GIS (geographic information system): avril 2014

Simplification of Patches and Identifying Separate "Clumps"

Publié par elharrak mardi 22 avril 2014 0 commentaires

The process of querying or classifying grids often leads to isolated small patches of cells and highly irregular boundaries around groups of cells of the same category. The "precision" of these may outweigh their utility as identifiers of hazard, suitability, or condition. Probably the easiest way to eliminate these two conditions is to use a "median" or "mode" filter (see "filter" in next notes section).. Other methods are available.

Growing and shrinking

This process simplifies a grid or vector cell group by alternately expanding then shrinking the size of the cell groups or vector polygons (or by shrinking then expanding). For grids, adding one cell around the perimiter of all cell groups and then trimming one has the effect of smooth the boundaries because small embayments are eliminated. See the explanation for Expand, Shrink, and BoundaryClean in theArcMap Spatial Analyst Functional Reference.

This is grow first then shrink. What would shrink then grow do?

In Raster Space

The top maps show a grow-shrink once using the BoundaryClean tool with the "oneway" option, which is not the default. These "clumps" are available as the raster file .../demo/clumps/clump_this.mxd. (made from the query "Maury_zoom > 1200 & mz_slope < 40").

Here is the same Grid Command, but with the TwoWay variable (runs twice, but the second time through it reverses the grow-shrink to shrink-grow. This eliminates the small islands and peninsulas.)

The other request available in ArcMap is "Nibble," which you can explore on your own. It is more complicated in is execution and effect.

Grouping contiguous cells into individual regions

This process allows us to identify contiguous cells as unique "clumps." This will be important for selecting them in an analyses such as assigning hazard. In ArcGIS, this process works on grids where the cells to be "clumped" share a common value, and the rest of the cells have "No Data." You can also choose whether of not diagonals count as "contiguous" and whether to save the original value in a "link" field or not. See RegionGroup in the ArcMap Spatial Analyst Functional Reference

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Overlay Types

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Binary Layers

overlay by multiplying two binary risk layers
low hazard
0 high harzard
1
low hazard
0 low risk low risk
high hazard
1 low risk high risk
or adding them

	low hazard 0	high harzard 1
low hazard 0	low risk	moderate risk
high harzard 1	moderate risk	high risk

open overlay\overlay trials.mxd to try these in a model

use the xxdemo_overlay toolbox in the overlay folder to find the binary plus and times models

Multiple Category Layers

multiple boolean operators become very complex but can be done with a script
overlay by adding or multiplying;

	High Hazard	Mod Hazard	Low Hazard
High Hazard	high risk	moderate risk	low risk
Mod Hazard	high risk	low risk	no risk
Low Hazard	moderate risk	low risk	no risk

But how would you "hand pick" various levels of risk as shown above, when the weighting is not symmetrical?

Index Layers

use reclassify or map algebra to create indices
overlay by adding or by multiplying has a different effect on the distribution of high and low results.
From the file overlay\overlay trials.mxd,
use the xxdemo_overlay toolbox in the overlay folder to run an index overlay.

Layer Weighting (can be combined with Overlay Types #1-3)

one layer (or more) is more important or has more influence on the outcome than others
use raster calculat or the "weighted sum" toolbox
can use weighted overlay toolbox

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querying theme tables and vector "spatial arithmetic"

Publié par elharrak 0 commentaires

Feature Selection

Another other kind of reclassification is simplification of a discrete or categorical theme based on attributes. In the below example, a US State data layer that has census information attached to the state spatial file can be subdivided based on population density (this was done in the layer's properties box, using the query builder but could also be "queried" using Select by Attributes option from teh Selection menu..
If two or more spatial units need to be "combined" we can use different types of spatial math. Combinations of spatially interseting features are available in ArcMap under the edit menu (for digitizing or for editing a single theme with multiple intersecting polygons) but are generally used across multiple layers from the Geoprocessing Toolbox . This image shows the effects of the union, intersect, combine and subtract commands on the "big box" and "little box" polygons. Little box was drawn first.

These are the types of spatial and attribute combinations that are available in the Geoprocessing Toolboxes--they are common to most vector-able GIS.

Dissolving will result in feature categories and lines that disappear between spatial units (example: detail land use to general land use categories)
Merging brings adjacent maps with the same attribute information (be careful, unstable results if the attributes aren't identical or the order of attributes is different together) \
Clipping (also known as stamping) is like a cookies cutter slicing off a piece of an original theme (example: taking a subset of a geologic map for a study area)
Intersect and Union both create a fused theme that incorporates the spatial and attribute nature of both (only extent is different?). This will result in a huge number of polygons for some maps. Imagine intersecting a landuse and geology theme..... If you intersect a line theme with a polygon theme (input is line, overlay is polygon), the information about the polygon is added to the line segements, and the lines are split into new segments where the polygons cross lines.......see below....note that any length attributes for the orginal line theme or "connection" information (what's upstream or downstream) will now be incorrect and must be updated.

Selection by location

in a GIS, selection using database attribute queries is a "nonspatial" operation
Because we know the spatial context of each feature class and each feature, we can use their location relative to another as a selection criteria. This is known generally as an "intersect" operation, but has lots of variety of seletion t ypes and names in different platforms. Here are some of the possibilities in ArcGIS for features in one layer that. . .

. . . the features of another layer
In my work I find "within a distance of" especially useful.
here the red rings show a distance around the points of interest, and the highlighted cyan features show the "selected" features of each type.

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Map Overlays and Boolean Logic

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Presented with two map layers and a situation involving suitability or habitability, a GIS operator is able to "look through" the map layers to select or recategorize the areas of the two maps based on the combination of attributes or features.

For example, given a hawk with a preference for high, flat nesting sites, one could use a DEM to inquire where to build that hawk nest observation tower. This kind of operation relies on Boolean logic which controls how the maps are made. In ArcMap, boolean logic terms are built into the Map Calculator (left below) and Map Query (right)

and satisfies both conditions simultaneously

or satisfies one or the other and both
xor satisfies one or the other but not both
not excludes the condition

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Reclassification

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Using Raster Calculator statments to simplify data

Using a Reclassify operation (Spatial Analyst Menu Reclassify or Reclassify and Slice tools)

This is referred to sometimes as a density slice or simply "slice," and reduces data to useable information.

It can be used with binary, multiple, and index layers.
1. Choose the number of classification bins (the example below is 5 classes)
2. Choose how you want the histogram divided The two most common methods are equal interval and equal area (known in ESRI-speak as "quantile"). The illustration above uses "equal interval." These choices are also available for symbology of any layer (without changing the data)
  These are the choices from the Reclassify tool, and similar choices are found in the Slice tool. The reclassification you choose has a huge impact on the nature of the output. Note the change in results and histograms from the data found in simplify\reclassify_elevation.mxd (to see the effect, I created histograms for the different reclass types illsutrated below left).
Reclassify is the only good way to deal with discrete, non-numeric layers (like land use, e.g., pasture, roads, forest, lakes, etc) that have to be combined by hand ("manual" reclassification")
Metadata are complicated. If you use a toolbox, it creates the geoprocessing history, but it is impossible to recover and difficult to read in that format (try it). Better to save the reclassification scheme directly from the tool, by clicking the "save" button. This operation stores an ArcMap table. You must highlight the classes you want to save before you save, or the table will be empty (way to go ESRI).

Lastly, Query operations (perhaps multiple ones) can be used to reclassify. This is an uncommon, labor intensive method to select criteria or input.

each separate category must be saved independently (for example, each of the four possible combinations of two binary layers)
more useful across multiple map layers

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overlay complexity

Publié par elharrak 0 commentaires

Grouping raster or vector data by attribute in order to SIMPLIFY the OVERLAY process

Need to ask 2 questions first;"With what complexity?" then "How to assign values?"

in order of Increasing Complexity simplification of continuous layers yields discrete categorical layers

binary layers (yes/no, on/off, 1/0, suitable/unsuitable)
multiple category layers( yes/maybe/no, on/bordering/off, highly suitable/less suitable/not suitable)
"index" layers (1, 2, 3, ....10, or 1, 2, 3,...1000, etc)

How does one divide up a continuous variable into discrete categories? (open simplify_elevation.mxd from your ...GIS\Demo\simplify folder)

Raster Calculator Map Algebra
1. "IF" statements (plus "if... then...else") in traditional programming
  1. BINARY DISRETE CATEGORIES : a simple "Con" statement has this syntax
    Con (conditional statement, yesGrid, noGrid) . . . . .see help -- it means "conditional testing"
    Con("elev" > 500, 1, 0) copy and paste this into the Spatial Analyst Raster Calculator
  2. MULTIPLE DISCRETE CATEGORIES: a "nested" .Con statement (where the "no" for the first conditional sends you to another conditional test)
    Con("elev" > 600, 2, Con("elev" > 300, 1, 0))
2. Using arithmetic (works for Binary, Multiple, or Index Category Layers)
  1. Determine the range and divide by the number of categories required
    index category width = ([range]/[# of divs needed])
    for our elevation example; 240 to 830 = 590 / 10 classes = 59 m per index level
  2. subtract the minimum from each cell or attribute value to set the origin of the index at the lowest value
    ([elev] - 240) / 59
  3. find the next lowest integer (or highest, depending if you want the index valuesto start at 0 or 1) using the Floor or Ceil functions
    Floor(([elev] - 240) / 59) however to get it into an INTEGER raster, you must use
    Int(Floor(([elev] - 240) / 59))
    which yields 10 categories from 0 to 9
    or
    Int(Ceil(([elev] - 240) / 59) )
    which yields 10 categories from 1 to 10
    
    What if you want the same number of cells in each category?
Reclassify geoprocessing

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Simplificaton in Remote sensing

Publié par elharrak 0 commentaires

One of the things us old folks used to do with paper maps was trace the data onto mylar or tracing paper, and put it over another map. This analytical feista allowed the comparison of more than one variable. This is known as an "overlay" for obvious reasons.
Simplificaton facilitates overlay
Using complex or continuous variables in an overlay leads to very complex output
For example, try looking for a certain combination of slope and elevation by combining two continuous variables.

but simplification leads to fewer resulting combinations (here this is a "binary" simplification)

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Formating data to attach to existing features in ArcMap

Publié par elharrak mardi 15 avril 2014 0 commentaires

This
tutorial
will
demonstrate
how
to
format
tabular
data
in
prepara3on
for
joining
a,ributes
to
exis3ng
features
(shapes,
including
points,
lines,
and
polygons).
It
is
very
generic
in
scope
but
covers
the
basics
of
forma&ng
and
joining.