Case study

Linked-data forLinked-data forIntegrated Catchment Integrated Catchment

ManagementManagement

Ian DickinsonEpimorphics Ltd

ian@epimorphics.com

@ephemerian

Tom GuilbertEnvironment Agency

tom.guilbert@environment-agency.gov.uk

Agenda

context and aims– catchment management data– vision

integrated catchment linked-data project conclusion

A better place for people and wildlife

Catchment management

Data overview

water bodiesrisk assessments

classification resultsreasons for failure

predicted outcomesactions

1. Data & evidence, consultations, local

knowledge, model outputs and plans collated in to a

shared central system “Local Community CPS”

Local Community Catchment Planning System

Local Community Catchment Planning System

MonitoringMonitoring Local Knowledge

Local Knowledge ActionsActions

2. Contents of Local Community CPS

published as Linked Data alongside EA

and research datasets

3. Linked Data (machine readable data) could be

automatically combined by applications such as the EVO, CCM Hub and any number of

web apps

CCMHUB

Slide used by kind permission of Michelle Walker, Rivers Trust

ICM: proof-of-concept project

ICM proof-of-concept project

16 weeks duration project team:

– 1 FTE app dev– 0.4 FTE user

research– 0.5 FTE data

7400 water bodies 7.8m triples

agile principles– four iterations– 2-3 week sprints– stakeholder

review

alpha/staging site

organizationscale

From data to linked open data

data modelling

extraction

transformation

publication

presentation

interpretationdownload

source data

SQL

Java

Apache Fuseki

explorer application

Elda

Modelling: considerations

every constant becomes a URI

plan for change

re-use vocabularies

complete is better than simple

Data complexity

WaterBody

SurfaceWater GroundWater

RiverOrLake Transitional Coastal

River Lake

SurfaceWaterTransfer Canal SSSI_Ditch

Data complexity

WaterBody

SurfaceWater GroundWater

RiverOrLake Transitional Coastal

River Lake

SurfaceWaterTransfer Canal SSSI_Ditch

Data transformation

in: CSV out: RDF triples

iterative, so automate!

Data publishing

Baseline goal:– provide access to the data

Practical considerations:– Just “follow-your-nose” linked data?– or SPARQL?– or an API?– ….

Published data: SPARQL

?

Published data: linked-data API

Published data: linked-data API

Published data: linked-data API

ICM data explorer

present the data in a meaningful way

provide meaningful and useful interactions

Data explorer key features

search– by name, catchment, location, ...

show classification items filter by properties

– e.g. classification value map and tabular output basic reports download data

Data explorer application

Specificunderstandingof user goalsand task

Genericdata-driven

interface

data explorer

Data explorer application

Interpretationandreporting

Extractand

download

data explorer

Data explorer application

Easy fornovicesto get started

Not toofrustrating

and slowfor experienced

users

data explorer

Typical user enquiry

“Please show me all:– rivers and lakes – near Glastonbury – that had overall ecological classification as

moderate, poor or bad – between 2009 and 2012.”

Dialogue moves

correspondingSPARQLquery

selectedRDF

resources

correspondingSPARQLquery

selectedRDF

resources

interactionstate

location,classifications,water-body types,...

interactionstate

add year constraint

Demo

Initial learnings

writing SPARQL by doing– in context– with feedback

hard to balance different user needs– explore vs. guide– real user input

download– important– RDF to useful CSV is hard

Dissemination

Conclusions & next steps

formal evaluation– involve partner organizations eg Rivers Trust

“generated excitement”– key engagement tool for catchment management

information– summer 2014 draft river basin management plans

big picture– reference spine for integrating data from other

environmental stakeholders

Photo courtesy of grisleyreg http://www.panoramio.com/photo/65014213 License CC BY-NA 3.0

Questions?