---
title: "geoknife package"
author: "Jordan Read"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output: 
  rmarkdown::html_vignette:
    toc: true
    fig_width: 7
    fig_height: 6
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{geoknife package guide}
  \usepackage[utf8]{inputenc}
---


```{r setup, include=FALSE}
library(rmarkdown)
options(continue=" ")
options(width=60)
library(knitr)
library(geoknife)
query <- geoknife::query
`values<-` <- geoknife::`values<-`
id <- geoknife::id

```


##Introduction

The **geoknife** package was created to support web-based geoprocessing of large gridded datasets according to their overlap with landscape (or aquatic/ocean) features that are often irregularly shaped. geoknife creates data access and subsequent geoprocessing requests for the USGS's Geo Data Portal to carry out on a web server. The results of these requests are available for download after the processes have been completed. This type of workflow has three main advantages: 1) it allows the user to avoid downloading large datasets, 2) it avoids reinventing the wheel for the creation and optimization of complex geoprocessing algorithms, and 3) computing resources are dedicated elsewhere, so geoknife operations do not have much of an impact on a local computer.

Because communication with web resources are central to geoknife operations, users must have an active internet connection. geoknife interacts with a remote server to discover processing capabilities, find already available geospatial areas of interest (these are normally user-uploaded shapefiles), get gridded dataset characteristics, execute geoprocessing requests, and get geoprocessing results. 

The main elements of setting up and carrying out a geoknife 'job' (geojob) include defining the feature of interest (the stencil argument in the geoknife function), the gridded web dataset to be processed (the fabric argument in the geoknife function), and the the processing algorithm parameters (the knife argument in the geoknife function). The status of the geojob can be checked with check, and output can be loaded into a data.frame with results. See below for more details.

##Installation
To install the stable version of **geoknife** package with dependencies:
```{r, eval=FALSE}
install.packages("geoknife", 
    repos = c("https://owi.usgs.gov/R","https://cran.rstudio.com/"),
    dependencies = TRUE)
```
Or to install the current development version of the package:
```{r, eval=FALSE}
install.packages("devtools")
devtools::install_github('USGS-R/geoknife')
```

## getting started
The `geoknife` package was created to support web-based geoprocessing of large gridded datasets according to their overlap with landscape (or aquatic/ocean) features that are often irregularly shaped. geoknife creates data access and subsequent geoprocessing requests for the USGS's Geo Data Portal to carry out on a web server. 

### geoknife concepts
geoknife has abstractions for web-available gridded data, geospatial features, and geoprocessing details. These abstractions are the basic geoknife arguments of `fabric`, `stencil` and `knife`.  
* `fabric` defines the web data that will be accessed, subset, and processed (see [the fabric section](#gridded-data-fabric) for more details). These data are limited to gridded datasets that are web-accessible through the definitions presented in [the OPeNDAP section](#opendap). Metadata for `fabric` include time, the URL for the data, and variables.  
* `stencil` is the geospatial feature (or set of features) that will be used to delineate specific regions of interest on the fabric (see [the stencil section](#spatial-features-stencil) for more details). `stencil` can include point or polygon groupings of various forms (including classes from the sp R package).  
* `knife` defines the way the analysis will be performed, including the algorithm and version used, the URL that receives the processing request, the statistics returned, and the format of the results (see [the knife section](#web-processing-details-knife) for more details).  
* The `geoknife()` function takes the `fabric`, `stencil`, and `knife`, and returns a `geojob`, which is a live geoprocessing request that will be carried out on a remote web server (see [the geojob section](#geojob-details) for more details). The `geojob` can be checked by users, and results can be parsed and loaded into the R environment for analyses. 

### remote processing basics
Because `geoknife` executes geospatial computations on a remote webserver, the workflow for to execute geoprocessing operations may feel a bit foreign to users who usually performing their analyses on a local computer. To find available datasets and their details (variables, time range, etc.), `geoknife` must query remote servers because data for use with `geoknife` is typically hosted on open access servers near the processing service. These operations are covered in detail below, but this section is designed to provide a quick overview. 

Interactions with web resources may take on the following forms, and each involve separate requests to various webservers:

(@) Using the `query` function to figure out what data exist for `fabric`. This function will request data from a CSW (catalog service for the web) resource and return results, or, if a dataset is already specified, it can be used to query for the variables or time dimension.
(@) Using the `query` function to use a web resource for the geometry of `stencil`, including a US State, Level III Ecoregion, and many others. 
(@) Submitting a `geojob` to be processed externally
(@) Checking the status of a `geojob`
(@) Loading the results from a successful `geojob`

### quick start guide
There are various ways to get up and running quickly with `geoknife`. See sections below for additional details on any of the following operations. As mentioned above, `geoknife` has the basic arguments of `fabric`, `stencil` and `knife`. `knife` is an optional argument, and if not used, a default `knife` will be used to specify the processing details. 

#### define a stencil that represents the geographic region to slice out of the data
There are many different ways to specify geometry (`stencil`) for `geoknife`. The two basic functions that support building `stencil` objects are `simplegeom` and `webdata`:

```{r}
library(geoknife)
```
Use a single longitude latitude pair as the geometry with the `simplegeom` function:
```{r}
stencil <- simplegeom(c(-89, 46.23))
```
Or specify a collection of named points in a `data.frame` (note that naming is important for multi-features because it specifies how the results are filtered):
```{r}
stencil <- simplegeom(data.frame(
              'point1' = c(-89, 46), 
              'point2' = c(-88.6, 45.2)))
```

Use a web-available geometry dataset with the `webgeom` function to specify state boundaries:
```{r, eval=FALSE}
stencil <- webgeom('state::New Hampshire')
stencil <- webgeom('state::New Hampshire,Wisconsin,Alabama')
```
or HUC8s (hydrologic unit code):
```{r, eval=FALSE}
stencil <- webgeom('HUC8::09020306,14060009')
```
```{r, echo=FALSE}
load(system.file('extdata', 'HUC8_stencil.RData', package = 'geoknife'))
```
display stencil:
```{r}
stencil
```
see what other HUCs could be used via the `query` function:
```{r, eval=FALSE}
HUCs <- query(stencil, 'values')
```
```{r, echo=FALSE}
load(system.file('extdata', 'HUC8_query.RData', package = 'geoknife'))
```
there are thousands of results, but `head()` will only display a few of them
```{r}
head(HUCs) 
```

#### define a fabric that represents the underlying data
The Geo Data Portal's web data catalog is quite extensive, and inludes many datasets that can all be processed with `geoknife`. Check it out at cida.usgs.gov/gdp. This is not a complete list of all relevant datasets that can be accessed and processed. The `geoknife` package has a number of quick access datasets build in (similar to quick start `webgeom` objects). 

An example of a quick start dataset:
```{r}
fabric <- webdata('prism')
fabric
```
which can be a starting point for the PRISM dataset, as the fields can be modified:
```{r}
times(fabric) <- c('2002-01-01','2010-01-01')
variables(fabric) <- c('tmx')
fabric
```


#### create the processing job that will carry out the subsetting/summarization task

```{r, eval=FALSE}
job <- geoknife(stencil, fabric)
```

use convienence functions to check on the job:
```{r, eval=FALSE}
check(job)
running(job)
error(job)
successful(job)
```

Cancel a running job:
```{r, eval=FALSE}
job <- cancel(job)
```

Run the job again, but have R wait until the process is finished:
```{r, eval=FALSE}
job <- geoknife(stencil, fabric, wait = TRUE)
```

Load up the output and plot it
```{r, fig.height=3.5, fig.width=7, eval=FALSE}
data <- result(job)
plot(data[,1:2], ylab = variables(fabric))
```
```{r, fig.height=3.5, fig.width=7, echo=FALSE}
load(system.file('extdata', 'prism_job.RData', package = 'geoknife'))
plot(data[,1:2], ylab = variables(fabric))
```

For long running processes, it often makes sense to use an email listener:
```{r, eval=FALSE}
job <- geoknife(webgeom('state::Wisconsin'), fabric = 'prism', email = 'fake.email@gmail.com')
```

## spatial features (`stencil`)
The `stencil` concept in `geoknife` represents the area(s) of interest for geoprocessing. `stencil` can be represented by two classes in `geoknife`: `simplegeom` and `webdata`. Any other classes can also be used that can be coerced into either of these two classes (such as `data.frame`). 

### `simplegeom` object
The `simplegeom` class is designed to hold spatial information from the R environment and make it available to the processing engine. `simplegeom` is effectively a wrapper for the `sp` package's `SpatialPolygons` class, but also coerces a number of different other types into this class. For example:  

Points can be specified as longitude latitude pairs:
```{r}
stencil <- simplegeom(c(-89, 45.43))
```
or as a data.frame:
```{r}
stencil <- simplegeom(data.frame(
              'point1' = c(-89, 46), 
              'point2' = c(-88.6, 45.2)))
```
Also, a `SpatialPolygons` object can be used as well (example from `sp` package): 
```{r}
library(sp)
Sr1 = Polygon(cbind(c(2,4,4,1,2),c(2,3,5,4,2)))
Sr2 = Polygon(cbind(c(5,4,2,5),c(2,3,2,2)))
Sr3 = Polygon(cbind(c(4,4,5,10,4),c(5,3,2,5,5)))
Sr4 = Polygon(cbind(c(5,6,6,5,5),c(4,4,3,3,4)), hole = TRUE)

Srs1 = Polygons(list(Sr1), "s1")
Srs2 = Polygons(list(Sr2), "s2")
Srs3 = Polygons(list(Sr3, Sr4), "s3/4")
stencil <- simplegeom(Srl = list(Srs1,Srs2,Srs3), proj4string = CRS("+proj=longlat +datum=WGS84"))
```

### `webgeom` object
The `webgeom` class is designed to hold references to web feature service (WFS) details and make it available to the processing engine.  

Similar to `webdata` (see below), the `webgeom` class has public fields that can be set and accessed using simple methods. Public fields in `webgeom`:  

 * `url`: the WFS endpoint to use for the data
 * `geom`: the feature collection name (can be namespaced shapefile names)
 * `attribute`: the attribute of the feature collection to be use 
 * `values`: the values of the chosen attribute to use (or `NA` for all)
 * `version`: the WFS version to use. 

To create a default `webgeom` object:
```{r}
stencil <- webgeom()
```
The user-level information in webgeom is all available with the webgeom "show" method (or print). 
```{r}
stencil
```
The public fields can be accessed in by using the field name:
```{r, eval=FALSE}
geom(stencil) <- "sample:CONUS_states"
attribute(stencil) <- "STATE"
values(stencil) <- c("Wisconsin","Maine")
```

### quick access to web available data for webgeoms
There are some built in `webgeom` templates that can be used to figure out the pattern, or to use these datasets for analysis. Currently, the package only supports US States, Level III Ecoregions, or HUC8s:
```{r, eval=FALSE}
stencil <- webgeom('state::Wisconsin')
webgeom('state::Wisconsin,Maine')
webgeom('HUC8::09020306,14060009')
webgeom('ecoregion::Colorado Plateaus,Driftless Area')
```

### query function for `webgeom`
The `query` function on `webgeom` can be used to find possible inputs for each public field (other than `version` and `url` currently):
```{r, eval=FALSE}
query(stencil, 'geoms')
```
```
##  [1] "sample:Alaska"                  
##  [2] "upload:CIDA_TEST_"              
##  [3] "sample:CONUS_Climate_Divisions" 
##  [4] "sample:CONUS_states"        
##  [5] "sample:CONUS_states"            
##  [6] "sample:CSC_Boundaries"          
##  [7] "sample:Landscape_Conservation_Cooperatives"             
##  [8] "sample:FWS_LCC"                 
##  [9] "sample:simplified_huc8"     
## [10] "sample:Ecoregions_Level_III"
## [12] "sample:Counties"         
## [13] "sample:nps_boundary_2013"       
## [14] "upload:nrhu_selection"          
## [15] "upload:nrhu_selection_Gallatin" 
## [16] "sample:simplified_HUC8s"        
## [17] "draw:test"
```
```{r, eval=FALSE}
query(stencil, 'attributes')
```
```
## [1] "STATE"
```


## gridded data (`fabric`)
The `fabric` concept in `geoknife` represents the gridded dataset that will be operated on by the tool. `fabric` can be a time-varying dataset (such as PRISM) or a spatial snapshot coverage dataset (such as the NLCD). At present, `fabric` is limited to datasets that can be accessed using the OPeNDAP protocol or WMS (web map service). Most helper functions in geoknife, including `query(fabric,'variables')` tend to work better for OPeNDAP datasets. 

### `webdata` object
The `webdata` class holds all the important information for webdatasets in order to make them available for processing by geoknife's outsourced geoprocessing engine, the Geo Data Portal. Public fields in `webdata`:

 * `times`: a POSIXct vector of length 2. This specifies the start and end time of the process request. If `times()[1]` is `NA`, the start time will be the begining of the dataset. If `times()[2]` is `NA` the end time will be the end of the dataset. `times` must be `as.POSIXct(c(NA,NC))` for datasets without a time dimension. 
 * `url`: a character for the location of a web available dataset. This URL will be queried for data access and used for the processing task.
 * `variables`: a character vector for the variables of the dataset to use. Must be valid variables that exist within the dataset specified with `url`.

To create a default `webdata` object:
```{r}
fabric <- webdata()
```
The user-level information in webdata is all available with the webdata "show" method (or print). 
```{r}
fabric
```
The public fields can be accessed in by using the field name:
```{r}
times(fabric)
url(fabric) <- 'https://cida.usgs.gov/thredds/dodsC/prism'
variables(fabric) <- 'tmx'

times(fabric)[1] <- as.POSIXct('1990-01-01')
```

### find data that is indexed by the Geo Data Portal catalog
The `fabric` is specified using the `webdata` function. `geoknife` can access a catalog of webdata by using the `query` function:
```{r, eval=FALSE}
webdatasets = query('webdata')
length(webdatasets)
```
```{r, echo=FALSE}
load(system.file('extdata', 'webdata_query.RData', package = 'geoknife'))
length(webdatasets)
```
Interrogating datasets can be done by printing the returned dataset list, which displays the title and the url of each dataset by default (this example truncates the `r length(webdatasets)` datasets to display 5):
```{r}
webdatasets[61:65]
```

Finding additional information about a particular dataset is supported by `title()` and `abstract()`, which return the dataset titles and abstracts respectively:
```{r}
title(webdatasets[87])
abstract(webdatasets[87])
```

indexing datasets based on order or title are equivalent  
```{r}
fabric <- webdata(webdatasets[99])
evapotran <- webdata(webdatasets['Monthly Conterminous U.S. actual evapotranspiration data'])
```

To modify the times in `fabric`, use `times()`:
```{r}
times(fabric) <- c('1990-01-01','2005-01-01')
```
Similar to `webgeom`, the query method can be used on `webdata` objects:
```{r, eval=FALSE}
query(fabric, 'times')
query(fabric, 'variables')
```

### find data that is *not* indexed by the Geo Data Portal catalog
There are hundreds (or potentially thousands) of additional OPeNDAP datasets that will work with geoknife, but need to be found through web searches or catalogs (e.g., www.esrl.noaa.gov/psd/thredds/dodsC/Datasets, apdrc.soest.hawaii.edu/data/data.php). One such example is Sea Surface Temperature from the Advanced Very High Resolution Radiometer (AVHRR) temperature sensing system. Specifying datasets such as this requires finding out the OPeNDAP endpoint (URL) for the dataset, and specifying it as the `url` to webdata (we found this example in the extensive apdrc.soest.hawaii.edu/data/data.php catalog):
```{r}
fabric = webdata(url='dods://apdrc.soest.hawaii.edu/dods/public_data/satellite_product/AVHRR/avhrr_mon')
```
`query` for `variables` doesn't work for this dataset, because it actually doesn't have units and therefore "valid" variables are not returned (instead you get an empty list). From the OPeNDAP endpoint, it is clear that this dataset has one variable of interest, which is called 'sst':
```{r, eval=FALSE}
variables(fabric) <- 'sst'
query(fabric, 'times')
```
```
[1] "1985-01-01 UTC" "2003-05-01 UTC"
```
```{r}
times(fabric) <- c('1990-01-01','1999-12-31')
```

plotting the July surface temperature of a spot on the Caspian Sea is done by:
```{r, eval=FALSE}
sst = result(geoknife(data.frame('caspian.sea'=c(51,40)), fabric, wait = TRUE))
head(sst)
july.idx <- months(sst$DateTime) == 'July'
plot(sst$DateTime[july.idx], sst$caspian.sea[july.idx], type='l', lwd=2, col='dodgerblue', ylab='Sea Surface Temperature (degC)',xlab=NA)
```
```{r, echo=FALSE, fig.height=4, fig.width=6}
load(system.file('extdata', 'sst_result.RData', package = 'geoknife'))
head(sst)
july.idx <- months(sst$DateTime) == 'July'
plot(sst$DateTime[july.idx], sst$caspian.sea[july.idx], type='l', lwd=2, col='dodgerblue', ylab='Sea Surface Temperature (degC)',xlab=NA)
```

### query function for `webdata`
The `query` function works on `webdata`, similar to how it works for `webgeom` objects. For the PRISM dataset specified above, the time range of the dataset can come from `query` with `times`:
```{r, eval=FALSE}
fabric = webdata('prism')
variables(fabric) <- 'ppt'
query(fabric, 'times')
```
```
## [1] "1895-01-01 UTC" "2013-02-01 UTC"
```
likewise, variables with `variables`:
```{r, eval=FALSE}
query(fabric, 'variables')
```
Note that a variable has to be specified to use the `times` query:
```{r}
variables(fabric) <- NA
```
```
## [1] "ppt" "tmx" "tmn"
```
This will fail:
```{r,eval=FALSE}
query(fabric, 'times')
```
```
 Error in times_query(fabric, knife) : 
  variables cannot be NA for fabric argument 
```
At present, the `geoknife` package does not have a query method for dataset urls.

## `knife` object
The `webprocess` class holds all the important information for geoknife processing details for the outsourced geoprocessing engine, the Geo Data Portal. Public fields in `webprocess`:

 * `url`: a character for the location of the web processing service to be used.
 * `algorithm`: a list specifying the algorithm to be used for processing. Defaults to `r names(algorithm(webprocess()))`.
 * `version`: a character specifying the version of the web processing service to be used. Defaults to `r version(webprocess())`.
 * `processInputs`: a list of processing details for the specified `algorithm`. These details vary depending on `algorithm` and are this field is automatically reset when the `algorithm` field is set. 
 * `wait`: a boolean that specifies whether to have R wait until the process is finished. Defaults to `FALSE`
 * `email`: a character that species an email address to send the finished process result to. 
 
### query function for `webprocess`
The `query` function works on `webprocess`, similar to how it works for `webgeom` and `webdata` objects. For a default `webprocess` object, the available algorithms can be queried by:
```{r, eval=FALSE}
knife <- webprocess()
query(knife, 'algorithms')
```
```
## $`Categorical Coverage Fraction`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureCategoricalGridCoverageAlgorithm"
## 
## $`OPeNDAP Subset`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureCoverageOPeNDAPIntersectionAlgorithm"
## 
## $`Area Grid Statistics (unweighted)`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureGridStatisticsAlgorithm"
## 
## $`Area Grid Statistics (weighted)`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureWeightedGridStatisticsAlgorithm"
## 
## $`WCS Subset`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureCoverageIntersectionAlgorithm"
```
Changing the `webprocess` url will modify the endpoint for the query, and different algorithms may be available:
```{r,eval=FALSE}
url(knife) <- 'https://cida.usgs.gov/gdp/process/WebProcessingService'
query(knife, 'algorithms')
```
```
## $`Categorical Coverage Fraction`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureCategoricalGridCoverageAlgorithm"
## 
## $`OPeNDAP Subset`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureCoverageOPeNDAPIntersectionAlgorithm"
## 
## $`Area Grid Statistics (unweighted)`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureGridStatisticsAlgorithm"
## 
## $`Area Grid Statistics (weighted)`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureWeightedGridStatisticsAlgorithm"
## 
## $`WCS Subset`
## [1] "gov.usgs.cida.gdp.wps.algorithm.FeatureCoverageIntersectionAlgorithm"
```

### algorithm
As noted above, the `algorithm` field in `webprocess` is a list, specifying the algorithm name and relative path to the algorithm endpoint. To access or change the algorithm:
```{r, eval=FALSE}
knife <- webprocess()
algorithm(knife) <- query(knife, 'algorithms')[1]
# -- or --
algorithm(knife) <- list('Area Grid Statistics (weighted)' = 
                           "gov.usgs.cida.gdp.wps.algorithm.FeatureWeightedGridStatisticsAlgorithm")
```
### inputs
getting and setting `processInputs` for `geoknife` is currently in. Check back later.

### url
The `url` field in `webprocess` can be accessed and set as expected:
```{r, eval=FALSE}
url(knife) <- 'https://cida.usgs.gov/gdp/process/WebProcessingService'
```

### `wait`
The `wait` boolean in `webprocess` can set during creation:
```{r, eval=FALSE}
knife <- webprocess(wait = TRUE)
knife
```
```
## An object of class "webprocess":
## url: https://cida.usgs.gov/gdp/process/WebProcessingService 
## algorithm: Area Grid Statistics (weighted) 
## web processing service version: 1.0.0 
## process inputs: 
##    SUMMARIZE_FEATURE_ATTRIBUTE: false
##    SUMMARIZE_TIMESTEP: false
##    REQUIRE_FULL_COVERAGE: true
##    DELIMITER: COMMA
##    STATISTICS: 
##    GROUP_BY: 
## wait: TRUE 
## email: NA
```

### `email`
The `email` field in `webprocess` can be accessed and set as expected:
```{r, eval=FALSE}
knife <- webprocess(email = 'fake.email@gmail.com')
knife
```
```
## An object of class "webprocess":
## url: https://cida.usgs.gov/gdp/process/WebProcessingService 
## algorithm: Area Grid Statistics (weighted) 
## web processing service version: 1.0.0 
## process inputs: 
##    SUMMARIZE_FEATURE_ATTRIBUTE: false
##    SUMMARIZE_TIMESTEP: false
##    REQUIRE_FULL_COVERAGE: true
##    DELIMITER: COMMA
##    STATISTICS: 
##    GROUP_BY: 
## wait: FALSE 
## email: fake.email@gmail.com
```

## `geojob` details
The `geojob` in the `geoknife` package contains all of the processing configuration details required to execute a processing request to the Geo Data Portal and check up on the state of that request. A `geojob` object is created using the high-level function `geoknife()` with the `stencil`, `fabric` and optional `knife` arguments as described above.

### `geojob` class and details
The `geojob` public fields include:  
 
 * `url`: the url where the processing job was sent to. Is defined by the `url` field of the `knife` argument used to create the job
 * `xml`: the XML used in the POST to the web geoprocessing service. This XML includes configurations set up by the `fabric`, `stencil`, and `knife` arguments.
 * `id`: the url of the process that is currently running, or `r id(geojob())` for no job.
 
### `check` and related functions
The status of a `geojob` can be checked with the `check` function:
```{r, eval=FALSE}
job <- geoknife(stencil, fabric = 'prism', wait = FALSE)
check(job)
```
```
## $status
## [1] "Process Started"
## 
## $URL
## NULL
## 
## $statusType
## [1] "ProcessStarted"
```

use other convienence functions related to `check` to return boolean (`TRUE` or `FALSE`) responses
```{r, eval=FALSE}
running(job)
error(job)
successful(job)
```

### `cancel` geojob
The `geoknife` package currently limits the user processing requests to single-running processes, so as to avoid creating thousands of requests in error, which could overwhelm the processing resources. If there is a reason to support additional jobs at one time, please email the package maintainers with your query.  

To cancel and existing job:
Cancel a running job but retain the details:
```{r, eval=FALSE}
id(job)
```
```
## [1] "https://cida.usgs.gov:80/gdp/process/RetrieveResultServlet?id=a264a88c-9672-4029-915b-a09b1403d26a"
```
```{r, echo=FALSE}
job <- geojob()
```
```{r}
job <- cancel(job)
id(job)
```

To cancel any running job without specifying the `geojob` reference:
```{r}
cancel()
```

## geoknife web resources
geoknife outsources all major geospatial processing tasks to a remote server. Because of this, users must have an active internet connection. Problems with connections to datasets or the processing resources are rare, but they do happen. When experiencing a connectivity problem, the best approach is often to try again later or email gdp@usgs.gov with any questions. The various web dependencies are described below. 

### Geo Data Portal
The U.S. Geological Survey's "Geo Data Portal" (GDP) provides the data access and processing services that are leveraged by the `geoknife` package. See [cida.usgs.gov/gdp](https://cida.usgs.gov/gdp/) for the GDP user interface.  

