GIS Lecture 11: Spatial Analyst

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GIS Lecture 11 Spatial Analyst
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Outline

• Spatial Analyst Overview
• Grid Maps
• Raster Layers
• Raster Masks
• Kernel Density Smoothing
• Site Suitability Study
• Poverty Risk Model

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Spatial Analyst Overview
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Spatial Analyst

• Traditional GIS
• Spatial queries, buffer analysis, overlay
  analysis
• Spatial Analyst
• Quantify spatial patterns and relationships
• Track changes over time and determine patterns
• Identify anomalous spatial patterns using
  mathematical statistics
• Statistical significance and verifying conclusions

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Primary Features

• Data analysis
• Allows rapid generation of maps that are based on
  complex statistics
• Surface modeling
• Creates predicted surface (grid maps) from
  unmeasured points based on statistical analysis
  of measured points

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Load Spatial Analyst Extension

• Tools, Extensions

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Grid Maps
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Grid Maps

• Divides geographic space into uniform blocks
  called cells
• Every cell represents a certain specified portion
  of the earth, such as a square mile, kilometer,
  meter, etc.
• Each cell is given a value that describes the
  site, such as elevation, land use type, number of
  crimes, etc.

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Grid Map Example

• Housing units in City of Pittsburgh

• Census Block Centroids

• Kernel Density Raster Map

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Raster Layers
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Raster Basemap

• Free from U.S. Geological Survey (USGS)
• Digital Elevation Model (DEM)
• NED shaded relief, 1/3 arc second

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Raster Basemap

• Land use map
• NLCD 2001

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Raster Layer Properties

• All raster maps are rectangular coordinate system

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Create Raster Mask

• Conversion Tools
• Feature to Raster

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Create Grid Map

• Convert TIF to Grid
• Export TIF

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Environment Settings

• Settings for environment tools
• Set once to use throughout project

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Extract Raster Masks

• Extract (Clip) Land Use to Pgh Mask

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Hillshade Raster

• Hillshade function simulates illumination of a
  surface
• Gives 3D appearance
• Parameters
• Altitude of light source above surfaces horizon
• Angle (azimuth) relative to true north

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Create Hillshade Raster

• DEM to Hillshade

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Resultant Hillshade Layer

• Original hillshade layer

• Modified hillshade layer

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Kernel Density Smoothing
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Kernel Density Smoothing

• Analyzes out of heart attack incidences in
  Pittsburgh
• Uses point shapefile of census block centroids
  and point shapefile of out of hospital cardiac
  arrests (OHCA shapefile)
• Estimates the incidence of heart attacks per unit
  area (density)
• Two parameters
• Cell size
• Search radius

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Existing Maps

• OHCA and existing raster land use map
• Heart attack locations are in developed areas

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Existing Maps

• Point centroids with population
• OHCA points

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Density Map

• Create density map for heart attack incidence
• Pittsburgh blocks average 300 ft per side in
  length
• 2.5 blocks reasonable for defibrillator locations
• Look at areas 5 blocks by 5 blocks or 1,500 ft
• 150 ft cell size

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Resultant Density Map

• Shown with standard deviation and custom color
  ramp

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Resultant Density Map

• OHCA added
• Note cluster in Downtown Pittsburgh where people
  work but dont live

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Raster Value Points

• Extracts point estimates from raster surface for
  each OHCA point
• Use extracted densities multiplied by block areas
  to estimate number of heart attacks
• Better predictor of heart attacks

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Extract Raster Value Points

• Extraction Toolset

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Resultant Layer

• OHCAPredicted shapefile
• Has attribute value, RASTERVALU, which is an
  estimate of heart attack density per sq foot, in
  the vicinity of each block

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Calculate Predicted Heart Attacks

• Resultant estimate will be larger than actual
  number of heart attacks in OCHA (these are only
  ones with bystander help)
• Expression to calculate prediction
• 5 RASTERVALU AREA
• five is for 5 year sample of heart attack data
  in OHCA

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Scatter Plot

• Actual vs. predicted heart attacks
• Pittsburgh has 7,466 blocks
• 1,509 blocks with heart attacks

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Resultant Graph

• At the scale of blocks, predicted values
  correlate poorly with actual values
• Factors other than population residence is needed

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Site Suitability Study
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Site Suitability Study

• Evaluates other factors for heart attack
  predictability (e.g. commercial land use)
• Uses 600 foot buffer around commercial areas

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Convert Buffers to Raster
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Kernel Density Map

• Create a kernel density map to use for
  calculations

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Calculate Simple Query

• Query kernel density map (HeartAttack) for areas
  that have high density and merit a defibrillator
• 25 block area(1,500 ft x 1,500 ft , 2.25 x 106 sq
  ft of area)
• 10 or more heart attacks every 5 years in
  locations where bystander help is possible
• Heart attack density
• 10 heart attacks/ 2.25 x 106 sq ft /0.000004444

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Calculate Simple Query

• Raster Calculator

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Simple Query Result
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Calculate Compound Query

• Add second criteria commercial buffer

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Compound Query Result
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Poverty Risk Model
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Model for Risk Index

• Poverty study as another risk for heart attacks
• Population below poverty income line
• Female headed household with children
• Population with less than HS education
• Workforce who are unemployed
• Improper linear model for poverty
• Calculate Z-score values
• Data selected by subtracting the mean and
  dividing by the standard deviation for above
  variables, then averaging them

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Risk Model Base Layers

• Block Groups
• NoHighSch2 (ho high school degree)
• Male16Unem (males in workforce who are
  unemployed)
• Poverty (population below poverty income)
• Blocks
• FHH (female headed households with children)

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Risk Model Base Layers

• Difficult to represent using vectors

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Create New Model

• New toolbox and model

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Kernel Density Layers

• Create kernel density layer for first input

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Kernel Density Layers

• Create kernel density layer for additional input

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Standard Deviation Statistics

• Mean and standard deviation for raster layers

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Raster Algebra Expression

• Poverty Index

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Finished Model
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Poverty Contour Map

• Poverty index of 1 or higher

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Summary

• Spatial Analyst Overview
• Grid Maps
• Raster Layers
• Raster Masks
• Kernel Density Smoothing
• Site Suitability Study
• Poverty Risk Model