v0.2.47..v0.2.48 changeset Introduction.asciidoc
Garret Voltz edited this page Sep 27, 2019
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1 revision
diff --git a/docs/user/Introduction.asciidoc b/docs/user/Introduction.asciidoc
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@@ -20,13 +20,13 @@ categories:
* Image to Image (I2I) - Aligns two images.
.Simple V2V Conflation
-image::user/images/SimpleConflation.png[]
+image::images/SimpleConflation.png[]
Hootenanny deals exclusively with vector to vector conflation. The following
sections discuss when Hootenanny is an appropriate tool for conflation and
general guidance in determining what strategy to employ when using Hootenanny.
-=== Conflation Workflow
+=== Conflation Workflow
Hootenanny is a tool to help the user work through a conflation problem.
Many aspects of the conflation workflow are automated or semi-automated to help
@@ -72,7 +72,7 @@ Appropriate data for conflation:
==== Normalizing and Preparing Data
Before conflation can occur Hootenanny expects that all data is provided in a
-modified OSM schema and data model that is a superset of the
+modified OSM schema and data model that is a superset of the
http://wiki.openstreetmap.org/wiki/Map_Features[OSMschema]. The http://wiki.openstreetmap.org/wiki/Element[OSM data model] provides
a connected topology that aids conflation of multiple "layers" at one time while
the extendable OSM schema provides flexibility in handling many data types.
@@ -82,27 +82,27 @@ common formats into the OSM data model as well as translating existing schemas
into OSM. See the <<Translation,Translation>> section for further details.
Hootenanny also provides some functions for automatically cleaning many bad data
-scenarios. Many of the operations are performed prior to conflation.
+scenarios. Many of the operations are performed prior to conflation.
See also:
-* <<clean,clean command>>
+* <<clean,clean command>>
* <<MapCleanerTransforms, map.cleaner.transforms>>
==== Conflation Strategies
There are three main conflation strategies supported by Hootenanny:
-* Vertical Conflation (aka Reference Conflation) - Both data sets contain useful information and significant overlap. Conflate the best
+* Vertical Conflation (aka Reference Conflation) - Both data sets contain useful information and significant overlap. Conflate the best
geometry and tag parts of map B into map A.
* Horizontal Conflation - There is a small amount of overlap between the two data sets.
-* Cookie Cutter & Horizontal Conflation - One data set is superior to the other in every way but does not cover the same region. The
-lesser data set will be cropped and the seams stitched back in using Horizontal Conflation. Define a specific region in map A that should
+* Cookie Cutter & Horizontal Conflation - One data set is superior to the other in every way but does not cover the same region. The
+lesser data set will be cropped and the seams stitched back in using Horizontal Conflation. Define a specific region in map A that should
not be modified at all and stitch in data from map B around it.
* Differential Conflation - Conflate map A with B where the only data added to the output from B is in areas that don't overlap with A. Optionally, you can configure to overwrite tags in A from B even when there is overlap.
-* Attribute Conflation - Conflate map A with B where only tags are transferred from B to A and no changes are made to A's geometry (with
+* Attribute Conflation - Conflate map A with B where only tags are transferred from B to A and no changes are made to A's geometry (with
some configurable exceptions).
-
+
See also:
* _Unifying Conflation_, <<hootalgo>>
@@ -112,11 +112,11 @@ NOTE: In the Hootenanny iD UI, there are three conflation options: _Reference_,
===== Vertical Conflation
.Vertical Conflation. Red and blue boxes represent different data sets. This is a good candidate for Vertical Conflation because of the high degree of overlap between the two data sets.
-image::user/images/VerticalConflation.png[]
+image::images/VerticalConflation.png[]
-Vertical conflation is most applicable when both data sets provide value and there is significant overlap between the data sets. In this scenario many of the features will be evaluated for conflation and possibly merged or marked as needing review. The primary advantage to using this strategy is maintaining much of the information available in both data sets. Because a large amount of the data is being evaluated for conflation it also increases the chances that errors will be introduced or unnecessary reviews may be generated.
+Vertical conflation is most applicable when both data sets provide value and there is significant overlap between the data sets. In this scenario many of the features will be evaluated for conflation and possibly merged or marked as needing review. The primary advantage to using this strategy is maintaining much of the information available in both data sets. Because a large amount of the data is being evaluated for conflation it also increases the chances that errors will be introduced or unnecessary reviews may be generated.
-To use, pass +ReferenceConflation.conf+ to the conflate command.
+To use, pass +ReferenceConflation.conf+ to the conflate command.
e.g.
--------
@@ -129,18 +129,18 @@ See also:
===== Horizontal Conflation
-.Horizontal Conflation. This is a good candidate for Horizontal Conflation because there is a small amount of overlap between the two data sets.
-image::user/images/HorizontalConflation.png[]
+.Horizontal Conflation. This is a good candidate for Horizontal Conflation because there is a small amount of overlap between the two data sets.
+image::images/HorizontalConflation.png[]
-Programmatically there is no difference between Vertical and Horizontal conflation. The difference is solely conceptual.
+Programmatically there is no difference between Vertical and Horizontal conflation. The difference is solely conceptual.
[[UnsupportedHorizontalConflation]]
.Unsupported Horizontal Conflation due to the complete lack of overlap between the two data sets.
-image::user/images/NotHorizontalConflation.png[]
+image::images/NotHorizontalConflation.png[]
As demonstrated in <<UnsupportedHorizontalConflation>> two vector layers that don't quite touch are not conflated. If this is of interest please create an issue at https://github.com/ngageoint/hootenanny.
-To use, pass +HorizontalConflation.conf+ to the conflate command.
+To use, pass +HorizontalConflation.conf+ to the conflate command.
e.g.
--------
@@ -156,19 +156,19 @@ See also:
[[CookieCutterImage]]
.Cookie Cutter & Horizontal. The left image depicts the overlap of a high quality, smaller area data set overlayed on a coarser regional data set that is typical for Vertical/Horizontal Conflation. The shaded area in the right image depicts the -1km buffer that is applied during the Cookie Cutter operation.
-image::user/images/CookieCutter.png[]
+image::images/CookieCutter.png[]
-The cookie cutter operation is designed for situations where two data sets contain significant overlap, but one data set is better in _every way_. A typical scenario that warrants this strategy is coarse country wide data that needs to be conflated with high quality city level data. When employing cookie cutter a polygon that approximates the bounds of the city will be removed from the coarse country data before conflation.
+The cookie cutter operation is designed for situations where two data sets contain significant overlap, but one data set is better in _every way_. A typical scenario that warrants this strategy is coarse country wide data that needs to be conflated with high quality city level data. When employing cookie cutter a polygon that approximates the bounds of the city will be removed from the coarse country data before conflation.
[[horizontalconflate_Boulder1]]
.Boulder, CO with Street centerlines (gray) and OpenStreetMap Highways (red). Right image depicts generate-alpha-shape (red polygon). Street centerline data obtained from the link:$$https://www-static.bouldercolorado.gov/docs/opendata/Streets.zip$$[City of Boulder] and Highway data set downloaded from an OSM data provider. The basemap shown here is OSM.
-image::user/images/hootid-horizconfl.png[]
+image::images/hootid-horizconfl.png[]
[[horizontalconflate_hootid]]
.Process depicted in the Hootenanny User interface. The Horizontal & Cookie Cutter conflation performs an edge matching to merge the Street centerline data with the OSM data. The resulting conflated dataset shown in bottom image (green). Boulder, CO with DigitalGlobe Global Basemap (GBM).
-image::user/images/hootiD_horizontalconflation_boulder.png[scaledwidth="50%"]
+image::images/hootiD_horizontalconflation_boulder.png[scaledwidth="50%"]
-To use, pass +HorizontalConflation.conf+ to the conflate command.
+To use, pass +HorizontalConflation.conf+ to the conflate command.
e.g.
--------
@@ -185,7 +185,7 @@ See also:
===== Differential Conflation
-To use, pass +DifferentialConflation.conf+ to the conflate command.
+To use, pass +DifferentialConflation.conf+ to the conflate command.
e.g.
--------
@@ -196,7 +196,7 @@ More details: <<hootalgo, DifferentialConflation>>
===== Attribute Conflation
-To use, pass +AttributeConflation.conf+ to the conflate command.
+To use, pass +AttributeConflation.conf+ to the conflate command.
e.g.
--------
@@ -213,7 +213,7 @@ NOTE: Fill in hoot:review tag details.
Each review flags one or more features. The features are referenced using the <<UUID,uuid>> field. A `hoot:review:note` field is also populated with a brief description of why the features were flagged for review.
-===== Reviewing from the Command Line Interface
+===== Reviewing from the Command Line Interface
Reviewable items are flagged with several `hoot:review` tags during the conflation process. The user can then edit the resulting output file with an editor of their choosing to resolve the reviewable items. It is worth noting that this review process should occur before the data is exported as exporting the data using the `convert` command or similar will likely strip the review tags.
@@ -233,7 +233,7 @@ The web interface exposes reviewable items through an intuitive interface that g
In some cases there are more than two files that must be conflated. If this is the case the data must be conflated in a pairwise fashion. For instance if you are conflated three data sets, A, B & C, then the conflation may go as follows:
.Pairwise Conflation Example
-[graphviz]
+
-------
digraph G
{