1. Lectura de datasets

Trabajaré con los datasets individualmente para mirar posibles inconsistencias antes de intentar juntar todo.

  • Haré una operación de enriquecimiento de la columna timestamp para crear distintos formatos de datas;
  • Haré un split en la columna code para facilitar la agregación de los datasets;

1.1 Satelite Data - Zone 7

sat_data_r07 <- readxl::read_excel("./data/Satellitedata_SpatialJoinISRAR07_fishing_2017_2021.xlsx") %>%
  janitor::clean_names() %>%
  mutate(isra_code = substr(code, 1, 4),
         isra_zone = substr(code, 5, 5),
         juri_zone = substr(code, 6, 8),
         juri_code = substr(code, 9, 12),
         datetime = as_datetime(timestamp),
         date = as_date(datetime),
         year_month = zoo::as.yearmon(date)) %>%
  janitor::clean_names() %>%
  relocate(starts_with('isra'), .before = 'name') %>%
  relocate(starts_with('juri'), .before = 'jurisdicti') %>%
  relocate(c(timestamp, date, datetime, year_month), .before = objectid)
head(sat_data_r07)
## # A tibble: 6 × 38
##   timestamp        date       datetime            year_month objectid join_count
##   <chr>            <date>     <dttm>              <yearmon>     <dbl>      <dbl>
## 1 <NA>             NA         NA                  <NA>              1          0
## 2 <NA>             NA         NA                  <NA>              2          0
## 3 <NA>             NA         NA                  <NA>              3          0
## 4 2021-06-06 02:1… 2021-06-06 2021-06-06 02:15:51 jun 2021          4          1
## 5 2018-05-10 02:2… 2018-05-10 2018-05-10 02:23:42 mai 2018          5          1
## 6 2019-04-20 14:3… 2019-04-20 2019-04-20 14:33:08 abr 2019          6          1
## # ℹ 32 more variables: target_fid <dbl>, join_fid <dbl>, code <chr>,
## #   status <chr>, isra_code <chr>, isra_zone <chr>, name <chr>,
## #   jurisdicti <chr>, juri_zone <chr>, juri_code <chr>, region <chr>,
## #   criteria <chr>, subcriteri <chr>, query_crite <chr>, query_subcr <chr>,
## #   min_depth <dbl>, max_depth <dbl>, order <chr>, family <chr>, url <chr>,
## #   scene_id <chr>, lat <dbl>, lon <dbl>, year <dbl>, mmsi <dbl>,
## #   matching_s <dbl>, fishing_sc <dbl>, length_m <dbl>, matched_ca <chr>, …
skim(sat_data_r07)
Data summary
Name sat_data_r07
Number of rows 1656
Number of columns 38
_______________________
Column type frequency:
character 20
Date 1
numeric 16
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
timestamp 101 0.94 25 25 0 1104 0
year_month 101 0.94 4 16 0 60 0
code 0 1.00 12 12 0 124 0
status 0 1.00 4 4 0 1 0
isra_code 0 1.00 4 4 0 1 0
isra_zone 0 1.00 1 1 0 1 0
name 0 1.00 4 35 0 125 0
jurisdicti 0 1.00 4 84 0 33 0
juri_zone 0 1.00 3 3 0 26 0
juri_code 0 1.00 4 4 0 124 0
region 0 1.00 20 57 0 4 0
criteria 0 1.00 26 121 0 13 0
subcriteri 0 1.00 2 214 0 38 0
query_crite 0 1.00 1 10 0 8 0
query_subcr 0 1.00 2 22 0 23 0
order 0 1.00 11 100 0 33 0
family 0 1.00 8 173 0 63 0
url 0 1.00 51 81 0 124 0
scene_id 101 0.94 67 67 0 1104 0
matched_ca 101 0.94 9 18 0 4 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
date 101 0.94 2017-01-08 2021-12-27 2019-05-26 799

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
objectid 0 1.00 828.50 478.19 1.00 414.75 828.50 1.24225e+03 1656.00 ▇▇▇▇▇
join_count 0 1.00 0.94 0.24 0.00 1.00 1.00 1.00000e+00 1.00 ▁▁▁▁▇
target_fid 0 1.00 85.26 31.67 0.00 71.00 108.00 1.08000e+02 124.00 ▁▂▁▂▇
join_fid 0 1.00 92573.01 57387.24 -1.00 36492.00 95688.00 1.41189e+05 186761.00 ▇▅▆▆▆
min_depth 0 1.00 4.05 28.13 0.00 0.00 0.00 0.00000e+00 220.00 ▇▁▁▁▁
max_depth 0 1.00 256.14 499.62 3.00 100.00 100.00 1.00000e+02 1928.00 ▇▁▁▁▁
lat 101 0.94 20.68 13.31 -28.86 20.77 25.86 2.65200e+01 29.99 ▁▁▁▁▇
lon 101 0.94 56.34 9.21 32.06 53.11 56.10 5.74100e+01 77.75 ▁▁▇▁▂
year 101 0.94 2018.91 1.41 2017.00 2018.00 2019.00 2.02000e+03 2021.00 ▇▇▇▇▆
mmsi 1347 0.19 469458307.39 113915512.66 8617172.00 419006400.00 422445300.00 4.70956e+08 994018107.00 ▁▂▇▂▁
matching_s 101 0.94 0.38 3.66 0.00 0.00 0.00 0.00000e+00 87.52 ▇▁▁▁▁
fishing_sc 101 0.94 0.94 0.02 0.90 0.93 0.95 9.60000e-01 0.96 ▁▁▂▂▇
length_m 101 0.94 21.46 5.81 9.80 17.49 20.01 2.42100e+01 45.07 ▃▇▃▁▁
overpasses 101 0.94 328.34 122.22 67.00 284.00 289.00 4.26000e+02 575.00 ▃▁▇▇▁
shape_length 0 1.00 26.51 18.21 0.01 5.21 42.43 4.24300e+01 91.28 ▆▁▇▁▁
shape_area 0 1.00 5.56 6.71 0.00 0.48 8.20 8.20000e+00 156.32 ▇▁▁▁▁

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
datetime 101 0.94 2017-01-08 00:55:46 2021-12-27 02:15:31 2019-05-26 14:33:35 1104

1.2 Satelite Data - Zone 12

sat_data_r12 <- readxl::read_excel("./data/Satellitedata_SpatialJoinISRAR12_fishing_2017_2021.xlsx") %>%
  janitor::clean_names() %>%
  mutate(isra_code = substr(code, 1, 4),
         isra_zone = substr(code, 5, 6),
         juri_zone = substr(code, 7, 9),
         juri_code = substr(code, 10, 13),
         datetime = as_datetime(timestamp),
         date = as_date(datetime),
         year_month = zoo::as.yearmon(date)) %>%
  janitor::clean_names() %>%
  relocate(starts_with('isra'), .before = 'name') %>%
  relocate(starts_with('juri'), .before = 'jurisdicti') %>%
  relocate(c(timestamp, date, datetime, year_month), .before = objectid)
head(sat_data_r12)
## # A tibble: 6 × 41
##   timestamp        date       datetime            year_month objectid join_count
##   <chr>            <date>     <dttm>              <yearmon>     <dbl>      <dbl>
## 1 2019-09-19 10:0… 2019-09-19 2019-09-19 10:08:12 set 2019          1          1
## 2 2019-10-25 10:0… 2019-10-25 2019-10-25 10:08:13 out 2019          2          1
## 3 2020-10-25 10:0… 2020-10-25 2020-10-25 10:09:08 out 2020          3          1
## 4 2020-01-23 10:0… 2020-01-23 2020-01-23 10:08:59 jan 2020          4          1
## 5 2018-11-28 23:1… 2018-11-28 2018-11-28 23:15:12 nov 2018          5          1
## 6 2018-10-23 23:1… 2018-10-23 2018-10-23 23:14:48 out 2018          6          1
## # ℹ 35 more variables: target_fid <dbl>, join_fid <dbl>, code <chr>,
## #   status <chr>, isra_code <chr>, isra_zone <chr>, name <chr>,
## #   jurisdicti <chr>, juri_zone <chr>, juri_code <chr>, region <chr>,
## #   criteria <chr>, subcriteri <chr>, query_crite <chr>, query_subcr <chr>,
## #   min_depth <dbl>, max_depth <dbl>, order <chr>, family <chr>, url <chr>,
## #   area_hseas <dbl>, field <dbl>, field1 <dbl>, scene_id <chr>, lat <dbl>,
## #   lon <dbl>, year <dbl>, mmsi <dbl>, matching_s <dbl>, fishing_sc <dbl>, …
skim(sat_data_r12)
Data summary
Name sat_data_r12
Number of rows 3083
Number of columns 41
_______________________
Column type frequency:
character 20
Date 1
numeric 19
POSIXct 1
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
timestamp 37 0.99 25 25 0 1459 0
year_month 37 0.99 4 16 0 60 0
code 0 1.00 12 13 0 65 0
status 0 1.00 4 4 0 1 0
isra_code 0 1.00 4 4 0 1 0
isra_zone 0 1.00 2 2 0 2 0
name 0 1.00 6 62 0 65 0
jurisdicti 0 1.00 4 100 0 18 0
juri_zone 0 1.00 3 3 0 13 0
juri_code 0 1.00 3 4 0 65 0
region 0 1.00 34 34 0 1 0
criteria 0 1.00 26 121 0 8 0
subcriteri 0 1.00 2 243 0 30 0
query_crite 0 1.00 1 10 0 8 0
query_subcr 0 1.00 2 26 0 27 0
order 0 1.00 10 128 0 38 0
family 0 1.00 9 226 0 55 0
url 0 1.00 53 96 0 64 0
scene_id 37 0.99 67 67 0 1461 0
matched_ca 37 0.99 9 18 0 4 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
date 37 0.99 2017-01-04 2021-12-26 2019-07-06 881

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
objectid 0 1.00 1542.00 890.13 1.00 771.50 1542.00 2312.50 3083.00 ▇▇▇▇▇
join_count 0 1.00 0.99 0.11 0.00 1.00 1.00 1.00 1.00 ▁▁▁▁▇
target_fid 0 1.00 31.55 17.42 0.00 23.00 24.00 48.00 64.00 ▂▇▃▂▃
join_fid 0 1.00 98654.87 54216.52 -1.00 57066.50 98574.00 144084.00 186670.00 ▇▇▇▇▇
min_depth 0 1.00 2.70 34.32 0.00 0.00 0.00 0.00 1650.00 ▇▁▁▁▁
max_depth 0 1.00 966.37 723.17 20.00 200.00 1000.00 1928.00 1928.00 ▇▁▆▁▆
area_hseas 0 1.00 733598.59 1210183.55 0.00 21700.00 60510.00 248693.00 3020979.00 ▇▁▁▁▂
field 0 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ▁▁▇▁▁
field1 0 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 ▁▁▇▁▁
lat 37 0.99 4.71 15.49 -43.70 -7.84 -2.56 24.00 31.36 ▁▁▇▃▅
lon 37 0.99 -88.39 13.61 -114.61 -107.27 -80.66 -79.48 -70.56 ▅▁▁▇▃
year 37 0.99 2018.99 1.42 2017.00 2018.00 2019.00 2020.00 2021.00 ▇▇▇▆▇
mmsi 2865 0.07 607281715.38 231968085.69 14334.00 371124500.00 760000421.00 760012394.00 990999121.00 ▁▂▁▇▁
matching_s 37 0.99 0.25 3.68 0.00 0.00 0.00 0.00 106.20 ▇▁▁▁▁
fishing_sc 37 0.99 0.94 0.02 0.90 0.93 0.95 0.96 0.96 ▂▂▂▂▇
length_m 37 0.99 22.97 6.57 10.72 18.54 21.44 25.52 61.78 ▇▇▂▁▁
overpasses 37 0.99 267.92 113.47 34.00 147.00 267.00 292.00 797.00 ▅▇▂▁▁
shape_length 0 1.00 61.25 78.22 0.04 12.53 23.87 32.48 208.76 ▇▁▁▁▂
shape_area 0 1.00 59.92 98.60 0.00 1.93 4.96 20.35 246.27 ▇▁▁▁▂

Variable type: POSIXct

skim_variable n_missing complete_rate min max median n_unique
datetime 37 0.99 2017-01-04 01:40:11 2021-12-26 13:06:12 2019-07-06 23:34:14 1459

Comparacion de columnas entre los datasets de cada región

janitor::compare_df_cols(sat_data_r07, sat_data_r12, strict_description = T)
##     column_name    sat_data_r07    sat_data_r12
## 1    area_hseas            <NA>         numeric
## 2          code       character       character
## 3      criteria       character       character
## 4          date            Date            Date
## 5      datetime POSIXct, POSIXt POSIXct, POSIXt
## 6        family       character       character
## 7         field            <NA>         numeric
## 8        field1            <NA>         numeric
## 9    fishing_sc         numeric         numeric
## 10    isra_code       character       character
## 11    isra_zone       character       character
## 12   join_count         numeric         numeric
## 13     join_fid         numeric         numeric
## 14    juri_code       character       character
## 15    juri_zone       character       character
## 16   jurisdicti       character       character
## 17          lat         numeric         numeric
## 18     length_m         numeric         numeric
## 19          lon         numeric         numeric
## 20   matched_ca       character       character
## 21   matching_s         numeric         numeric
## 22    max_depth         numeric         numeric
## 23    min_depth         numeric         numeric
## 24         mmsi         numeric         numeric
## 25         name       character       character
## 26     objectid         numeric         numeric
## 27        order       character       character
## 28   overpasses         numeric         numeric
## 29  query_crite       character       character
## 30  query_subcr       character       character
## 31       region       character       character
## 32     scene_id       character       character
## 33   shape_area         numeric         numeric
## 34 shape_length         numeric         numeric
## 35       status       character       character
## 36   subcriteri       character       character
## 37   target_fid         numeric         numeric
## 38    timestamp       character       character
## 39          url       character       character
## 40         year         numeric         numeric
## 41   year_month         yearmon         yearmon
Column name Column definition
OBJECTID Numero de fila
Join_Count Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
TARGET_FID Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
JOIN_FID Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
code Codigo numerico de la ISRA (poligono)
Status Todos los poligonos en el mapa son ISRAS entonces esta columna tampoco sirve
Name Nombre particular de cada ISRA
Jurisdicti Pais en el que esta la ISRA
Region Region del mundo, en este caso van a ser 3 regiones (12 Central and South American Pacific, 7 Western Indian Ocean, 9 Asia)
Criteria Las caracteristicas biologicas y ecologicas que cumplen las especies que cualificaron para que se determinara la ISRA
Subcriteri Las caracteristicas biologicas y ecologicas que cumplen las especies que cualificaron para que se determinara la ISRA
QueryCrite Codigos del criteria
QuerySubcr Codigos del subcriteria
MinDepth profundidad minima en metros de la isra (0 es desde la superficie)
MaxDepth profundidad maxima de la ISRA
Order Orden de las especies que cualificaron para la ISRA (QS)
Family Familia de las especies que cualificaron para la ISRA (QS)
URL En esta direccion puedes encontrar el factseet de cada area donde explican porq ha sido designada como una ISRA
AreaHseas Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
Field Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
Field1 Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
scene_id Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
timestamp Fecha y hora del evento de pesca
lat latitud del evento de pesca
lon longitud del evento de pesca
year ano del evento de pesca
mmsi NO SIRVE. Valores del estudio de Paolo et al. 2024 que usaron para determinar si estaban pescando o no esos barcos
matching_s NO SIRVE. Valores del estudio de Paolo et al. 2024 que usaron para determinar si estaban pescando o no esos barcos
fishing_sc Aqui ya filtre todos los que tenian un valor igual o mayor a 0.9 lo que significa que estan pescando
length_m longitud en metros del barco
matched_ca Aqui solo aparecen los que hicieron match para decir que estaban pescando
overpasses Esto CREO que es el numero de veces que el satelite paso sobre ese punto durante el estudio 2017-2021
Shape_Length Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
Shape_Area Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)

Consideraciones sobre los datasets

  • La zona 7 no tiene las columnas field, field1 y area_hseas;
  • Los tipos de columnas están iguales;
  • La zona 12 posee 82 días a más de muestreo;
  • La zona 7, tienes más sítios muestreados;
  • En la zona 7, la juri_zone 0514, Oman, posee los name Gulf of Masirah y Muscat. Todavía, en la zona 12, el número de juri_zone y name son iguales. Confirmar si es un error;

Note 1: había piensado en crear una columna llamada n_regions para tener el número de regiones que posee cada ISRA, todavía solamente hay 5 registros de ISRAS que poseen más de una region.

sat_data_r07 %>%
  mutate(n_regions = case_when(region == unique(sat_data_r07$region)[1] ~ 1,
                               region == unique(sat_data_r07$region)[2] ~ 2,
                               region == unique(sat_data_r07$region)[3] ~ 3,
                               region == unique(sat_data_r07$region)[4] ~ 4)) %>% count(n_regions)
## # A tibble: 4 × 2
##   n_regions     n
##       <dbl> <int>
## 1         1  1651
## 2         2     1
## 3         3     1
## 4         4     3

Definicion de columnas

El dataset final para satelite fue definido con las columnas: objectid, code, timestamp, datetime, date, year, year_month, isra_zone, jurisdicti, juri_zone, juri_code, name, region, criteria, subcriteri, query_crite, query_subcr, min_depth, max_depth, order, family, lat, lon, fishing_sc, length_m, matched_ca, overpasses

sat_data <- full_join(sat_data_r07, sat_data_r12) %>% 
  select(objectid, code, timestamp, datetime, date, year, year_month, 
                        isra_zone, jurisdicti, juri_zone, juri_code, name, region,
                        criteria, subcriteri, query_crite, query_subcr, min_depth,
                        max_depth, order, family, lat, lon, fishing_sc, length_m,
                        matched_ca, overpasses)

head(sat_data)
## # A tibble: 6 × 27
##   objectid code        timestamp datetime            date        year year_month
##      <dbl> <chr>       <chr>     <dttm>              <date>     <dbl> <yearmon> 
## 1        1 ISRA7ARE02… <NA>      NA                  NA            NA <NA>      
## 2        2 ISRA7ARE02… <NA>      NA                  NA            NA <NA>      
## 3        3 ISRA7ARE03… <NA>      NA                  NA            NA <NA>      
## 4        4 ISRA7ARE03… 2021-06-… 2021-06-06 02:15:51 2021-06-06  2021 jun 2021  
## 5        5 ISRA7ARE03… 2018-05-… 2018-05-10 02:23:42 2018-05-10  2018 mai 2018  
## 6        6 ISRA7ARE03… 2019-04-… 2019-04-20 14:33:08 2019-04-20  2019 abr 2019  
## # ℹ 20 more variables: isra_zone <chr>, jurisdicti <chr>, juri_zone <chr>,
## #   juri_code <chr>, name <chr>, region <chr>, criteria <chr>,
## #   subcriteri <chr>, query_crite <chr>, query_subcr <chr>, min_depth <dbl>,
## #   max_depth <dbl>, order <chr>, family <chr>, lat <dbl>, lon <dbl>,
## #   fishing_sc <dbl>, length_m <dbl>, matched_ca <chr>, overpasses <dbl>

1.1.3 GPS Data - Zone 7

# RECORDAR: list.files no ejectuta si algunos de los archivos de Excel está abierto

gps_data_r7 <- list.files(path = "./data/Fishing Region 07 AIS/", pattern = "\\.xlsx$", full.names = TRUE) %>%
  map_df(~ readxl::read_excel(.x, col_types = c("text", "numeric", "numeric", "numeric", "text", "text", "text", "text", "text", "text", "text", "text", "text", "numeric", "numeric", "text", "text", "text", "text", "numeric", "numeric", "date", "text", "text", "text", "numeric", "numeric", "numeric"))) %>%
  janitor::clean_names() %>%
  mutate(isra_code = substr(code, 1, 4),
         isra_zone = substr(code, 5, 5),
         juri_zone = substr(code, 6, 8),
         juri_code = substr(code, 9, 12),
         date = as_date(time_range),
         year = year(date),
         year_month = zoo::as.yearmon(date)) %>%
  relocate(starts_with('isra'), .before = 'name') %>%
  relocate(starts_with('juri'), .before = 'jurisdicti') %>%
  relocate(objectid, .before = 'objectid_1') %>%
  relocate(c(time_range, date, year_month), .before = 'objectid') %>%
  mutate(time_range = as.character(time_range))
head(gps_data_r7)
## # A tibble: 6 × 35
##   time_range date       year_month objectid objectid_1 join_count target_fid
##   <chr>      <date>     <yearmon>  <chr>    <chr>           <dbl>      <dbl>
## 1 <NA>       NA         <NA>       <NA>     1                   0          0
## 2 <NA>       NA         <NA>       <NA>     2                   0          1
## 3 <NA>       NA         <NA>       <NA>     3                   0          2
## 4 <NA>       NA         <NA>       <NA>     4                   0          3
## 5 <NA>       NA         <NA>       <NA>     5                   0          4
## 6 2014-05-01 2014-05-01 mai 2014   1        6                   1          5
## # ℹ 28 more variables: join_fid <dbl>, code <chr>, status <chr>,
## #   isra_code <chr>, isra_zone <chr>, name <chr>, jurisdicti <chr>,
## #   juri_zone <chr>, juri_code <chr>, region <chr>, criteria <chr>,
## #   subcriteri <chr>, query_crite <chr>, query_subcr <chr>, min_depth <dbl>,
## #   max_depth <dbl>, order <chr>, family <chr>, url <chr>, lat <dbl>,
## #   lon <dbl>, flag <chr>, geartype <chr>, vessel_i_ds <chr>, apparent_f <dbl>,
## #   shape_length <dbl>, shape_area <dbl>, year <dbl>

Hay que verificar las diferencias en el formato de fechas antes de dar merge en los datasets

skim(gps_data_r7)
Data summary
Name gps_data_r7
Number of rows 407406
Number of columns 35
_______________________
Column type frequency:
character 23
Date 1
numeric 11
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
time_range 154165 0.62 10 10 0 84 0
year_month 154165 0.62 4 16 0 84 0
objectid 895 1.00 1 5 0 64349 0
objectid_1 0 1.00 1 5 0 65274 0
code 0 1.00 12 12 0 124 0
status 0 1.00 4 4 0 1 0
isra_code 0 1.00 4 4 0 1 0
isra_zone 0 1.00 1 1 0 1 0
name 0 1.00 4 35 0 125 0
jurisdicti 0 1.00 4 84 0 33 0
juri_zone 0 1.00 3 3 0 26 0
juri_code 0 1.00 4 4 0 124 0
region 0 1.00 20 57 0 4 0
criteria 0 1.00 26 121 0 13 0
subcriteri 0 1.00 2 214 0 38 0
query_crite 0 1.00 1 10 0 8 0
query_subcr 0 1.00 2 22 0 23 0
order 0 1.00 11 100 0 33 0
family 0 1.00 8 173 0 63 0
url 0 1.00 51 81 0 124 0
flag 5658 0.99 3 3 0 85 0
geartype 895 1.00 7 18 0 17 0
vessel_i_ds 895 1.00 1 2 0 50 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
date 154165 0.62 2014-01-01 2023-12-01 2018-11-01 84

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
join_count 0 1.00 1.00 0.05 0.00 1.00 1.00 1.00 1.00 ▁▁▁▁▇
target_fid 0 1.00 62.99 48.82 0.00 7.00 71.00 112.00 124.00 ▇▁▂▁▇
join_fid 0 1.00 24112.64 16270.51 -1.00 10053.25 21914.00 36394.00 64349.00 ▇▆▆▃▂
min_depth 0 1.00 1.89 18.63 0.00 0.00 0.00 0.00 220.00 ▇▁▁▁▁
max_depth 0 1.00 389.44 505.25 3.00 180.00 256.00 300.00 1928.00 ▇▁▁▁▁
lat 895 1.00 -14.75 20.03 -45.20 -33.00 -9.90 -2.20 29.90 ▇▂▇▂▂
lon 895 1.00 55.67 16.03 18.80 46.40 50.90 72.50 86.80 ▁▃▇▂▅
apparent_f 895 1.00 7.03 25.31 0.00 1.33 2.74 6.01 1864.13 ▇▁▁▁▁
shape_length 0 1.00 50.50 21.27 0.01 42.43 54.45 57.58 91.28 ▃▂▇▇▂
shape_area 0 1.00 75.22 52.11 0.00 16.26 64.04 125.88 156.32 ▆▁▇▁▇
year 154165 0.62 2019.56 3.08 2014.00 2017.00 2018.00 2023.00 2023.00 ▂▃▃▁▇

1.1.4 GPS Data - Zone 12

# RECORDAR: list.files no ejectuta si algunos de los archivos de Excel está abierto
gps_data_r12 <- list.files(path = "./data/Fishing Region 12 AIS/", pattern = "\\.xlsx$", full.names = TRUE) %>%
  map_df(~ readxl::read_excel(.x, col_types = c("text", "numeric", "numeric", "numeric", "text", "text", "text", "text", "text", "text", "text", "text", "text", "numeric", "numeric", "text", "text", "text", "text", "numeric", "numeric", "guess", "text", "text", "text", "numeric", "numeric", "numeric"))) %>%
  janitor::clean_names() %>%
  mutate(isra_code = substr(code, 1, 4),
         isra_zone = substr(code, 5, 6),
         juri_zone = substr(code, 7, 9),
         juri_code = substr(code, 10, 13),
         date = ym(time_range),
         year = year(date),
         year_month = zoo::as.yearmon(date)) %>%
  relocate(starts_with('isra'), .before = 'name') %>%
  relocate(starts_with('juri'), .before = 'jurisdicti') %>%
  relocate(objectid, .before = 'objectid_1') %>%
  relocate(c(time_range, date, year_month), .before = 'objectid')
head(gps_data_r12)
## # A tibble: 6 × 35
##   time_range date       year_month objectid objectid_1 join_count target_fid
##   <chr>      <date>     <yearmon>  <chr>    <chr>           <dbl>      <dbl>
## 1 2014-03    2014-03-01 mar 2014   105      1                   1          0
## 2 2014-03    2014-03-01 mar 2014   107      2                   1          0
## 3 2014-03    2014-03-01 mar 2014   109      3                   1          0
## 4 2014-03    2014-03-01 mar 2014   110      4                   1          0
## 5 2014-03    2014-03-01 mar 2014   111      5                   1          0
## 6 2014-03    2014-03-01 mar 2014   113      6                   1          0
## # ℹ 28 more variables: join_fid <dbl>, code <chr>, status <chr>,
## #   isra_code <chr>, isra_zone <chr>, name <chr>, jurisdicti <chr>,
## #   juri_zone <chr>, juri_code <chr>, region <chr>, criteria <chr>,
## #   subcriteri <chr>, query_crite <chr>, query_subcr <chr>, min_depth <dbl>,
## #   max_depth <dbl>, order <chr>, family <chr>, url <chr>, lat <dbl>,
## #   lon <dbl>, flag <chr>, geartype <chr>, vessel_i_ds <chr>, apparent_f <dbl>,
## #   shape_length <dbl>, shape_area <dbl>, year <dbl>
skim(gps_data_r12)
Data summary
Name gps_data_r12
Number of rows 319544
Number of columns 35
_______________________
Column type frequency:
character 23
Date 1
numeric 11
________________________
Group variables None

Variable type: character

skim_variable n_missing complete_rate min max empty n_unique whitespace
time_range 317 1.00 7 7 0 119 0
year_month 317 1.00 4 16 0 119 0
objectid 317 1.00 1 5 0 41301 0
objectid_1 0 1.00 1 5 0 52242 0
code 0 1.00 12 13 0 65 0
status 0 1.00 4 4 0 1 0
isra_code 0 1.00 4 4 0 1 0
isra_zone 0 1.00 2 2 0 2 0
name 0 1.00 6 62 0 65 0
jurisdicti 0 1.00 4 100 0 18 0
juri_zone 0 1.00 3 3 0 13 0
juri_code 0 1.00 3 4 0 65 0
region 0 1.00 34 34 0 1 0
criteria 0 1.00 26 121 0 8 0
subcriteri 0 1.00 2 243 0 30 0
query_crite 0 1.00 1 10 0 8 0
query_subcr 0 1.00 2 26 0 27 0
order 0 1.00 10 128 0 38 0
family 0 1.00 9 226 0 55 0
url 0 1.00 53 96 0 64 0
flag 26895 0.92 3 3 0 68 0
geartype 317 1.00 7 18 0 15 0
vessel_i_ds 317 1.00 1 3 0 92 0

Variable type: Date

skim_variable n_missing complete_rate min max median n_unique
date 317 1 2014-01-01 2023-12-01 2020-10-01 119

Variable type: numeric

skim_variable n_missing complete_rate mean sd p0 p25 p50 p75 p100 hist
join_count 0 1 1.00 0.03 0.00 1.00 1.00 1.00 1.00 ▁▁▁▁▇
target_fid 0 1 29.80 14.07 0.00 23.00 23.00 50.00 64.00 ▁▇▂▃▁
join_fid 0 1 14857.44 10706.00 -1.00 5479.00 12879.00 22522.00 41301.00 ▇▆▅▃▂
min_depth 0 1 0.83 13.88 0.00 0.00 0.00 0.00 1650.00 ▇▁▁▁▁
max_depth 0 1 1164.70 775.52 20.00 200.00 1112.00 1928.00 1928.00 ▅▁▃▁▇
lat 317 1 0.27 7.87 -43.70 -4.50 1.00 4.10 31.50 ▁▁▇▇▁
lon 317 1 -91.39 11.28 -116.70 -98.50 -90.20 -81.10 -70.50 ▃▃▇▇▅
apparent_f 317 1 7.75 36.69 0.00 1.01 2.20 5.62 4293.34 ▇▁▁▁▁
shape_length 0 1 95.93 84.57 0.04 23.87 54.14 208.76 208.76 ▇▅▁▁▇
shape_area 0 1 117.67 100.17 0.00 20.35 95.59 246.27 246.27 ▇▅▁▁▇
year 317 1 2019.96 2.38 2014.00 2018.00 2020.00 2022.00 2023.00 ▂▃▅▇▇ 
janitor::compare_df_cols(gps_data_r7, gps_data_r12, strict_description = T)
##     column_name gps_data_r7 gps_data_r12
## 1    apparent_f     numeric      numeric
## 2          code   character    character
## 3      criteria   character    character
## 4          date        Date         Date
## 5        family   character    character
## 6          flag   character    character
## 7      geartype   character    character
## 8     isra_code   character    character
## 9     isra_zone   character    character
## 10   join_count     numeric      numeric
## 11     join_fid     numeric      numeric
## 12    juri_code   character    character
## 13    juri_zone   character    character
## 14   jurisdicti   character    character
## 15          lat     numeric      numeric
## 16          lon     numeric      numeric
## 17    max_depth     numeric      numeric
## 18    min_depth     numeric      numeric
## 19         name   character    character
## 20     objectid   character    character
## 21   objectid_1   character    character
## 22        order   character    character
## 23  query_crite   character    character
## 24  query_subcr   character    character
## 25       region   character    character
## 26   shape_area     numeric      numeric
## 27 shape_length     numeric      numeric
## 28       status   character    character
## 29   subcriteri   character    character
## 30   target_fid     numeric      numeric
## 31   time_range   character    character
## 32          url   character    character
## 33  vessel_i_ds   character    character
## 34         year     numeric      numeric
## 35   year_month     yearmon      yearmon
Column name Column definition
OBJECTID_1 Numero de fila
Join_Count Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
TARGET_FID Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
JOIN_FID Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
code Codigo numerico de la ISRA (poligono)
Status Todos los poligonos en el mapa son ISRAS entonces esta columna tampoco sirve
Name Nombre particular de cada ISRA
Jurisdicti Pais en el que esta la ISRA
Region Region del mundo, en este caso van a ser 3 regiones (12 Central and South American Pacific, 7 Western Indian Ocean, 9 Asia)
Criteria Las caracteristicas biologicas y ecologicas que cumplen las especies que cualificaron para que se determinara la ISRA
Subcriteri Las caracteristicas biologicas y ecologicas que cumplen las especies que cualificaron para que se determinara la ISRA
QueryCrite Codigos del criteria
QuerySubcr Codigos del subcriteria
MinDepth profundidad minima en metros de la isra (0 es desde la superficie)
MaxDepth profundidad maxima de la ISRA
Order_ Orden de las especies que cualificaron para la ISRA (QS)
Family Familia de las especies que cualificaron para la ISRA (QS)
URL En esta direccion puedes encontrar el factseet de cada area donde explican porq ha sido designada como una ISRA
OBJECTID Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
Lat latitud del evento de pesca
Lon longitud del evento de pesca
Time_Range mes y ano del evento de pesca
Flag bandera del barco de pesca
Geartype tipo de arte de pesca (esta te tengo que enviar la lista definitiva porque hay algunas artes de pesca que las vamos a agrupar en una sola categoria)
Vessel_IDs Numero de la embarcacion (no sirve)
Apparent_F tiempo de pesca en horas (este sirve para calcular el esfuerzo pesquero y es lo que hay que sacar con la ecuacion de los datos satelitates
Shape_Length Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)
Shape_Area Estas columnas son generadas por ARCGIS Pro al hacer el spatial joint (no sirven)

Consideraciones sobre los datasets

Definicion de columnas

El dataset final para satelite fue definido con las columnas: objectid, objectid_1, code, time_range, date, year, year_month, isra_zone, jurisdicti, juri_zone, juri_code, name, region, criteria, subcriteri, query_crite, query_subcr, min_depth, max_depth, order, family, lat, lon, geartype, vessel_i_ds, apparent_f

gps_data <- full_join(gps_data_r7, gps_data_r12) %>% 
  select(objectid, objectid_1, code, time_range, date, year, year_month, 
                        isra_zone, jurisdicti, juri_zone, juri_code, name, region,
                        criteria, subcriteri, query_crite, query_subcr, min_depth,
                        max_depth, order, family, lat, lon, geartype, vessel_i_ds,
                        apparent_f)
head(gps_data)
## # A tibble: 6 × 26
##   objectid objectid_1 code      time_range date        year year_month isra_zone
##   <chr>    <chr>      <chr>     <chr>      <date>     <dbl> <yearmon>  <chr>    
## 1 <NA>     1          ISRA7ARE… <NA>       NA            NA <NA>       7        
## 2 <NA>     2          ISRA7ARE… <NA>       NA            NA <NA>       7        
## 3 <NA>     3          ISRA7ARE… <NA>       NA            NA <NA>       7        
## 4 <NA>     4          ISRA7ARE… <NA>       NA            NA <NA>       7        
## 5 <NA>     5          ISRA7BHR… <NA>       NA            NA <NA>       7        
## 6 1        6          ISRA7BNJ… 2014-05-01 2014-05-01  2014 mai 2014   7        
## # ℹ 18 more variables: jurisdicti <chr>, juri_zone <chr>, juri_code <chr>,
## #   name <chr>, region <chr>, criteria <chr>, subcriteri <chr>,
## #   query_crite <chr>, query_subcr <chr>, min_depth <dbl>, max_depth <dbl>,
## #   order <chr>, family <chr>, lat <dbl>, lon <dbl>, geartype <chr>,
## #   vessel_i_ds <chr>, apparent_f <dbl>