1 8/11/2015 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals GIS Data Preparation and Integration...

104/19/23 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals

GISData Preparation and Integration

Digesting the Food


Data Preparation and Integration: the necessary steps• Geocoding: assigning geographic coordinates to points

– Perhaps the most basic form of spatial data entry

• data media conversion– scanning– digitizing

• data format conversion– raster & vector

• data reduction• Topology, error detection and topological editing• rectification and registration (one on top of the other)

– overlaying sheets and referencing to the real world

• edge matching & image adjustment (side by side)– linking & balancing adjacent sheets

• interpolation• conflation

Geocoding:assigning spatial coordinates to point data

Address Matching assigns spatial coordinates (explicit location) to addresses (implicit location)Address matching requires street network file with street attribute information (street name and number range) for all street segments (block sides)

– “Zone” variable required if data spans multiple cities (to handle duplicated street names)– precise matching of street names can be problematic– completeness (esp. for ‘new’ streets) important– PO boxes, building names, and apartment complex names cause problems.

Implementation in ArcGIS is 3-step process– In ArcToolbox (9.2), process street network file to create a Geocoding Service– In ArcMap, load appropriate geocoding service via Tools/Geocoding/Services Manager – In ArcMap, geocode a table of addresses using Tools/Geocoding/Geocode Addresses

Point Location Files containing lat/long or x,y coordinates (e.g derived via GPS)

– bring table (e.g. in .csv or .dbf format) into ArcGIS using add data icon– Right click table name in T of C and select Display X,Y data– Displays as “event layer.” Export to shapefile or gdb feature class for spatial data set.

Input table must contain 3 variables at minimum: Feature ID, x, y


Data Media Conversion--Scanning:

automated recording of map or aerial • Produces “dumb” raster data

– vectorize using conversion software – Create “smart” image using digital image

processing techniques• electromechanical

– $100-$50,000 instruments– drum or flatbed– scan resolution depends on price!

• down to 20 microns (millionth of m)

• Scanners v. sensors– Sensors collect data directly in digital

form (e.g. digital cameras)– Sensor resolution now (2005>) matches

that of photos, so scanning photos becoming old technology

– Still lots of paper maps around e.g. property ownership records

• Great if need only raster representation• Automated creation of vector data from

scanning very problematic:– docs must be clean– complex line work adds error– lines shouldn’t be broken with text.– text may be interpreted as lines– automatic feature detection (road versus

railroad) difficult

• ESRI’s ArcScan for ArcGIS (included with ArcEditor) provides interactive, semi-automated raster to vector conversion.

– Other vendors offer specialized conversion software

• Digital image processing techniques used to create “smart raster”

– Identify feature type within each raster


Data Media Conversion--Digitizing:manually tracing a map or aerial

• Applied to map or aerial photo• Use hard copy map/photo on

table/tablet, or scanned image on screen (heads-up digitizing)

• pen or cursor detects x, y coords• coordinates are in inches/cms from

lower left (0,0)• control points (tic marks) relate

digitized coordinates to real world lat/long coordinates

• coordinates captured in stream or point mode

• accuracy of table (but not user!) usually better than 0.1 mm

• all nodes and polygons should be marked and numbered first

• essentially a vector approach

Problems:• paper maps unstable

– crease and fold

– stretch with humidity ( up to 3%)

– photos more stable (0.2%)

• map errors transferred to GIS– maps often prepared for display

not accuracy

• human hand very shaky

• often generates undershoots, overshoots, & double lines

– editing and clean-up essential


Data Format Conversion:• Vector to Vector

– e.g. whole polygon (e.g SAS map data) to point/arc/polygon

– computationally intense

– no accuracy loss providing data is ‘clean’

– perfectly transitive

• raster to raster– may involve resampling (see under data

reduction)

– may involve conversion between different vendor’s raster formats (e.g. GRID to BIL)

• vector to raster: point– node x,y assigned to closest raster cell

– locational shift almost inevitable; error depends on raster size.

– two points in one cell indistinguishable

– not transitive; cannot retrieve original data without error

vector to raster: line– cells assigned if touched by line

– stair step appearance of diagonal lines (called aliasing)

– can be visually improved through anti aliasing: brightness of cells varied based on fraction of cell covered by the line

• raster to vector– by far the most difficult

Transitive: the ability to reproduce the original data after conversion.

vectorraster

Vector raster

4 possibilities


Vector to Raster Conversion

Orthogonal Line Diagonal Line

(more problemmatic)

Point

Vector

RasterNote the use of anti-aliasing to improve line’s visual appearance


Raster to Vector Data Conversion: 3-step process– skeletonizing (or thinning): to reduce rasters to unit width

• peeling approach successively removes outer edges

• medial axis approach determines set of interior pixels farthest from outer edges

– vector extraction: to identify lines• 4-connected reconstruction

– joins center points of 4-connected neighbors if present– particularly bad for diagonal line reproduction

• 8-connected reconstruction– joins center points of 8-connected neighbors if present– diagonal lines reproduced but adds extra lines

• 8-connected reconstruction with redundancy elimination– if 4-connected neighbor line exists, don’t draw diagonal– reduces redundant lines

– topological reconstruction: recreates topological structure– create nodes at line junctions– construct arcs– define polygons (manual designation required)

Available via the ArcScan extension for ArcGIS, as well as via several specialized packages from other vendors


Raster to Vector ConversionSkeletonizing

For example, go to: http://www.cosc.canterbury.ac.nz/people/mukundan/covn/Thin.html

http://www.cosc.canterbury.ac.nz/people/mukundan/covn/Thin.html

http://www.cosc.canterbury.ac.nz/people/mukundan/covn/Thin.html


Raster to Vector Conversion: Vector Extraction

4-connect reconstruction

Vector Raster

4-connect reconstruction:search the 4 surrounding cells andjoin center points if present


Raster to Vector Conversion:Vector Extraction

8-connect reconstruction

Vector Raster

8-connect reconstruction:search the 8 surrounding cells and join center points if present.


Raster to Vector Conversion:Vector Extraction

8-connect reconstruction with redundancy elimination

Vector Raster

8-connect with redundancyelimination:draw diagonal from 8-cell search only if not alreadyconnected by orthogonal from 4-cell search


Data Format Conversion Implementation in ArcGIS 9

Arctoolbox>Conversion Tools>To Raster> Raster To Other (multiple)

Converts one or more raster dataset formats supported by ArcGIS to a GRID, IMAGINE, TIFF, or geodatabase raster dataset format

Can also be accomplished thru ArcCatalog, Export function

Arctoolbox>Conversion Tools>From Raster> Raster to Point

Raster to Polygon

Raster to PolyLineConverts raster datasets in GRID, IMAGINE, or TIFF formats to shapefiles or feature classes.

Results may not be what you expect!

Can also be accomplished thru ArcCatalog, Export function

Arctoolbox>Conversion Tools>To Raster> Feature to Raster

Converts any shapefile, coverage, or geodatabase feature class containing point, line, or polygon features to a raster dataset

Can also be accomplished thru ArcCatalog, Export function.

Use ArcCatalog, Export function for conversions between shapefiles, gdb feature classes, coverages and CAD

ArcGIS Data Interoperability Extension

for the most comprehensive set of conversions

To Raster

FromRaster

FromVector

To Vector


Data Reduction• Why?– conserve space

• Disk in past• Comm. bandwidth today

– conserve time• reduce processing time (batch)• speed response time (interactive)

• Resampling (raster data)– ‘average’ the 4 values in a 2by2

neighborhood – use this 1 value in a single cell

occupying the location of the 4 original cells

– use mean for interval data; rules required for ordinal or nominal data

– not transitive!

• Thinning (vector data)– often applied to data digitized

in stream mode– tolerance elimination: remove

nearest-neighbor points which are ‘too close’ (e.g. output device resolution insufficient to distinguish)

– topological elimination*: remove points unnecessary for topo structure

– model-based elimination: fit polynomial by least squares and record fewer points along its path3

2 4

7

4

16 bytes

4 bytes

1 byte

*Normally uses the Douglas/Poiker (or Peucker) algorithm: David H.

Douglas & Thomas K. Peucker Algorithms for the reduction of the

number of points required to represent a digitized line or its caricature, Canadian Cartographer, 1973

Implement in ArcGis via Advanced Editing toolbar, Generalize tool


Topology --knowledge about relative spatial positioning --spatial relationships between features and rules about these

relationships --managing data cognizant of shared geometry

Implies knowledge of the three Cs: – connectivity (linked): – congruency (coincident/same as/on top of)– contiguity (adjacent)

It is critical that spatial data be created and managed so that it is topological clean--free from topological errors

--editing must always aim to maintain topological structure

In topological editing, changes made to one feature (line, polygon, etc.) are also reflected in all other features to which it is connected, coincident, or adjacent

In the classic GIS data structure model (as discussed in GIS Data Structures lecture) this implies that, for example

--all arcs have nodes at end points --there is a node wherever arcs intersect or connect--a single arc forms the border between contiguous polygons (e.g. Dallas and Tarrant county)--a single arc represents a common boundary

(e.g. state and county boundary)

Topology & Errors

Tarrant Dallas


Errors: detection and removal

• GIS packages commonly use topological structure checking to detect errors

• Editing based on node snapping used to correct errors: moving a feature so its coordinates correspond exactly with another’s

• snapping conducted based on tolerances -- snap if within 1 foot, for example

• Care must always be taken to assure that topological “cleaning” does not itself introduce errors (e.g. snapping nodes and lines together which shouldn’t be)


Topological errors or real world occurrences?common problems

• dangling arc (node missing at one end)• No node at arc intersection (overpass?)• Overshoot (or missing node)?• undershoot?• pseudo node (but perhaps road surface

changes)

• pseudo arc (connects to itself)

• open polygon

• Sliver polygon

• gap


How ArcGIS Handles Topology• The original Coverage data model, introduced with

ArcInfo in 1981, incorporated topology as a part of the data– The CLEAN command checked for, and automatically “fixed”,

topological errors based on a set tolerance• It could introduce errors into the data

– The BUILD command then rebuilt polygon structures• ArcGIS 8.3 introduced the concept of topological rules for

geodatabases in which the topological relationships are stored as a topology feature class separate from the data itself– The user can generate an error report, review each error, and then

fix it in the data if desired, or mark it as an “exception”


Georeferencing: Rectification and Registrationproviding true earth location/overlaying layers

• rectification: rearrangment of location of objects to correspond to a specific reference system (usually geodetic)

• registration: rearrangment of location of objects of one set so they correspond with those of another, without reference to a specific reference system

Despite formal difference, often used interchangeably

Two methods• homogeneous transformation

via rotation, translation, scaling, skewing– used for map projection and

similar conversions• differential transformation via

rubber sheeting– used to correctly position

distorted images or scanned maps or documents

•Most commonly used to relate images (e.g. scanned photo) to a vector layer, but can also be used to “fix” incorrect positioning of features in a vector layer•Implemented in ArcMap: via the Georeferencing toolbar for images

via the Spatial Adjustment toolbar for vector layers


Transformation:(homogeneous conversion)

• translation of origin – from digitizer origin for sheet

to ‘true’ origin of GIS file• rotation of axis

– e.g to true north• scaling of axis

• homogenous: • differential (ovals to circles)

• skewing of axis

Changing map projections may involve all 4

translation

rotation

differential scaling

skewing


Rubber Sheeting(differential conversion)

• GIS file is differentially ‘stretched’ so that tic points in file overlay corresponding ground control (tie) points on earth’s surface (or tic points in a second file)

• polynomial fitted by least squares between known ground control coords and tic point coords in GIS

– “Least squares” minimizes the sum of the squared distances between tic/tie pairs

• derived parameters then applied to all coordinates in file

• after conversion, tic points are on average closer to ground control points, but not identical

• can’t do this with a paper map!

ground control (tie)

map locations (tic)

GIS file

--the more the better

--well distributed

--known lat/long of ground control tie points (usually obtained from GPS) needed for rectification

--common identifiable points in each file needed for registration


Edge Matching:Joining map sheets to create a seamless GIS

Process• required for topo. consistency even if

features line-up visually• snapping used to connect featuresIssues• acceptable tolerance before

‘further investigation’ of mismatch• ‘how far back’ to go on sheet(s) with

adjustments for mismatchCauses of mismatch• paper map shrinkage/expansion• errors from digitizing/scanning

– georeferencing errors– accuracy of equipment– extrapolation or round-off errors

• overlapping map coverageImplement in ArcGIS 9 by:1. ArcToolbox>Data Management>General>Append (replaces Geoprocessing Tools>Merge in AG 8)

– combines two (or more) files, but does not link features2. Spatial Adjustment toolbar, edge match tool

– links features (after links have been manually identified)

Corresponding features fail to match on two sheets:

Edge matching in this examplewould likely require ‘further research’


Image Adjustmentsraster/image data issues

Raster data is made from separate images (photos) or tiles which are mosaiced to produce “seamless image”

Collars: must be removed for seamless image– Overlap between adjacent images– Borders of scanned maps

Image Balancing and Feathering: adjusting radiometry for consistent and/or desired image color, brightness, contrast

– Checker board appearance – Abrupt line between adjacent images– Brightness levels wash out detail in highly reflective areas, but enhance detail in low

reflectance areas– Inconsistent signature for same features, especially water as function of wind or sun

relative to camera (and is it blue?)

Digital Ortho adjustments:– Ground control (usually with GPS for visible points) to obtain ‘real world’ location– Ground control for camera’s angle relative to ground– Camera calibration data to remove lens distortion– Digital terrain model (dtm) to remove elevation “distance”(5 mi. on map to mountain top, but 6 mi walking or on photo if mountain is 5,280 feet high!)

Collar removal required.

Image Balancing/feathering required


Tiles AfterBefore

2005 NCTCOG Digital Orthos


Interpolation:to create regular spacings from irregular data

(e.g creating raster elevation surface from set of point height measurements) • estimating values for

locations with no data based on:– known values, and

– understanding of spatial behavior of phenomena

• generally, should assign more importance to closer known values than those further away

• weighting functions– average closest n (2?) points

• ignores distance– fit line between closest 2 – fit surface between closest 3

• trend surface approaches– one high order polynomial

• oscillation a problem– finite element approach:

fit separate polynomials for each local area

– kriging: uses correlations of values with distance

Estimated values

Implemented in ArcGIS 9 via ArcToolbox>Spatial Analyst Tools>Interpolation


Conflation• create new master coverage from the best spatial and attribute qualities

of two or more source coverages– combine multiple coverages into one to simplify support– updated data obtained (e.g. new TIGER file) but need to preserve

enhancements made to earlier version– two groups modify a single file, then need to recreate single version

which preserves mods

• create new master coverage from quality spatial data in one source and quality attribute data in another– somewhat narrower definition

• Depending on the situation, can require application of a variety of processing tools and can be labor intensive:

• Approaches available within ArcGIS 9 include – Spatial Adjustment toolbar, specifically attribute transfer tool– ArcToolbox>Analysis Tools>Overlay>Update

• other add-ins available such as • MapMerge from ESEA, Mountain View CA for ArcGIS• GIS/T-Conflate for transportation applications

NAVSTAR Global Positioning System (gps)

NAVSTAR Satellite Program • 24 (NAVigation Satellite Time and Ranging)

satellites in 11,00 mile orbit provide 24 hour coverage worldwide

• first launched 1978; full system operational December 1993.

• gps receiver computes locations/elevations via signals from simultaneously visible satellites (minimum 3 for 2-D, 4 for 3-D)

• Selective Availability (SA) security system– 100m accuracy with single receiver, if active– 10-15m accuracy if inactive

• SA turned off May 1st, 2000– Multiple ways to counteract SA– Even USCG broadcasted correction signal!– Europeans threatened to compete– Regional denial of signal possible

• Russia’s 21-satellite GLONASS (Global Navigation Satellite System) also available.

Types of Ground Collection and CorrrectionAutonomous – Hand-held unit provides 10m accuracy (with SA off)– $150-$1,500 per unit

WAAS (wide area augmentation system)– <3 meter accuracy in practice (spec. is 7m vert/horiz)– Base stations (25 across US) monitor satellites– 2 master stations (E & W coast) calculate corrections – upload to two geosynchronous satellites over equator– correction signal broadcast to GPS receivers (no special extra

equipment needed unlike DGPS)– Began operation June, 1998– To be expanded to cover Canada, Mexico, Panama – European EGNO, Asian MSAS under development

Differential (DGPS-predecessor to WAAS)– accuracy 1-5m depending on equipment/exact method– equipment $1,500-$15,000 per receiver– correct for SA and other errors via either

• real time correction signals over FM radio • post process with data from Internet

Kinematic: – high accuracy engineering (within cms); – two receivers (base station and rover– must lock-on to satellites– equipment $15-30K per station

–use to collect ground control for imagery/orthos –or for point/line data (manholes, roads, etc)


Factors Affecting GPS Accuracy• Ionosphere

– worst in evening at low altitudes (but ephemerous best there)

• troposhere– especially water vapor which slows signal

• multipath– reflected signals from buildings, cliffs, etc

• ephemerous– position and number of satellites in sky

– 4 required for 3D (horiz. and vertical), 3 for 2D (no elevation)

– ideallly, 3 every 120° horizon. with 20° elev., 1 directly above

• blockage (of satellite signal)– by foliage, buildings, cliffs, etc.

– WAAS signal espec. subject to blocking by terrain & buildings ‘cos is from geostationary equatorial satellite

Overall, accuracy better at night than during day.


Conclusion

Most of the effort in most GIS projects involves data preparation and integration!

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1 8/11/2015 Ron Briggs, UTDallas GISC 6381 GIS Fundamentals GIS Data Preparation and Integration...

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