TADA Module 2: Geospatial Functions

Overview and Setup

Welcome!

Thank you for your interest in Tools for Automated Data Analysis (TADA). TADA is an open-source tool set built in the R programming language. This RMarkdown document walks users through how to download the TADA R package from GitHub, access and parameterize several important functions, and create basic visualizations with a sample data set.

Note: TADA is still under development. New functionality is added weekly, and sometimes we need to make bug fixes in response to tester and user feedback. We appreciate your feedback, patience, and interest in these helpful tools.

If you are interested in contributing to TADA development, more information is available at:

Contributing

We welcome collaboration with external partners.

Geospatial Functions in TADA

This example Module 2 workflow focuses on the following tasks:

Creating a ML/AU Crosswalk: Use TADA_CreateAUMLCrosswalk to establish a crosswalk between ATTAINS assessment units and WQP monitoring locations.
Map Review: Utilize TADA_ViewATTAINS to review assessment units and monitoring locations on a map, ensuring accuracy and completeness.
Submitting the Crosswalk: Employ TADA_UpdateATTAINSAUMLCrosswalk to submit an up-to-date assessment unit/WQP monitoring location crosswalk to ATTAINS.
Assigning Uses: Use TADA_AssignUsesToAU to assign specific uses to the assessment units.

A Note About ATTAINS:

The Assessment, Total Maximum Daily Load (TMDL) Tracking and Implementation System (ATTAINS) is an online platform that organizes and combines each state and participating tribe’s Clean Water Act reporting data into a single data repository. The geospatial component of ATTAINS includes spatial representations of each entity’s surface water assessment units as well as their assigned designated uses, their most recent EPA reporting category (i.e., their impairment status), their impaired designated uses, and the parameter(s) causing the impairment.

Within an assessment unit, the criteria or thresholds used to assess water quality typically remain the same and all water features are assessed as one entity (although there are some exceptions, for example if a single assessment unit crosses multiple ecoregions). Depending on the state or tribe, these assessment units can be a specific point or series of points along a waterbody such as a river or lake, a river reach (line), an entire waterbody such as a river or lake (polygon), or even an entire watershed. In other words, assessment units can take the form of point, line, and area (polygon) features, or some combination of all of them. Moreover, it is possible that some assessment units are not geospatially referenced at all, meaning they are not captured in the ATTAINS geospatial database.

Install and Load the EPATADA R Package

First, install and load the remotes package specifying the repo. This is needed before installing EPATADA because it is only available on GitHub (not CRAN).

install.packages("remotes",
  repos = "http://cran.us.r-project.org"
)
# Load the remotes library
library(remotes)

Next, install and load TADA using the remotes package. TADA R Package dependencies will also be downloaded automatically from CRAN with the TADA install. You may be prompted in the console to update dependency packages that have more recent versions available. If you see this prompt, it is recommended to update all of them (enter 1 into the console).

remotes::install_github("USEPA/EPATADA",
  ref = "develop",
  dependencies = TRUE
)

Finally, use the library() function to load the TADA R Package into your R session.

library(EPATADA)

Help pages

All EPATADA R package functions have their own individual help pages, listed in the Package index on the Reference tab of the GitHub website. Users can also access the help page for a given function in R or RStudio using the following format (example below): ?[name of TADA function]

# Access help page for TADA_CreateAUMLCrosswalk
?TADA_CreateAUMLCrosswalk

Module 1 Prerequisites

Get bacteria and pH data from Missoula County, Montana. This example is included in the EPATADA R package, and can be loaded into your R environment like this:

# Load the dataset
utils::data("Data_MT_MissoulaCounty", package = "EPATADA")

# Un-comment to generate Data_MT_MissoulaCounty example data using TADA_DataRetrieval
# # get MT data
# tada.MT <- TADA_DataRetrieval(
#   startDate = "2020-01-01",
#   endDate = "2022-12-31",
#   statecode = "MT",
#   characteristicName = c(
#     "Escherichia",
#     "Escherichia coli",
#     "pH"
#   ),
#   countycode = "Missoula County",
#   ask = FALSE
# )
#
# # clean up data set (not comprehensive)
# tada.MT.clean <- tada.MT  |>
#   TADA_RunKeyFlagFunctions()  |>
#   TADA_SimpleCensoredMethods()  |>
#   TADA_HarmonizeSynonyms()

Rename the example data to tada.MT.clean:

# Assign it to tada.MT.clean
tada.MT.clean <- Data_MT_MissoulaCounty

# Optionally, remove the original dataset from the environment
rm(Data_MT_MissoulaCounty)

Step A: Discover ML/AU Matches

The TADA_CreateAUMLCrosswalk function efficiently creates a crosswalk between ATTAINS assessment units and WQP monitoring locations. It uses three prioritized data sources:

User-Supplied Crosswalk: An optional crosswalk provided by the user (e.g., see example user.supplied.cw in next code chunk).
ATTAINS Crosswalk: Utilizes TADA_GetATTAINSAUMLCrosswalk to incorporate crosswalk information stored by participating organizations in ATTAINS. There may not be an ATTAINS crosswalk available for all organizations as storing this information in ATTAINS is optional for states and some tribes are still in the process of developing their crosswalks.
ATTAINS Catchments/Geospatial Join: Employs TADA_CreateATTAINSAUMLCrosswalk to connect monitoring locations to assessment units using ATTAINS catchments through a geospatial join. This function converts WQP monitoring locations into a geospatial sf object and associates them with their intersecting NHDPlus high resolution catchments containing entity-defined assessment units in ATTAINS.

Process

The function prioritizes these sources in the order listed. It automatically attempts to assign unassigned monitoring locations using the next available data source. For example, if any monitoring locations remain unassigned after checking the user-supplied crosswalk, the function will check the ATTAINS crosswalk, then the geospatial join, as needed.
This entire process is automated within TADA_CreateAUMLCrosswalk, so users do not need to run each step individually, though they have the option to do so if desired.
To make this function run more efficiently, the default is to not return catchments from the ATTAINS geospatial service for matches found in the user-supplied crosswalk or ATTAINS crosswalk (#1 or #2 above). If the user does want to return ATTAINS geospatial service catchments for matches from the user-supplied or ATTAINS crosswalks, they can set fill_ATTAINS_catch = TRUE. This will return the catchments, but increases function run time significantly. ATTAINS catchments are always returned for TADA_CreateATTAINSAUMLCrosswalk assessed WQP monitoring locations.

Current Limitations

Currently, the fill_USGS_catch argument applies only to monitoring locations that are not included in the user-supplied or ATTAINS crosswalks and that cannot be matched to existing ATTAINS assessment units and catchments during TADA_CreateATTAINSAUMLCrosswalk.

Future Improvements

Plans are underway to develop a separate process to more efficiently return all ATTAINS catchments and NHD catchments associated with all WQP monitoring locations in the future, to support mapping and crosswalk review.

Create An Example User-Supplied Crosswalk

Get existing data from ATTAINS using TADA_GetATTAINSAUMLCrosswalk and subset a few rows to create an example user-supplied ATTAINS Assessment Unit and WQP Monitoring Location crosswalk for demonstration purposes. If you have your own crosswalk, this step can be skipped.

# review valid ATTAINS org IDs
ATTAINS_orgs <- rExpertQuery::EQ_DomainValues("org_id")

# get crosswalk from ATTAINS
attains.existing.MT <- TADA_GetATTAINSAUMLCrosswalk(org_id = "MTDEQ")

# clean existing crosswalk from ATTAINS to make sure WQP monitoring location IDs pulled from ATTAINS are WQP compatible (adds org ID if missing)
clean.existing.attains.MT <- TADA_UpdateATTAINSAUMLCrosswalk(org_id = "MTDEQ")

# create example user supplied crosswalk (select a few Monitoring Locations from the tada df to use in the example for demonstration purposes)
user.supplied.cw <- clean.existing.attains.MT  |>
  dplyr::select(
    ATTAINS.AssessmentUnitIdentifier,
    ATTAINS.MonitoringLocationIdentifier,
    ATTAINS.WaterType
  )  |>
  dplyr::filter(ATTAINS.MonitoringLocationIdentifier %in% c(
    "MDEQ_WQ_WQX-C04CKFKR05", "MDEQ_WQ_WQX-C04KNDYC01", "MDEQ_WQ_WQX-C04KNDYC02",
    "MDEQ_WQ_WQX-C04KNDYC04", "MDEQ_WQ_WQX-C04KNDYC54"
  ))  |>
  dplyr::rename(
    AssessmentUnitIdentifier = ATTAINS.AssessmentUnitIdentifier,
    MonitoringLocationIdentifier = ATTAINS.MonitoringLocationIdentifier,
    WaterType = ATTAINS.WaterType
  )  |>
  # Add an example new assessment unit for demonstration purposes
  dplyr::bind_rows(c(
    AssessmentUnitIdentifier = "NEW:EX_MDEQ_WQ_WQX",
    MonitoringLocationIdentifier = "NARS_WQX-NWC_MT-10184",
    WaterType = "LAKE, FRESHWATER"
  ))


rm(attains.existing.MT, clean.existing.attains.MT, ATTAINS_orgs)

Run `TADA_CreateAUMLCrosswalk`

# make AU assignments for unassigned MLs
MT.AUMLRef <- TADA_CreateAUMLCrosswalk(
  tada.MT.clean,
  au_ref = user.supplied.cw,
  org_id = "MTDEQ",
  fill_ATTAINS_catch = TRUE,
  return_nearest = TRUE,
  batch_upload = TRUE
)

This example is also included in the EPATADA R package, and can be loaded into your R environment like this:

utils::data("Data_MT_AUMLRef", package = "EPATADA")

MT.AUMLRef <- Data_MT_AUMLRef

rm(Data_MT_AUMLRef)

Advanced: Deep Dive into `TADA_CreateATTAINSAUMLCrosswalk()`

TADA_CreateAUMLCrosswalk() automatically runs TADA_CreateATTAINSAUMLCrosswalk (see #3 under step A above). This function pulls in ATTAINS data from the EPA’s ATTAINS Assessment Geospatial Service and links it to TADA-pulled Water Quality Portal observations. For the function to work properly, the input dataframe must have - at a minimum - WQP observation coordinates in “LongitudeMeasure” and “LatitudeMeasure” columns and a “HorizontalCoordinateReferenceSystemDatumName” column.

By default, TADA_CreateATTAINSAUMLCrosswalk() returns a dataframe with ATTAINS-linked Water Quality Portal entries. Users have the added option of returning the intersecting ATTAINS geospatial shapefile objects with their ATTAINS-linked Water Quality Portal dataframe. If return_sf = TRUE, the function returns a list containing the dataframe and shapefile objects named ATTAINS_catchments, ATTAINS_lines, ATTAINS_points, and ATTAINS_polygons. Note, if any of these shapefile objects are empty, this indicates that there are no ATTAINS objects of that type intersecting any WQP-linked ATTAINS catchment.

Regardless of the user’s decision on returning the ATTAINS shapefile objects, TADA_CreateATTAINSAUMLCrosswalk() always returns a dataframe (or dataframes if fill_USGS_catch = TRUE, see section Filling in missing ATTAINS assessment units) containing the original TADA WQP dataframe, plus new columns representing the ATTAINS assessment unit(s) that fall within the same NHDPlus HiRes catchment as them. This means that it is possible for a single TADA WQP observation to have multiple ATTAINS assessment units linked to it. If the user would like to link all of these features, set return_nearest = FALSE. In these instances of multiple assessment units in the same catchment, the returned dataframe will have more than one row of data for each WQP observation. Such WQP observations can be identified using the ResultIdentifiercolumn (i.e., multiple rows with the same ResultIdentifier value are the same observation).

If the user would like to only return a single ATTAINS assessment unit, set return_nearest = TRUE. This will return only the closest assessment unit to the WQP observation in instances where a catchment contains more than one assessment unit. In this example workflow, return_nearest = TRUE.

TADA_CreateATTAINSAUMLCrosswalk also runs TADA_MakeSpatial() within it to convert any Water Quality Portal (WQP)-style dataframe with latitude/longitude data into a geospatial shapefile object. The user supplies a WQP dataframe and the coordinate reference system that they want the spatial object to be in [the default is CRS 4326 (WGS 84)]. For the function to work properly, the input dataframe must have - at a minimum - WQP observation coordinates in “LongitudeMeasure” and “LatitudeMeasure” and a “HorizontalCoordinateReferenceSystemDatumName” column.

Let’s run TADA_MakeSpatial() to make the water quality data spatial.

# default CRS is WGS84 (4326)
TADA_spatial <- TADA_MakeSpatial(.data = tada.MT.clean, crs = 4326)

## [1] "Data after CRS assignment:"
## # A tibble: 426 × 164
## # Rowwise: 
##    ResultIdentifier ActivityTypeCode TADA.ActivityType.Flag ActivityMediaName
##    <chr>            <chr>            <chr>                  <chr>            
##  1 NWIS-118797649   Sample-Routine   Non_QC                 Water            
##  2 NWIS-118797650   Sample-Routine   Non_QC                 Water            
##  3 NWIS-118797697   Sample-Routine   Non_QC                 Water            
##  4 NWIS-118797698   Sample-Routine   Non_QC                 Water            
##  5 NWIS-118887962   Sample-Routine   Non_QC                 Water            
##  6 NWIS-118887963   Sample-Routine   Non_QC                 Water            
##  7 NWIS-118888011   Sample-Routine   Non_QC                 Water            
##  8 NWIS-118888012   Sample-Routine   Non_QC                 Water            
##  9 NWIS-118888061   Sample-Routine   Non_QC                 Water            
## 10 NWIS-118888062   Sample-Routine   Non_QC                 Water            
## # ℹ 416 more rows
## # ℹ 160 more variables: TADA.ActivityMediaName <chr>,
## #   ActivityMediaSubdivisionName <chr>, CountryCode <chr>, StateCode <chr>,
## #   CountyCode <chr>, MonitoringLocationName <chr>,
## #   TADA.MonitoringLocationName <chr>, MonitoringLocationTypeName <chr>,
## #   TADA.MonitoringLocationTypeName <chr>,
## #   MonitoringLocationDescriptionText <chr>, LatitudeMeasure <chr>, …
## [1] "Processing CRS: NAD27"
## [1] "Processing CRS: NAD83"
## [1] "Processing CRS: UNKWN"
## [1] "Processing CRS: WGS84"

This new spatial object is identical to the original TADA dataframe, but now includes a “geometry” column that allows for mapping and additional geospatial capabilities. Enter ?TADA_MakeSpatial into the console to review another example of this function in use and additional information.

Now we can review the monitoring locations on a map:

leaflet::leaflet()  |>
  leaflet::addProviderTiles("Esri.WorldTopoMap",
    group = "World topo",
    options = leaflet::providerTileOptions(
      updateWhenZooming = FALSE,
      updateWhenIdle = TRUE
    )
  )  |>
  leaflet::clearShapes()  |>
  leaflet.extras::addResetMapButton()  |>
  leaflet::addLegend(
    position = "bottomright",
    colors = "black",
    labels = "Water Quality Observation(s)",
    opacity = 1
  )  |>
  leaflet::addCircleMarkers(
    data = TADA_spatial,
    color = "grey", fillColor = "black",
    fillOpacity = 0.8, stroke = TRUE, weight = 1.5, radius = 6,
    popup = paste0(
      "Site ID: ",
      TADA_spatial$MonitoringLocationIdentifier,
      "<br> Site Name: ",
      TADA_spatial$MonitoringLocationName
    )
  )

Using either our original tada.MT.clean or the geospatial version TADA_spatial, we can pull in the ATTAINS catchment features that intersect our observations:

TADA_with_ATTAINS <- TADA_CreateATTAINSAUMLCrosswalk(
  .data = tada.MT.clean,
  return_sf = FALSE,
  return_nearest = FALSE
)

## [1] "Data after CRS assignment:"
## # A tibble: 426 × 164
## # Rowwise: 
##    ResultIdentifier ActivityTypeCode TADA.ActivityType.Flag ActivityMediaName
##    <chr>            <chr>            <chr>                  <chr>            
##  1 NWIS-118797649   Sample-Routine   Non_QC                 Water            
##  2 NWIS-118797650   Sample-Routine   Non_QC                 Water            
##  3 NWIS-118797697   Sample-Routine   Non_QC                 Water            
##  4 NWIS-118797698   Sample-Routine   Non_QC                 Water            
##  5 NWIS-118887962   Sample-Routine   Non_QC                 Water            
##  6 NWIS-118887963   Sample-Routine   Non_QC                 Water            
##  7 NWIS-118888011   Sample-Routine   Non_QC                 Water            
##  8 NWIS-118888012   Sample-Routine   Non_QC                 Water            
##  9 NWIS-118888061   Sample-Routine   Non_QC                 Water            
## 10 NWIS-118888062   Sample-Routine   Non_QC                 Water            
## # ℹ 416 more rows
## # ℹ 160 more variables: TADA.ActivityMediaName <chr>,
## #   ActivityMediaSubdivisionName <chr>, CountryCode <chr>, StateCode <chr>,
## #   CountyCode <chr>, MonitoringLocationName <chr>,
## #   TADA.MonitoringLocationName <chr>, MonitoringLocationTypeName <chr>,
## #   TADA.MonitoringLocationTypeName <chr>,
## #   MonitoringLocationDescriptionText <chr>, LatitudeMeasure <chr>, …
## [1] "Processing CRS: NAD27"
## [1] "Processing CRS: NAD83"
## [1] "Processing CRS: UNKWN"
## [1] "Processing CRS: WGS84"

# Can also be performed on the spatial data:
# TADA_with_ATTAINS <- TADA_CreateATTAINSAUMLCrosswalk(.data = TADA_spatial, return_sf = FALSE, return_nearest = TRUE)

This new TADA_with_ATTAINS object is a modification of the original TADA Water Quality Portal dataframe that now has additional columns associated with the ATTAINS assessment unit(s) that lie in the same NHD HiRes catchment as them (these columns are prefixed with “ATTAINS”). Moreover, because our TADA_with_ATTAINS object contains more rows than the original TADA dataframe, we can deduce that some Water Quality Portal observations fall within an NHD catchment that contains more than one ATTAINS assessment unit.

TADA_with_ATTAINS_list <- TADA_CreateATTAINSAUMLCrosswalk(
  .data = tada.MT.clean,
  return_sf = TRUE,
  return_nearest = TRUE
)

## [1] "Data after CRS assignment:"
## # A tibble: 426 × 164
## # Rowwise: 
##    ResultIdentifier ActivityTypeCode TADA.ActivityType.Flag ActivityMediaName
##    <chr>            <chr>            <chr>                  <chr>            
##  1 NWIS-118797649   Sample-Routine   Non_QC                 Water            
##  2 NWIS-118797650   Sample-Routine   Non_QC                 Water            
##  3 NWIS-118797697   Sample-Routine   Non_QC                 Water            
##  4 NWIS-118797698   Sample-Routine   Non_QC                 Water            
##  5 NWIS-118887962   Sample-Routine   Non_QC                 Water            
##  6 NWIS-118887963   Sample-Routine   Non_QC                 Water            
##  7 NWIS-118888011   Sample-Routine   Non_QC                 Water            
##  8 NWIS-118888012   Sample-Routine   Non_QC                 Water            
##  9 NWIS-118888061   Sample-Routine   Non_QC                 Water            
## 10 NWIS-118888062   Sample-Routine   Non_QC                 Water            
## # ℹ 416 more rows
## # ℹ 160 more variables: TADA.ActivityMediaName <chr>,
## #   ActivityMediaSubdivisionName <chr>, CountryCode <chr>, StateCode <chr>,
## #   CountyCode <chr>, MonitoringLocationName <chr>,
## #   TADA.MonitoringLocationName <chr>, MonitoringLocationTypeName <chr>,
## #   TADA.MonitoringLocationTypeName <chr>,
## #   MonitoringLocationDescriptionText <chr>, LatitudeMeasure <chr>, …
## [1] "Processing CRS: NAD27"
## [1] "Processing CRS: NAD83"
## [1] "Processing CRS: UNKWN"
## [1] "Processing CRS: WGS84"

# return only the closest ATTAINS AU for observations within a catchment with multiple AUs
# TADA_with_ATTAINS_list <- TADA_CreateATTAINSAUMLCrosswalk(.data = TADA_spatial, return_sf = TRUE, return_nearest = TRUE)

If we set return_sf = TRUE as done to create the TADA_with_ATTAINS_list object above, we also now have all the raw intersecting ATTAINS features associated with these ATTAINS catchment observations stored in a list along with the TADA dataframe.

Assign NHD catchments to WQP observations that are missing ATTAINS assessment units

As you can see in the above examples, not all WQP observations have an intersecting ATTAINS catchment: see that in the returned dataframes, some WQP observations have NAs where there should be ATTAINS information. In these instances, the user can optionally fill in catchment information from the NHD by entering fill_USGS_catch = TRUE:

TADA_with_ATTAINS_filled <- TADA_CreateATTAINSAUMLCrosswalk(
  tada.MT.clean,
  fill_USGS_catch = TRUE,
  return_sf = TRUE,
  return_nearest = TRUE
)

## [1] "Data after CRS assignment:"
## # A tibble: 426 × 164
## # Rowwise: 
##    ResultIdentifier ActivityTypeCode TADA.ActivityType.Flag ActivityMediaName
##    <chr>            <chr>            <chr>                  <chr>            
##  1 NWIS-118797649   Sample-Routine   Non_QC                 Water            
##  2 NWIS-118797650   Sample-Routine   Non_QC                 Water            
##  3 NWIS-118797697   Sample-Routine   Non_QC                 Water            
##  4 NWIS-118797698   Sample-Routine   Non_QC                 Water            
##  5 NWIS-118887962   Sample-Routine   Non_QC                 Water            
##  6 NWIS-118887963   Sample-Routine   Non_QC                 Water            
##  7 NWIS-118888011   Sample-Routine   Non_QC                 Water            
##  8 NWIS-118888012   Sample-Routine   Non_QC                 Water            
##  9 NWIS-118888061   Sample-Routine   Non_QC                 Water            
## 10 NWIS-118888062   Sample-Routine   Non_QC                 Water            
## # ℹ 416 more rows
## # ℹ 160 more variables: TADA.ActivityMediaName <chr>,
## #   ActivityMediaSubdivisionName <chr>, CountryCode <chr>, StateCode <chr>,
## #   CountyCode <chr>, MonitoringLocationName <chr>,
## #   TADA.MonitoringLocationName <chr>, MonitoringLocationTypeName <chr>,
## #   TADA.MonitoringLocationTypeName <chr>,
## #   MonitoringLocationDescriptionText <chr>, LatitudeMeasure <chr>, …
## [1] "Processing CRS: NAD27"
## [1] "Processing CRS: NAD83"
## [1] "Processing CRS: UNKWN"
## [1] "Processing CRS: WGS84"

When fill_USGS_catch = TRUE, the returned list splits observations into two dataframes: WQP observations with ATTAINS catchment data, and WQP observations without ATTAINS catchment data. Instead of listing ATTAINS information in TADA_without_ATTAINS, it links basic information about the catchment including its unique identifier, catchment area, and the resolution of the NHD used. As a default, TADA_CreateATTAINSAUMLCrosswalk() will use the NHD HiRes (resolution = "Hi") for filling in missing ATTAINS catchments. However, the user can choose to change the resolution to the NHDPlus V2 by setting resolution = "Med".

Moreover, when return_sf = TRUE as above, the function will additionally return the raw catchment features associated with the observations in TADA_without_ATTAINS in a new shapefile called without_ATTAINS_catchments.

Arguments for TADA_CreateATTAINSAUMLCrosswalk()

.data: Your input TADA-style Water Quality Portal data.
fill_USGS_catch: If TRUE, it will find intersecting NHD catchments to fill in information for samples not covered by ATTAINS.
resolution: Specifies which version of the NHD to use if filling catchments: the “Med”, or “Hi”. The default option is “Hi”.
return_nearest: If TRUE, returns only the nearest ATTAINS feature to each WQP observation.
return_sf: If TRUE, returns spatial data in addition to tabular data.

Step B: Review MLs/AUs on a Map

The TADA_ViewATTAINS() function creates a map that visually represents the monitoring locations and assessment units. When the param ref_icons is equal to TRUE, the results from the three different crosswalk sources are shown using distinct circle marker icons:

User-Supplied Crosswalk: Monitoring locations derived from a user-supplied reference are indicated by a user icon.
ATTAINS Crosswalk: Locations that match with the crosswalk downloaded from ATTAINS are marked with a circle that contains a check mark.
ATTAINS Catchments/Geospatial Join: Matches found by joining monitoring locations with ATTAINS catchments in TADA_CreateATTAINSAUMLCrosswalk() when return_sf = TRUE are shown as solid fill circle markers.

Additionally, when a user clicks on any circle marker, a pop-up window appears displaying the assessment unit crosswalk source for that particular monitoring location.

Alternately, users can set ref_icons equal to FALSE to display all monitoring locations as plain circle markers regardless of the crosswalk source.

Let’s view the data associated with MT.AUMLRef!

TADA_ViewATTAINS(MT.AUMLRef, ref_icons = TRUE)

You may have noticed that because we set fill_USGS_catch = TRUE, TADA_ViewATTAINS() also mapped the without_ATTAINS_catchments. These are represented as clear polygons with black outlines.

Enter ?TADA_ViewATTAINS into the console to review another example of this function in use and additional information.

Step C: Upload ML/AU crosswalk to ATTAINS

The TADA_UpdateATTAINSAUMLCrosswalk function generates an output that can be batch uploaded to ATTAINS, enabling the creation or updating of Monitoring Location Identifiers within ATTAINS Assessment Unit profiles.

Workflow Example:

Access the Element:
Begin by accessing the ATTAINS_batchupload element from the list generated by the TADA_CreateAUMLCrosswalk function (e.g. MT.AUMLRef).
Review and Modify:
Review the ATTAINS_batchupload element and make any necessary modifications.
Execute the Function:
Execute TADA_UpdateATTAINSAUMLCrosswalk. This process adds new links to WQP site pages, preparing the data for upload to ATTAINS.
Choose Update Option:
When running TADA_UpdateATTAINSAUMLCrosswalk(), with the attains_replace function input users have the option to:
- Overwrite: Replace all existing records.
- Append: Add new Monitoring Location Identifiers to the current records.

For more detailed instructions, enter ?TADA_UpdateATTAINSAUMLCrosswalk into the console.

batch.upload.MT <- MT.AUMLRef$ATTAINS_batchupload  |>
  TADA_UpdateATTAINSAUMLCrosswalk( # selected attains_replace = TRUE because all matches currently in ATTAINS are included in this new crosswalk
    attains_replace = TRUE,
    batch_upload = TRUE,
    wqp_data_links = "add",
    # ml ids have already  been corrected if needed
    update_mlid = FALSE,
    org_id = "MTDEQ"
  )

Step D: Assign Uses to AUs

After mapping the WQP monitoring locations (MLs) to ATTAINS assessment units (AUs), we will use the TADA_AssignUsesToAU function to assign specific uses to these assessment units. Additionally, we will demonstrate how to automatically assign uses to the new assessment unit, NEW:EX_MDEQ_WQ_WQX with the water type “LAKE, FRESHWATER,” that was created in step A, which is not yet present in ATTAINS.

# extract ATTAINS_crosswalk data frame from the list
Final.MT.AUMLRef <- MT.AUMLRef$ATTAINS_crosswalk

Now, we will assign uses to each unique assessment unit (AU) in our finalized monitoring location to assessment unit crosswalk (e.g., Final.MT.AUMLRef). The process of assigning uses to AUs follows these prioritized steps:

User-Supplied Crosswalk (Optional): Utilize a crosswalk provided by the user that maps specific uses to assessment units.
Existing Uses from ATTAINS: Import existing uses for assessment units as defined by ATTAINS organizations.

MT.UseAURef <- TADA_AssignUsesToAU(
  AUMLRef = Final.MT.AUMLRef,
  org_id = "MTDEQ"
)

## [1] "TADA_AssignUsesToAU: Importing existing uses by AU from Expert Query."

Advanced: Assigning Uses to New AUs

For any new assessment unit (AU), users must determine how to assign the appropriate uses. The TADA_AssignUsesToWaterType function is designed to assist with this process. It imports combinations of water types and use names associated with the specified organization from ATTAINS. By doing so, it automatically assigns uses to new assessment units based on their water type, thereby streamlining the integration of new AUs into existing frameworks.

For more information on how to customize this function to suit your needs, enter ?TADA_AssignUsesToWaterType into the R console.

MT.UseAURef_with_WaterUseRef <-
  TADA_AssignUsesToAU(
    waterUseRef = TADA_AssignUsesToWaterType(org_id = "MTDEQ"),
    AUMLRef = Final.MT.AUMLRef,
    org_id = "MTDEQ"
  )

## [1] "TADA_AssignUsesToAU: Importing existing uses by AU from Expert Query."
## [1] "TADA_CreateWaterusesRef: Importing unique water types and uses by organization from Expert Query."

Users also have the option to manually assign use names to new AUs if they prefer not to use the TADA_AssignUsesToWaterType function. This approach allows for greater flexibility and customization when integrating new AUs into existing frameworks.

MT.UseAURef_manual <- MT.UseAURef  |>
  dplyr::left_join(
    data.frame(
      ATTAINS.UseName = c("Aquatic Life", "Drinking Water"),
      ATTAINS.AssessmentUnitIdentifier = c("NEW:EX_MDEQ_WQ_WQX", "NEW:EX_MDEQ_WQ_WQX")
    ),
    by = ("ATTAINS.AssessmentUnitIdentifier")
  )  |>
  dplyr::mutate(ATTAINS.UseName = dplyr::coalesce(ATTAINS.UseName.x, ATTAINS.UseName.y))  |>
  dplyr::select(-ATTAINS.UseName.x, -ATTAINS.UseName.y)

Summary

In conclusion, this example TADA Module 2 workflow significantly enhances the ability to maintain up-to-date information on WQP Monitoring Locations and the uses being assessed at various ATTAINS Assessment Units. By systematically guiding users through the creation of crosswalks, map reviews, data submission, and use assignments, this workflow supports efficient data management and improves reproducibility.

TADA Team

2025-12-12

Overview and Setup

Geospatial Functions in TADA

Install and Load the EPATADA R Package

Module 1 Prerequisites

Step A: Discover ML/AU Matches

Create An Example User-Supplied Crosswalk

Run `TADA_CreateAUMLCrosswalk`

Advanced: Deep Dive into `TADA_CreateATTAINSAUMLCrosswalk()`

Step B: Review MLs/AUs on a Map

Step C: Upload ML/AU crosswalk to ATTAINS

Step D: Assign Uses to AUs

Advanced: Assigning Uses to New AUs

Summary

TADA Module 2: Geospatial Functions

TADA Team

2025-12-12

Overview and Setup

Geospatial Functions in TADA

Install and Load the EPATADA R Package

Module 1 Prerequisites

Step A: Discover ML/AU Matches

Create An Example User-Supplied Crosswalk

Run TADA_CreateAUMLCrosswalk

Advanced: Deep Dive into TADA_CreateATTAINSAUMLCrosswalk()

Step B: Review MLs/AUs on a Map

Step C: Upload ML/AU crosswalk to ATTAINS

Step D: Assign Uses to AUs

Advanced: Assigning Uses to New AUs

Summary

Run `TADA_CreateAUMLCrosswalk`

Advanced: Deep Dive into `TADA_CreateATTAINSAUMLCrosswalk()`