Outline

1
(No Transcript)
2
Outline

• Land Administration and Data Integration
• Data Integration Drivers
• Data Integration Issues and Problems
• Sources of the Problems
• Data Integration Levels
• Data Integration Approaches
• Conclusion

3
- The land admin definition and evolution - The
land administration response - Cadastral concept
and principles

• - Overview
• People to land relationships

• Ten LA statements
• Intro to the LA toolbox
• The role of governance in spatially enabled LAS

Growth of Land Administration

• Managing
• Property RRRs
• Land marine
• interface
• Human rights
• Women's access
• Environment and
• sustainability
• accounting
• Benchmarking and
• Monitoring
• and evaluation
• - Spatially enabling
• governments
• Accessing
• land information
• New tools for
• developing
• countries

Introduction

• Land policy
• Legal principles
• Institutional Principles
• Cadastral mapping and boundary options
• Land tenure and registration
• - ICT options for land admin
• Process of Land Admin
• e-Land Admin
• SDIs principles
• Land and Marine administration,
• Human resource capacity building

Land Administration 451-418/607
Current issues and Future trends
The Land Administration Toolbox
Technology and SDI
Case Studies

• - Digital Cadastral Databases
• Land administration data models
• Information access technologies
• eLand administration
• iLand spatially enabling governments
• Spatial hierarchy problem and the SDI solution

• Data Integration and LA
• Developed countries
• Newly industrialized
• countries
• Developing countries

4
LA and Data Integration
Incorporating sustainable development objectives
into ICT enabled land administration systems
(Adopted from Enemark, Williamson and Wallace,
2004)
5
LA and Data Integration
Data Integration
6
LA and Data Integration
Data Integration
7
LA and Data Integration
Data Integration
8
Marine SDI and Seamless SDI
Coastal Zone

• Development of SDI in the marine environment
  would provide basis for integration of marine
  terrestrial environments. The ultimate aim is to
  include a marine dimension to SDI models so that
  they work seamlessly both on land and at sea
  through.
• This seamless model will bridge the gap between
  the terrestrial and marine environments, creating
  the spatially enabled land-sea interface to more
  effectively meet sustainable development
  objectives.

Spatial Data Integration
9
Marine SDI and Seamless SDI
Data Integration Framework

• National Mapping Agencies and Hydrography
  Organizations generally use
• Different coordinate systems
• Different projections
• Different datums (Hz V)
• Different content

10
Data Integration and land-related Services

• Land use
• Land development
• Land valuation
• Land taxation
• Property planning
• Resource Management
• Environment protection
• Land-Sea Management

11
Need for Data Integration

• Spatial Technology is growing dramatically
• Citizens and governments are recognizing the
  value of spatial data to manage the assets
  including land and resources
• Demands are growing

12
Data Integration Drivers

• Sustainable Development objectives
• Sustainable development objectives (economical
  growth, environmental protection and social
  cohesion)
• Equal access to land
• Poverty elimination through better land
  management
• The impact of human activities (built environment
  and economic systems) on natural environment
• Integration of Spatial data (natural and built
  environmental data) with socio-economic data
  (taxation, land valuation)

13
Data Integration Drivers

• Producing richer datasets
• Each spatial dataset represents an aspects of the
  real world
• Topography
• Cadastre
• Business-oriented data (e.g. Utilities)
• Socio-economic data, and
• etc.
• Data integration obtains the unique advantages of
  each data

14
Data Integration Drivers

• Raising the analytical level/Creating knowledge
• Integrated data elements reinforce, augment or
  contradict others
• Spatial data integration generates new
  information

15
Data Integration Drivers

• Horizontal and vertical integration
• Vertical data integration from different themes
  for a certain area
• Horizontal data integration from same themes

16
Data Integration Drivers

• Complying with a uniform framework
• One-stop shop
• Single Point of Truth

17
Data Integration Drivers

• Facilitating multi-disciplinary science
• Land Administration
• Resource Management
• Emergency Management
• Urban Planning
• Health

Utilities
Critical Infrastructures
Cadastre
Bathymetry
18
Spatial Data Integration is problematic

• Inconsistencies, heterogeneities,
  interoperability, legal, institutional, policy
  and social issues hinder effective data
  integration
• Different Understanding
• Different Objectives
• Different Policies
• Different Drivers

19
Source of Problems

• Different understandings and purposes

20
Land Parcel Different Understandings
Entity

Legal Objects
Physical Objects
Location
Social Entity
Living being
Database
Animal
Country
Organization
Geographical Region
Natural Objects
Property
Land Legal
Land Surveyor
Land GIS view
Natural resource
Land Environment Expert
21
Data Integration Scenario1

• Hazard management, Tsunami (EICUs Experience)

• Gaps of Data, No Vertical Topology, Currency,
  Collaboration, Data Exchange Policy,

Utilities
Critical Infrastructures
Datasets Cadastre Access Network Underground
Features
Cadastre
Bathymetry
22
Data Integration Scenario2

• Nationally consistent data
  (PSMAs Experience)

Inconsistencies in Data Model, Metadata,
Standards, Specifications, Collaboration Model,
and IP issues in Victoria, NSW, GA,

• G-NAF
• CadLite
• Transport and Topography
• Admin Boundaries
• Points of Interest

IDM
23
Data Integration Scenario3
Band 2
Locational
Police
Fire
Ambulance
SES
Band 3
Public Schools
Band 1
Private Schools
More publicly available Rich metadata Consistent
data models Low price Clear policies Low access
and use restriction Interoperable data National
coverage Single point of access
Infrastructure
Socio-economic
Public Hospitals
Fundamental Data
Private Hospitals
Aged care
Demography
Electricity
Less Publicly available Poor metadata Inconsistent
data models High price Not clear policies Access
and use restriction Not Interoperable data No
Single point of access
Community Centers
Cadastre
Employment
Sub-stations
CCTV
Roads
Valuations
Gas
Icons
Imagery
Public Transport
Water
Key Buildings
Topography
Schedules
Hydrants
People at Risk
Census
Pedestrians Flow
Sewerage
Rural Property Buildings
Administrative Boundaries
Floor Plans
Storm-water
Hazardous Chemicals
Street Directory
Hazard Models
Telecoms
Oil Spill Response Atlas
Waterways
Buildings Database
Ferry Routes
Flora and Fauna Atlas
Vegetation
Bush Fire Prone Zones
Points of Interest
Flood Planes
3D Buildings
24
Fundamental and Value-added Data
25
Data Integration Issues and Problems

• There are at least seven sources of road centre
  line in Victoria with different road alignments
  and mismatched road names.

26
Data Integration Issues and Problems

• Completeness

27
Data Integration Issues and Problems

• Data classes and categories

28
Data Integration Issues and Problems

• Different Specifications and Standards Railway,
  Tank Point and Cableway have been differently
  Categorized in different jurisdictions

29
Data Integration Issues and Problems

• Feature Level and Theme Level Metadata

Victoria, GA Theme Level (ANZLIC Guidelines)
NSW Feature Level
30
Data Integration Issues and Problems

• Data Model (Feature Type as a Class or as an
  Attribute Roads)

Harmonized Data Model (ICSM)
NSWs Data Model

Integrated Data Model (PSMA)
31
Data Integration Issues and Problems

• Lack of single channel of data access for users
• No obligation to follow national spatial policies
  
• Custodians are not defined for all data
• Immature institutional arrangements and
  user/provider relationships
• IP and restrictions on data hinder integration
• Software does not support integration and
  integration functions

32
Data Integration Issues and Problems

• Metadata does not contain all information
  required for integration
• Different priorities of mapping organizations
• Different data models utilized by data providers
• Minimal awareness of the importance of
  integration among senior managers
• Data providers create data for a particular
  purpose in contrast to multi-disciplinary data
• Incomplete knowledge about the availability and
  quality of spatially referenced data, especially
  natural environmental data

33
Data Integration Issues and Problems

• Spatial data integration issues
• Technical
• Legal
• Policy
• Institutional
• Social

Data Integration
Technical Integration
34
What is Data Integration?

• Data Integration is not only to geometrically and
  topologically match data, and having the
  correspondence of attributes, but also the
  establishment of all legal, policy, institutional
  and social mechanisms in order to facilitate the
  integration of data.

35
What is Data Integration?

• An Effective Data Integration requires technical
  and non-technical guidelines and tools to remove
  the integration barriers
• The establishment of collaboration
• Capacity building
• Technical tools (metadata and data access tools)
• Consistent access and privacy policies
• Custodianship arrangements
• etc

36
Data Integration Levels

• Data Integration
• Process level
• Data level

Data Level
37
Data Integration Levels

• Data Level Integration
• Visual integration
• Matching coordinate systems and projections
• Compatible scale
• Compatible formats
• Visualization
• Analytical level
• Visual integration
• Attribute matching
• Data model synchronization

Analytical Integration
Visual Integration
38
Data Integration Approaches

• Federated databases
• Virtual database
• Integration of distributed databases
• Technical issues
• Institutional arrangements, access and privacy
  policies
• Etc.

39
Data Integration Approaches

• Ontology-driven spatial data integration
• diversity of the requirements, purposes and
  backgrounds
• Share understandings

40
Data Integration Approaches

• Spatial mediation
• A third party service to integrate data and
  services across internet
• metadata

41
Data Integration Approaches

• Feature Manipulation Engines (FMEs)
• An integrated collection of Spatial ETL (Extract,
  Transform, Load) tools
• FME has been introduced as a complete
  interoperability solution

42
Data Integration Approaches

• Interoperability
• Semantic, structure and syntactic
• Integration and Interoperability

43
Interoperability

• Interoperability is the ability of a system or a
  product to work with other systems or products
  without special effort on the part of the
  customer

Technical Interoperability
Legal Interoperability
Social Interoperability
Institutional Interoperability
Policy Interoperability
44
Data Integration Approaches

• Generalization (Multi-scale data integration)
• transforms a highly detailed map into one with
  fewer details
• One-stop services

45
Conclusion

• Spatial data plays a significant role in many
  land-related services
• They integrate spatial data from different
  sources
• The diversity of approaches, understandings and
  backgrounds of the custodians causes
  heterogeneity among spatial data

46
Conclusion

• The technical and non-technical issues
  (institutional, policy, legal and social) needs a
  holistic framework (SDI) to facilitate the
  effective data integration
• Many approaches have been proposed to overcome
  data integration problems

47

• Any Questions

48
(No Transcript)