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![Page 1: Spatial analysis in GIS. GIS for mineral and hydrocarbon exploration Used for integrating data (map layers) to identify most prospective areas ∫ Integrating.](https://reader036.fdocuments.in/reader036/viewer/2022062717/56649e225503460f94b0f4be/html5/thumbnails/1.jpg)
Spatial analysis in GIS
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GIS for mineral and hydrocarbon explorationUsed for integrating data (map layers) to identify most prospective areas
∫Integrating function • linear or non-linear• parametersInput spatial datasets
• Categoric or numeric• Binary or multi-class
Output mineral potential map • Grey-scale or binary
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Data Types
Nominal/categorical
Ordinal
Interval
Ratio
Nominal data are items which are differentiated by a simple label, usually a name. May have numbers assigned to them. This may appear ordinal but is not. Nominal items are usually categorical, in that they belong to a definable category.Can be counted, but not ordered or measured.
Ordinal data can be ranked (put in order) or have a rating scale attached. Can be counted and ordered, but not measured.
Interval data is where the distance between any two adjacent units of measurement (or 'intervals') is the same but the zero point is arbitrary.
Ratio data are measured in terms of the ratio between a magnitude of a continuous quantity and a unit magnitude of the same kind. The zero value is absolute
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Data Types
Parametric vs. Non-parametric
Interval and ratio data are parametric, and are used with parametric tools in which distributions are predictable (e.g., Normal).
Nominal and ordinal data are non-parametric, and do not assume any particular distribution. They are used with non-parametric tools such as the histogram.
4’ 7” 5’ 5’5” 5’10’ 6’3” 6’8” 4’ 7” 5’ 5’5” 5’10’ 6’3” 6’8”
Height of women Height of men
Normal distribution – parameters are mean and standard deviation
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Data TypesContinuous and DiscreteContinuous measures are measured along a continuous scale.
Discrete data have a set of fixed values.
Continuous dataDiscreet data
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Multi-class/continuous and binary dataContinuous
Multiclass
Binary magnetic map
Binary Geological map
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What is GIS?
• GIS = Geographic Information System– Links databases and maps– Manages information about places– Helps answer questions such as:
• Where is it?• What else is nearby?• Where is the highest concentration of ‘X’?• Where can I find things with characteristic ‘Y’?• Where is the closest ‘Z’ to my location?
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Definition of GIS(Ron Briggs, UT Dallas)
A system of integrated computer-based tools for end-to-end processing (capture, storage, retrieval, analysis, display) of data using location on the earth’s surface.
• set of integrated tools for spatial analysis• encompasses end-to-end processing of data
– capture, storage, retrieval, analysis/modification, display• uses explicit location on earth’s surface to relate data • aimed at decision support, as well as on-going operations and
scientific inquiry
Because of the link between spatial locations and non-spatial data, it is possible to apply non-spatial statistical modeling methods to spatial data
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SPATIAL DATA MODELS
What do you mean by spatial data?
How real world spatial data are represented? How would you represent a real world river? Land-use?
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SPATIAL DATA MODELSTwo models:1. Vector model2. Raster model
SPATIAL DATA TYPESSpatial data come in three basic forms:• Spatial Data• Attribute data
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Vector Model: Map data
Map data contains the location and shape of geographic features. Maps use three basic shapes to present real-world features: • points, • lines, and• areas (called polygons/regions).
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Vector ModelThe spatial locations of features are defined on the basis of coordinate pairs.
• These can be discrete, taking the form of points (Point or Node data) or lines (Arc or polyline data) or areas (Area or polygon data)
• Attribute data pertaining the individual spatial features is maintained in an external database.
• Topology – A set of rules that models how points, lines and polygons share geometry and are related to each other.
Area Population
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ROCK
SPATIAL DATA MODELS: Vector Model
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VECTOR MODELPoints represent anything that can be described as an x, y location on earth’s surface, for example, mineral deposits, gas fields
Lines objects described by length only (zero width) such as faults, streets, highways, and rivers
A Polygon describes a geographic feature that is characterized by a boundary, whether natural, or artificial, such as the boundaries of countries, states, cities, census tracts, postal zones, and market areas or rock types
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SPATIAL DATA TYPES: Image data(Raster Model)
Image data ranges from satellite images, digital elevation models, potential field data data and aerial photographs to scanned maps (maps that have been converted from printed to digital format).
We can represent point, line and polygon data in image form
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• Every cell represents a unit area on the ground. All unit areas are equal
• The smaller the area the cells represent, the larger the resolution.
• Cell values represent a specific property of the ground in that unit area:
For example, - Surface reflectance- Magnetic field- Gravity field- Elevation- Rock type
- The values can nominal, ordinal, interval or ratio, they can be integers or floating points.
• Georeferenced
10 m x 10 m grid cell
SPATIAL DATA MODELS: Raster Model
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- Most spatial analysis are done in raster format because it facilitates mathematical calculations, e.g.,
INGRID1/ INGRID 2 INGRID1 * INGRID 2
SPATIAL DATA MODELS: Raster Model
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VECTOR TO RASTER CONVERSION
The area of interest is covered by a fine mesh or matrix of grid cells and the surface attribute value occurring at the centre of each cell point is recorded as the value for that cell.
1
2
3
Id Type Area
1 Granite 25
2 Sandstone 63
3 Limestone 42
1
2
3
123
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Raster to vector conversion (Digitization)
For vectorization, trace the boundaries using a digitizing tablet/on-screen.
Essentially, the X,Y coordinates of features are stored
However, often it is necessary to convert raster to vector format, and then back to the raster format (why??)
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SPATIAL DATA TYPES: Attribute DataAttribute (tabular) data is the descriptive data that GIS links to map features.
Attribute data is collected and compiled for specific areas like states, census tracts, cities, and so on and often comes packaged with map data.
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GEOPROCESSING IN GIS• Processing of spatial data to derive predictor
map layers
Primary data- Geological map - Structural map- Remote sensing- Geophysical data- geochemical
PROCESSING & INTERPRETATION
Derivative (Input) layers- Proximity to granites - Proximity to deep faults- Proximity to fold axes - Reactive rocks- Competency
differences- Alteration - Metal anomalies
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GEOPROCESSING IN GIS
• Querying and conditional evaluation• Density calculations• Distance calculations• Interpolation• Reclassification
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QUERYING INGIS
• Query by attributes• Query by location
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SELECT BY ATTRIBUTESSQL is used for selecting features in a map layer by attributes that full-fill specified condition.
for example,
SELECT * FROM MapLayer WHERE “field1”>= 10
OPTIONS:NEW_SELECTION ADD_TO_SELECTION REMOVE_FROM_SELECTION SUBSET_SELECTION SWITCH_SELECTION
IMPORTANT OPERATORS• = • >• <• <>• >=• <=
• LIKE• AND• OR • NOT
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QUERY BY ATTRIBUTES
ROCK
SELECT * FROM GEOLOGY WHERE “ROCK” = ‘Dolerite’
ROCK
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QUERY BY ATTRIBUTES
ROCK Map of dolerite
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SELECT BY LOCATIONUsed for selecting features from a map layer based on spatial relationship (adjacency, connectivity, containment) with another layer.
For example, SELECT * FROM MapLayer1 CONTAINS MapLayer2
ArcGIS syntax:SelectLayerByLocation MapLayer1 Type_of_relationship MapLayer2 Buffer_distance NEW_SELECTION
Types of spatial relationships that can be queried:• Intersect• Are within a distance of • Contain• Completely contain• Are within• Are completely within• Have their centroid in• Share a line segment with• Are identical to
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SELECT BY LOCATION
FaultsGold deposits
Gold deposits within 1 km from Faults
SELECT * FROM GOLD_DEPOSITS WITHIN _ 1_km FROM FAULTS
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Density estimationDensity is defined as number of (point/line) features per unit area
Density surfaces show where point or line features are concentrated.
For example, you have a point shape file showing mineral deposit locations. You want to learn more about the metal distribution in the area.
Can be used for cluster studies (mineral deposits, population, roads/infrastructure, natural resources such as minerals, forest, agriculture etc., animal inhabitations, ecology…
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Density estimation
Gold deposits Distribution of gold
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Density estimation
Faults Fault density (distribution of faults)
Faults
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Density estimation
Distribution of gold Distribution of faults
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Distance estimationEuclidean distance is calculated from the center of the source cells to the center of each of the surrounding cells. True Euclidean distance is calculated to each cell in the distance functions.
For each cell, the distance is calculated to each source cell by calculating the hypotenuse, with the x-max and y-max as the other two legs of the triangle. This calculation derives the true Euclidean, not cell, distance. The shortest distance to a source is determined, and if it is less than the specified maximum distance, the value is assigned to the cell location on the output raster.
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Distance estimation
Faults Distance to faults
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Distance estimation
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GEOPROCESSING IN GIS
• Interpolation: used for determining the unknown value at any point from the known values at the given sample points in the spatial neigbourhood.
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• Non-interpolative methods
• Interpolative methods
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Non-interpolative methods
1. Assign each sample point to a grid cell (or pixel).2. Buffer the sample points.3. Draw a Thiessen or Voronoi polygon around
each sample point; assign the value at the sample point to the entire area within the Voronoi polygon.
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Delaunay triangles
a Delaunay triangulation for a set of points is a triangulation of the points in such a way that no point is inside the circumcircle of any triangle.
Delaunay triangulations maximize the minimum angle of all the angles of the triangles in the triangulation.
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• Voronoi polygons Connecting the centres of the circumcircles produces the Voronoi polygons.
The property of a Voronoi ploygon of a point is that all points with that polygon are closest to that point.
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Interpolation:Estimating values at points
intermediate between sample points.
• Triangulation• Inverse distance weighting• Natural Neighbours• Krigging
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Triangulation• Draw Delaunay triangles for all sample points
5
4
3
1
2
FID X Y Z
1 1 1 26
2 4 2 32
3 2 3 28
4 5 4 35
5 3 5 42
6 3 4 ?
5
4
3
1
2
6
The equation for every triangular facet is given by
z = a + bx + cy where z is the value, x and y are X and Y coordinates of a sample point, respectively,a, b and c are unknown coefficients
Three unknown coefficients, three equations, hence the values of the coefficients can be estimated. Once you have coefficients, you can estimate values at any point within the triangle
6
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Inverse distance weighing5
43
1
2
6
pij
i
n
ii
n
iii
j
dw
w
zwz
),(
1
1
1
,
Point Z Distance from 6
1 26 3.62 32 2.23 28 1.44 35 25 42 1
Where z is the value at the point i;w is the weight of i;d(j,i) is the distance between the point i and the point j where the value needs to be calculated;p is the power;n is total number points in the neighbourhood with known values.
5
43
1
2
6
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Natural neighbor
• Draw Vornoi polygons for all points (green colour)
• Draw a Voronoi polygon around the point at which the value is to be determined (orange colour)
• Apply weights to each point value in proportion to the area of intersection between the Voronoi polygon of that point and the the Voronoi polygon of the query point.
Natural neighbor interpolation finds the closest subset of sample points for the query point and applies weights to them based on proportionate areas.
iji
n
ii
n
iii
j
Aw
w
zwz
,
1
1 Aij is the area of intersection between the Vornoi polygons of the points i and j.
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Krigging
n
iiizwz
1
ˆ
The value at the queried point is given by:
Where zi are the values at sample pointswi are the weights of sample points
1
......
01...1
1...
............
1...
0
101
1
111
nnnnn
n
C
C
w
w
CC
CC
C ● w = D
C-1 ● C ● w = D ● C-1
Or w = D ● C-1
C – Spatial covariance values between the pair of sample points D – Spatial covariances between sample points and the point where the value is required to be estimated
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Krigging: Spatial covarianceCovariance between two variables x and y is given by
)()(1
1
yyxxn
C i
n
i Measures the degree to which x co-varies with y
Moment of inertia measures the deviation from the perfect correlation
2
1
)(2
1i
n
i yxn
In the above equation, suppose we substitute zt for x and z(t+h) for y, where z is a spatial variable measured at a location t and at another location (t+h), where h is the separation distance called a shift or lag.
The spatial covariance of z with itself at separate distance of h can also be measured by γ, (or by C).
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Krigging: VariogramsBy changing the separation distance h (called lag or shift), a series of scatter plots can be generated showing how the variable z is correlated with itself as a function of h.
The plot of the moment of inertia as a function of h is called variogram, the plot with covariance is called autocovariance diagram
Sill
Range
Scatter plotExponential model fitted to the scatter plot
γ(h) = C0 if h =0 γ(h) = C0 + C1(1-exp(-3 h/a) ) if h >0
Sill and range are estimated so the model is a reasonable fit to the observed data
Variogram autocovariance diagram
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Krigging: Variogram Models
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Krigging: Variogram Models
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Krigging: VariogramFitting a model to data
Longer range smaller range
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Krigging: Spatial covarianceAutocovariance diagram can be used to calculate covariance at different distances, hence different covariances in the equations below:
1
......
01...1
1...
............
1...
0
101
1
111
nnnnn
n
C
C
w
w
CC
CC
C ● w = D
n
iiizwz
1
ˆ
The following equation is then used to estimate the value at the query point
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ELEVATION IN METERS
100 m
100 m
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Auto-covarianceCase 1: Shift of 100 meters
X Y = X+100
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Elevation in 100 meters
100 m
100 m
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X Y = X+100
Auto-covarianceCase 1: Shift of 100 meters
X Y = X+100
40 4240 43
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Elevation in 100 meters
100 m
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Auto-covarianceCase 1: Shift of 100 meters
X Y = X+100
40 4240 4342 4442 45
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Elevation in 100 meters
100 m
100 m
100 m
100 m
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Auto-covarianceCase 1: Shift of 100 meters
X Y = X+100
40 4240 4342 4442 4544 4844 5048 5243 4543 45
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Auto-covarianceCase 1: Shift of 100 meters
X Y = X+100
40 4240 4342 4442 4544 4844 5048 5243 4543 4545 5045 4950 5250 5552 56
Mean X = 44.85Mean Y = 48.28
Covariance = 13.94
MOI = 6.71
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Auto-covarianceCase 2: Shift of 200 meters
X Y = X+200
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Elevation in 100 meters
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Auto-covarianceCase 2: Shift of 200 meters
X Y = X+200
40 44
40 45
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Elevation in 100 meters
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Auto-covarianceCase 2: Shift of 200 meters
X Y = X+200
40 44
40 45
42 48
42 49
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Auto-covarianceCase 2: Shift of 200 meters
X Y = X+200
40 44
40 45
42 48
42 49
44 55
48 56
43 50
45 52
Mean X = 43Mean Y = 49.8
Covariance = 8.718
MOI = 25.56
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Auto-covarianceCase 2: Shift of 300 meters
X Y = X+300
40 48
43 52
45 56
Mean X = 42.66Mean Y = 52
Covariance = 7.8889
MOI = 44.33
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Auto-covarianceDistance -vs- Covariance
Distance Covariance
100 13.94
200 8.718
300 7.8889
0 50100
150200
250300
3500
10
20
30
Distance vs Covariance
Covariance
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range
VariogramDistance -vs- MOI
Distance MOI
100 6.71
200 25.56
300 44.33
sill
nugget effect
50 100 150 200 250 300 350 400 4500
5
10
15
20
25
30
35
40
45
50