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Agenda
What is geospatial data
What does “structured” geospatial data look like?
General data modelling issues regarding geospatial data
In search of the BLPU
A brief history of OS maps – how structured are they (then and now)
Raster map data
EDRM
Geo-parsers/gazetteers/metadata
Web-based systems
Future directions?
What is Geospatial Information? - 1
Spatial data which relates to the surface of the Earth
Geodetic reference system as base e.g. WGS84 used for Global Positioning System (Earth as an ellipsoid), Latitude and Longitude (Earth as a sphere)
Ordnance Survey (GB) define National Grid – projection onto flat surface – NB: OS(NI) use Irish grid
Spatial relationships – defined around concept of neighbourhood – relates to two “laws” of geography:
• Most things influence most other things in some way• Nearby things are usually more similar than things
which are far apart
What is Geospatial Information? - 2
Unstructured – spaghetti data
Topology – information structured as networks, polygons
GeoSpatial information requires metadata – e.g. minimal information such as map projection used
GeoSpatial information may also temporal modelling – e.g. farm subsidies vary as utilisation and legislation change
Field-based model versus object-based model of space, e.g. rainfall versus buildings on which rain falls
GeoSpatial information requires ontology
– What is the “real world”, how classifiedRelates to semantics
What are GeoSpatial Systems?
Known as Geographic Information Systems, Spatial Information Systems
Enables capture, modelling, storage, retrieval, sharing, manipulation and analysis of geographically referenced data
Database is at the heart – as is “attribute” data
Model developing – perhaps GeoSpatial data better seen as “attribute” of alphanumeric business information
Presentation does not have to be map-based in all cases
Key element is spatial indexing – uses different techniques to alphanumeric indexing
Where used? Examples
Central government – DEFRA, ODPM, Land Registry, ONS
Local government – planning, highways authorities
Utilities – physical and logical network
Insurance – flood plains
Health – epidemiology
Travel, multi-modal route planning
More widespread use – addresses, postcode based data against regional boundaries, infrastructure (“geographies” used to divide country, catchment area)
Fiat boundaries verus “bona fide” boundaries – what is “real world” how do we structure it?
Structured geo-databaseParadigm shift?
Relational Database
(Attribute data)
SpatialData
(proprietary format)
ERP
CRM
Real
Tim
e/Engineering
System
s
Spatially extended RDBMS-Complex data types for spatial data
-Computational geometry-Spatial indexing
-DDL and DML extensions
Geospatial data modelling
Field-based model versus object-based model
Geographic Information Systems are object-based in practice
Most common field based information, e.g. Digital Elevation Model (line of sight applications), attached to objects
Objects rely on field-based model, i.e. spatial co-ordinates
Initiatives such as Digital National Framework encourage organisations to structure data on references to objects, not re-capture and duplicate data
GeoSpatial equivalent of “referential integrity”
Nevertheless duplication, lack of (referential) integrity is common place and hard to eradicate
In search of the BLPU
Basic Land and Property Unit “Holy grail” of industry – no Da Vinci code produced yet!Example of Ordnance Survey Master Map (OSMM):"St Mary's football stadium, Southampton" is one objectTypical detached house and its plot of land, likewiseComplex entities such as "Southampton railway station" are defined in terms multiple objects: one for the main building, several for the platforms, one more for pedestrian bridge over the tracks. (NB: See Wikipedia article on TOID)Defining the candidate BLPU, their lifecycles and their attribute data and verifying that these are meaningful/practicable from the wide variety of business processes which apply to the BLPU and the aggregate entities which are created from them Dependencies so that data sets are based on the BLPU wherever possible limited by business use, e.g. field use change quite different from a tenant/owner perspective
Evolution of geographic information
1950 2010
paperrecords
digital
records
databaserecords
paper mapping
digital mapping
geographicinformation
1970 1990
Raster map data
Scanned ortho-rectified map or map-based data – metadata is co-ordinates, projection, extent
For example Google Maps/Google Earth, Microsoft Virtual Earth
Traditionally stored outside the database as external files, analogous to vector data storage, e.g. Oracle 10g GeoRaster
Data stored as BLOBs, metadata required regarding number of bytes per pixel, compression algorithms and so on
Benefits limited as “intelligence” in map requires interpretation
Still limited progress on map-based pattern recognition – there are semi-automated solutions from companies such as Laser-Scan
EDRM
Electronic document and records management
Increase usage in local/central government due to Freedom of Information act
Contain potentially significant geospatial data
Most common example is address
Requires capture of appropriate metadata or appropriate pattern recognition to identify addresses
Requires gazetteers to provide reference to spatial co-ordinates
NB: most familiar gazetteer – list of streets in AtoZ maps
Geo-parsers/gazetteers/metadata
Geo-parsers: identify spatial tags (geo-tags) in data
Context sensitivity and patterns of usage required
E.g. Jordan (country) != Jordan (Katie Price)
Can see an example at:
http://edina.ac.uk/projects/geoxwalk/geoparser.html
Relies on and populates gazetteer of associated names
Emerging standards for geo-parsing, e.g. Open GIS Consortium looking at:
– Gazetteer service– Geo-coder service– Web services (WMS/WFS)
Web-based systems
World wide wild west of unstructured data
Increasing use of systems to control, coordinate and make this accessible
Geo-enabled semantic web – raises issues of ontology
www.metacarta.com – provide web-based Geographic Text Search (GTS), has the ability to confine searches by geography and retrieve information that it detects using the keywords, and then displays this information geographically on a map interface (working now with Google Earth).
They know where you live
MetaCarta(R), Inc., a leading provider of geographic intelligence, announced today that it had won a one-year contract with … the Department of Homeland Security [which] identifies and assesses current and future threats to the homeland, maps those threats against the nation's vulnerabilities, issues timely warnings and takes preventative and protective action… The product automatically identifies geographic references using advanced natural language processing (NLP) from any type of unstructured content in a customer's archives such as email, web pages, newswires or cables. It assigns a latitude and longitude to these references so that users can analyze their text archives using geographic maps, keywords and time as filters. The results of a query are displayed on a map with icons representing the locations found in the natural language text of the documents and as a text results list. Both the icons and text summaries are hyperlinked to the documents they represent. (Source: http://www.prnewswire.com/cgi-bin/stories.pl?ACCT=109&STORY=/www/story/03-14-2005/0003193909&EDATE=)
The future (and summary)
Structured environment – will contain more “unstructured” data
Web will continue to provide unstructured distributed data
Success of semantic-based approach yet to be determined, experience with geospatial data indicates there are significant complexities based around our representations of the “real world”
One issue is clear – increasingly less privacy, location is already accessible through mobile phones and linking this to other data can provide significant intelligence information
Also clear – data quality issues will persist
They will still get it wrong!