ksonda · September 20, 2024 04:43
diff --git a/R-geoconnex.qmd b/R-geoconnex.qmd
 ---
 title: "Using the geoconnex Reference Feature Server with R"
 format:
  html:
    page-layout: full
 ---

 Below we demonstrates how to use the geoconnex reference feature server API with R's spatial packages, particularly `sf`. We'll explore various use cases for working with hydrological and related spatial data.


 ## Setup

 First, let's load the necessary libraries and set up our API base URL.

 ```{r setup, message=FALSE}
 library(sf)
 library(dplyr)
 library(mapview)
 library(jsonlite)
 library(knitr)
 library(DT)

 base_url <- "https://reference.geoconnex.us"
 ```

 ## Use Case 1: Identifying features and their spatial geometry

 The Geoconnex Reference Feature server is a source for identifiers and geometries for real-world features that many organizations may collect and publish data about. The attributes of these features vary, but all include a "uri" which serves as a [persistent identifer](https://docs.geoconnex.us/contributing/minting). First, let's discover what kinds of features are available:

 ```{r collections}
 url <- paste0(base_url, "/collections?f=json")
 collections <- jsonlite::fromJSON(url)

 datatable(collections$collections)
 ```

 We see a number of options available, including watershed boundaries like the Hydrologic Unit Codes, administrative boundaries like counties, states, and public water systems, and hydrologic features like mainstems (rivers) and aquifers, and hydrometric features such as dams and gages. Let's see how to use these collections!


 The reference feature server lets us find features according to attribute and spatial filters. Let's say we're interested in a specific river, one we think is called the "Animas river". We can pass an attribute filter query to the "mainstems" collection", and then use the R `sf` package to read into a dataframe, and the `mapview` package to visualize.

 ```{r mainstem}

 # construct a query for a river name that includes the string "Animas"
 query <- URLencode("name_at_outlet LIKE '%Animas%'") 
 url <- paste0(base_url, "/collections/mainstems/items?f=json&filter=", query)

 animas_rivers <- st_read(url, quiet = TRUE) %>% select(uri, name_at_outlet)

 # Display the results
 animas_rivers %>% 
  select(uri, name_at_outlet) %>% 
  knitr::kable()

 # Map the results
 mapview(animas_rivers, zcol = "name_at_outlet")
 ```

 There are evidently 3 rivers that include the word "Animas". Let's say we were interested in the "Animas River", shown on the map in Green. We find that it's Geoconenx URI is `https://geoconnex.us/ref/mainstems/35394`.
	---
	title: "Using the geoconnex Reference Feature Server with R"
	format:
	html:
	page-layout: full
	---

	Below we demonstrates how to use the geoconnex reference feature server API with R's spatial packages, particularly `sf`. We'll explore various use cases for working with hydrological and related spatial data.


	## Setup

	First, let's load the necessary libraries and set up our API base URL.

	```{r setup, message=FALSE}
	library(sf)
	library(dplyr)
	library(mapview)
	library(jsonlite)
	library(knitr)
	library(DT)

	base_url <- "https://reference.geoconnex.us"
	```

	## Use Case 1: Identifying features and their spatial geometry

	The Geoconnex Reference Feature server is a source for identifiers and geometries for real-world features that many organizations may collect and publish data about. The attributes of these features vary, but all include a "uri" which serves as a [persistent identifer](https://docs.geoconnex.us/contributing/minting). First, let's discover what kinds of features are available:

	```{r collections}
	url <- paste0(base_url, "/collections?f=json")
	collections <- jsonlite::fromJSON(url)

	datatable(collections$collections)
	```

	We see a number of options available, including watershed boundaries like the Hydrologic Unit Codes, administrative boundaries like counties, states, and public water systems, and hydrologic features like mainstems (rivers) and aquifers, and hydrometric features such as dams and gages. Let's see how to use these collections!


	The reference feature server lets us find features according to attribute and spatial filters. Let's say we're interested in a specific river, one we think is called the "Animas river". We can pass an attribute filter query to the "mainstems" collection", and then use the R `sf` package to read into a dataframe, and the `mapview` package to visualize.

	```{r mainstem}

	# construct a query for a river name that includes the string "Animas"
	query <- URLencode("name_at_outlet LIKE '%Animas%'")
	url <- paste0(base_url, "/collections/mainstems/items?f=json&filter=", query)

	animas_rivers <- st_read(url, quiet = TRUE) %>% select(uri, name_at_outlet)

	# Display the results
	animas_rivers %>%
	select(uri, name_at_outlet) %>%
	knitr::kable()

	# Map the results
	mapview(animas_rivers, zcol = "name_at_outlet")
	```

	There are evidently 3 rivers that include the word "Animas". Let's say we were interested in the "Animas River", shown on the map in Green. We find that it's Geoconenx URI is `https://geoconnex.us/ref/mainstems/35394`.