S H A W N L . P E N M A N

E A R T H D A T A A N A LY S I S

C E N T E R

U N I V E R S I T Y O F N E W M E X I C O

RASTER ANALYSIS

Spatial Analyst basics

Raster / Vector conversion

Raster data fundamentals

Cells, cell values, zones and regions

Basic spatial properties of raster

Points, lines, polygons, distance, adjacency, buffer, network

Types of raster functions

Raster reclassification

Raster overlay

Raster query and map algebra

Distance functions

Zonal statistics

Other raster analysis tools

TOPICS COVERED

SPATIAL ANALYST

BASICS

SPATIAL ANALYST TO WORK WITH RASTER DATA

Extremely useful extension to ArcGIS

Same for all levels (ArcView, ArcInfo)

Needs to be purchased separately.

Spatial Analyst uses grids

essentially makes ArcGIS into a raster program

Spatial analysis and modeling tools

queries, overlay, distance, proximity, density, slope, aspect, hillshade,

viewshed, contours, etc.

ARCGIS SPATIAL ANALYST

TURNING SPATIAL ANALYST ON

SPATIAL ANALYST TOOLBAR &

ARCTOOLBOX

ArcMap 9.3

Spatial Analyst Toolbar

ArcMap 10.1

Spatial Analyst & Image

Classification Toolbars

Tools now in

ArcToolbox

ADRG (ARC Digitized Raster Graphics)

CADRG (Compressed ADRG)

CIB (Controlled Image Base)

DTED (Digital Terrain Elevation Data)

ERDAS GIS, LAN, RAW, IMG

ERMapper (ERS)

BIL, BIP, BSQ

ESRI GRID

GIF, JPG, SID, TIF, BMP

RASTER FORMATS

GRIDS

Grids are the main raster format

of ArcGIS Spatial Analyst. Output

created by Spatial Analyst consist

of grids – these can be converted

to other formats using ArcCatalog.

WORKING WITH GRIDS

Grids are stored very

much like coverages ….

Always set a proper

working directory and

never delete files

manually in Windows

Explorer but use

ArcCatalog.

BUILDING PYRAMIDS

Pyramids are used to improve performance. They are a downsampled version of the

original raster dataset and can contain many downsampled layers. Each successive

layer of the pyramid is downsampled at a scale of 2:1. Pyramids can speed up the

display of raster data by retrieving only the data at a specified resolution that is

required for the display.

SINGLE-BAND RASTERS

MULTIBAND RASTERS

Grid data files can be classified using the Symboloy properties

Grid cells are given a solid color based on cell values

Both discrete and continuous data are used

discrete data are stored an integer grid theme, e.g. land use, soil type,

land ownership, etc.

continuous data are stored as an integer or floating point grid theme,

e.g. population density, elevation, price of land etc.

Various classification methods can be used

Unique values

Classified

Stretched

DISPLAYING GRIDS

Housing units in city of Pittsburgh

GRID MAP EXAMPLE

GIS TUTORIAL 1 - Basic Workbook

Census block centroids Kernel density raster map

UNIQUE VALUES

CLASSIFIED

STRETCHED

ANALYSIS OPTIONS

Now settings under

Environments

ENVIRONMENTS

ANALYSIS MASK

The mask identifies those cells within the analysis extent that will

not be considered when performing an operation or a function.

All identified cells will be "out" and assigned to the nodata value

on all subsequent output raster datasets.

ANALYSIS MASK

The analysis extent be a vector or raster file – in the example, a vector

polygon is used as a mask to create slope map from a DEM but only

within the study area.

ANALYSIS EXTENT

When performing analysis, the area of interest may be a portion of a larger raster dataset. If the

area of interest is a portion of a larger raster dataset, the analysis extent can be set to encompass

only the desired cells. All subsequent results from analysis will be to this extent. The analysis extent

is a rectangle and is specified by identifying the coordinates of the window in map space.

RASTER RESOLUTION

CELL SIZE

The output cell size, or resolution, for any operation or function can be set to any size desired.

The default output resolution is determined by the coarsest of the input raster datasets.

RASTER / VECTOR

CONVERSION

RASTER / VECTOR CONVERSION

RASTER / VECTOR CONVERSION

RASTER / VECTOR CONVERSION

RASTER DATA

FUNDAMENTALS

CELLS, ROWS, COLUMNS AND VALUES

Raster data sets are an

organized matrix of cells.

Cells are organized into

rows & columns which

have an index position

number. The top left cell is

at the (0,0) position. The

notation uses column first,

followed by row.

7

0 1 2 3 4 5

0

1

2

3

4

5

Rows

Columns

(4,1)

RASTER DATA AND COORDINATE

SYSTEMS

Raster data layers are

stored with a Cartesian

coordinate system.

Positions on the grid

have real-world

locations.

Each cell can be

referenced by an X,Y

location.

All cells are square & are

the same size.

Raster origin

(135,982;1,251,821)

Coordinate origin (0,0) X axis

Y a

xis

25

25

CELL VALUES

2 2 3 3 3

1 3 3 2 3

2 2 3 1 2

2 2 4 2 2

4 3 2 3 1

2 3 4 4 2

2

1

1

2

2

1

INTEGER VS. FLOATING POINT

2 2 3 3 3

1 3 3 2 3

2 2 3 1 2

2 2 4 2 2

4 3 2 3 1

2 3 4 4 2

2

1

1

2

2

1 5.2389 5.2389 5.2389 5.2389 5.2389 5.2389

5.2389 5.2389 5.2389 5.2389 5.2389 5.2389

5.2389 5.2389 5.2389 5.2389 5.2389 5.2389

5.2389 5.2389 5.2389 5.2389 5.2389 5.2389

5.2389 5.2389 5.2389 5.2389 5.2389 5.2389

5.2389 5.2389 5.2389 5.2389 5.2389 5.2389

DATA TYPES AND CELL VALUES

WORKING WITH NO DATA

ZONES

2 2 5 6 3

1 3 3 2 3

2 2 3 1 2

5 2 4 2 2

4 3 2 3 1

2 3 4 1 2

2

1

4

5

5

1

Any two or more cells within the same value belong to the same zone –

a zone can consist of cells that are connected, disconnected or both.

An example

with 6 zones.

ZONES AND ATTRIBUTE DATA

Value Count

1 6

2 13

3 8

4 4

5 4

6 1

2 2 5 6 3

1 3 3 2 3

2 2 3 1 2

5 2 4 2 2

4 3 2 3 1

2 3 4 1 2

2

1

4

5

5

1

BASIC SPATIAL

PROPERTIES OF RASTER

Points

Line

Polygons

Distance

Adjacency

Buffer

Network

Spatial Coincidence

BASIS SPATIAL PROPERTIES IN RASTER

RASTER - POINTS

RASTER - LINES

RASTER - POLYGONS

RASTER - DISTANCE

RASTER - ADJACENCY

Orthogonal

only

Orthogonal

and diagonal

RASTER - BUFFER

RASTER - NETWORK

12 14

15 24

23 27

21

18

17

RASTER – SPATIAL COINCIDENCE

TYPES OF RASTER

FUNCTIONS

RASTER FUNCTIONS

Local

Focal

Zonal

Global

LOCAL FUNCTION EXAMPLE: RASTER

CALCULATOR

2 2 5

1 3 3

2 2 3

5 2 4

2

1

4

5

* 2 =

4 4 10

2 6 6

4 4 6

10 4 8

4

2

8

10

GLOBAL FUNCTION EXAMPLE: STRAIGHT-

LINE DISTANCE

-

1 =

0

1 -

-

- -

-

-

-

-

-

-

- - - -

1

1

1.4

1.4

2

2

2.2

2.2

2.2

3

3.1

3.1

2.8 3.6

Distance of

RASTER

RECLASSIFICATION

RECLASSIFY

Reclassifying your data simply means replacing input cell values with new output cell values. The

input data can be any supported raster format. If you add a multiband raster, the first band will be

taken and used in the reclassification.

ELEVATION RASTER

RECLASSIFY

There are many reasons why you might want to reclassify your

data. Some of the most common reasons are:

To replace values based on new information

To classify certain values together for display

To classify certain values together for conversion to vector format for

analysis

To reclassify values to a common scale

To set specific values to nodata or to set nodata cells to a value

RECLASSIFY

Reclassification is useful when you want to replace the values in

the input raster with new values. This could be due to finding

out that the value of a cell or a number of cells should actually

be a dif ferent value.

For example, this may happen if the landuse in an area changed over

time.

You may want to simplify the information in a raster.

For instance, you may want to group together various types of forest

into one forest class.

RECLASSIFY

Another reason to reclassify is to assign values of preference,

sensitivity, priority, or some similar criteria to a raster. This may

be done on a single raster (a raster of soil type may be assigned

values of 1–10 that represent erosion potential) or with several

rasters to create a common scale of values .

For example, when finding slopes most at risk of avalanche activity,

input rasters might be slope, soil type, and vegetation. Each of these

rasters might be reclassified on a scale of 1–10 depending on the

susceptibility of each attribute in each raster to avalanche activity (that

is, steep slopes in the slope raster might be given a value of 10

because they are most susceptible to avalanche activity).

RECLASSIFY

Sometimes you want to remove specific values from your

analysis.

This might be, for example, because a certain landuse type has

restrictions (such as wetland restrictions), which means you cannot

build there. In such cases, you might want to change these values to

nodata in order to remove them from further analysis .

In other cases, you may want to change a value of NoData to be

a value such as in the case where new information means a

value of NoData has become a known value.

RECLASSIFY

RECLASSIFY

RECLASSIFY

f tp://edacftp.unm.edu/outgoing/pub/spenman/geog581L/exam

ples_raster_analysis/part1

FTP SITE

Open Tutorial1-1 mxd

DEM Properties, Source tab – how many columns & rows and

what is their size?

What is the projection?

Statistics – what is the range of elevation?

Examine attribute table of LandUse

How many records are there?

Set Environment – turn on spatial analyst toolbar

Set Raster Analysis Environment Cell size, select “as specified as

below”, “50”, mask Pittsburgh

Extract Land Use using a mask

Use layer file to symbolize new land use layer

DEMO/PRACTICE

Reclassify Tool

Reclassify LandUse

All developed into one category

All forest into one category

All wetlands into one category

Have to look at classification (symbology tab) to figure out which

numbers belong to developed, forest and wetlands categories

Examine changes

Try reclassify with Classification window

Use reclassified LandUse and export to polygon

DEMO/PRACTICE