viz oceano bottle data (temp, o2, oa, dic)

[bbest]

• read the data
• map by station id. for now use the lon/lat in the file; maybe later lookup an authoritative location.
• extract points by area of interest (aoi)
• interpolate points to surface with idw() or krige() and extract from surface
• create read_bottle() in calcofi4r
• visualize initial variables

Background

Appendix 1: CalCOFI Data Inventory 2021 - Google Sheets:
- Ocean temperature & HCI: temperature, salinity, oxygen, nutrients
- Hypoxia: temperature, salinity, oxygen, nutrients
- [OA/DIC: separate db; on website under pH]: Dissolved inorganic carbon (DIC) & total alkalinity (TA)

[bbest]

Profiling

• SWFSC folks made pretty maps with commercial s/w Surfer | Golden Software Support:
  
• How?
  • across survey segments on only or any place?

[bbest]

• noaa-iea.github.io/fl-wq
  

I. Detailed Views for Scientific Audience

A. Survey Tracks

• Initial Map View: survey tracks
• Selectable: segments along each line (that could include underway data along surface)
• Plot: Profile plot along track, so starting distance (min(date)) on x axis

B. Interpolated Surface

• Initial Map View: kriged surface, possibly with light tracks overlaid
• Selectable: draw a transect (within extent of kriged surface)
• Plot:
  • Profile plot along arbitrary transect
  • Measures of uncertainty with plot

II. Synoptic Views for General Audience

A. Survey Tracks

• Initial Map View: survey tracks
• Selectable:
  • Management polygon
• Plot: Profile plot along track, so starting distance (min(date)) on x axis

B. Interpolated Surface

• Initial Map View: kriged surface, possibly with light tracks overlaid
• Selectable: draw a transect (within extent of kriged surface)
• Plot:
  • Profile plot along arbitrary transect
  • Measures of uncertainty with plot

[bbest]

See knitted Rmarkdown html with first read of data here:

https://calcofi.github.io/scripts/oceano-bottle.html

Let's consider the following example of CalCOFI survey tracks and wanting to extract information for an Area of Interest (AOI) for some management purpose:

https://shiny.ecoquants.com/calcofi/

Summarize by AOI

1. Summary stats on station data as points

Function to generate basic statistics (avg, min, max, stdev, hi90, lo10) given some parameters:

• aoi: area of interest, as well known text (sf::st_text()) for eventually using in an API function mode
• date_beg: filter by start date
• date_end: filter by end date
• date_step: some notion of summarizing by annual / seasonal / quarterly
• depth_min: filter by depth from surface (which direction +/-?)
• depth_max: filter by depth from surface (which direction +/-?)
• stats: a vector of functions as strings like: mean, min, max, stdev, hi90, lo10

Channel Islands Sanctuary as example AOI

You can quickly read this noaa-onms/onmsR:sanctuaries.geojson to pull Channel Islands as an example AOI with the following R code:

sanctuaries_geo <- "https://github.com/noaa-onms/onmsR/raw/12a87dfd4b90f2e3009ccb4913315fb2df7afddc/data-raw/sanctuaries.geojson"

cinms_ply <- sf:st_read(sanctuaries_geo) %>%
  dplyr::filter(nms == "CINMS")

cinms_txt <- sf::st_text(cinms_ply$geometry)

2. Extract from interpolated surface

Lots of geostatistical methods here:

• IDW: inverse distance weighting
• kriging

References:

• Chapter 11 Statistical learning | Geocomputation with R
• Chapter 12 Spatial interpolation of point data | Introduction to Spatial Data Programming with R
  

[bbest]

@cdobbelaere,

You can use oceano-bottle.Rmd and set path to Google Drive folder calcofi - Google Drive where it's sync'd to your local machine (using Download - Google Drive)

[bbest]

See #3 for more AOIs