GI Science spatial data modelling Dr Nigel Trodd

GI Science spatial data modelling Dr Nigel Trodd Science and the Environment Coventry University

Aim & objectives To understand the process of building a digital representation of reality • • to characterise reality (as a spatial concept) to identify the process of modelling geographical phenomena to identify & exemplify entitation & data modelling to identify vector and raster data & their data structures

Building GI models I am a Geographer I model places & spatial phenomena GI Science = Geography + Computer Science I am a Computer Scientist I model information in a database

How can we store geographical data in a computer?

the real world is complex and computers are very simple and computers need us to model the geography for them.

model the real world in a computing environment ? How do we from reality to spatial data structures in 1. . 2. . 3. . 4 (easy) steps

1. from reality to geographical phenomena 2. from geographical phenomena to spatial entities 3. from spatial entities to spatial data models 4. from spatial data models to spatial data structures

terminology overload. . . there’s more

1. from reality to geographical phenomena How do we view reality? Values at different places • Continuous variation – biomass, elevation, SST Points and edges with associated data • Discrete objects - house, pipeline, river, coastline

1. from reality to geographical phenomena 2. from geographical phenomena to spatial entities the famous 5 ive. . . points lines areas networks & surfaces

3. from spatial entities to spatial data models object-based 4 Geometry 4 Topology field (or layer) of geography 4 Concepts -based

wakey … here comes the easy bit

vector & raster

4. from spatial data models to spatial data structures

Summary I am NOT a Geographer I am NOT a Computing Scientist I am a GI Scientist. I model. I am crude. I use basic IT. I work.

I am a GI Scientist. Are you? • outline the art of building a digital representation of reality. • identify & exemplify 5 spatial entity types. • explain the elements of a spatial data model. • identify differences between vector and raster data & exemplify their data structures.