Introduction to Spatial Data Types

Transcription

1 Introduction to Spatial Data Types Daniel McInerney Urban Institute Ireland, University College Dublin, Richview Campus, Clonskeagh Drive, Dublin th June 2009

2 Presentation Outline 1 Objectives Basics Synthesising Reality 2 3 Vector vs. Raster Models Examples of Data Conclusion

5 Objectives of lecture Objectives Basics Synthesising Reality To provide some theory and background to GIS, data models and spatial data Provide some examples of spatial data available within and outside of UCD.

6 What is a GIS? Objectives Basics Synthesising Reality A Geographic Information System (GIS) has been defined as a system for organised collection of computer hardware, software, geographic data and personnel designed to efficiently capture, store, update, manipulate, analyse and display all forms of geo-referenced information Defined by Burroughs and McDonnell as a means of checking, manipulating and analysing data, which are spatially referenced to the Earth. At its core is a spatial database that optimally stores both spatial and attribute data

7 What is a GIS? Objectives Basics Synthesising Reality A Geographic Information System (GIS) has been defined as a system for organised collection of computer hardware, software, geographic data and personnel designed to efficiently capture, store, update, manipulate, analyse and display all forms of geo-referenced information Defined by Burroughs and McDonnell as a means of checking, manipulating and analysing data, which are spatially referenced to the Earth. At its core is a spatial database that optimally stores both spatial and attribute data

8 Conceptual of a GIS Objectives Basics Synthesising Reality

9 Representing Reality Objectives Basics Synthesising Reality REALITY - what actually exists DATA MODEL - Abstraction of the real world incorporating only properties relevant to application DATA STRUCTURE - A representation of the data model, expressed using arrays and programming structures that are incorporated in computer programs FILE STRUCTURE - The representation of the data in storage hardware in terms of bits and bytes on disk sectors

13 The Data Model Critical decision is the choice of the data model, which is the basis of a GIS Different data models are required for spatial and attribute data They are a set of constructs for describing and representing selected aspects of the real world in a computer Models need to cope with: maintaining data, modelling tasks, analysis tasks and presentation.

17 Two fundamental data models exist: Vector & Raster... but also Vector representation of linked lines and Delauney Triangulation (TINs) irregular tesselations of fields.

18 Two fundamental data models exist: Vector & Raster... but also Vector representation of linked lines and Delauney Triangulation (TINs) irregular tesselations of fields.

19 What are Spatial Data? A geographical entity is defined in terms of: Location (spatial reference) Dimensions Attribute Time It is common in spatial analysis to refer to places as (spatial) objects.

20 Spatial Reference Latitude Longitude National Grid Reference An address A postcode

21 Coordinate Reference Systems A flat map distorts geometry because the Earth is curved. If you plot lat long coordinates of a Cartesian system a straight line will appear bent and areas will be distorted. 3D points are projected from the Earth s surface onto the 2D map surface. Despite these projections, this process can lead to distortions over large areas.

22 Map Projections Many types of map projections exist, perhaps the most commonly used one is: Universal Transverse Mercator Coordinate System It divides the Earth into zones that are six degrees in longitude & from 80 south latitude to 84 north latitude

23 Map Projections Bear in mind that map projections distort five geographic relationships: Areas Angles Gross Shapes Distances Directions Some projections will preserve local angles but not areas and vice versa!

24 Irish National Grid

25 Zero Dimensional Object Types Point - as pairs of coordinates in lat/long or some other reference system Node A point feature is a zero-dimensional cartographic object. It specifies the geometric location and no other meaningful measurement The size of the point may vary, but the area of those symbols is meaningless Four types of points exist: entity point, label point, area point and node

26 One Dimensional Object Types Line - ordered sequence of points connected by straight lines Line features are one dimensional features, despite occupying two-dimensional space. A line segment is the direct connection between two points A line feature is typically represented as a sequence of vectors An Arc is the location of points that are defined by a mathematical function to form a curve Link or edge is the connection between two nodes

27 Two Dimensional Object Types Areas as ordered rings of points connected by straight lines to form polygons Area is a two dimensional, bounded and continuous object Interior area is an area not including its boundary Simple polygon consists of an interior area and an outer ring. The boundary does not intersect itself Typically refers to vector polygons, but also relates to pixels and grid cells.

28 Attribute Data Attribute (Aspatial) information is the label / name / categorisation / descriptiong associated with a spatial object The attributes can be as important as the spatial data themselves May be more complex than the spatial data May be a simple text label (e.g. town name, river name, county population) Attributes usually stored in some form of database (i.e. DBF, ORACLE table...) Relational or object oriented DBMS used to spatial objects to complex attribute tables

34 Common types of attribute data Nominal Attribute that distinguishes between locations without any ranking or potential for arithmetic Ordinal Attribute that implies a ranking, but arithmetic calculations are nonsensical Interval Attributes where differences make sense Ratio Attributes where it makes sense to divide one measurement by another

35 Raster Data Popular means of referencing continuous fields Designed for rapid response to queries e.g. What is at a given location? Where does attribute X occur?

36 Types of continuous fields Elevation Air Temperature Land ownership Ice thickness Timber Volume or Biomass Population density

37 What is a raster? A raster is a geographic data set in which values are assigned to a rectangular array of objects (Goodchild, 1997) A regular grid of cells with a fixed resolution completely covering an area (Burrough, 1986)

38 What is a raster? A raster is a geographic data set in which values are assigned to a rectangular array of objects (Goodchild, 1997) A regular grid of cells with a fixed resolution completely covering an area (Burrough, 1986)

39 Basic raster definition Space is tesselated at a fixed resolution, the cell size Cell values are stored in a fixed order (e.g. row by row from top left to bottom right) No need for coordinates of each cell as the scheme is ordered and regular Store the coordinates of one cell and cell size to register the raster to an external coorindate system

40 Advantages Vector vs. Raster Models Examples of Data Conclusion Raster Advantages Simple data structure Simple overlay operations High spatial variability is efficiently represented Satellite & other formats already in this format Vector Advantages Vector representation is more compact Provides efficient encoding of topology, i.e. better for network operations Better suited to produce maps with crisp line-work

41 Advantages Vector vs. Raster Models Examples of Data Conclusion Raster Advantages Simple data structure Simple overlay operations High spatial variability is efficiently represented Satellite & other formats already in this format Vector Advantages Vector representation is more compact Provides efficient encoding of topology, i.e. better for network operations Better suited to produce maps with crisp line-work

42 Disadvantages Vector vs. Raster Models Examples of Data Conclusion Raster Disadvantages Raster data are less compact Topological relationships difficult to represent Poorer graphical output (step-like effect / blocky appearance) Vector Disadvantages More complex structure than a simple raster Overlay operations are more difficult to implement Representing high spatial variability is inefficient Handling image data is not possible

43 Disadvantages Vector vs. Raster Models Examples of Data Conclusion Raster Disadvantages Raster data are less compact Topological relationships difficult to represent Poorer graphical output (step-like effect / blocky appearance) Vector Disadvantages More complex structure than a simple raster Overlay operations are more difficult to implement Representing high spatial variability is inefficient Handling image data is not possible

44 Vector vs. Raster Models Examples of Data Conclusion Demonstration of different types of data Frequently, access to data is the biggest hinderence to carrying out spatial analysis Multitude of data exist, some proprietary others free...

45 Vector vs. Raster Models Examples of Data Conclusion Demonstration of different types of data Frequently, access to data is the biggest hinderence to carrying out spatial analysis Multitude of data exist, some proprietary others free...

46 Vector vs. Raster Models Examples of Data Conclusion Concluding remarks Hopefully this lecture has provided you with some background and theory to GIS An understanding of data models and data types will facilitate your spatial analysis Type of data model are dependent on your needs and analysis Questions, comments?

47 Vector vs. Raster Models Examples of Data Conclusion Concluding remarks Hopefully this lecture has provided you with some background and theory to GIS An understanding of data models and data types will facilitate your spatial analysis Type of data model are dependent on your needs and analysis Questions, comments?