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Making a map from thematically multi- sourced data: the potential of - - PowerPoint PPT Presentation

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23 Septembre 2014 17th ICA Generalisation workshop Making a map from thematically multi- sourced data: the potential of making inter-layers spatial relations explicit Ccile Duchne IGN, COGIT Laboratory cecile.duchene@ign.fr

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Cécile Duchêne

IGN, COGIT Laboratory cecile.duchene@ign.fr

23 Septembre 2014 – 17th ICA Generalisation workshop

Making a map from “thematically multi- sourced data”: the potential of making inter-layers spatial relations explicit

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  • One kind of map commonly produced:

« Thematically Multi-Sourced » maps

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Base data => backdrop map Thematic data Source 1 Sources 2, …, n

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  • One kind of map commonly produced:
  • Rest of the presentation:
  • 1 base dataset + 1 thematic dataset (each from 1 source)
  • Each internally consistent (no redundancies, etc.)

« Thematically Multi-Sourced » maps

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Base data => backdrop map Thematic data Source 1 Sources 2, …, n

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  • One kind of map commonly produced:
  • Rest of the presentation:
  • 1 base dataset + 1 thematic dataset (each from 1 source)
  • Each internally consistent (no redundancies, etc.)

« Thematically Multi-Sourced » maps

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Base data => backdrop map Thematic data Source 1 Sources 2, …, n

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  • These maps :
  • have existed for a long time, produced by expert cartographers

(Das et al. 2012)

  • have exploded over the web (web mapping, by non cartographers)
  • ften have a bad legibility => potentially bad decision making

(Jaara et al. 2011; Das et al 2012; Gaffuri 2011; Balley et al. 2014; Sester et al. 2014)

  • …often due to a bad management of relationships btw thematic

and background layer

« Thematically Multi-Sourced » maps

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« Thematically Multi-Sourced » maps

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« Thematically Multi-Sourced » maps

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« Thematically Multi-Sourced » maps

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« Thematically Multi-Sourced » maps

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  • Conviction: thematically multi-sourced maps could be improved

by a better, explicit management of relations

  • Objective: analyse where we are now, and what could be done
  • Additional motivation:

“Despite all advances in digital generalisation, no overall generalisation theory has been worked out, nor are there convincing solutions for digital generalisation of all relationships between cartographic objects” Ormeling (2011)

MOTIVATION

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Intro Rels in TMS maps Managing rels Open issues

OUTLINE

  • Introduction
  • Relations in thematic maps: role and evolution through scale
  • Managing thematic-background relations
  • Open research issues

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Intro Rels in TMS maps Managing rels Open issues

ROLES OF RELATIONS IN THEMATIC MAPS

  • Relations at least as important as features : well known

(Papadias and Theodoridis 1997; Ruas and Mackaness 1997; Mackaness and Edwardes 2002; Touya et al. 2012; Mackaness et al. 2014)

  • Topographic maps: generalist. What relations are interpreted?
  • Thematic maps:

The theme of interest for the user is known Backdrop data = spatial context for thematic data (Sester et al. 2014) Relations with the backdrop map are interpreted:

=> spatial + semantic

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Intro Rels in TMS maps Managing rels Open issues

RELATIONS EVOLUTION THROUGH SCALE?

  • Some relations are (should be) invariant (Bobzien and Morgenstern 2003):

adjacency/inclusion, reachability, relative position

  • Generalisation should caricature « almost present » relations

(Duchêne et al., 2012)

  • Thematic-background relations should be abstracted when the LOD

decreases (Jaara et al., 2013)

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Intro Rels in TMS maps Managing rels Open issues

OUTLINE

  • Introduction
  • Relations in thematic maps: role and evolution through scale
  • Managing thematic-background relations
  • Open research issues

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Intro Rels in TMS maps Managing rels Open issues

Managing relations… when?

At two main stages:

Integration of thematic data and (one) background dataset Derive map at intended LOD

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LOD 1 LOD 2 LOD n … Generalisation Replacement of background + « migration » (Jaara et al. 2011)

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Intro Rels in TMS maps Managing rels Open issues

Managing… what kinds of relations?

  • Existing VS expected relations

This route is locally very close to the road This route should be locally equal to the road

  • Relations holding at instances VS type level

This accident is close to that junction Accidents are on roads

  • « Hosting » (Jaara et al. 2012) VS « Peer to peer » relations

The road « hosts » the accident The accident happened west to the bridge

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Intro Rels in TMS maps Managing rels Open issues

Managing relations… how?

  • For integration and migration, two (complementary) approaches:
  • Define expected relations at types level
  • Analyse initial relations at instances level

Deduce expected relations at instances level

  • For generalisation:
  • Constrained-based approaches enable to define expected relations at

instances level

  • A few studies specifically focused on relations management (Edwardes

2007; Gaffuri et al. 2008; Duchêne et al. 2003, 2012)

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Intro Rels in TMS maps Managing rels Open issues

MODELLING RELATIONS: HOW?

  • How to model relations between foreground and background

instances ?

  • (Too) few studies, to our knowledge
  • Model for relations and constraints on them (Touya et al. 2012, 2014;

Jaara et al. 2013)

  • Object « hosting » another one thus influencing its behaviour

(Picault et Mathieu 2011; Maudet et al. 2013; on-going)

  • Extension of CityGML with a framework for relations (Bucher et al.

2012)

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Intro Rels in TMS maps Managing rels Open issues

OUTLINE

  • Introduction
  • Relations in thematic maps: role and evolution through scale
  • Managing thematic-background relations
  • Open research issues

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Intro Rels in TMS maps Managing rels Open issues

Better know/describe relations

  • Taxonomy of common thematic-background relations?
  • Spatial + some sematic attached that might guide generalisation?
  • Generic knowledge on how relations are « allowed » to be

transformed through LODs? Does it vary a lot with the use case?

  • Typical relations attached to typical LODs? (cf. CityGML)

Dependency on use case?

  • … Typical use cases??

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Network section hosts punctual objects… … landmarks …events

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Intro Rels in TMS maps Managing rels Open issues

Generalisation of background data

  • … how is it influenced by the presence of background data?

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Intro Rels in TMS maps Managing rels Open issues

Could we set up advanced SDIs…

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LODn LOD2 LOD1

… Backdrop data: MRDB structure

Themes (high level) Thematic-backdrop relations…

Knowledge on thematic data semantic: ontologies

Typical use cases?

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Intro Rels in TMS maps Managing rels Open issues

Could we set up advanced SDIs…

…able to semi-automatically integrate external thematic data at the relevant LOD…

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LODn LOD2 LOD1

… Backdrop data: MRDB structure

Themes (high level) Thematic-backdrop relations…

Knowledge on thematic data semantic: ontologies

Typical use cases? LOD?

  • r
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Intro Rels in TMS maps Managing rels Open issues

Could we set up advanced SDIs…

…able to semi-automatically integrate external thematic data at the relevant LOD…

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LODn LOD2 LOD1

… Backdrop data: MRDB structure

Themes (high level) Thematic-backdrop relations…

Knowledge on thematic data semantic: ontologies

Typical use cases?

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Intro Rels in TMS maps Managing rels Open issues

Could we set up advanced SDIs…

…able to semi-automatically integrate external thematic data at the relevant LOD…

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LODn LOD2 LOD1

… Backdrop data: MRDB structure

Themes (high level) Thematic-backdrop relations…

Knowledge on thematic data semantic: ontologies

Typical use cases?

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Intro Rels in TMS maps Managing rels Open issues

Could we set up advanced SDIs…

…and to derive meaningful maps at coarser level(s)?

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LODn LOD2 LOD1

… Backdrop data: MRDB structure

Themes (high level) Thematic-backdrop relations…

Knowledge on thematic data semantic: ontologies

Typical use cases?

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Intro Rels in TMS maps Managing rels Open issues

Could we set up advanced SDIs…

…and to derive meaningful maps at coarser level(s)?

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LODn LOD2 LOD1

… Backdrop data: MRDB structure

Themes (high level) Thematic-backdrop relations…

Knowledge on thematic data semantic: ontologies

Typical use cases?

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Intro Rels in TMS maps Managing rels Open issues

Could we set up advanced SDIs…

…ideally: on the fly? ...how to combine generalisation and migration for that?

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LODn LOD2 LOD1

… Backdrop data: MRDB structure

Themes (high level) Thematic-backdrop relations…

Knowledge on thematic data semantic: ontologies

Typical use cases?

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Intro Rels in TMS maps Managing rels Open issues

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Thank you! Questions?