Integrating geoinformation Dr Nigel Trodd Coventry University

an integrated geodatabase

1 st Law of [a] GIS [salesman]* 2 sets of GI, 1 combination 20 data sets, 190 pairs, 1 million+ combinations The more data linked, many more potential applications * You get something for nothing by bringing together GI from different sources and using it in combination BUT  Can raise complex problems of GI ownership  produces uncertainty where data collected to different standards  Data linking procedures may partially determine results

To integrate spatial data you need… A compatible geodata file format A specified data projection & coordinate system A common spatial data model To understand the meaning of your geographical objects

Aim & objectives To explain key processes & issues in integrating geospatial data We will identify & exemplify techniques to apply a data projection & coordinate system georegister data convert spatial data models specify ontologies for geographical objects

Geodata stream

spatial referencing Some (most?) georeferences are metric They define location using measures of distance from fixed places e.g. distance from the Equator or from the Greenwich Meridian Others are based on ordering E.g. street addresses in most parts of the world order houses along streets, sometimes even numbers one-side and odd numbers the other Others are only nominal Placenames do not involve ordering or measuring

Georeferencing for mapping and analysis Placenames can be converted to coordinates using gazetteers Street addresses must be converted to coordinates Using address-matching and geocoding functions Metric referencing systems can be converted between map projections Using mathematical transformations Not surprisingly, these are standard GIS functions

Metric Geographic Coordinates: Lat / Long (0,0) Equator Prime Meridian

Latitude and Longitude Longitude line (Meridian) N S WE Range: 180ºW - 0º - 180ºE Latitude line (Parallel) N S WE Range: 90ºS - 0º - 90ºN (0ºN, 0ºE) Equator, Prime Meridian

But… Earth to Globe to Map Real world simplified as a sphere Sphere ‘flattened’ to a map

Coordinate Systems (  o, o ) (x o,y o ) X Y Origin A planar coordinate system is defined by a pair of orthogonal (x,y) axes drawn through an origin

Map projection system Datum Coordinate system - direction distance area shape Metric georeferencing systems

More than one map projection? John Savard’s homepage explains the basics: Peter Dana’s notes for the Geographers Craft website (using map projections): approj/mapproj_f.html approj/mapproj_f.html Henry Bottomley does an impressive job at demonstrating the effects of different map projections: a way of representing a spherical world using only a flat piece of paper.

equidistant conic projection

The Universal Transverse Mercator (UTM) Projection A type of cylindrical projection Implemented as an internationally standard coordinate system Initially devised as a military standard Uses a system of 60 zones Maximum distortion is 0.04% Transverse Mercator because the cylinder is wrapped around the Poles, not the Equator

Zones are each six degrees of longitude, numbered as shown at the top, from W to E Universal Transverse Mercator System

100 km Easting 524 Northing 739 SP OSGB National Grid

The Coventry-at-the- centre-of-the-world grid George Eliot building used to set the datum Coordinates measured by angle and distance

Georegistering spatial data 1. Identify common points on two data layers – at least one (the registered control) with a known projection, datum & coordinate system  Describe a (mathematical) relationship between the points 2. Apply that relationship to transform the entire unregistered data layer.  The unregistered layer inherits the georeferencing system of the registered layer 3. (for raster) Resample to a common grid

Ground control points (GCPs)

Rotate Flip Stretch Translate Transformations

transforming an image rubber sheeting

resampling a grid

resampling a grid: NN

resampling an image NN Bilinear Original

Geodata stream

But… why bother? The real world is 3-dimensional Modern data capture technologies record XYZ coordinates Use a 3D model for data storage Visualise geospatial data in 3D Only project the data in 2D when you have to publish a flat paper map!

If only it was that simple…..Archived data ….Maps used as ‘base maps’ ……Images assume a ‘flat’ Earth You will still need to understand projections & coordinate systems for many years

Converting spatial data models Most, but not all, GIS can handle raster & vector data Many GIS functions have been implemented for only one data model Users of GIS frequently have to rasterise vectors & vectorise rasters

rasterisation

a complication? Length or area measurements Topology

Vectorisation: many more complications

Data interoperability Low level: data file format Medium level: data standards High level: ontologies

interoperability What? Why? In a perfect world all data would come in one data format… but the world is not perfect!! Therefore, we adopt standard procedures & protocols to describe data If a GIS complies with these standards then it can read the data, …no matter what its’ internal format. Partial interoperability

Making interoperability happen: formats & standards, procedures & protocols de facto industry formats  software vendors e.g. ESRI ‘shp’, Google Earth KML National / international standards e.g. Ordnance Survey NTF Web-based open standards… HTML, XML  Open Geospatial Consortium (OGC) specifications GML, WMS, WFS 

Semantic difficulties Multiple application domains Means multiple languages Simple questions… what is a road? Numerous answers… Transport route Source of air pollution Drainage pathway Lump of concrete How do you ‘translate’ data between languages?

Ontologies an ontology is a specification of a concept In GI systems, we use concepts like land parcel, highway, lake, etc. In Architecture Engineering and Construction (AEC) systems we use concepts like building, room, garden, backyard, etc. If our specification includes unique, unambiguous names for concepts, and descriptions of the meaning of those names then we could develop a mapping between ontologies AEC has produced well-specified BIMs e.g. IFC GIS has produced…. very little

Geo-ontologies GIS has produced…. very little e.g. ESRI Geodatabase Schemas Data Models for GIS Users about/data-models.html about/data-models.html Most of the time we (GISers) create implicit geo-ontologies on-the-fly… we know what we mean! … what is a mountain?

and… perfect integration is not always possible …the geography of data sources

summary We have tools to support spatial data integration Geodata file format exchange Georeferencing Geodata model conversion X Geospatial ontologies and (sometimes) we lack data worthy of integration