geospatial data types
DESCRIPTION
Geospatial Data Types. Data Types. Two general views to organizing spatial data: Objects Monitoring measurement points, rivers, structures Have attributes or features attached to them Point, line or area format Values exist at entity locations - PowerPoint PPT PresentationTRANSCRIPT
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Geospatial Data Types
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Data Types
• Two general views to organizing spatial data:
– Objects • Monitoring measurement points, rivers, structures• Have attributes or features attached to them• Point, line or area format• Values exist at entity locations• Commonly stored and rendered in raster format (grids)
– Fields • Continuous data such as temperature gradient fields and satellite imagery• Values exist over an area• Every location has a value• Commonly stored and rendered in raster format (grids)
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Haining, 2003
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Vector RepresentationX-AXIS
500
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600500400300200100
Y-AXIS
River
House
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Trees
Trees
BB
B BB
BBB G
GBK
BBB
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Raster Representation
1 2 3 4 5 6 7 8 9 1012345
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8910
Real World
G G
Raster and Vector Data Models
adapted from Lembo, 2003
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Vector – Advantages and Disadvantages
• Advantages– Good representation of reality– Relatively compact data structure– Accurate graphics
• Disadvantages– Complex data structures– Some spatial analysis is difficult or impossible to perform
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• Advantages– Simple data structure– Uniform size and shape– Computationally cheaper to process
• Disadvantages– Large amount of data– Less visually pleasing (“blocky”)– May lose information due to generalization– Projection transformation is difficult– Different scales between grids can make comparison difficult
Raster – Advantages and Disadvantages
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Objects and Fields
Objects and fields can be transformed to the other type
ObjectsVectors
FieldsRaster
adapted from Bolstad, 2002
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Vector vs. Raster
Burroughs, 1996
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Landcover Raster Grid
Legend
Mixed coniferDouglas fir
Oak savannahGrassland (1-5)
(6-10)
(11-15)
(16-20)
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Raster = Grid
columns
row
s
The bounding box defines the geographic extent of the grid in terms of its coordinates
[min_x, max_x, min_y, max_y]
Abbreviation for PICTURE
ELEMENT, which is the
smallest unit in an image.
In raster based GIS
systems, attribute
information can be
assigned to each pixel.
Pixel
Matrix of Equal-Area Cells
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Grid File Format (ASCII)
ncols 6 nrows 6 xllcorner 210yllcorner 370cellsize 20 nodata_value 0 5, 6, 7, 8, 10, 135, 7, 8, 10, 12, 134, 5, 8, 12, 15, 153, 4, 5, 13, 15, 163, 5, 11, 14, 15, 172, 4, 5, 16, 16, 17
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Table Format
X Y Value
220 380 2
220 400 3
220 420 3
220 440 4
220 460 5
220 480 5
240 380 4
240 400 5
240 420 4
240 440 5
240 460 7
240 480 6
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Contoured Plots
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Also known as an Isopleth Plot
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Map Scale
• Map scale is based on the representative fraction, the ratio of a distance on the map to the same distance on the ground.
• Most maps used in GIS fall between 1:1 million and 1:1000.
• A GIS is scaleless because maps can be enlarged and reduced and plotted at many scales other than that of the original data.
• To meaningfully compare maps in a GIS, both maps MUST be at the same scale
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Scale of a baseball earth
• Baseball circumference = 226 mm• Earth circumference approx 40
million meters• Scale is 1:177 million
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Scale Dependent Measurements
How long is Maine’s coastline?
length=340 km
length=355 km
length=415 km
From Longley et al., 2001
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Resolution25 meter 5 meter
1 meterSame number of pixels (rows and columns)
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Resolution
1 meter 5 meter
25 meterSame geographic area (m X m)
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Spatial Dimensionality
0-dimensional, L0
points and nodes
1-dimensional, L1
lines
2-dimensional, L2 (x,y)areas, polygons
3-dimensional, L3 (x, y, z)volumes
4-dimensional, L4 (x, y, z, t)3-D plus time
Another way to classify spatial object types is by their dimensionality
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2.5 Dimensions
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Attributes
Attributes are the values and properties of an object or entity
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Types of Attributes
• Nominal – Simply identifies or classifies an entity so that it can be distinguished from another. e.g. sensor ID, building name
– Cannot be manipulated using mathematical operations. However, frequency distributions are meaningful.
• Ordinal – Values based on an order or ranking, e.g. agricultural potential classes
– Cannot be manipulated using mathematical operations. However, frequency distributions are meaningful.
• Interval – Differences between entities are defined using fixed equal units, e.g. Celsius temperature
– Can be manipulated using addition and subtraction
• Ratio - Differences between entities can be defined using ratios, e.g. distance
– Can be manipulated using multiplication and division
• Cyclic - differences between entities depending on repeating sequence, e.g. wind direction
A common approach to classifying attributes is based on their level of measurement
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Structured Query Language (SQL)
SELECT column name
SQL is a formal search language that allows you to work with, access and filter data stored in a relational database format
FROM data table name
WHERE data condition
The most common use for SQL is to retrieve subsets of data based on specified conditions
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ArcGIS Select by Attribute
SELECT *FROM MO_STNWHERE O3 > 80 AND PM25 > 15
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Defining Reclassification Categories
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Classification Schemas
Natural breaks: classes are defined according to apparently natural groupings of data values. (GIS programs that automatically determine classes usually base them on relatively large jumps in data values.)
Quantile breaks: classes are defined by having an equal number of observations
Equal interval breaks: classes are defined by uniform intervals
Standard deviation breaks: classes are defined by differences from the mean value.
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Color Brewer
http://www.personal.psu.edu/faculty/c/a/cab38/ColorBrewerBeta.html
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Graphic Visualization Components
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Summary
Two general data types: object & field
Generally, “handled” as either vector or raster
Data can have multiple attributes (properties) associated with features or grid cells
Levels of measurement helps formalize the arithmetic and statistics that are appropriate for a particular dataset
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Date Topic Reading
Problem Set Tutorial
31-Aug GIS Overview Bolstad Chp 1Gorr, Chp1
7-Sep Geospatial Data Longley Chp 3Gorr Chp2-3
14-SepProjections and Coordinate Systems
Bolstad Chp 3
Problem Set 1 distributed
Gorr Chp4, Chp 5 (p. 172-180)
21-Sep Feature Analysis Bolstad Chp 9Gorr Chp 8 (p. 272-290), Chp 9
28-Sep Surface Analysis Bolstad Chp 10/11
PS1 due; PS2 distr.
Handout: Suitability Analysis
5-Oct Spatial Data Analysis Bolstad Chp 12
Handout: California Air Pollution
12-Oct Spatial Modeling / Web GIS Bolstad Chp 13
PS2 due Gorr Chp 8 (p. 291-299), Handout: Groundwater Modeling
19-Oct Exam / Project Presentations
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Gistutorial\UnitedStatesStatesCountiesCitiesCapitalsUtahNevadaPennsylvania
Gistutorial\Layers
Tutorial3-1.mxdTutorial3-NativeAmericans.mxd