : 324446 Median : 2.3004 without_PA:50
## Mean : 96084821 Mean : 7849823 Mean : 26.1657
## 3rd Qu.:163607945 3rd Qu.: 3142025 3rd Qu.: 50.1699
## Max. :262131668 Max. :90067926 Max. :100.0000
##
## id_dummy mang_plan mang_comitte
## Min. : 1.0 Não :15 Não :39
## 1st Qu.: 64.0 Sem_info:50 Sem_info:29
## Median : 90.0 Sim :17 Sim :14
## Mean : 99.9 TI : 0 TI : 0
## 3rd Qu.:116.0
## Max. :201.0
##
list_PA_by_treatment[[6]] %>%
group_by(ORIG_NAME, CD_GEOCODI) %>%
dplyr::select(PA_total_area, intersected_PA_area, area_occupied_pct) %>%
paged_table()
list_PA_by_treatment[[6]] %>%
group_by(ORIG_NAME, threshold) %>%
summarise(ct_within_category=sum(length(unique(CD_GEOCODI)))) %>%
paged_table()
## Figure - CT amount by US
prep_US <- list_PA_by_treatment[[6]] %>%
group_by(ORIG_NAME) %>%
arrange(CD_GEOCODI) %>%
mutate(
cumulative_area = cumsum(intersected_PA_area),
x_position = row_number()
) %>%
ungroup()
unique_US_names <- unique(prep_US$ORIG_NAME)
for (name in unique_US_names) {
data_filtered <- prep_US %>% filter(ORIG_NAME == name)
p <- ggplot(data_filtered, aes(x = x_position, y = cumulative_area)) +
geom_line(aes(color = threshold, group = 1), size = 1) +
geom_point(aes(color = threshold, group = 1 ), size = 2) +
scale_color_manual(values = c("with_PA" = "green", "without_PA" = "red"))+
labs(title = name,
y="Total acumulado da intersecção da área da PA por CT",
x="Número de CT") +
theme_classic()
print(p)
}
