EV398 Geospatial Workbook - West Point - Home EV398... · EV398 Geospatial Workbook ... and the other fixed and a . Scanning: ... – Installed on RDBMS (Oracle, SQL Server, DB2,

EV398 Geospatial Workbook

COL Michael D. Hendricks Academy Professor United States Military Academy Geography & Environmental Engineering West Point, New York 10996 845-938-2472

Property of the U.S. Government

Fal l 2013


Part One: Geospatial Basics 3. Geospatial Information Science 4. Representing Geography 5. Geospatial Data Models 6. Database Basics 7. Joining Example 8. Raster Data Models 9. Vector Data Models 10. The Geodatabase 11. Datums 12. Projections and Coordinate Systems 13. Defining Projection vs. Projecting

INDEX

Part Two: Collecting Geospatial Data 14. Geo Data Sources 15. Geospatial Data Quality 16. Raster Data Collection – Scanning 17. Raster Resolution 18. Vector Data Collection – GPS 19. Vector Data Collection – Digitizing 20. Military Geospatial Data

Part Three: Geospatial Analysis 21. Geospatial Queries 22. Classification and Dissolve 23. Distance 24. Overlay 25. Geospatial Process Modeling - ArcGIS Model Builder 26. Raster Analysis 27. Terrain Surface Analysis 28. Routing Analysis 29. Case Study: Site Selection

EV398 Geospatial Workbook

Partially Derived from ESRI ArcMap Documentation

2

DATUMS & PROJECTIONS

GEOSPATIAL DATABASE

VECTOR DATA

GEOCODING

FIELD DATA COLLECTION

REMOTE SENSING SCANNED MAPS

ELEVATION

GEOSPATIAL VISUALIZATION GEOSPATIAL ANALYSIS

United States Military Academy Dept of Geography & Environmental Engineering

Geospatial Information Science Group

Geocoding: The process of finding the location of a street address on a map. In GIS, geocoding requires a reference dataset that contains address attributes for the geographic features in the area of interest.

10996

10998

10899

10786

DEM Creation Orthorectification

Georeferencing: Assigning coordinates from a known reference system, such as latitude/longitude, UTM, or State Plane, to the page coordinates of a raster (image) or a planar map.

Georeference

Orthorectification: Elimination of image displacement resulting from topographic relief and other factors

Scanning: Transferring an analog product into a digital raster based on color

Satellite Aircraft

Digital Image Digital

Image

Digital Image

Georeference

Feature Data Attributed vector

data of map features

Elevation Data Uniformly-spaced grid

of terrain elevation values

Controlled Image Base (CIB)

Black & white imagery for visualization

Scanned Maps (CADRG)

Scanned paper maps

Multi-spectral Imagery (MSI)

Imagery from various energy wavelengths

.

Transverse Mercator Projection

Lambert Conformal Conical Projection

Polar Sterographic Projection

Scan

GPS Total Station

STREETS ZIP CODES

ADDRESS 550a Winans Place 330 Ohio Street 5 90th Street 2045 W. Elm St.

X, Y 300000, 4598300 300000, 4598300 300000, 4598300 300000, 4598300

Differential Correction

Differential Correction: A technique for increasing the accuracy of GPS measurements by comparing the readings to two receivers – one roving and the other fixed and a known location.

Stereo Imagery

Line of sight

Routing

Proximity

Geospatial Query

Site Selection

Pattern Analysis

EV378 Cartography

EV377 & EV477 Remote Sensing

EV398 & EV498 GIS

EV380 Surveying

EV379 Photogrammetry

Vector Data: Points, Lines and Polygons associated with attributes that describe the geographic phenomena

COORDINATES

Universal Transverse Mercator - UTM Zone 52, 300669 m E, 4196075 m N

Military Grid Reference System - MGRS 52S CG 00669 96075

Geographic 37°53’42.3”N 126°43.990’ E


3

http://www.planar.com/Products/flatpanel_monitors/stereoscopic/stereoscopic.cfm�

Geographic Atomic Elements

Scale

Raster

A grid of squares Raster representations divide the world into arrays of cells and assign attributes to the cells

Vector

Points, Lines, Polygons

Discrete Phenomena is best represented as Vector Data

Continuous Phenomena is best represented as Raster Data

Data model

Attributes (what)

Time (when)

Measurement Levels Nominal: Showing difference only - Example (Apples, Oranges, Pears) Ordinal: Showing order - Example (Large, Medium, Small) Interval: Zero does not mean lack of something - Example (temperature -20 F, 0 F, 40 F) Ratio: Full range of mathematical operations - Example (population 0, 100, 1500)

Users Mental Model

Traditional Paper Map

All geographic data are built up from primitives, or facts. A geographic data explicitly or implicitly link these three atomic elements; Place, Attributes, and Time.

Place (where)

See Data Models

Model

Continuous

Discrete Vector The world is empty except

for the well defined objects. Discrete objects can be counted. Typical

Dimensionality

1-D line

2-D polygon

3-D volume

4-D space-time

0-D point

Generalizations Simplification

Collapse Amalgamation

Refinement Enhancement

Smoothing Aggregation

Merge Exaggeration Displacement

See Datums,

Projections, and Coordinate

Systems

Representative Fraction (RF)

Ratio of map distance to distance on the earth. Note: Scale of digital databases is a vague concept.

Small Scale Small amount of detail, Large area

Large Scale Large amount of detail, Small area

Representing Geography



Property of the U.S. Government Partially Derived from ESRI ArcMap Documentation

4

Geodatabase Feature Datasets

Feature Classes, subtypes, attribute rules (Point, Line, Polygon)

Spatial Reference

Geometric Networks

Planar Topologies

Attribute Domains

Rasters

Topology

Point

Segment: Start and endpoint and a function defining a curve between points

Point: Start and endpoint and a function defining a curve between points

Ring: Path that is closed

Path: Sequence of connected segments that cannot intersect

Vector Primitives

Topological relationships help ensure that geospatial data is correctly in the computer &

can assist in processing

Feature Class

Geodatabases are relational databases containing geographic information • Geodatabases contain feature classes and tables. • Feature classes can be organized into feature datasets.

Benefits:

• Uniform depository of geo data • Data entry/editing more accurate because of intelligent validation • Relational Database standards and capabilities leveraged

• Personnel Geodatabase: – Based on a Microsoft Access Access Database – Limitation of 250,000 object and 2Gb total size

Shape File

Main file (*.shp)

Index file (*.shx)

dBase file (*.dbf)

• Non-Topologic (Spaghetti) • Shape file is really a grouping of files • Each shape file can only store 1 kind of geometry i.e.

point, line, or polygon • Does not store topology

Advantages: Draws Quickly Simple

Disadvantages: No topology to identify errors

•Other files: •Metadata <name>.shp.xml •Projection <name>.prj •Even more …

Coverage • Topologic (GeoRelational Data Model) • Using arcs (lines, or edges) as the basic unit • Avoids double representation of internal boundaries • Keeps track of topology:

– Which nodes are connected by which arcs – Which polygons are separated by which arcs

Advantages:

Easy to edit & maintain Disadvantages:

Computational Intensive

NODE LIST

ARC LIST

POLY LIST

• A collection of geographic features with the same –Geometry type (such as point, line, or polygon) –Attributes Fields –Spatial reference (datum/proj/coordinate system)

• Can be inside or our outside or outside a feature dataset

• A set of governing rules applied to feature classes that explicitly define the spatial relationships that must exist between feature data.

• Topology Rule: An instruction to the geodatabase defining the permissible relationships of features within a given feature class or between features in two different feature classes.

Topology Rule Examples

ArcGIS Data Types

names or other textual qualitiesvariesup to 64,000 charactersText

date and/or time8mm/dd/yyyyhh:mm:ssA/PM

Date

images or other multimediavariesvariesBLOB

numeric values with fractional values within specific range8approximately

-2.2E308 to 1.8E308

Double precision floating point number (Double)


-3.4E38 to 1.2E38

Single precision floating point number (Float)

numeric values without fractional values within specific range4-2,147,483,648 to

2,147,483,647Long integer

numeric values without fractional values within specific range; coded values2-32,768 to 32,767Short integer

ApplicationsSize (Bytes)

Specific range, length, or formatName



Date



-2.2E308 to 1.8E308



-3.4E38 to 1.2E38







NominalNominal

OrdinalOrdinal

IntervalIntervalRatio

CategoryCategory

NumericNumeric

NominalNominal

OrdinalOrdinal


CategoryCategory

NumericNumeric

Subtypes: A subset of features in a feature class that share the same attributes.

For example, the streets in a streets feature class could be categorized into three subtypes: local streets, collector streets, and arterial streets.

• Creating subtypes can be more efficient than creating many feature classes or tables in a geodatabase.

For example, a geodatabase with a dozen feature classes that have subtypes will perform better than a geodatabase with a hundred feature classes.

• Subtypes also make editing data faster and more accurate because default attribute values and domains can be set up. • Enterprise (Multi-user) Geodatabase (ArcSDE):

– Installed on RDBMS (Oracle, SQL Server, DB2, Informix)

Array of CellsArray of Cells

0 1 2 3 4 5

123

0

row

4

colu

mn

Coordinates = 4,3

IntegersIntegers

1

2

3

4No data

NominalNominalOrdinalOrdinal

CategoryCategory

EXAMPLES •Derived values such as land use, objects

Real NumbersReal Numbers

0 - 10

10.1 - 20

20.1 - 30

30.1 - 40

No data

IntervalInterval Ratio

NumericNumeric

EXAMPLES •Light reflectance

•Pictures•Scanned maps

• Energy (Satellite Images)• Z values (elevation, slope, etc.)

Resolution 100m 4 cells

Double the resolution, you quadruple the storage size

50m 16 cells

25m 64 cells

Raster Rasters do not store geo-coordinates information for all cells, only: (1) Raster Resolution (2) x, y coordinate of upper left cell

Possibility exists to join table

to other tables

Value Attribute Table (VAT): does not store data for every cell, just for each value

value count type 1 6 Woods 2 7 Road 3 4 River 4 2 Building 5 0 Clear

• Raster: 2-D array of uniform cells • Covers space, no gaps • Topology is inherent in grid structure

Relating tables with JOIN Attribute Data

Relational Database Management System

Disk

RDBMS

Applications

OS (Operating System)

Relational Database

Disk


RDB (example: MS Access)

Applications

RDBMS: structured computer information storage and retrieval system where the basic unit is a Table with Rows and Columns. Data is defined, accessed and

modified with SQL statements.

Join Fields

Rows =

Record

Column = Field

Computer File Structures • Simple Lists • Ordered Sequential Lists • Index Files

Relational Database: A series of related simple tables

Advantage: •Flexible •Simple structure

Disadvantage: •Key fields must be available

Attribute Domains

• Types of attribute domains

• A mechanism for enforcing data integrity.

• Attribute domains define what values are allowed in a field in a feature class or nonspatial attribute table.

If the features have been grouped into subtypes, different attribute domains can be assigned to each of the subtypes.

–Range: Defines the range of permissible values for a numeric attribute. For example, (1 to 32 inches). –Coded Value: Defines a set of permissible values for an attribute in a geodatabase. values appear in the attribute table. Coded value domains consist of a code and its equivalent value. For example, for a road feature class, the numbers 1, 2 and 3 might correspond to three types of road surface: gravel, asphalt and concrete. Codes are stored in the geodatabase and corresponding

Coordinates = 4,2

• File Geodatabases: – Stored as folders in a file system. – Each dataset is held as a file that can scale up to 1 TB in size

• Triangular: points form triangles

• Irregular: points space irregular

• Network: triangles have information about neighbors

face

node

(x,y,z)

(x,y,z)

(x,y,z)

TIN Triangular Irregular Network

KML Keyhole Markup Language

<?xml version="1.0" encoding="UTF-8"?> <kml xmlns="http://www.opengis.net/kml/2.2"> <Placemark> <name>New York City</name> <description>New York City</description> <Point> <coordinates> -74.006393, 40.714172,0 </coordinates> </Point> </Placemark> </kml>

• XML based file format used in Google Earth and many other programs

• Coordinates always geographic in WGS 1984 Datum

Example KML File

XY Coordinate Table

• A table containing AT LEAST two fields, one for the x-coordinate and one for the y-coordinate

• Table can be an EXCEL File, DBF file, deliminated text file, or many others.

XCoord YCoord City

-74.00639 40.714172 New York

-73.95917 41.391194 West Point

Example XY Table

Geospatial Data Models




5

ArcGIS Data Types



Date



-2.2E308 to 1.8E308



-3.4E38 to 1.2E38









Date



-2.2E308 to 1.8E308



-3.4E38 to 1.2E38







NominalNominal

OrdinalOrdinal


CategoryCategory

NumericNumeric

NominalNominal

OrdinalOrdinal


CategoryCategory

NumericNumeric

Relating tables with JOIN

Attribute Data

Relational Database Management System

Disk

RDBMS

Applications


Relational Database

Disk


RDB (example: MS Access)

Applications

RDBMS: structured computer information storage and retrieval system where the basic unit is a Table with Rows and Columns. Data is defined, accessed and

modified with SQL statements.

Join Fields

Rows =

Record

Column = Field

Computer File Structures • Simple Lists • Ordered Sequential Lists • Index Files

Relational Database: A series of related simple tables

Advantage: •Flexible •Simple structure

Disadvantage: •Key fields must be available

Database Basics

Primary Key: An attribute or set of attributes in a database that uniquely identifies each record. A primary key allows no duplicate values and cannot be null. Foreign Key: An attribute or set of attributes in one table that match the primary key attributes in another table. Foreign keys and primary keys are used to join tables in a database.

Joining Tables




6

PE_Cities.OID

PE_Cities.name PE_Cities.pop

0 Edgerton 24001 Lake Wilson 5552 Sioux Falls 3400003 Pipestone 450004 Trotsky 400

Step 1: Copy the fields in the Cities table into the join result table. Add the table name as a prefix to each field name, ex. OID = PE_Cities.OID

Step 2: Add the fields from the PE_Location table

OID X Y1 414000 52040002 405000 51960003 408000 52040004 410000 52020000 412000 5101000

PE_Location

Hey… How do I join these tables?

Step 3: For the first record, get the value in the OID field (OID = 0) and find a match in the PE_Location’s OID field

PE_Cities.OID

PE_Cities.name PE_Cities.pop PE_Location.OID PE_Location.X PE_Location.Y

0 Edgerton 24001 Lake Wilson 5552 Sioux Falls 3400003 Pipestone 450004 Trotsky 400

PE_Cities.OID


0 Edgerton 2400 0 412000 5101001 Lake Wilson 5552 Sioux Falls 3400003 Pipestone 450004 Trotsky 400

Step 4: Add the field values for the matched feature

EV398

PE_Cities.OID

PE_Cities.name PE_Cities.pop PE_Location.OID PE_Location.X PE_Location.Y PE_Festivals.OID

PE_Festivals.festival PE_Festivals.city

PE_Festivals.dates

0 Edgerton 2400 0 412000 510100 0 Dutch Festival Edgerton July 13-151 Lake Wilson 555 1 414000 52040002 Sioux Falls 340000 2 405000 51960003 Pipestone 45000 3 408000 52040004 Trotsky 400 4 410000 5202000

Step 5: Complete Step 3 & 5 for remaining features.

Step 6: Add the fields from the PE_Festivals table

OID festival city dates0 Dutch Festival Edgerton July 13-151 Song of Hiaw atha Pageant Pipestone July 21-232 Artfalls Fine Arts Festival SiouxFalls June 263 Perch Festival Lake Wilson 2 Jun

PE_Festivals

PE_Cities.OID



PE_Festivals.dates

0 Edgerton 2400 0 412000 5101001 Lake Wilson 555 1 414000 52040002 Sioux Falls 340000 2 405000 51960003 Pipestone 45000 3 408000 52040004 Trotsky 400 4 410000 5202000

PE_Cities.OID


0 Edgerton 2400 0 412000 5101001 Lake Wilson 555 1 414000 52040002 Sioux Falls 340000 2 405000 51960003 Pipestone 45000 3 408000 52040004 Trotsky 400 4 410000 5202000

Step 7: For the first record, get the value in the name field (name = “Edgerton”) and find a match in the PE_Festival’s city field

Step 8: Add the field values for the matched feature

PE_Cities.OID



PE_Festivals.dates

0 Edgerton 2400 0 412000 510100 0 Dutch Festival Edgerton July 13-151 Lake Wilson 555 1 414000 5204000 3 Perch Festival Lake Wilson 2-Jun2 Sioux Falls 340000 2 405000 5196000 null null null null3 Pipestone 45000 3 408000 5204000 1 Song of Hiaw atha Pageant Pipestone July 21-234 Trotsky 400 4 410000 5202000 null null null null

Step 9: Complete Step 7 & 8 for remaining features

Why is this null? Note that Sioux Falls in the Festival table is missing the space “SiouxFalls” and does not match “Sioux Falls” in the Cities table. Therefore a join is not made on this record.

Why is this null? “Trotsky” does not exist in the festivals table and therefore these is nothing to join.

PE_Cities.OID



PE_Festivals.dates

0 Edgerton 2400 0 412000 510100 0 Dutch Festival Edgerton July 13-151 Lake Wilson 555 1 414000 5204000 3 Perch Festival Lake Wilson 2-Jun2 Sioux Falls 340000 2 405000 5196000 null null null null3 Pipestone 45000 3 408000 5204000 1 Song of Hiaw atha Pageant Pipestone July 21-234 Trotsky 400 4 410000 5202000 null null null null

Now are you happy!

Join the PE_location table to the PE_cities table based on their common field of OID, and also joining the PE_ festival table’s city field to the PE_cities table’s name field. Use the ArcMap Joining conventions

Joining Example




7

Raster Basics

Types of Rasters

Value Attribute Table (VAT)

Storing a Raster in a Simple Text File

Raster Data Models

Real Numbers

0 - 10

10.1 - 20

20.1 - 30

30.1 - 40

No data

Interval Ratio

Numeric

EXAMPLES •Light reflectance

•Pictures •Scanned maps

• Energy (Satellite Images) • Z values (elevation, slope, etc.)

Integers

1

2

3

4

No data

Nominal Ordinal

Category

EXAMPLES •Derived values such as land use, road, river, etc.

Cell Size: The dimensions on the ground of a single cell in a raster, measured in map units. Cell size is often used synonymously with pixel size.

Spatial Resolution

100m 4 cells

Double the resolution,

you quadruple the storage size

Cell Array of Cells

• 2-dimensional array of uniform cells • Covers space, no gaps • Topology is inherent in grid structure

0 1 2 3 4 5

1

2

3

0

row

4

colu

mn

Coordinates = 4,2

height

width =

50m 16 cells

25m 64 cells

12.5m 256 cells

Raster: A spatial data model that defines space as an array of equally sized cells arranged in rows and columns, and composed of single or multiple bands. Each cell contains an attribute value and location coordinates. Unlike a vector structure, which stores coordinates explicitly, raster coordinates are contained in the ordering of the matrix. Groups of cells that share the same value represent the same type of geographic feature.

Note that the attribute table does not store data for every cell, just for each value

value count type 1 6 Woods 2 7 Road 3 4 River 4 2 Building 5 0 Clear

3 3

3 3 4

4

2 2

2 2

2

2

2

1

1 1

1 1

1

ncols 5 nrows 5 xllcorner 500000 yllcorner 4500000 cellsize 100 nodata_value -32768 1 3 3 2 2 4 2 2 3 3 2 1 1 4 -32768 2 1 1 -32768 -32768 2 1 -32768 -32768 -32768

File header

Raster.txt

Dimensionality Issues 0-D point = 1 cell

– Issues: 1 cell has an area

1-D Line = Sequence of equal values cells

– Issues: diagonals & distance measures

2-D Line = Group of equal values cells

– Issues: area & perimeter measures

Value Attribute Table (VAT)

Real Number Rasters CANNOT have a Values Attribute Table (VAT). Too many potential values.

Raster File Types

• ESRI Grid (no file extension) • Erdas Imagine File (.img) • TIFF (.tif,tiff)

• GeoPDF (.pdf) • JPEG (.jpg) • JPEG2000 (.jpg) • MrSid (.sid) • DoD Raster Data – Raster Product Format (RPF): CIB,

CADRG • Many more




8

Vector Data Models Point





Vector Primitives

Shape File

Main file (*.shp)

Index file (*.shx)

dBase file (*.dbf)








Advantages:



NODE LIST

ARC LIST

POLY LIST

XY Coordinate Table • A table containing AT LEAST two fields, one for the

x-coordinate and one for the y-coordinate • Table can be an EXCEL File, DBF file, deliminated text file, or

many others.

XCoord YCoord City

-74.00639 40.714172 New York

-73.95917 41.391194 West Point

Example XY Table

KML – Leyhole Markup Language • XML based file

format used in Google Earth and many other programs

• Coordinates always geographic in WGS 1984 Datum

<?xml version="1.0" encoding="UTF-8"?> <kml xmlns="http://www.opengis.net/kml/2.2"> <Placemark> <name>New York City</name> <description>New York City</description> <Point> <coordinates> -74.006393, 40.714172,0 </coordinates> </Point> </Placemark> </kml>

Example KML File




9

Geodatabase Feature Datasets

Feature Classes, subtypes, attribute rules (Point, Line, Polygon)

Spatial Reference

Geometric Networks

Planar Topologies

Attribute Domains

Rasters

Topology Topological relationships help ensure that

geospatial data is correctly in the computer & can assist in processing

Feature Class

Geodatabases are relational databases containing geographic information • Geodatabases contain feature classes and tables. • Feature classes can be organized into feature datasets.

Benefits:

• Uniform depository of geo data • Data entry/editing more accurate because of intelligent validation • Relational Database standards and capabilities leveraged

• Personnel Geodatabase: – Based on a Microsoft Access Access Database – Limitation of 250,000 object and 2Gb total size

Shape File

Main file (*.shp)

Index file (*.shx)

dBase file (*.dbf)








Advantages:



NODE LIST

ARC LIST

POLY LIST

• A collection of geographic features with the same –Geometry type (such as point, line, or polygon) –Attributes Fields –Spatial reference (datum/proj/coordinate system)

• Can be inside or our outside or outside a feature dataset

• A set of governing rules applied to feature classes that explicitly define the spatial relationships that must exist between feature data.

• Topology Rule: An instruction to the geodatabase defining the permissible relationships of features within a given feature class or between features in two different feature classes.

Topology Rule Examples

Subtypes: A subset of features in a feature class that share the same attributes.

For example, the streets in a streets feature class could be categorized into three subtypes: local streets, collector streets, and arterial streets.

• Creating subtypes can be more efficient than creating many feature classes or tables in a geodatabase.

For example, a geodatabase with a dozen feature classes that have subtypes will perform better than a geodatabase with a hundred feature classes.

• Subtypes also make editing data faster and more accurate because default attribute values and domains can be set up.

• Enterprise (Multi-user) Geodatabase (ArcSDE): – Installed on RDBMS (Oracle, SQL Server, DB2, Informix)

Attribute Domains

• Types of attribute domains

• A mechanism for enforcing data integrity.

• Attribute domains define what values are allowed in a field in a feature class or nonspatial attribute table.

If the features have been grouped into subtypes, different attribute domains can be assigned to each of the subtypes.

–Range: Defines the range of permissible values for a numeric attribute. For example, (1 to 32 inches). –Coded Value: Defines a set of permissible values for an attribute in a geodatabase. values appear in the attribute table. Coded value domains consist of a code and its equivalent value. For example, for a road feature class, the numbers 1, 2 and 3 might correspond to three types of road surface: gravel, asphalt and concrete. Codes are stored in the geodatabase and corresponding

• File Geodatabases: – Stored as folders in a file system. – Each dataset is held as a file that can scale up to 1 TB in size

The Geodatabase Point





Vector Primitives




10

Datums (A set of parameters linking an ellipsoid to the Earth that help define a coordinate system)

GRS 80 Clarke 1866 WGS 84

Horizontal Datums (base reference for a coordinate system)

Vertical Datums (Height)

Local Datums Global Datums

Datum Components 1.The parameters defining the shape of an Ellipsoid 2.The location of an initial point of origin 3.The orientation of the ellipsoid

NAD 83 Used by newer USGS maps

NAD 27 Used by older USGS maps

WGS 84 Used by GPS & most DoD maps

Sphere Simplest

representation of the earth. OK for

scales greater than 1:5,000,000

Ellipsoid Slightly flattened

sphere. Revolution of an ellipse about

one of its axes.

Geoid Surface of equal

gravity. If the earth was completely

covered with oceans this is the surface that

would result

(SOE) Surface of the Earth

The actual shape of the earth. Too complicated

to model mathematically

others

Relationship of models

SOE

(H) Height above Geoid- orthometricMSL (Mean Sea Level)

(h) Height above EllipsoidHAE

(N) Geoid-EllipsoidSeparation

Map Elevation uses Height above GeoidGPS Elevation uses Height above Ellipsoid

ReferenceEllipsoid

Geoid Sphere

Others

Models the surface of the earth in the region of interest. Origin is at the surface of the earth

SOE

originEllipsoid

SOE

originEllipsoid Origin is at the center of the

earth and provides acceptable approximation of the earth’s shape over the entire earth. SOEorigin

Ellipsoid

SOEorigin

Ellipsoid

Mod

els

of th

e Ea

rth’

s Sh

ape

Un-Projected Coordinate Systems

Universal Transverse Mercator (UTM)

Military Grid Reporting System (MGRS)

Datums




11

Datums (A set of parameters linking an ellipsoid to the Earth that help define a coordinate system)

Un-Projected Coordinate Systems A set of parameters linking an ellipsoid to the Earth that helps define a coordinate system.

Geographic (Latitude & Longitude)

Universal Transverse Mercator (UTM)

Geocentric

Z

Prime Meridian

X0º Long

Y90°E

(X,Y,Z)Z

Prime Meridian

X0º Long

Y90°E

(X,Y,Z)

-3014213.2 m, 4038687.9 m, 3895223.3 m

3-D Cartesian right hand Coordinate System with an origin at the center of the earth and the X axis oriented at the Prime Meridian and the Z at the North Pole. GPS & Other Satellites use this.

Latitude & Longitude are defined by the Prime Meridian and the Equatorial reference planes

Geographic latitude Vertical angle from the equator to the normal of ellipsoid, positive in northern hemisphere and negative in the southern Geographic longitude Horizontal angle from the prime meridian positive in the eastern hemisphere and negative in the western Geodetic height Distance normal from the reference ellipsoid

37° 53.423’ N, 126° 43.990’ E

Others; State Plane, etc.

Transverse Mercator Projection

Zone 52, 300669 m E, 4196075 m N

Military Grid Reporting System (MGRS)

Universal Polar Stereographic and

Others

Angular Measures Dividing a circle: A circle can be divided into 360 degrees. Degrees are unitless.

The distance of a degree varies Note that the two 45 degree arcs may

have different lengths!

0°360°

45°

45°

Fractions of a degree: A degree can be divided into 60

minutes (“) A minute can be divided into 60

seconds (‘)

Types: • Decimal Degrees DD • Decimal Minutes DM • Decimal Seconds DMS

1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960

Military Grid Reference System (MGRS)

• Subdivides the UTM into 6°X 8° zones numbered 1-60 west to east and c-x from south to north

• Each 6°X 8° area is divided into 100,000m squares

• A level of detail is achieved by moving so many meters east and north within the zone (i.e. 8 digit coordinate)

Grid Zone Designator

100,000msquare identifier

NumericalCoordinates

MGRS: 18T WL 8701682707

Example:West Point Base Station:Zone18 587016 easting 4582707 northing

Reporting System

0°

500,000m

NorthernHemisphere

False origin

Centralmeridian

0°

500,000m

NorthernHemisphere

False origin

Centralmeridian

52S CG 00669 96075

Based on the UTM Coordinate System

• Uses 60 six degree wide zones each with the Transverse Mercator projection. • False origin at 500,000m east of central meridian for both Northern and Southern Hemispheres •Coordinates in meters northing and meters easting

• A coordinate consists of: –Grid Zone Designator –100,000 identifier –4 to 10 numeric coordinate

Projections and Coordinate Systems




Lambert Conformal Conical Projection

Polar Sterographic Projection

12

Header

ID X Y

1 45.0000 30.0000

2 44.0000 31.0000

3 43.0000 32.0000

Header: GCS_WGS_1984

ID X Y

1 45.0000 30.0000

2 44.0000 31.0000

3 43.0000 32.0000

Only header updated

Define Projection: Records the coordinate system information for the

specified input dataset or feature class including any associated projection parameters, datum and

spheroid. It creates or modifies the feature class's projection parameters.

Definitions from ArcGIS Help File

Points_GCS.shp Points_GCS.shp

Undefined Coordinate System

Header: UTM_18_WGS_1984

ID X Y

1 500000 3318785

2 404532 3430031

3 311072 3542183

Project: Changes the coordinate system of

your Input Dataset or Feature Class to a new Output Dataset or Feature

Class with the newly defined coordinate system including the datum

and spheroid

New File created with coordinates in different

Coordinate system. In this case converted from Geographic to UTM

Points_UTM.shp

Defining Projections vs. Projecting

13

Data Types Analog Digital Geo-Aware

GPS Data Collection

DEM Creation

Orthorectification


Geocode: The process of finding the location of a street address on a map. The location can be an x,y coordinate or a feature such as a street segment, postal delivery location, or building. In GIS, geocoding requires a reference dataset that contains address attributes for the geographic features in the area of interest.

Georeference: Assigning coordinates from a known reference system, such as latitude/longitude, UTM, or State Plane, to the page coordinates of a raster (image) or a planar map.

Scan Raw

Scanned Map

Georeferencing

Scan

Georeference

Tablet Digitize

Stereo Imagery

Raw GPS Data

DiffCor GPS Data

Geocoding

ADDRESS 550a Winans Place 330 Ohio Street 5 90th Street 2045 W. Elm St.

Onscreen Digitize

Orthorectification: Elimination of image displacement resulting from topographic relief and other factors


DEM Creation: Advanced topic, see EV379 Photogrammetry

X, Y 300000, 4598300 300000, 4598300 300000, 4598300 300000, 4598300

Geolocate

GeoLocate: The process of creating geographic features from tabular data by matching the tabular data to a spatial location. An example of geolocation is creating point features from a table of x,y coordinates.

COGO

x,y,z Data

Geolocate

Manual Text Entry

X, Y 300000, 4598300 300000, 4598300 300000, 4598300 300000, 4598300

Bearing, Dist 100o, 300 m 320o, 450 m 030o, 329 m 158o, 247 m


X, Y 300000, 4598300 300000, 4598300 300000, 4598300 300000, 4598300

Manual Text Entry

x,y,z Data

COGO (Coordinate geometry): Using bearings and distances to locate points on the ground.

x,y,z Data

Paper Map

Field Geo Data Collection

Remote Sensing

Manual Text Entry

Tablet Digitizing: The process of converting features on a paper map into digital format. To digitize a map, you use a digitizing tablet (also know as a digitizer) connected to your computer to trace over the features that interest you. The x,y coordinates of these features are automatically recorded and stored as spatial data.

Onscreen Digitizing: Converting features on a Digital into a vector format .

Information about the content, quality, condition, and other characteristics of data. Metadata for geographical data may document its subject matter; how, when, where, and by whom the data was collected; accuracy of the data; availability and distribution information; its projection, scale, resolution, and accuracy; and its reliability with regard to some standard. Metadata consists of properties and documentation. Properties are derived from the data source (for example, the coordinate system and projection of the data), while documentation is entered by a person (for example, keywords used to describe the data).

Metadata

Air Photo

DEM (elevation)

DEM (elevation)

Metadata Elements

Identification Information Data Quality Information Spatial Data Organization Info Spatial Reference Information Entity and Attribute Info Distribution Information Metadata Reference Information

Georef Image

Digital Image

Raw Scanned

Image Georef Image

Ortho- Imagery

Ortho- Imagery

GeoLocate: The process of creating geographic features from tabular data by matching the tabular data to a spatial location.

Georef Image (Map)

Georef Image (Map)

10996

10998

10899

10786

STREETS ZIP CODES

Analysis

Geodatabase

Feature DatasetsFeature Datasets

Feature Classes, subtypes, attribute rules(Point, Line, Polygon)

Feature Classes, subtypes, attribute rules(Point, Line, Polygon)

Spatial Reference

Geometric NetworksGeometric Networks

Planar TopologiesPlanar Topologies

Attribute DomainsAttribute Domains

TEXT REPORT

Digital Files

Map Maker

Satellite

Aircraft

Survey Equipment


Transformations

Reclassification

Raster 2 Vector

Vector 2 Raster

Reproject

Format Conversion




Geo Data Sources

14

Geospatial Data Quality

Uncertainty

Accuracy Precision

lack of:

lack of:

Confidence

Data Quality Basic Concepts

Blunder

Error

Systematic Random

Lineage

Dimensions of Data Quality

Positional Accuracy

Attribute Accuracy

Logical Consistency

Completeness

Temporal Accuracy

A measured, observed, calculated, or interpreted value that differs from the true value or the value that would be obtained by a perfect observer using perfect equipment and perfect methods under perfect conditions.

The degree to which the measured value of some quantity is estimated to vary from the true value.

The closeness of a repeated set of observations of the same quantity to one

another. Precision is a measure of the control over random error. For example, assessment of the quality of a surveyor's work is based in part on the precision of

their measured values.

The degree to which a measured value conforms to true or accepted values.

Accuracy is a measure of correctness. It is distinguished from precision, which

measures exactness.

Reliability Accuracy

Fitness for Use/task

Currency

Relevancy

Completeness

Precision

Data Model

DATUM/PROJECTION

SCALE

CLASSIFICATIONS

CARTOGRAPHIC PROPERTIES

TASK Fitness for use DATA

Database World “truth” Database

Data Collection Errors

Data Transfer Errors

Data Use Errors

User

In GIS data processing, the persistence of an error into new datasets calculated or created using datasets that originally contained errors. The study of error propagation is concerned with the effects of combined and accumulated errors throughout a series of data processing operations.

Error Propagation

Types of

Operational (User/Process

Inherent

Sources of

Models/ Algorithms




15

Raster Data Collection

Affine Transformation (1st order) A geometric transformation that scales, rotates, skews, and/or translates images or coordinates between any two Euclidean spaces. It is commonly used in GIS to transform maps between coordinate systems. In an affine transformation, parallel lines remain parallel, the midpoint of a line segment remains a midpoint, and all points on a straight line remain on a straight line.

x’ = Ax + By + C y’ = Dx + Ey + F

Least Squares Adjustment to

Solve for Parameters A,B,C,D,E,F

Create Links

Adjust ALL

Coordinates

Check RMS

Scan Raw

Scanned Map

Georeferencing Paper Map

Georef Image (Map)

Map Maker

Georeference: Assigning coordinates from a known reference system, such as latitude/longitude, UTM, or State Plane, to the page coordinates of a raster (image) or a planar map.


Type of Scanners • Flat Bed • Sheet Feed • Drum

• Scanner settings

• Spatial Resolution (dpi) • Number of Colors • File Format

dots per inch (dpi)

dpi = 1 dpi = 10

Dots = Raster cells

Lossy Compression: Data compression that provides high compression ratios (for example 10:1 to 100:1), but does not retain all the information in the data. In GIS, lossy compression is used to compress raster datasets that will be used as background images, but is not suitable for raster datasets used for analysis or deriving other data products.

JPEG: Acronym for Joint Photographic Experts Group. A lossy image compression format commonly used on the Internet. JPEG is well-suited for photographs or images that have graduated colors.

Lossless Compression: Data compression that has the ability to store data without changing any of the values, but is only able to compress the data at a low ratio (typically 2:1 or 3:1). In GIS, lossless compression is often used to compress raster data when the pixel values of the raster will be used for analysis or deriving other data products.

GIF: Acronym for Graphic Interchange Format. A low-resolution file format for image files, commonly used on the Internet. It is well-suited for images with sharp edges and reduced numbers of colors.

TIFF: Acronym for Tagged Image File Format A lossless file format that retains original pixel values.

RESAMPLING The process of interpolating new cell values when transforming rasters to a new coordinate space or cell size. Methods:

• Nearest Neighbor • Bilinear interpolation • Cubic convolution

e Control Point: Where the point

should be

Current Location: The wrong location of the point

Transformed Location: The coordinate that results from the

GLOBAL transformation equation for this location

Residual Error (e): The distance between your

Control Point (Truth) and the Transformed

Location

Type Nearest Neighbor

Bi-Linear/Cubic

Scanned Map

3 Band (RGB) Not Best Best

1 Band (Grey Scale) Not Best Best

Imagery

Multiband - Energy Best Not Best

3 Band Visible Not Best Best

1 Band Visible Not Best Best

Discrete Classified Raster

Best BAD

Elevation Depends Depends




16

Com

plex

ity

Translation Rotation Scale Skew Diff Scale

1st order Polynomial (AFFINE)

X X X

2nd order Polynomial X X X X X

3rd order Polynomial X X X X X

Rubber Sheet X X X X X

Project

X

X

Loca

l G

loba

l

Minimum Number of

Control Points.

3 / 4

6 / 7

10 / 11

3

Dpi: 600dpi File format: TIF File Size: 83.454 Mb High quality, but very large file size

Scanning Resolution and Formatting

Notice file compression regions and resulting discontinuities in Dr. Dalton’s Eye

Lossless Compression: Data compression that has the ability to store data without changing any of the values, but is only able to compress the data at a low ratio (typically 2:1 or 3:1). In GIS, lossless compression is often used to compress raster data when the pixel values of the raster will be used for analysis or deriving other data products.

Lossy Compression: Data compression that provides high compression ratios (for example 10:1 to 100:1), but does not retain all the information in the data. In GIS, lossy compression is used to compress raster datasets that will be used as background images, but is not suitable for raster datasets used for analysis or deriving other data products.

JPEG: Acronym for Joint Photographic Experts Group. A lossy image compression format commonly used on the Internet. JPEG is well-suited for photographs or images that have graduated colors. GIF: Acronym for Graphic Interchange Format. A

low-resolution file format for image files, commonly used on the Internet. It is well-suited for images with sharp edges and reduced numbers of colors.

TIFF: Acronym for Tagged Image File Format A lossless file format that retains original pixel values.

DPI: Acronym for dots per inch. A measure of the resolution of scanners, printers, and graphic displays. The more dots per inch, the more detail can be displayed in an image.

Dpi: 600dpi File format: JPG File Size: 3.346 Mb

Dpi: 300dpi File format: TIF File Size: 20.863 Mb

Dpi: 300dpi File format: JPG File Size: 0.410 Mb

Notice increased pixilation Notice both increased pixilation and discontinuities

A 8 x 10 glossy photo is scanned at 600 dpi and 300 dpi, then both saved as TIF and JPG respectively

Scanning: The process of capturing data from hard-copy maps or images in digital format using a device called a scanner.

Scanner: A device that captures a print or hard-copy image, such as a text document or map, and records the information in digital format.

Flatbed Scanner: A type of scanner with a flat, clear surface on which a map or image remains stationary while a sensor beam moves across it and captures a digital image.

Roller Feed Scanner: A type of scanner that moves a document through a roller assembly over camera sensors that capture a digital image.

Drum Scanner: A type of scanner in which a hard-copy image or map is attached to a cylinder that spins while a sensor captures a digital image from the surface of the page.




17

GPS Precision and Accuracy

PDOP

Vector Data Collection - GPS

Geometry

Multi path Error




GPS Data Collection


Raw GPS Data

DiffCor GPS Data

x,y,z Data


Measure X,Y,Z at a point

COGO

x,y,z Data

Geolocate

Manual Text Entry

X, Y

300000, 4598300 300000, 4598300 300000, 4598300 300000, 4598300



X, Y

300000, 4598300 300000, 4598300 300000, 4598300 300000, 4598300

Manual Text Entry

Field Geo Data Collection

GeoLocate: The process of creating geographic features from tabular data by matching the tabular data to a spatial location. Survey Equipment

COGO (Coordinate geometry): Using bearings and distances to locate points on the ground.

From a known point (x,y,z) Measure ANGLES & DISTANCES

GPS Systems

How GPS Works • A constellation of 24 satellites (SVs) in medium earth orbit

Therefore: Each GPS Satellite, over time, is in a different location over the earth

• Location is calculated by the passive receiver through trilateration of weak pseudo-code radio waves from four satellites.

• Corrections made for ionospheric and tropospheric delays (and some other error sources, depending on unit).

• Measures distance from satellites to GPS receiver using [travel time] x [speed of light]. Therefore: Orientation of satellites is important for accuracy and

the orientation is always changing


• The accuracy of a 3D GPS position based on (1) # satellites and (2) the geometry of satellite positions.

• The lower the #, the more accurate the data!

• Any position with a PDOP over 7 or 8 is probably not worth collecting

• If a base station GPS receiver is operating at a

known location in the same area (200 km radius), it can log a record of the magnitude/direction of inaccuracies in GPS readings received per time epoch.

• By synchronizing base station readings to rover readings, GPS data can be corrected by applying adjustments equal to, but in the opposite direction of the observed inaccuracies

• GPS locations contain inherent inaccuracies due to: – clock timing errors – ephemeris errors – atmospheric conditions

18

http://en.wikipedia.org/wiki/File:Galileo_logo.svg�

Using Existing Features to Help Digitize

Vector Data Collection - Digitizing

Basic Digitizing Process 1. Start Editing 2. Ensure proper layer is target i.e. ID What feature class or shapefile are you adding data to 3. Set Snapping Environment 4. For each feature a. Digitize geometry i. Point Digitizing or ii. Stream Digitizing

b. Update attribute fields 5. Save Edits

Digitizing: The process of converting

the geographic features on an analog map into digital format

using a digitizing tablet, or digitizer, which is

connected to a computer.

Snapping An automatic editing operation in which points or features within a specified distance (tolerance) of other points or features are moved to match or coincide exactly with each others' coordinates.

edge

end

end

edge edge

Parts of a Feature

Mouse Click

Generating Point Features with Heads-up Digitizing

Trace Existing Features

Auto Complete Polygon

1.Zoom to feature 2.Start Editing 3.Get Coordinates by either:

a. Clicking where feature is, based on georeferenced map or image

b. Typing in Coordinates 4.Update Attributes 5.Save Edits

Generating Polyline Features with Heads-up Digitizing


a. Clicking on each node of the linear feature

b. Tracing existing features and using existing feature’s coordinates

c. Stream digitizing 4.Update Attributes 5.Save Edits

Generating Polygon Features with Heads-up Digitizing

existing polygons

New polygon to digitize

1

2 3

4 5

6

New polygon to digitize

existing polygons

Shared Vertices perfectly aligned without

explicitly being

clicked

existing polygons

a

a

New polygon after being digitized


a. Clicking on each node of the linear feature

b. Tracing existing features and using existing feature’s coordinates

c. Stream digitizing 4.Update Attributes 5.Save Edits

Find and Fix Errors

Start Editing

Add/Edit Features

Validate Topology

Inspect Errors

For each error

Fix error

make error

exception

Save Edits




19

ExistingFeature

(for examplea river)

Data Formats

CIB Controlled Imagery Base Black & white imagery for visualization

CADRG Compressed Arc Digitized Raster Graphics Scanned paper maps

Source

NTM National Technical

Means

Satellite

Commercial Satellites

GeoNames

Product Specific

Cartographic Digital

Files

SRTM

Other

Digitize

VMAP & UVMAP Vector Map & Urban Vector Map Attributed vector data of map features

Database

DTED Digital Terrain Elevation Data Uniformly-spaced grid of terrain elevation values

Military Geospatial Data

Orthorectification

Paper Maps

STANDARD MAP PRODUCT SCALES

GNC Global Nautical Chart 1:5M JNC Joint Nautical Chart 1:2M ONC Operational Nautical Chart 1:1M TPC Tactical Pilot Chart 1:500K JOG Joint Operational Graphic 1:250K TLM100 Topographic Line Map 1:100K TLM50 Topographic Line Map 1:50K MM Military Installation Graphic various CG City Graphics various

.

Scan

Georeferencing

Feature Foundation Data Vector Data Attributed vector data of map features

Multi-spectral Commercial Imagery Imagery from various energy wavelengths

CIB 10: 10 meter spatial resolution CIB 5: 5 meter spatial resolution CIB 1: 1 meter spatial resolution

RASTER PRODUCT FORMAT VECTOR PRODUCT FORMAT DIGITAL TERRAIN ELEVATION DATA

OTHER FORMATS HRTe 5

HRTe 4

HRTe 3

DTED 2

DTED 1

~1m ~3m ~10m ~30m ~100m Post Spacing

2.6GB 291.8MB 26MB 0.196MB 0.021MB File Size 28km x 24 km

Two Methods: (1) Photogrammterically derived bare earth elevation (2) Shuttle Radar Topographic Mission (SRTM) reflective surface elevation

• Accuracy: – Absolute Horizontal Accuracy: ± 23 meters @ 90% Confidence – Absolute Vertical Accuracy: N/A

• Orthoimage Advantages: – Correct Horizontal Terrain Positions – “Look and Feel” of an Image – Cheaper and faster than making maps




20

GeoPDF

Graphical Selection

Select Features Completely Within the Selection Box

Spatial Query Operators in ArcMap

Geospatial Queries

Boolean Logic

SQL – Structures Query Language A syntax for retrieving and manipulating data from a relational database. SQL has become an industry

standard query language in most relational database management systems.

Basic Form of SQL: SELECT:what fields to show,

ArcGIS returns all fields (*)

FROM: what table to select from. This is a layer in ArcMap WHERE: what features to select

PRIMARY • Intersect • Are within a distance of • Are within • Are completely within • Contain • Completely contain SECONDARY • Have their centroid in • Share a line segment with • Touch the boundary of • Are identical to • Are crossed by the outline of • Contain (Clementini) • Are Within (Clementini)

A and B and C A or B or C

INTERSECT UNION

Not (A or B or C)

Query: A request that selects features or records from a database. A query is often written as a statement or logical expression.

Identify: In ArcGIS, a tool that, when applied to a feature (by clicking it), opens a window showing that feature's attributes

Selection topology

Tolerance

Selection Tolerance: The buffer searched at a mouse click point when selecting features graphically.

Select Features Partially or Completely

Within Selection Box

Select Features the Selection Box

Is Completely Within

(A and B B and C) (A or C)

21

SELECTION METHODS • Create a New Selection • Add to current Selection • Remove from Current Selection • Select from Current Selection

Classification: When you perform a classification, you group similar features into classes by assigning the same value or symbol to each member of the class. • Aggregating features into classes allows you to spot patterns in the data more easily. • The definition of a class range determines which features fall into that class and affect the appearance of the map. • By altering the class breaks (the boundary between classes), you can create very different-looking maps. • Classes can be created manually, or you can use a standard classification scheme. Defined Interval: This classification scheme allows you to specify an

interval by which to equally divide a range of attribute values. Rather than specifying the number of intervals as in the equal interval classification scheme, with this scheme, you specify the interval value. ArcMap automatically determines the number of classes based on the interval. The interval specified in the example below is 0.04 (or 4 percent).

Classification and Dissolve

Dissolve: A geoprocessing command that removes boundaries between adjacent polygons that have the same value for a specified attribute. Dissolve fields Features with the same value combinations for the specified fields will be

aggregated (dissolved) into a single feature. The Dissolve fields are written to the Output Feature Class table.

Multipart features: Dissolve may result in multipart features being created. A multipart

feature is a single feature that contains noncontiguous elements and is represented in the attribute table as one record.

Summarizing attributes: As part of the Dissolve process, the aggregated features can

also include summaries of any of the attributes present in the input features.




22

Raster Reclassify Tool: The process of taking input cell values and replacing them with new output cell values. Reclassification is often used to simplify or change the interpretation of raster data by changing a single value to a new value, or grouping ranges of values into single values—for example, assigning a value of 1 to cells that have values of 1 to 50, 2 to cells that range from 51 to 100, and so on.(ESRI GIS Dictionary

Reclassify

0

16,927

10 Classes

continuous discrete

Equal Interval

10 9 8 7 6 5 4 3 2 1

Equal Interval: This classification scheme divides the range of attribute values into equal-sized subranges, allowing you to specify the number of intervals while ArcMap determines where the breaks should be. For example, if features have attribute values ranging from 0 to 300 and you have three classes, each class represents a range of 100 with class ranges of 0–100, 101–200, and 201–300. This method emphasizes the amount of an attribute value relative to other values, for example, to show that a store is part of the group of stores that made up the top one-third of all sales. It’s best applied to familiar data ranges such as percentages and temperature.

Quantile: The range of possible values is divided into unequal-sized intervals so that the number of values is the same in each class. Classes at the extremes and middle have the same number of values. Because the intervals are generally wider at the extremes, this option is useful to highlight changes in the middle values of the distribution.

Distance Euclidean Distance: The straight-line distance between two points on a plane. Euclidean distance, or distance 'as the crow flies,' can be calculated using the Pythagorean theorem.

Buffer A zone around a map feature measured in units of distance or time. A buffer is useful for proximity analysis. Alternately, a polygon enclosing a point, line, or polygon at a specified distance. Computer’s Steps

Buffer each feature • Constant value • or field based

Optionally Dissolve Interior boundaries • None – Polygon for each feature • All - Dissolves all buffers into one feature • List – Field based, i.e. all features with equal

value in a field their buffers will be dissolved

Dissolve Type = NONE

10 Buffer features

Dissolve Type = ALL

Dissolve Type = List On Field “Gap Width Range”

1 Buffer features

2 Buffer features




23

Other Distances • Manhattan Distance

Rectilinear distance between two points along a regular grid, such as driving/walking in the city of Manhattan

• Geodesic Distance The shortest line between any two points on the earth's surface on a spheroid (ellipsoid). One use for a geodesic is when you want to find the shortest distance between two cities for an airplane's flight path.

• Loxodrome Distance A loxodrome is not the shortest distance between two points but instead defines the line of constant bearing, or azimuth. Geodesic routes are often broken into a series of loxodromes, which simplifies navigation. This is also known as a rhumb line.

The Overlay Process --The Intersection Case—

Step 2: Select Features

Step 1: Cracking

Overlay Operations

Cracking: A part of the topology validation process in which vertices are created at the intersection of feature edges.

Overlay Tools

ERASE

Where does A occur but not B

UNION INTERSECT

A B

Where does A and B both occur

These are NEW shapes (features)!

Not selections of existing features

Where does A or B occur

Intersect: A geometric integration of spatial datasets that preserves features or portions of features that fall within areas common to all input datasets.

Union: A topological overlay of two or more polygon spatial datasets that

preserves the features that fall within the spatial extent of either input

dataset; that is, all features from both datasets are retained and extracted

into a new polygon dataset.

Erase: A topologic overlay that deletes features from one spatial dataset that overlap features in

another spatial dataset

Forest

Clay

Loam

ID type 1 Loam 2 Clay

ID type 1 Forest

ID Soil type 1 Loam 2 Clay 3 Clay 4 Loam

Veg type null

Forest Forest

null

Tools in

ArcGIS

Example: “Forest” INTERSECTS “Loam”

1

2

3 4

ID Soil type 1 Loam 2 Clay 3 Clay 4 Loam

Veg type null

Forest Forest

null

INTERSECT: Soil type = Loam AND Veg type = Forest

Step 3: Write to New Output File

4 ID Soil type 4 Loam

Veg type Forest

1

2

3 4




Spurious Polygon Problem

24

Richly Annotating Models

Case Study: Selection

Geospatial Process Modeling – ArcGIS Model Builder

Spatial Operation (Tool)

Tool

Input

Intermediate Output

Input

Tool

Input

Output

Select Layer By Attribute

(2)

Palustrine Emergent Wetlands

Lakes and Ponds

Select Layer By Attribute

Lakes and Ponds >

10,000sq m

Select Layer By Location

Latrines within 100 meters of a lake > 10,000 sq Latrine

Select Layer By Location

(2)

Latrines meeting both

CLASS = 'Palustrine Emergent'

SHAPE.area >10000

Latrine INTERSECT Lakes & Ponds > 10,000 sq m

100 m buffer

Latrines within 100 meters of a lake > 10,000 sq m Union or Add to current selection

Palustrine Emergent Wetlands

What is the OBJECTID of the latrines that intersect a Palustrine Emergent Wetlands or are within 100 meters of a lake greater that 10,000m2?

Basic Premise: Someone could use your model printout to make the model themselves

Example of a portion of a model sketch. Ensure each part includes (1) the operation,(2) inputs, (3) output, (4) description, and if applicable (5) criteria. Also include (6) Model Title, Name, and Date

SELECT Key Features Police Stations

bfc = 12

Select Police Stations

2 1 3

4

5

Title Name Date

6




25

Map Algebra

Raster Process Issues

Snap To Raster

Raster Analysis Reclassify

The process of taking input cell values and replacing them with new output cell values. Reclassification is often used to simplify or change the interpretation of raster data by changing a single value to a new value, or grouping ranges of

values into single values—for example, assigning a value of 1 to cells that have values of 1 to 50, 2 to cells that range

from 51 to 100, and so on. (ESRI GIS Dictionary

Reclassify

0

16,927

10 Classes

continuous discrete

Equal Interval

10 9 8 7 6 5 4 3 2 1




Cell Size

Cell Size: The dimensions on the ground of a single cell in a raster, measured in map units. Cell size is often used synonymously with pixel size.

Default settings. IF INPUT IS RASTER: Maximum of Inputs - The largest cell size of the input raster datasets. IF INPUT IS VECTOR: The width or height (whichever is shorter) of the extent of the feature dataset and divide by 250.

Extent Extent: The minimum bounding rectangle (xmin, ymin and xmax, ymax) defined by coordinate pairs of a data source. All coordinates for the data source fall within this boundary.

Default settings. Only cells overlapped in both will be in output file .

Mask: In ArcGIS, a means of identifying areas to be included in analysis. Such a mask is often referred to as an analysis mask, and may be either a raster or feature layer.

Resampling

Bilinear interpolation Nearest Neighbor Cubic convolution

Division Example

0 1

2 4

5 1

4 6

Raster A Raster B Output

1st Input raster = Raster A 2nd Input raster = Raster B Output raster = Output 0 1

.50 .67

Output = Raster A / Raster B

0 1

2 4

10 10

10 10 Raster A CONSTANT Output

1st Input raster = Raster A 2nd Input = 10 Output raster = Output

0 .1

.2 .4

Output = Raster A / 10

+ =

+ =

Conditional Statements

0 1

2 4

5 1

4 6

Raster A Raster B

Output

F F

T T

0 1

4 6 4 6

0 1

For each cell IF (Raster A >= 2) Then Raster B Else Raster A

Input conditional raster = Raster A Input true raster or constant value = Raster B Input false raster or constant value = Raster A Output raster = “output” Expression = value >= 2

Divide – Subtracts the value of the second input raster from the value of the first input raster on a cell-by-cell basis Times Minus Plus Float – Converts integer values to floating-point values on a cell-by-cell basis Int – Converts input floating-point values to integer values through truncation on a cell-by-cell basis Many Others

Map Algebra: A language that defines a syntax for combining map themes by applying mathematical operations and analytical functions to create new map themes. In a map algebra expression, the operators are a combination of mathematical, logical, or Boolean operators (+, >, AND, tan, and so on), and spatial analysis functions (slope, shortest path, spline, and so on), and the operands are spatial data and numbers

26

Terrain Surfaces

Field Data Collection

Stereo Imagery

Terrain Surface

LIDAR Stereo Radar (SRTM)

Contours Points TIN Point cloud DEM GRID

Terrain Data Models

Derived Terrain Products

Collection Methods

Slope Aspect Hill Shading

Layer Tinting Viewshed Vertical

Profile Curvature

Cartographic Relationship of models

SOE

(H) Height above Geoid- orthometricMSL (Mean Sea Level)

(h) Height above EllipsoidHAE

(N) Geoid-EllipsoidSeparation

Map Elevation uses Height above GeoidGPS Elevation uses Height above Ellipsoid

ReferenceEllipsoid

Geoid Sphere

Vertical Datums “Height from What?

Elevation: The vertical distance of a point or object above or below a reference surface or datum (generally mean sea level). Elevation generally refers to the vertical height of land

Vertical Geodetic Datum: A geodetic datum for any extensive measurement system of heights on, above, or below the earth's surface. Traditionally, a vertical geodetic datum defines zero height as the mean sea level at a particular location or set of locations; other heights are measured relative to a level surface passing through this point. Examples include the North American Vertical Datum of 1988; the Ordnance Datum Newlyn (used in Great Britain); and the Australian Height Datum

DEM: Acronym for digital elevation model. The representation of continuous elevation values over a topographic surface by a regular array of z-values, referenced to a common datum. DEMs are typically used to represent terrain relief.

TIN: A dataset containing a triangulated irregular network (TIN). The TIN dataset includes topological relationships between points and neighboring triangles.

Contour: A line on a map that connects points of equal elevation based on a vertical datum, usually sea level.

Slope: The incline, or steepness, of a surface. Slope can be measured in degrees from horizontal (0–90), or percent slope (which is the rise divided by the run, multiplied by 100). A slope of 45 degrees equals 100 percent slope. As slope angle approaches vertical (90 degrees), the percent slope approaches infinity. The slope of a TIN face is the steepest downhill slope of a plane defined by the face. The slope for a cell in a raster is the steepest slope of a plane defined by the cell and its eight surrounding neighbors.

Aspect: The compass direction that a topographic slope faces, usually measured in degrees from north. Aspect can be generated from continuous elevation surfaces. For example, the aspect recorded for a TIN face is the steepest downslope direction of the face, and the aspect of a cell in a raster is the steepest downslope direction of a plane defined by the cell and its eight surrounding neighbors.

Hillshading: The hypothetical illumination of a surface according to a specified azimuth and altitude for the sun. Hillshading creates a three-dimensional effect that provides a sense of visual relief for cartography, and a relative measure of incident light for analysis.

Viewshed: The locations visible from one or more specified points or lines. Viewshed maps are useful for such applications as finding well-exposed places for communication towers, or hidden places for parking lots.

Z-value: The value for a given surface location that represents an attribute other than position. In an elevation or terrain model, the z-value represents elevation; in other kinds of surface models, it represents the density or quantity of a particular attribute.




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Least Cost Path: The path between two locations that costs the least to traverse, where cost is a function of time, distance, or some other criteria defined by the user.

Routing Analysis What is Cost? What is cost?

Time Money Danger Visibility Others?




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Optimal Sites

Majority Filter

Replaces cells of a raster based upon the

majority value of contiguous neighboring

cells.

In simple terms, it removes single cells of

optimal value, since they are too small for a

school.

Conditional Statement

Performs a conditional if/else evaluation on each input cell of a

raster

IF Value = 9 Then Value remains 9

IF Value <> 9

Then Value is changed to NoData

Schools

Recreation Sites

Land Use

Elevation

Reclassify The process of taking input cell values

and replacing them with new output cell values. Reclassification is often used to simplify or change the interpretation of raster data by changing a single value to a new value, or grouping ranges of

values into single values—for example, assigning a value of 1 to cells that have values of 1 to 50, 2 to cells that range

from 51 to 100, and so on. (ESRI GIS Dictionary

Slope The steepness of a surface. The slope

for a cell in a raster is the steepest downhill slope of a plane defined by the

cell and its eight surrounding neighbors. Slope can be measured in

degrees from horizontal (0–90), or percent slope (which is the rise divided

by the run, multiplied by 100). (ESRI GIS Dictionary)

Euclidean distance The straight-line distance between two points, normally on a plane. Euclidean

distance, or distance "as the crow flies," can be calculated using the

Pythagorean theorem (ESRI GIS Dictionary

Case Study: Site Selection

Weighted Overlay

Suitable Areas

landuse

Con Output

Optimal Majority Filter Output filtered

Slope Slope output elevation Reclassify Reclassed slope

Euclidean Distance

Distance to Schools

schools Reclassify Reclassed distance

Euclidean Distance

Distance to Recreation rec_sites Reclassify

Reclassed distance to

Scenario The town of Stowe, Vermont, USA, has experienced a substantial increase in population. Demographic data suggests this increase has occurred due to families with children moving to the region, taking advantage of the many recreational facilities located nearby. It has been decided that a new school must be built to take the strain off the existing schools, and as a town planner you have been assigned the task of finding the potential site.

How does it work? For each cell in the raster dataset, sum the weighted values of each input raster. In this case, the GIS (landuse) * weight) +(DisttoSchools * weight) + (DistoRec Sites * weight) + (slope * weight)

The values of the derived datasets representing slope, distance to recreation sites, and distance to schools have all been reclassified to a common measurement scale (more suitable cells have higher values). The landuse dataset is still in its original form because you can weight the cell values for this dataset as part of the weighted overlay process. Values representing areas of water and wetlands will be restricted. You'll also mark slope values that are less than 4 (the least suitable because they are too steep) as restricted so these values can be excluded. If all datasets were equally important, you could simply combine them, giving each equal influence; however, you have been informed that it is preferable to locate the new school close to recreational facilities and away from other schools. You will weight all the inputs, giving each a percentage of influence. The higher the percentage, the more influence a particular input will have in the suitability model.

0

53.1038

10 Classes

continuous discrete

10 Classes

continuous discrete

Equal Interval

Equal Interval

0

16,927

10 Classes

continuous discrete

Equal Interval

12% Weighted Overlay

A technique for combining multiple rasters by applying

a common measurement scale of values to each raster, weighting each

according to its importance, and adding

them together to create an integrated analysis.

(ESRI GIS Dictionary

25%

50%

13%

• Brush/Transitional = 5 • Barren land = 10 • Built up = 3 • Agricultural = 9 • Forest = 4 • Water = Restricted • Wetland = Restricted

FOR EXAMPLE: A cell that has the following values: Land use = 3 (Forest) Distance to School = 4 Distance to Rec Sites = 8 Slope = 10

* 12% ( ) + * 25% ( ) + * 50% ( ) + * 13% ( )

= 0.36 + + + 1 0.13 4 5.49

Environmental Settings

•Working Directory

•Scratch Directory

•Extent = constant

•Cell Size = 30 meters

Restricted and No Data Values

10 9 8 7 6 5 4 3 2 1

10 9 8 7 6 5 4 3 2 1

0

13,487

10 9 8 7 6 5 4 3 2 1

10

9

5

4

3

10 8 4 3




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Documents

EV398 Geospatial Workbook - West Point - Home EV398... · EV398 Geospatial Workbook ... and the other fixed and a . Scanning: ... – Installed on RDBMS (Oracle, SQL Server, DB2,