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GEOG 328 - GIS Analysis > GEOG 328 Midterm II > Flashcards

GEOG 328 Midterm II Flashcards

1
Q

What is the definition of Interpolation?

A

Estimating (predicting) values at unsampled sites within an area for which there exist some sampled (point) observations

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2
Q

What is the goal of Interpolation?

A

To predict values for cells in a raster from a limited number of sample data points.

It can be used to predict unknown values for any geographic point data, such as elevation, rainfall, chemical concentrations, noise levels, and so on.

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3
Q

T/F Different interpolation methods will almost always produce different surfaces?

A

True

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4
Q

Interpolation from which of these three is most common? Points/Lines/Polygons?

A

Points

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5
Q

With Interpolation, Values are often subject to which law? What does that mean?

A

Tobler’s 1st law of Geography, Near things are more alike than things further away, same goes for values.

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6
Q

What are the criteria for comparison with Interpolation?

A
  1. Abrupt vs. gradual transitions 

  2. Exact vs. approximate (does it honour source data?)
    
3. Global vs. Local
    
4. Stochastic vs Deterministic
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7
Q

What do Global Interpolators do?

A

Determine a single function which is mapped across the whole region; a change in one input value affects the entire map. (Trend surface analysis)

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8
Q

What do Local Interpolators do?

A

Apply an algorithm repeatedly to a small portion of the total set of points; a change in an input value only affects the results within the window.

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9
Q

What are Exact Interpolators?

A

Interpolators where the surface passes through all points whose values are known.

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10
Q

What are Approximate Interpolators?

A

Are Interpolation methods used when there is some uncertainty about the given surface values.

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11
Q

What are Gradual Interpolators?

A

Interpolators which produce an interpolation surface with gradual changes

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12
Q

What are Abrupt Interpolators?

A

Interpolators which produce quickly changing but continuous values
e.g. (impermeable barriers, e.g. geological faults)

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13
Q

What is a Stochastic Method?

A

Stochastic techniques use both mathematical and statistical functions for prediction.
Stochastic methods incorporate the concept of randomness: the interpolated surface is conceptualized as one of the many that might have been observed, all of which could have produced the known data points.

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14
Q

What are examples of Stochastic methods?

A

Stochastic interpolators include, Trend surface analysis (TSA) and Kriging;

These methods allow for the statistical significance of the surface and uncertainty of the predicted values to be calculated.

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15
Q

What are Deterministic Methods?

A

Deterministic techniques are based on surrounding measurements (mathematical functions) to calculate the surface.

These techniques are based on the measured
values of a parameter at samples near the unmeasured location for which a prediction is made

Deterministic methods are methods which do not use probability theory.

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16
Q

What is a Thiessen Polygon?

A

They divide and entire area into polygons with one polygon at each observed point, where every location within the polygon is closer to that point than any other point.

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17
Q

What are Thiessen Polygons classified as?

A

Abrupt: they generate sharp edges
Exact: they honour all observations
Local: they repeat an algorithm over small sections of terrain

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18
Q

What is a Moving Average?

A

A moving average calculates value at the centre of the neighbourhood based on the average value of all values shown within the window.

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19
Q

What is a Moving average classified as?

A

Gradual: Smooth transitions
Approximate: Does not honour existing points
Local: Repeats algorithm using small areas.

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20
Q

What is the IDW interpolation method?

A

Inverse distance weighted interpolation is considered a method that assumes that the estimated value of a point is influenced more by nearby known points than those further away.

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21
Q

What are considerations when using the IDW interpolation method?

A

Cell size - output cell size can be as big or small as you want, 2m, 500m, 1000m


Power Function - control the significance of known data points on interpolated values based on their distance.


Search Radius (fixed/variable) - Limits the number of input points that cane used for calculating each interpolated cell. (What distance should it be looking for points)


Barriers - stop lines which can be included in the interpolation, will not move past that location

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22
Q

What is Power Function and why is it important to IDW?

A

Power function controls the significance of known data points on interpolated values based on distance.

Higher power - means emphasis is placed on the nearest points and the resulting surface will have a more detailed but less smooth surface.

Lower power - will give more influence to the points that are further away resulting in a smoother surface.

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23
Q

What are Barriers with regards to IDW? What effect to they have?

A

Line features used as a break line that limits the search for input points. Only points on the same side of the break-in will be included. Examples are cliffs and ridges.

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24
Q

What are some considerations concerning your data when using IDW?

A

Number of observed points taken into account (function of size and shape of neighbourhood)
Small number you’re increasing the emphasis on short range / Large - an increased edge effect.
Distribution of observed points
Density of points

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25
Q

What are some Considerations for using IDW?

A

Output value for a cell using IDW is limited to the range of the values used to interpolate.

The best results from IDW are obtained when sampling is “sufficiently dense” with regard to the local variation you are attempting to simulate.

The influence of an input point on an interpolated value is isotropic.

26
Q

What is a TSA?

A

TSA is a Global polynomial interpolation that fits a smooth surface defined by a mathematical function (a polynomial) to input sample points.

27
Q

When would you use a TSA?

A

Fitting a surface to sample points when a surface varies gradually from region to region over the area of interest - for example, pollution over an industrial area.

Meant to find general tendencies of sample data, rather than to model a surface precisely.

Is however susceptible to outliers in the data, especially around the edges.

28
Q

What is a Trend RMS Error?

A

A test for how well your trend surface matches your input points using RMS error output from the interpolation.

29
Q

Describe IDW’s characteristics.

A

IDW represents geographic data well, uses continuous datasets, integrates a weighted component (toblers 1st law), does not get an error surface (it looks instead at density and data distribution)

IDW is Deterministic (uses surrounding measurements to calculate the surface, deterministic allows for only one value at each location)

Local Interpolator as it calculates predictions from measured points within neighbourhoods (better overall fit)

Exact interpolator as it honours all known data points and the interpolated surface passes through all of them.

30
Q

Describe Thiessen Polygons characteristics.

A

Theissien polygons are an exact interpolator, are abrupt, assumes that no data is best data, and provides a modelled surface based on known data.

31
Q

Describe Kriging’s Characteristics.

A

Kriging embraces toblers first law, allows for the randomness of the variable when you’re modelling a surface. It also provides a measure of uncertainty of the interpolated surface.

Kriging is Stochastic (uses mathematical and statistical functions for prediction, incorporates randomness through autocorrelation) attempts to provide BLUE

a Local Interpolator as it calculates predictions from measured points within neighbourhoods (better overall fit)

an Exact interpolator as it honours all known data points and the interpolated surface passes through all of them.

32
Q

What is Kriging?

A

An interpolation technique that uses regionalized variable theory to incorporate information about the stochastic aspects of spatial variation when estimating interpolation weights.

33
Q

What are the Data requirements for Ordinary Kriging?

A

Normal distribution - histogram/bell curve

Stationary - (data pairs in all directions have the same distance) variance between the value of points of a particular distance apart should be constant. Use voronoi map (theissen polygons)

No trends - no systematic changes in the data across the entire study area. Use trend analysis to determine.

34
Q

What is Semivariance with regards to Geostatistics?

A

The basic measurement of geotatistics is the semivariance (a measure of the degree of spatial dependance between samples at a specific point) in essence this is the inverse of calculating SAC.

As you increase semivariance the differences are increasing between data pairs.

As you compare data variables they become more different as the distance increases.

Rather than the similarities (correlation) semivariance looks at the (Variance) in the data set.

35
Q

Definition of Semivariance

A

“measures the difference between attribute values as a function of their spatial separation (h), or rate of change of a regionalized variable”

36
Q

What are some exceptions in your data with Semivariance?

A

At 0 the difference should be 0
At a small distances you would expect a shorter distance and smaller differences in values
At greater distance the distance and difference increases.
At a critical distance, data pairs are no longer correlated (important for modelling)

37
Q

What is a Semivariogram?

A

A chart of plotted semevariance values.

38
Q

What do the elements of a semivariogram represent? (Range / Sill / Nugget)

A

The Range of a semivariogram tells you at what distance your correlation fall off (range) (gives you an exact distance for modelling, a threshold of data to include)

The Sill tells you at the max distance and what the expected difference is between those values.

Nugget refers to the short term randomness that cannot be explained (the error present in the data) over a short distance you do not see the same variance. (Whats the typical variation you’d expect to see.)

39
Q

What is a BLUE?

A

(Best/Linear/Unbiased/Estimate)

Best - tries to minimize variance of the errors
Linear - looks at a linear association of the data
Unbiased - tries to make sure your residual errors are at zero
Estimate - because it is a modelling process

40
Q

What is a Model?

A

a model is a simplified representation of a phenomena or a system.

41
Q

What can the word ‘model’ refer to in GIS?

A 
Data representations
Interpolated surfaces
Geoprocessing models
Regression modelling
Decision support tools/multicriteria models
42
Q

What are some of the roles of modelling in GIS?

A

GIS is a tool that can process, display, and integrate different data sources including maps, digital elevation models (DEMs), GPS (global positioning system) data, images, and tables.

GIS can be used to build a vector-based or raster-based model.

GIS has algorithms for conversion between vector and raster data.

43
Q

What are models used for?

A

Solve complex problems with multiple data.

Provide dynamic solutions. 


Support a decision or design process.

44
Q

What is a Multi-Criteria Model?

A

Multi-Criteria Model is the Integration of several GIS layers to model the spatial variability of “something”.

Something = risk, vulnerability, likelihood, impact.

A model of the real world, incorporating spatial data and relationships, used to aid understanding of spatial form and process.

A methodology or set of analytical procedures used to derive information about spatial relationships between geomorphic phenomena.

45
Q

What are the steps for multi-criteria Modelling?

A
  1. Define the goals of the model
. what are you ranking?
 What result are you looking for?
  2. Breakdown the model into elements and define the properties of each element and the interactions between elements
  3. Explore and standardize input datasets
  4. Determine your evaluation criteria & develop scoring weights.
  5. Implement and calibrate the model
  6. Validate the model.
46
Q

How are Distances calculated between features?

A

Distance between any two features is calculated as the shortest separation.

GIS Tools: Near, Spatial Join * with closest match option*

47
Q

What is Euclidian Distance? What are its assumptions?

A

Euclidian distance is the straight line distance between two features (point/line/polygon). It assumes the unconstrained ability to move, thus a straight line is the shortest & Best Path.

Distance here is always calculated to the boundary of a polygon feature, not to the centre or centroid of the polygon.

48
Q

What is Cost Distance?

A

Cost distance takes into account various cost factors and finds the best route or allocation of cells based upon those factors.

49
Q

What is a proximity analysis? Why is it useful?

A

A type of analysis in which geographic features (points, lines, polygons, or raster cells) are selected based on their distance from other features or cells.

Can be calculated based upon distance or nearest neighbour sampling.

50
Q

What is Corridor Analysis?

A

A method which uses two cost surfaces as inputs and then finds the cells that minimize cost based on both inputs.

Example: land cover and slope as costs for moving across a landscape

51
Q

What are the Steps for Least-Cost Path Analysis?

A
  1. Consider the projection of all data sets
  2. Consider the scale and resolution of each data set
  3. Covert to raster data type
  4. Reclassify Raster on a common scale
    ◦High Value = do not travel
    ◦Low Value = travel through
  5. Integrate Rasters (Cost Surface)
  6. Calculate Cost Distance and Backlink Rasters
  7. Calculate Cost Path
52
Q

How do you get the least cost surface in GIS?

A

By integrating your weighted and classified raster surfaces together.

53
Q

What is the Backlink Raster?

A

The direction to take to follow the least cost path back from the destination back to the origin (reverse least cost path)

54
Q

What are absolute and relative barriers with regards to Cost surface?

A

Absolute barriers you cannot pass through, relative barriers you can but the cost is high.

55
Q

What potential “costs” would a cost raster include for something like the building of a pipeline?

A 
Construction and operational costs:
•Distance from destination
•Topography (slope, grading)
•Number of stream, road, rail crossings)
•Proximity to population centres
Potential environmental impacts costs:
•Cultural resources
•Land-use, recreation and aesthetics
•Vegetation and wildlife
•Water use and quality
•Wetlands, sensitive landscapes
56
Q

The the guest lecture presentation about Modelling fire perimeter formation in the Canadian rocky mountains, how was the data analyzed?

A

Fire dates of burning were based upon a weighted average assigning value to each 30m raster cell based on a code written to analyze satellite imagery. Tobler’s 1st Law was also used as a part of this.

57
Q

The fire management example presented in class was an example of a multi-criteria analysis T/F?

A

True

58
Q

What considerations taken into account when modelling the fire cessation study?

A

topography, weather, climate, fuels, and anthropogenic influences on the landscape.

59
Q

What is MAUP? What does this impact?

A

Occurs when data are aggregated at the location of boundaries and can significantly impact the results of statistical tests.

60
Q

What is the boundary problem? how does it effect your data?

A

The boundary problem is a phenomenon in which geographical patterns are differentiated by the shape and arrangement of boundaries that are drawn for administrative or measurement purposes.

The boundary problem occurs because of the loss of neighbors in analyses that depend on the values of the neighbors.

GEOG 328 - GIS Analysis (4 decks)

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