Introduction To Raster Based GIS Dr. Zhang GISC 1421 Fall 2016, 10/19

Introduction To Raster Based GIS Dr. Zhang GISC 1421 Fall 2016, 10/19

Model of the course Using and making maps Navigating GIS maps Map design Working with spatial data Geoprocessing Spatial data infrastructure Digitizing File geodatabases Geocoding Interactive maps Map Animations Map layouts Spatial analysis 3D GIS Proximity analysis Raster analysis Analyzing Spatial data Network analysis Data mining Spatial regression

Outline http://www.esri.com/products/arcgiscapabilities/insights Spatial Analyst overview Grid maps Raster layers Raster masks Kernel density smoothing Site suitability study Poverty risk model 3

SPATIAL ANALYST OVERVIEW GIS TUTORIAL 1 - Basic Workbook 4

Spatial Analyst Vector GIS Discrete spatial features including points, lines, and polygons Spatial queries, buffer analysis, overlay analysis Spatial Analyst Continuous spatial features such as elevation and incidence of diease Provide image base layers of physical features Estimate continuous surfaces from point and polygon data Combine continuous surfaces to produce indices 5

Spatial Analyst extension Customize, extensions Spatial Analyst Toolbar 6

GRID MAPS GIS TUTORIAL 1 - Basic Workbook 7

Grid maps Divides geographic space into uniform square or rectangular cells Each cell is given a value that describes the site, such as elevation, land use type, number of crimes, etc. 8

Grid map example Housing units in city of Pittsburgh Census block centroids Kernel density raster map 9

RASTER LAYERS GIS TUTORIAL 1 - Basic Workbook 10

Raster basemap Free from U.S. Geological Survey (USGS) National Elevation Dataset (NED) NED shaded relief, 1/3 arc second 11

Raster basemap Land use map NLCD 2001 12

Raster layer properties All raster maps are rectangular coordinate system 13

Set raster environment ArcMap automatically uses the environment settings saving time Geoprocessing, environments, raster analysis 14

Extract land use using mask The land-use layer in your map document is a TIFF file format Need to convert to ESRI format 15

Hillshade raster Hillshade simulates illumination of a surface, giving it a 3D appearance Parameters Altitude of light source above surface s horizon, (default 45 ) Angle (azimuth) relative to true north, measured clockwise (default 315 ) Colors of light and shadow, shades of gray by default

Create hillshade raster Hillshade function 17

Resultant hillshade layer Original hillshade layer Better symbolized hillshade layer 18

Using hillshade To make a raster layer appear 3D, give it 35% transparency and place hillshade below it Land use Land use with hillshade

KERNEL DENSITY SMOOTHING GIS TUTORIAL 1 - Basic Workbook 20

KDS versus histogram Each kernel has unit area and spreads each observation out spatially Stacked bars Summed kernels http://en.wikipedia.org/wiki/kernel_density_estimation

Kernel density smoothing Example: out-of-hospital heart attack incidences in Pittsburgh Uses point feature classes of census block centroids and out-of-hospital cardiac arrests (OHCA) Estimates the incidence of heart attacks per unit area (density) Two parameters Cell size Search radius GIS TUTORIAL 1 - Basic Workbook 22

Existing maps The following map display for estimated incidence using block centroids with point markers is as good as possible, but is difficult to interpret A better representation of incidence is by estimating the smoothed mean of the spatial distribution using kernel density smoothing 23

Existing maps Point centroids with population OHCA points 24

Density map Create density map for heart attack incidence Pittsburgh blocks average 300 feet per side in length 2.5 blocks is reasonable for defibrillator locations Look at areas 5 blocks by 5 blocks or 1,500 feet 150 foot cell size 25

Resultant density map Shown with standard deviation and green-tored color ramp 26

Lab and assignments Lab: Tutorial 9-1 to 9-3 Project Outline GIS TUTORIAL 1 - Basic Workbook 27

SITE SUITABILITY STUDY GIS TUTORIAL 1 - Basic Workbook 28

Site suitability study Evaluates factors for defibrillator location (e.g. commercial land use) Uses a buffer around commercial areas 29

Convert buffers to raster 30

Kernel density map Create a kernel density map to use for calculations 31

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 10 6 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 10 6 sq ft =/0.000004444 32

Reclassify settings Reclassify Tool to query for high heart attack densities 33

Simple query result 34

Calculate compound query Add second criterion: commercial buffer 35

Compound query result 36

POVERTY RISK MODEL GIS TUTORIAL 1 - Basic Workbook 37

Model for risk index Poverty is another risk for heart attacks Female-headed households with children Population below poverty income line Population with less than HS education Workforce of males who are unemployed How to combine these attributes into a single risk index? 38

Improper linear models Basic idea: Use human intelligence to select X variables to model some behavior, Y Define X so that increasing X implies increasing Y Combine standardized X variables with unit weights to predict behavior/create a risk index Standardized X (z-score) X = (X Xbar)/Stdev where Xbar = sample mean Stdev = sample standard deviation Then the predictor of Y is Yhat = (X1 + X2 + + Xn )/n (Dawes, Robyn (1979). The robust beauty of improper linear models in decision making, The American Psychologist, 572, pp 571 582)

Risk model base layers Raster maps can combine layers with different geographies Converts any polygons to uniform cells Blocks FHH (female headed households with children) Block groups NoHighSch2 (no high school degree) Male16Unem (males in workforce who are unemployed) Poverty (population below poverty income) 40

Risk model base layers Difficult to represent using vectors 41

Set geoprocessing options Geoprocessing, Geoprocessing options 42

Standardize input variables Calculate the Z-score in an attribute table of one of the input feature classes Open attribute table for AllCoBlocks Right-click FHH, Statistics Copy and paste the statistics to Notepad Copy and paste them later to the field calculator 43

Create new model Can create index interactively, but is a good candidate for a ModelBuilder model Provides documentation Can rerun model New toolbox and model in ArcCatalog Tool called UnweightedIndices.tbx Model called PovertyIndex (no spaces allowed) 44

Kernel density layers Create kernel density for first input Use FHHChild field from AllCoBlocks Cell size 150 Search radius 1,500 45

FHHChild kernel density map 46

Kernel density layers Create kernel density layers for additional inputs Block groups Search radius 3000 47

Partial model 48

Weighted sum Averages kernel density maps 49

Model

Male16Unem FHHChld PovertyIndex NoHighSch Poverty

Poverty index map 52

Poverty contour Policy analysts might wish to use the poverty index density of 0.0000009 or higher to define poverty A feature class that has the contour line for that index elevation can be created 53

Contour tool 54

Poverty contour map 55

Summary Spatial Analyst overview Grid maps Raster layers Raster masks Kernel density smoothing Site suitability study Poverty risk model 56

Lab and Assignments Lab: Tutorial 11-4 to 11-5 Assignments: Tutorial 11-6, Assignment 11-1, 11-2. Due: 11/02 GIS TUTORIAL 1 - Basic Workbook 57