The Geospatial Web provides data as well as processing functionality using web interfaces. Typical examples of such processes are models and predictions for spatial data, known as spatial statistics. Such analyses are written by domain experts in scripting languages and rarely exposed as web services. We present a concept of script annotations for automatic deployment in server runtime environments and demonstrate it with an implementation based on the open standards and open source components OGC Web Processing Service and R. Short paper: http://www.agile-online.org/Conference_Paper/CDs/agile_2013/Short_Papers/SP_S3.1_Hinz.pdf

1. Spatial Statistics on the
Geospatial Web
Matthias Hinz, Daniel Nüst, Benjamin Proß,
Edzer Pebesma

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Web Service (mediation layer)
Domain
Scientists
Scripting Environment
(software / languages)
Web Developer /
Admin
Heterogeneous
Audience
…
browser clients
community platforms
GIS-applications
services
Process Sharing
mobile devices
Geospatial Web

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Run the script as a
web process
Upload script
to server
Enhance
script

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The Annotation Concept
metadata
technical instructions
has name + parameter list
as code comments
locally and on a server

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Four Annotation Types
general process description
dynamic inputs
process outputs
resources

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1) General Process Description
# des: id = my42process, title= “my script”,
# abstract = “analyze 42 things”,
# author= “me”;
# myRuntime.des: id = …

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# number for factorization
myFactor <- 10
2) Dynamic Inputs

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2) Dynamic Inputs
# number for factorization
myFactor <- 10
# in: id = myFactor, type = integer,
# title = “numerical factor”,
# abstract = “the number to be used
# for factorization”,
# value = 1,
# minOccurs = 0, maxOccurs = 1;

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# number for factorization
# off;
myFactor <- 10
# on;
# in: id = myFactor, type = integer,
# title = “numerical factor”,
# abstract = “the number to be used
# for factorization”,
# value = 1,
# minOccurs = 0, maxOccurs = 1;
2) Dynamic Inputs

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3) Process Outputs
# number for factorization
myResult <- myAlgorithm(1, 2, myDataSet)
# out: id = myResult, type = string,
# title = “factorized output”,
# abstract = “output number as text in
# scientific notation (a x 10^b)”;

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4) Resources
# res: file1.data, file2.zip, file3.txt,
...;
# load auxiliary data
data1 <- foo("file2.zip")
data2 <- bar(“fixed_path/file3.txt")

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Ignore flags
# off;
script.part.to = be_ignored_on_server()
# on;
script.part.to = run_on_the_server()

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Application Example: WPS4R
Rserve
WPS Client
HTTP
GET /
POST
TCP /
IP
Web Admin Console
- Upload R scripts
- Manage processes
- Config parameters
WPS4R
XML
Requests /
Responses
R
HTTP
GET /
POST
User Administrator
Apache Tomcat Server
Processing
Service

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Example: Interpolation
+Kappasys WPS-Client
Quantum GIS

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WPS Process Description
[…] <ProcessDescription statusSupported="true"
storeSupported="true" wps:processVersion="1.0.0">
<ows:Identifier>org.n52.wps.server.r.Idw</ows:Identifier>
<ows:Title>Inverse distance weighted
interpolation</ows:Title>
<ows:Abstract>A simple interpolation carried out by R,
uses packages gstat, sp and rgdal</ows:Abstract>
<ows:Metadata xlin:title="R Script used for this process"
xlin:href="http://localhost:8080/wps/R/scripts/Idw.R"/>
<ows:Metadata xlin:title="Resource Directory URL"
xlin:href="http://localhost:8080/wps/R/resources" />
<ows:Metadata xlin:title="R Session Info"
xlin:href="http://localhost:8080/wps/R/sessioninfo.jsp"/>
<DataInputs> […]
# wps.des: id = idw,
# title = "Inverse distance weighted interpolation",
# abstract = "A simple interpolation carried out
# by R, uses packages gstat, sp and rgdal",
# author = "Matthias Hinz";

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Example: Live WeatheR Plots
live sensor data
analysis on server
image output

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Conclusion
annotation framework
script-based web processing
demo with open source framework
open approach
reproducibility

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Outlook
security
provenance
rights
further implementations / tools
usability
bidirectional communication

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Thanks for your Attention!

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Resources
WPS4R Website:
https://wiki.52north.org/bin/view/Geostatistics/
WPS4R

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Further Information

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Server Environment Variables
if(exists(“server”) && server == TRUE){
srv.metadata = get_server_url();}

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Annotated R script: IDW

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WPS Process Description
[…]
<Input minOccurs="1" maxOccurs="1">
<ows:Identifier>attributename</ows:Identifier>
<ows:Title>Attribute name</ows:Title>
<ows:Abstract>Name of the attribute to be
interpolated, musst match the
observations</ows:Abstract>
<LiteralData>
<ows:DataType ows:reference="xs:string" />
<ows:AnyValue />
</LiteralData>
</Input>
[…]
# wps.in: attributename, string, "Attribute name",
# "Name of the attribute to be interpolated,
# must match the observations";

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WPS Process Description
[…]
<DataInputs>
<Input minOccurs="1" maxOccurs="1">
<ows:Identifier>points</ows:Identifier>
<ows:Title>Observations</ows:Title>
<ows:Abstract>The point observations and
measurements
to be interpolated</ows:Abstract>
<ComplexData>
<Default>
<Format>
<MimeType>application/x-zipped-
shp</MimeType>
</Format>
</Default>
<Supported>
<Format>
<MimeType>text/xml;subtype=gml/3.0.0</MimeType>
<Encoding>UTF-8</Encoding>
<Schema>http://schemas.opengis.net/gml/3.
0.0/base/feature.xsd</Schema>
</Format>
<Format> […]

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WPS Process Description
[…]<Output>
<ows:Identifier>result</ows:Identifier>
<ows:Title>Interpolated predictions</ows:Title>
<ows:Abstract>A raster file that contains
predictions for the observed area</ows:Abstract>
<ComplexOutput>
<Default>
<Format>
<MimeType>image/geotiff</MimeType>
</Format>
</Default>
<Supported>
<Format>
<MimeType>application/image-ascii-
grass</MimeType>
<Encoding>base64</Encoding>
</Format> […]
# wps.out: result, image/geotiff,
# "Interpolated predictions",
# "A raster file that contains predictions
# for the observed area";