Modelling spatial data

• Learning Outcomes
• Define spatial data model
• Explain how spatial entities are used to create a data model
• Examine raster and vector formats and how they are stored on a computer
• Describe a spatial data structure
• Explain what topology is and how it is stored on a computer

Introduction

• GIS Model
  • Spatial form
    • Structure & distribution of features in geographic space
  • Spatial process
    • Interaction between the features is considered
  • We'll concentrate on form for the moment

Spatial data models

• Raster
  • Tessellation
  • Cell is building block for creating images of point, line, area, network and surface entities
• Vector
  • 2-dimensional Cartesian (x,y) coordinates
  • More complex shapes require more points to represent them
  • Too few points: measures of length, perimeter area compromised
  • Too many points: duplicate information – costly to store and manage

Building a model

• Identify spatial features from the real world that are of interest in the context of the application and choose how to represent them in a conceptual model
• Represent the conceptual model by an appropriate spatial data model (raster or vector)
• Select the appropriate data structure to store the model within the computer

Spatial data structures

• Provide the information needed by the computer to represent the model in digital form
• Raster
  • Range of different encoding methods
  • Problem of size
  • Data compaction
    • Run length encooding
    • Block encoding
    • Chain encoding
    • Quadtrees

Spatial data structures

• Vector
  • Simplest structure: list of coordinate pairs ('spaghetti' model)
  • Limited for more complex entities
    • To avoid duplication (polygon case) use point dictionary i.e. Which ploygons are made up of which points
  • Special cases
    • Networks -> need to encode linkages
    • 'Island' polygons enclosed by another polygon
  • Both cases – set of instructions needed to tell computer where line or polygon lies with respect to its neighbours
  • This implies a topological structure

Spatial data structures

• Considerable range of possible topological structures but all try to ensure that
  • No node or line segment is duplicated
  • Line segments and nodes can be referenced in more than one polygon
  • All polygons have a unique identifier
  • Island and hole polygons can be represented

Shapefile

• The Esri shapefile, or simply a shapefile, is a popular geospatial vector data format for geographic information system software. It is developed and regulated by Esri as a (mostly) open specification for data interoperability among Esri and other GIS software products.[1] Shapefiles spatially describe vector features: points, lines, and polygons, representing, for example, water wells, rivers, and lakes. Each item usually has attributes that describe it, such as name or temperature.

• While the term "shapefile" is quite common, a "shapefile" is actually a set of several files. Three individual files are mandatory to store the core data that comprise a shapefile: .shp, .shx, and .dbf. The actual shapefile relates specifically to .shp files but alone is incomplete for distribution, as the other supporting files are required.

• Mandatory files :
  • .shp — shape format; the feature geometry itself
  • .shx — shape index format; a positional index of the feature geometry to allow seeking forwards and backwards quickly
  • .dbf — attribute format; columnar attributes for each shape, in dBase IV format

• Optional files :
  • .prj — projection format; the coordinate system and projection information, a plain text file describing the projection using well-known text format
  • .sbn and .sbx — a spatial index of the features
  • .fbn and .fbx — a spatial index of the features for shapefiles that are read-only
  • .ain and .aih — an attribute index of the active fields in a table
  • .ixs — a geocoding index for read-write shapefiles
  • .mxs — a geocoding index for read-write shapefiles (ODB format)
  • .atx — an attribute index for the .dbf file in the form of shapefile.columnname.atx (ArcGIS 8 and later)
  • .shp.xml — geospatial metadata in XML format, such as ISO 19115 or other XML schema
  • .cpg — used to specify the code page (only for .dbf) for identifying the character encoding to be used

GeoJSON

{"type":"FeatureCollection",
 "features":[{"type":"Feature","id":"stgeom.T19014",
  "geometry":{"type":"MultiPolygon","coordinates":[[[[-9.800881544808641,52.113307011713744],
    [-9.800071435579255,52.11333079728532],[-9.799806439355441,52.11335803165582],
    [-9.799654042628184,52.1133710984878],[-9.799331374760715,52.11339130265084],
    ...
    [-9.800881544808641,52.113307011713744]]]]},
  "geometry_name":"geom","properties":
  {"settlement":"19014","settl_name":"Killorglin",
   ...
   "popdensity":897,
   "bbox":[-9.806061919649077,52.094103345241784,-9.766986215834313,52.11477198930869]}}],
   "crs":{"type":"EPSG","properties":{"code":"4326"}},
   "bbox":[-9.806061919649077,52.094103345241784,-9.766986215834313,52.11477198930869]}~~~~~~~~~~~~~~~~~~~~~~

GeoTiff

• GeoTIFF is a public domain metadata standard which allows georeferencing information to be embedded within a TIFF file. The potential additional information includes map projection, coordinate systems, ellipsoids, datums, and everything else necessary to establish the exact spatial reference for the file.
• An alternative to the "inlined" TIFF geospatial metadata is the *.tfw World File sidecar file format which may sit in the same folder as the regular TIFF file to provide very similar functionality to the standard GeoTIFF.

ASCII Grid

The format consists of a header that specifies the geographic domain and resolution, followed by the actual grid cell values. Usually the file extension is .asc, but recent versions of ESRI software also recognize the extension .grd. It looks like this:

• Records 1 - 6 Geographic header
  • Coordinates may be in decimal or integer format.
  • ncols xxxxx refers to the number of columns in the grid and xxxxx is the numerical value
  • nrows xxxxx refers to the number of rows in the grid
  • xllcorner refers to the western edge of the grid and xxxxx is the numerical value
  • yllcorner refers to the southern edge of the grid
  • cellsize refers to the resolution of the grid and xxxxx is the numerical value
  • nodata_value xxxxx refers to the value that represents missing data and xxxxx is the numerical value.
• Record 7 -> end of file Data values

ASCII Grid Example

ncols        1228
nrows        972
xllcorner    428212.210000000020
yllcorner    5566050.730000000400
cellsize     30.000000000000
NODATA_value      0
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... removed for brevity

ASCII Grid Example (accompanying .prj file)

PROJCS["NAD_1983_UTM_Zone_10N",
 GEOGCS["GCS_North_American_1983",
  DATUM["D_North_American_1983",SPHEROID["GRS_1980",6378137,298.257222101]],
  PRIMEM["Greenwich",0],
  UNIT["Degree",0.017453292519943295]],
  PROJECTION["Transverse_Mercator"],
  PARAMETER["latitude_of_origin",0],
  PARAMETER["central_meridian",-123],
  PARAMETER["scale_factor",0.9996],
  PARAMETER["false_easting",500000],
  PARAMETER["false_northing",0],
  UNIT["meters",1]]

Modelling surfaces

• Surface examples: height, pollution, rainfall, land use
• Digital terrain model (DTM) – digital data set used to model a topographic surface (height data)
• Approximates a continuous surface using a finite number of observations
• Regular (equal interval) grid of heights usually referred to as a digital elevation model or matrix (DEM)
• Resolution determined by frequency

Raster approach to DTM

• DEM is relatively simple model – grid of values for height with on other information about the surface being modelled
• Accuracy depends on complexity of surface and spacing of observations

Vector approach to terrain modelling

• Mimics raster DTM in its simplest form
• Triangulated Irregular Network (TIN) – more complex form
  • Joins height observations together with straight lines to create a mosaic of irregular triangles
  • Triangle vertices represent features such as peaks, depressions and passes
  • Triangle surfaces represent area, slope and aspect
  • Can incorporate further features to refine model
    • Ridges, troughs, cliff edges, rivers etc
    • Water features special case as rivers flow in valeys and thus help to create a more accurate model
  • Advantage: efficiency of data storage – less complex topography needs fewer points

Modelling networks

• Network: a set of interconnected linear features through which materials, goods and people are transported or along which communications flow.
• Network models are abstract representations of their real-world counterparts
• Adaptations of vector model – raster GIS usually not good at network analysis
• Vector network model made up of same arc/node data as any other vector model but with addition of certain attributes

Network model

• Arcs: network links
• Nodes: endpoints – represent junctions, switches, stops etc.
• Turns: transition between network links at a node
• Network characteristics stored as attributes
• Impedance: cost associated with traversing the network
  • Impedance values very important in determining outcome in route-finding algorithm
• Supply and demand: quantity of a resource available at a centre to satisfy demand
• Correct topology and connectivity important

Building computer worlds

• How to group raster and vector entities together
• Option:
  • Layers
    • Most common
    • Thematic
    • Tiles – chop area up into tiles – assist with storage of large volumes of information
  • Objects
    • Real world as set of individual objects
    • Draws on ideas in OO programming
    • Features not divided into separate layers but grouped into classes & hierarchies

Modelling the third dimension

• Most GIS takes 2D view
• Wire-frame, TIN etc. – 2.5D
• 3D representation still experimental – raster grid extension known as voxel

Modelling the fourth dimension

• Need to consider
  • Work-practice time
    • Current state of the data
  • Database time
    • That which is believed to be correct at the moment – lags behind work-practice time
  • Future time
    • Forecasting/prediction
    • Alternative scenarios
• Issues
  • What to include
  • How frequently to update
  • When to drop old data
  • Modelling moving objects