Spatial Analysis and Modeling

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Spatial Analysis and Modeling

• Vector Analysis
• Raster Analysis

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Modeling Spatial World

• A model is a representation of reality
• Models are created as a simplified, manageable
  view of reality
• Models help you understand, describe, or predict
  how things work in the real world
• Data Models.
• Representation models.
• Process models.

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Representation Models

• Represent the objects of real world through a
  sets of layers (Data Models)
• Common data models/descriptive models.
• Spatial relationships within an object (the shape
  of a building).
• Between the other objects in the landscape (the
  distribution of buildings).
• Model the attributes of the objects (who owns
  each building).

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Process Models

• Describe the interaction or processes of the
  objects of real world, which are modeled in the
  representation model, by using map calculation

• Suitability modeling where should I put it?
• Distance modeling
• how far is it?
• Hydrologic modeling where will the water flow
  to?
• Surface modeling
• what is the pollution level?

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Creating a conceptual model to solve a spatial
problem (1)

• Step 1. Stating the problem.
• What is the goal?
• Step 2. Breaking the problem down.
• What are the objectives.
• What are the objects and their interactions
  (process model).
• What datasets (data model and presentation model)
  will be needed

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Creating a conceptual model to solve a spatial
problem (2)

• Step 3. Exploring the datasets
• What is contained in the datasets
• what relationships between the datasets
• Step 4. Performing analysis (spatial analysis)
• Which tools to run the process models and build a
  overall model
• Step 5. Verifying the models result
• Does any thing in the model need to be changed?
• If yes, go back to step 4
• Step 6. Implementing the result

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Vector Spatial Analysis

• Map Overlay
• Union, Intersect, Identity, Erase, Symmetrical
  Difference, Update Extract
• Point in Polygon, Line in Polygon, Polygon on
  Polygon
• Clip, Select, Split, Table Select
• Proximity
• Buffer, Multiple Ring Buffer, Near, Point
  Distance
• Statistics
• Frequency, Summary Statistics

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Map Overlay Tools

• Intersect
• Union
• Identity
• Erase
• Symmetrical Difference
• Update

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Intersect

• This tool builds a new feature class from the
  intersecting features common in both feature
  classes. Available with any ArcGIS license

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Intersect

• How Intersect (Analysis) works
• The Intersect tool calculates the geometric
  intersection of any number of feature classes and
  feature layers. The features or portion of
  features that are common to (intersect) all
  inputs will be written to the Output Feature
  Class.
• Intersect does the following
• Determines the spatial reference for processing.
  This will also be the Output Feature Classes'
  spatial reference. For details on how this is
  done, see Spatial Reference. All the input
  features are projected into this spatial
  reference for processing.
• Cracks and clusters the features. Cracking
  inserts vertices at the intersection of feature
  edges clustering snaps together vertices that
  are within the cluster tolerance.
• Discovers geometric relationships (intersections)
  between features from all the feature classes or
  layers.
• Writes these intersections as features (point,
  line, or polygon) to the output.
• To explicitly control the output spatial
  reference (coordinate system and domains), set
  the appropriate environments, the Output Z Aware,
  and Output M Aware.
• The inputs can be any combination of geometry
  types (point, multipoint, line, polygon). The
  output geometry type can only be of the same
  geometry or a geometry of lower dimension as the
  input feature class with the lowest dimension
  geometry (point 0 dimension, line 1
  dimension, poly 2 dimension). Specifying
  different OUTPUT TYPE will produce different
  types of intersection of the input feature
  classes. These are not a different representation
  of the same intersections they are intersections
  that can only be represented by that geometry
  type (point, line, or polygon).
• Intersect can run with a single input. In this
  case, instead of discovering intersections
  between the features from the different feature
  classes or layers, it will discover the
  intersections between features within the single
  input. This can be useful to discover polygon
  overlap and line intersections (as points or
  lines).

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Union

• This tool builds a new feature class by combining
  the features and attributes of each feature
  class. Available with any ArcGIS license.

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Union

• How Union (Analysis) works
• Union calculates the geometric intersection of
  any number of feature classes and feature layers.
  
• All inputs must be of a common geometry type and
  the output will be of that same geometry type.
  This means that a number of polygon feature
  classes and feature layers can be unioned
  together. The output features will have the
  attributes of all the input features that they
  overlap.
• Union does the following
• Determines the spatial reference for processing.
  This will also be the output spatial reference.
  For details on how this is done, see Spatial
  Reference. All the input feature classes are
  projected (on the fly) into this spatial
  reference.
• Cracks and clusters the features. Cracking
  inserts vertices at the intersection of feature
  edges clustering snaps together vertices that
  are within the cluster tolerance.
• Discovers geometric relationships (overlap)
  between features from all feature classes.
• Writes the new features to the output.
• To explicitly control the output spatial
  reference (coordinate system and domains), set
  the appropriate environments, the Output Z Aware,
  and Output M Aware as desired. Note that the
  spatial reference used during processing is the
  same as the output spatial reference therefore,
  all Input Features must be within the X, Y, Z,
  and M domains.
• Union can run with a single input feature class
  or layer. In this case, instead of discovering
  overlap between the polygon features from the
  different feature classes or layers, it will
  discover the overlap between features within the
  single input. The areas where features overlap
  will be separated into new features with all the
  attribute information of the input feature. The
  area of overlap will always generate two
  identical overlapping features, one for each of
  the features that participates in that overlap.

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Identity

• This tool combines the portions of features that
  overlap the identity features to create a new
  feature class. Requires an ArcInfo license.

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Identity

• How Identity (Analysis) works
• Identity calculates the geometric intersection of
  the input and identity feature classes and
  feature layers.
• Identity does the following
• Determines the spatial reference for processing.
  This will also be the output spatial reference.
  For details on how this is done, see Spatial
  Reference. All the input feature classes are
  projected (on the fly) into this spatial
  reference.
• Cracks and clusters the features. Cracking
  inserts vertices at the intersection of feature
  edges clustering snaps together vertices that
  are within the cluster tolerance.
• Discovers geometric relationships (overlap)
  between the input features and the identity
  features.
• Input Features or portions of Input Features that
  do not overlap Identity Features are written to
  the output. Input features or portions of Input
  Features that overlap Identity Features get the
  attribute information from the Identity Feature
  and are written to the output.
• To explicitly control the output spatial
  reference (coordinate system and domains), set
  the appropriate environments, the Output Z Aware,
  and Output M Aware as desired. Note that the
  spatial reference used during processing is the
  same as the output spatial reference therefore,
  all Input Features and Identity Features must be
  within the X, Y, Z, and M domains.

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Erase

• This tool creates a feature class from those
  features or portions of features outside the
  erase feature class. Requires an ArcInfo license.

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Symmetrical Difference

• This tool creates a feature class from those
  features or portions of features that are not
  common to any of the other inputs. Requires an
  ArcInfo license.

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Update

• This tool updates the attributes and geometry of
  an input feature class or layer by the Update
  feature class or layer that they overlap.
  Requires an ArcInfo license.

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Point in polygon

• Uses the Intersect or Union Command with a
  polygon and a point feature class
• Returns a point feature class with attributes
  from the points and polygons

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Line in Polygon

• Uses the Intersect or Union Command with a
  polygon and a line feature class
• Returns a line feature class with attributes from
  the lines and polygons

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Extract Tools

• Clip
• Select
• Split
• Table Select

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Clip

• Extracts those features or portions of features
  from an input feature class that overlap with a
  clip feature class. The Clip tool is similar to
  the Intersect tool, however, the Clip tool does
  not transfer any attributes from the clip feature
  class to the output. Available with any ArcGIS
  license.

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Select

• Extracts features from an input feature class or
  input feature layer and stores them in a new
  output feature class. The output feature class
  may be created with a subset of features based on
  a Structured Query Language (SQL) expression.

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Split

• The spatial extraction of features by clipping
  portions of the input feature class into multiple
  feature classes. Requires an ArcInfo license.

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Table Select

• Extracts selected attributes from an input table
  based on an attribute query and stores them in
  the output table. Available with any ArcGIS
  license.

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Proximity Tools

• Buffer
• Multiple Ring Buffer
• Near
• Point Distance

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Buffer

• The construction of area features by extending
  outward from point, line, or polygon features
  over a specified distance. Available with any
  ArcGIS license.

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Multiple Ring Buffer

• Creates a new feature class of buffer features
  using a set of buffer distances. The new features
  may be dissolved using the distance values or as
  a set of individual features. Available with any
  ArcGIS license.

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Near

• Computes the distance from each point in a
  feature class to the nearest line or point in
  another feature class. Requires an ArcInfo
  license.

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Point Distance

• Computes the distances between point features in
  one feature class to all points in a second
  feature class that are within the specified
  search radius. Requires an ArcInfo license.

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Statistics Tools

• Frequency
• Summary Statistics

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Frequency

• Produces a list of the unique code occurrences
  and their frequency in an output table for a
  specified set of fields from an input feature
  class or table. Optionally, summary items may be
  totaled for each unique code combinationfor
  example, the total area for unique combinations
  of zoning and land use. Requires an ArcInfo
  license.

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Summary Statistics

• Generates summary statistics for fields from an
  input table or feature class and saves them in an
  output table. This tool contains the following
  statistics types sum, mean, minimum, maximum,
  standard deviation, range, first, and last.
  Available with any ArcGIS license.

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Raster Spatial Analysis

• Local, Focal, Zonal, and Global Functions
• Map Algebra
• Terrain Analysis
• Hydrologic Functions

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Local Functions
Local or per-cell functions compute an output
raster dataset where the output value at each
location is a function of the value associated
with that location on one or more raster
datasets. Dem_2x dem 2
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Focal Functions

• Focal, or neighborhood, functions produce an
  output raster dataset in which the output value
  at each location is a function of the value at a
  location and the values of the cells in a
  specified neighborhood around that location
• Dem_blur focalmean(dem, circle, 3)

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Zonal Functions

• Zonal functions compute an output raster dataset
  where the output value for each location depends
  on the value of the cell at the location and the
  association that that location has within a
  cartographic zone.
• ZONALMEAN (ltzone_gridgt, ltvalue_gridgt, DATA
  NODATA)

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Global Functions

• Global functions compute an output raster dataset
  in which the output value at each cell location
  is potentially a function of all the cells in the
  input raster datasets.
• There are two groups of global functions
• Euclidian Distance and Weighted Distance

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Raster calculator
Operators
Functions
Boolean operators And, Or, XOr, Not Relational
operators , gt, lt, ltgt, gt, lt Arithmetic
operators , /, -, , Log, Exp, Sin, Cos, Sqrt
Exponential and logarithmic Abs, Ceil, Floor,
Int, Float, IsNull Sin, Cos, Tan, Asin, Acos,
Atan Sqrt, Pow
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Terrain Analysis

• Contour
• Slope
• Aspect
• Hillshade
• Viewshed
• Cut/fill

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Performing surface analysis Contours

• Contours are polylines that connect points of
  equal value, such as elevation, temperature,
  precipitation, pollution, or atmospheric
  pressure.

DEMO !!!
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Slope
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Aspect
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Hillshade
Setting a hypothetical light source and
calculating the illumination values for each
cell in relation to neighboring cells. It can
greatly enhance the visualization of a surface
for analysis or graphical display.
Azimuth 315, altitude 45
DEMO !!!
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Viewshed

• Viewshed identifies the cells in an input raster
  that can be seen from one or more observation
  points or lines.
• It is useful for finding the visibility. For
  instance, finding a well-exposed places for
  communication towers

hillshaded DEM as background
DEMO !!!
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Cut/Fill

• Understanding cut/fill volumetric analysis
• Related topics
• Cut/Fill summarizes the areas and volumes of
  change between two surfaces. It identifies the
  areas and volume of the surface that have been
  modified by the addition or removal of surface
  material.
• Learn how to calculate cut/fill using the Spatial
  Analyst toolbar
• Learn how to calculate cut/fill using the
  cut/fill tool
• By taking two surface rasters of a given area
  from two different time periods, the Cut/Fill
  function will produce a raster displaying regions
  of surface material addition, surface material
  removal, and areas where the surface has not
  changed over the time period. Negative volume
  values indicate areas that have been filled
  positive volume values indicate regions that have
  been cut.
• Taking river morphology as an example, to track
  the amount and location of erosion and deposition
  in a river valley, a series of cross sections
  need to be taken through the valley and surveyed
  on a regular basis to identify regions of
  sediment erosion and deposition.

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Command line exampleCutFill_sa
C/data/surface01 C/data/surface02
C/data/cutfill 0.3048
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Hydrologic Analysis

• Basin
• Creates a raster delineating all drainage basins
  within the analysis window.
• Fill
• Fills sinks in a surface raster to remove small
  imperfections in the data.
• Flow Accumulation
• Creates a raster of accumulated flow to each
  cell by accumulating the weight for all cells
  that flow into each downslope cell.
• Flow Direction
• Creates a grid of flow direction from each cell
  to its steepest downslope neighbor.
• Flow Length
• Calculates upstream or downstream distance along
  a flow path for each cell.
• Sink
• Creates a grid identifying all sinks or areas of
  internal drainage.

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Hydrologic analysis

• Snap Pour Point
• Snaps selected pour points to the cell of
  highest flow accumulation within a specified
  neighborhood.
• SnapPour
• Snaps selected pour points to the cell of
  highest flow accumulation within a specified
  neighborhood.
• Stream Link
• Assigns unique values to sections of a raster
  linear network between intersections.
• Stream Order
• Assigns a numeric order to segments of a grid
  representing branches of a linear network.
• Stream To Feature Converts a raster representing
  a raster linear network to a feature class.
• StreamShape
• Converts a grid representing a raster linear
  network to a shapefile.
• Watershed
• Determines the contributing area above a set of
  cells in a grid.

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Reclassification
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GIS is not perfect

• A GIS cannot perfectly represent the world for
  many reasons, including
• The world is too complex and detailed.
• The data structures or models (raster, vector, or
  TIN) used by a GIS to represent the world are not
  discriminating or flexible enough.
• We make decisions (how to categorize data, how to
  define zones) that are not always fully informed
  or justified.
• It is impossible to make a perfect representation
  of the world, so uncertainty is inevitable
• Uncertainty degrades the quality of a spatial
  representation

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A Conceptual View of Uncertainty
Real World
Conception
Measurement Representation
Data conversion and Analysis
error propagation
Result
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Map representation error
Map scale Ground distance, accuracy, or resolution (corresponding to 0.5 mm map distance)
11,250 0.625 m
12,500 1.25 m
15,000 2.5 m
110,000 5 m
124,000 12 m
150,000 25 m
1100,000 50 m
1250,000 125 m
11,000,000 500 m
110,000,000 5 km
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• The ecological fallacyThe ecological fallacy is
  the mistake of assuming that an overall
  characteristic of a zone is also a characteristic
  of any location or individual within the zone.
• The Modifiable Areal Unit Problem (MAUP)The
  results of data analysis are influenced by the
  number and sizes of the zones used to organize
  the data. The Modifiable Area Unit Problem has at
  least three aspects
• The number, sizes, and shapes of zones affect the
  results of analysis.
• The number of ways in which fine-scale zones can
  be aggregated into larger units is often great.
• There are usually no objective criteria for
  choosing one zoning scheme over another.
• - An example of the influence of the number of
  zones on analysis is the 1950 study by Yule and
  Kendall which found that the correlation between
  wheat and potato yields in England changed from
  low to high as the data were grouped into fewer
  and fewer zones (starting with 48 and ending with
  2).
• - An example of the influence of zone shape is
  gerrymandering, in which voting district
  boundaries are manipulated in order to engineer a
  desired election outcome.

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zone shape change
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Living with uncertainty

• uncertainty is inevitable and easier to find,
• use metadata to document the uncertainty
• sensitivity analysis to find the impacts of input
  uncertainty on output,
• rely on multiple sources of data,
• be honest and informative in reporting the
  results of GIS analysis.
• US Federal Geographic Data Committee lists five
  components of data quality attribute accuracy,
  positional accuracy, logical consistency,
  completeness, and lineage (details see
  www.fgdc.gov)