Let’s pretty it up!

Border around project area

Everything else is hardly

noticeable… but it’s there

Big circles… and semi-

transparent

Color distinction is

clear

Spatial Analysis

Introduction to GIS – Yoh Kawano

Spatial Analysis

» Spatial analysis refers to the formal techniques to conduct analysis using their topological, geometric, or geographic properties.

» In a narrower sense, spatial analysis is the process of analyzing geographic data.

» Types of spatial analysis you have already been doing:˃ Buffering˃ Select by location

» Layers can be overlaid - placed one over the other based on a shared geographic reference – allows analysis of the relationships between layers

» Raster analysis is one method of Spatial Analysis.

Raster Data

» Why do we have to use raster data?˃ Vector data (points, lines and polygons) are limited to only certain

spatial analyses+ Point in polygon (which points are within+ Line intersections

˃ Vector data only knows about the space it occupies

» Raster data covers the entire region» Provides a more powerful format for advanced

spatial and statistical analysis

Raster Data

» A matrix of cells» Rows and columns

(grid)» Examples: aerial

photographs, digital photos, scanned maps

» Examples in spatial analyst…

Vector vs Raster

Types of Raster Data

» As basemaps

» As surface maps

» As thematic maps

The grid data structure

» Grid size is defined by extent, spacing and no data value information˃ Number of rows, number of column˃ Cell sizes (X and Y) ˃ Top, left , bottom and right coordinates

» Grid values ˃ Real (floating decimal point)˃ Integer (may have associated attribute table)

Definition of a Grid

Numberof

rows

Number of Columns(X,Y)

Cell size

NODATA cell

Value attribute table for categorical (integer) grid data

So now that I know what a raster is… what can I do with it?

» derive new information from your existing data, » analyze spatial relationships, » build spatial models, and » perform complex raster operations.

Applications of spatial analysis

» Find suitable locations» Model and visualize crime

patterns » Analyze transportation corridors» Perform land use analysis » Conduct risk assessments» Predict fire risk» Determine erosion potential » Determine pollution levels» Perform crop yield analysis

How to find “suitable” locations

» Step 1: State the Problem˃ Find the most suitable location for a new long-term care facility in Long

Beach

» Step 2: Identify the Parameters and Weight˃ Supply: needs to be far from existing facilities (weighted by number of

beds in the facilities) (25%)˃ Demand: number of persons over 65 (50%)˃ Access: close to major streets (25%)

» Step 3: Prepare Your Input Datasets˃ Long Beach Facilities (point)˃ Census Tracts – Age>65 (polygon)˃ Major Streets (line)

» Step 4: Perform the Analysis

ArcGIS Workflow

» ArcCatalog: ˃ Make sure all your layers are in the same projection (eg: UTM Zone 11N)

» ArcMap: 1. Load all your layers, double check that you are on the right projection

and units (eg: miles)2. Turn on Spatial Analyst Toolbar3. Set the Environment (very important, ensures that raster layers have the

same cell size!)4. Load your indicator layers5. “Rasterize” your layers (Ex: Kernel Density, Feature to Raster, Euclidean

Distance)6. Reclassify7. Apply weights8. Generate final raster

Step 1Ensure that each layer in your project has the SAME projection

Step 2Check the map units*Even if you change the “display” units, spatial analysis will be conducted using the “map” units

Step 3Access ArcToolbox Environments

Right click

Step 4Set the environment1. Processing Extent

Usually set this to the extent of your project, or the largest layer

2. Raster AnalysisCell size and Mask

Best site for new facility

Kernel Density on Number of

Beds

Feature to Raster on Age>65

Euclidean Distance

Long Beach Facilities

Long Beach Census Tracts

Long Beach Major Roads

Far from existing facilities

Close to areas with high

numbers of senior citizens

Close to major streets

25% 50% 25%

Example: Kernel Density

Example: Euclidean Distance

Example: Feature to Raster

Long Beach Facilities

Long Beach Census Tracts

Long Beach Major Roads

Kernel Density on Number of

Beds

Feature to Raster on Age>65

Euclidean Distance

Reclassify:3 most desirable

1 = least desirable

Reclassify:3 most desirable

1 = least desirable

Reclassify:3 most desirable

1 = least desirable

3 3

1 3

3

2

1 1 2

1 1

1 2

3

3

2 2 3

2 1

2 1

3

1

2 2 1

3 3

1 3

3

2

1 1 2

1 1

1 2

3

3

2 2 3

2 1

2 1

3

1

2 2 1

Long Beach Facilities

Long Beach Census Tracts

Long Beach Major Roads

25%

50%

25%

.75 .75

.25 .75

.75

.5

.25 .25 .5

.5 .5

.5 1

1.5

1.5

1 1 1.5

.5 .25

.5 .25

.75

.25

.5 .5 .25

1.75 1.5

1.25 2

3

2.25

1.75 1.75 2.25

Spatial Analysis Tools > Map Algebra > Raster Calculator