Introduction to Geographic Information Systems
Spring 2013 (INF 385T-28437)
Dr. David Arctur
Lecturer, Research Fellow
University of Texas at Austin
Lecture 7
Feb 21, 2013
Spatial Data and
Geoprocessing
Outline

Bolstad, Ch 5, 6, 7: Data Sources, cont’d
 GPS, Aerial/Satellite Imagery, Digital Data

Gorr & Kurland, Ch 8: Geoprocessing





Attribute extraction
Feature location extraction
Location proximities
Geoprocessing tools
Model builder
INF385T(28437) – Spring 2013 – Lecture 7
2
Lecture 7
MORE ON DATA SOURCES:
GPS, IMAGERY, DIGITAL
INF385T(28437) – Spring 2013 – Lecture 7
3
Measuring location & data

Three main approaches, many technologies:
 In situ: make field observations on site

Stream flow & other gauges, GPS location
 Remote sensing: observe from a distance

Aerial photos, satellite sensors, LiDAR
 Model results: products derived from working on
other products
INF385T(28437) – Spring 2013 – Lecture 7
4
Global Navigation Systems
Aka,

Global Positioning Systems (GPS)

Global Navigation Satellite Systems (GNSS)
Uses WGS84 for coordinate reference system
Bolstad, p.184
INF385T(28437) – Spring 2013 – Lecture 7
5
GPS Ranging: get 4+
Bolstad, p.189
INF385T(28437) – Spring 2013 – Lecture 7
6
GPS Errors due to receiver sensitivity
PDOP: Positional Dilution of Precision
(see Bolstad, p.192)
INF385T(28437) – Spring 2013 – Lecture 7
7
GPS: Differential Correction


Depends on
having GPS
receivers with
precisely known
location
Differential
correction can
be applied in
real-time or
calculated later
Bolstad, p.195
INF385T(28437) – Spring 2013 – Lecture 7
8
Remote Sensing

Aerial photography

Satellite multispectral / hyperspectral

LiDAR – Light Detection and Ranging

Sensor webs
Bolstad, chapter 6
INF385T(28437) – Spring 2013 – Lecture 7
9
Industrial
Process
Monitor
Sensor Webs






Sensors connected to and discoverable on Web
Sensors have position & generate observations
Sensor descriptions available
Automobile
Services to task and access sensors
as Sensor Probe
Local, regional, national scalability
Enabling the Enterprise
Airborne
Imaging
Device
Traffic, Bridge Temp
Sensor
Monitoring
Stored
Sensor
Data
Webcam
Strain
Gauge
Satellite-borne
Device
Source:
OGC – Spring 2013Imaging
INF385T(28437)
– Lecture 7
Environmental
Monitor
Health
Monitor
10
LiDAR – Laser-based imagery


Hi-resolution topography
Can separate forest
cover from ground layer
Bolstad, p.260
INF385T(28437) – Spring 2013 – Lecture 7
11
LiDAR point clouds
Bolstad, p.261
INF385T(28437) – Spring 2013 – Lecture 7
12
LiDAR Applications

Agriculture yields

Biology, conservation

Archaeology beneath forest canopy

Geology, soil science

3D cave maps, hi-resolution beach topography

Meteorology, law enforcement, robotics

Adaptive cruise control (autos)
Source: Wikipedia
INF385T(28437) – Spring 2013 – Lecture 7
13
Spatial Processing





Attribute extraction
Feature location extraction
Location proximities
Geoprocessing tools
Model builder
INF385T(28437) – Spring 2013 – Lecture 7
14
Lecture 7
SPATIAL PROCESSING:
ATTRIBUTE EXTRACTION
INF385T(28437) – Spring 2013 – Lecture 7
15
Attribute query extraction
You have tracts for an
entire state, but want
tracts for one county
only
INF385T(28437) – Spring 2013 – Lecture 7
16
Attribute query extraction

Select tracts by County FIPS ID
 Cook County = 031
INF385T(28437) – Spring 2013 – Lecture 7
17
Attribute query extraction


Cook County tracts
selected
Export to new feature
class or shapefile
INF385T(28437) – Spring 2013 – Lecture 7
18
Export selected features

Right-click to export selected features
INF385T(28437) – Spring 2013 – Lecture 7
19
Add new layer

Cook County tracts
INF385T(28437) – Spring 2013 – Lecture 7
20
Lecture 7
FEATURE LOCATION
EXTRACTION
INF385T(28437) – Spring 2013 – Lecture 7
21
Select by location



Powerful function unique to GIS
Identify spatial relationships between layers
Finds features that are within another layer
INF385T(28437) – Spring 2013 – Lecture 7
22
Select by location

Have Cook County census tracts but
want City of Chicago only
 Can’t use Select By Attributes
 No attribute for Chicago

Use “Municipality” layer
 City Chicago is a municipality within Cook County
INF385T(28437) – Spring 2013 – Lecture 7
23
Select by location

Select “Chicago” from municipalities layer
INF385T(28437) – Spring 2013 – Lecture 7
24
Select by location

Selection, Select By location
INF385T(28437) – Spring 2013 – Lecture 7
25
Export selected features
INF385T(28437) – Spring 2013 – Lecture 7
26
Lecture 7
LOCATION PROXIMITIES
INF385T(28437) – Spring 2013 – Lecture 7
27
Points near polygons

Health officials want to know polluting
companies near water features
INF385T(28437) – Spring 2013 – Lecture 7
28
Points near points

School officials want to know what schools
are near polluting companies
INF385T(28437) – Spring 2013 – Lecture 7
29
Polygons intersecting lines

Transportation planner wants to know what
neighborhoods are affected by construction
project on major highway
INF385T(28437) – Spring 2013 – Lecture 7
30
Lines intersecting polygons

Public works official wants to know what
streets or sidewalks will be affected by
potential floods
INF385T(28437) – Spring 2013 – Lecture 7
31
Polygons completely within polygons

City planners want to know what buildings
are completely within a zoning area.
INF385T(28437) – Spring 2013 – Lecture 7
32
Lecture 7
GEOPROCESSING TOOLS
INF385T(28437) – Spring 2013 – Lecture 7
33
Geoprocessing overview



GIS operations to manipulate data
Typically take input data sets, manipulate,
and produce output data sets
Often use multiple data sets
INF385T(28437) – Spring 2013 – Lecture 7
34
Geoprocessing enables decisions
Assess Wildfire Danger
Geoprocessing Workflow
To create derived &
value-added products
Decision Support
Client
Internet
Base map from
NASA Data Pool
Coordinate
transformation
Classify fire areas
from aerials
Overlay and buffer
Roads layer
…
Data
Source:
OGC – Spring 2013 – Lecture
INF385T(28437)
7
Servers (web services)
35
Common geoprocessing tools

Analysis
 Extract – Clip
 Overlay – intersect and union

Data management
 Generalization - dissolve

General
 Append
 Merge
INF385T(28437) – Spring 2013 – Lecture 7
36
Finding the tools

Geoprocessing menu
(slight differences between
10.0 and 10.1)
INF385T(28437) – Spring 2013 – Lecture 7
37
Finding the tools

ArcToolbox
INF385T(28437) – Spring 2013 – Lecture 7
38
Finding the tools

Search window
INF385T(28437) – Spring 2013 – Lecture 7
39
Clip

Acts like a “cookie cutter” to create a subset
of features
Input features (streets)
INF385T(28437) – Spring 2013 – Lecture 7
Clip features
(Central Business District)
Output features (CBD streets)
40
Clip
INF385T(28437) – Spring 2013 – Lecture 7
41
Clip vs. select-by-location

Clip
 Clean edges
 Looks good

Select by location
 Dangling edges
 Better for geocoding
INF385T(28437) – Spring 2013 – Lecture 7
42
Dissolve

Combines adjacent polygons to create new,
larger polygons
Uses common field value to remove interior
lines within each polygon, forming the new
polygons

Aggregate (sums) data while dissolving

INF385T(28437) – Spring 2013 – Lecture 7
43
Dissolve

Create regions using U.S. states
 Use SUB_REGION field to dissolve
 Sum population
INF385T(28437) – Spring 2013 – Lecture 7
44
Dissolve
Statistics Fields (optional)
(may not be initially visible,
scroll down to see)
INF385T(28437) – Spring 2013 – Lecture 7
45
Dissolve results


States dissolved to form regions
Population summed for each region
INF385T(28437) – Spring 2013 – Lecture 7
46
Append

Appends one or more data sets into an
existing data set
 Features must be of the same type
 Input datasets may overlap one another and/or the target
dataset
 TEST option: field definitions of the feature classes must be
the same and in the same order for all appended features
 NO TEST option: Input features schemas
do not have to match the target feature classes'
schema
INF385T(28437) – Spring 2013 – Lecture 7
47
Append

DuPage and Cook County are combining
public works and need a new single street
centerline file.
INF385T(28437) – Spring 2013 – Lecture 7
48
Append

Append will add DuPage streets to Cook
County streets
INF385T(28437) – Spring 2013 – Lecture 7
49
Resultant layer

One street layer (Cook County) with all
records and field items
INF385T(28437) – Spring 2013 – Lecture 7
50
Merge

Combines multiple input datasets of the
same data type into a single, new output
dataset
 Illinois campaign manager needs a single voting
district map but wants to preserve the original
layers
INF385T(28437) – Spring 2013 – Lecture 7
51
Merge
INF385T(28437) – Spring 2013 – Lecture 7
52
Resultant layer

New voting district layer
INF385T(28437) – Spring 2013 – Lecture 7
53
Union

Overlays two polygon layers
 Resulting output layer has combined attribute
data of the two inputs
 Contains all the polygons from the inputs,
whether or not they overlap
INF385T(28437) – Spring 2013 – Lecture 7
54
Union

Neighborhoods and ZIP Codes
INF385T(28437) – Spring 2013 – Lecture 7
55
Union
INF385T(28437) – Spring 2013 – Lecture 7
56
Union

Better describes characteristics of a
neighborhood.
 Central business district 15222 vs 15219
INF385T(28437) – Spring 2013 – Lecture 7
57
Union

Attributes tables contain different fields and data
INF385T(28437) – Spring 2013 – Lecture 7
58
Union results

New polygons with combined data
INF385T(28437) – Spring 2013 – Lecture 7
59
Union vs. Merge vs. Dissolve
Operation
# Input
Feature
Classes
Change in
Geometry
Schema Restrictions
Union
Multiple
Combines all input
geometries
Includes all fields from all input
feature classes; input tables do
not have to be identical
Merge
Multiple
Combines all input
geometries
Input tables must be identical;
retains one set of attributes
Dissolve
Single
Combines feature
geometries based
on shared attribute
values
N/A – single feature class schema
INF385T(28437) – Spring 2013 – Lecture 7
60
Intersect

Computes a geometric intersection of the
Input Features
Features (or portions of features which
overlap in all layers and/or feature classes)
will be written to the Output Feature Class

Inputs can have different geometry types

INF385T(28437) – Spring 2013 – Lecture 7
61
Intersect

City manager needs to know what buildings
intersect flood zones and wants the flood
data attached to each intersecting building
INF385T(28437) – Spring 2013 – Lecture 7
62
Intersect
INF385T(28437) – Spring 2013 – Lecture 7
63
Intersect result

Only building polygons that intersect flood
zones with combined data fields
INF385T(28437) – Spring 2013 – Lecture 7
64
Lecture 8
MODEL BUILDER
INF385T(28437) – Spring 2013 – Lecture 7
65
Model builder overview




Workflow processes can be complicated
Models automate and string functions together
Simplifies sensitivity / parametric studies
Example
 You have census tracts for a county and want to
create neighborhoods for a city
 Many steps are needed to create neighborhoods
(join, dissolve, etc)
INF385T(28437) – Spring 2013 – Lecture 7
66
Starting map

TIGER census tracts and municipalities
INF385T(28437) – Spring 2013 – Lecture 7
67
Final map

Tracts dissolved to create neighborhoods
INF385T(28437) – Spring 2013 – Lecture 7
68
Crosswalk table


Neighborhood names are
not included with the
census tracts, so a
crosswalk table was
created with the name of
neighborhood for each
census tract
Some neighborhoods are
made of multiple tracts
INF385T(28437) – Spring 2013 – Lecture 7
69
Geoprocessing
options
INF385T(28437) – Spring 2013 – Lecture 7
70
Create a new toolbox

Catalog
INF385T(28437) – Spring 2013 – Lecture 7
71
Create a new model

Right-click new Toolbox
INF385T(28437) – Spring 2013 – Lecture 7
72
Add tool to model

Add Join Tool
 To join crosswalk table to tracts…
INF385T(28437) – Spring 2013 – Lecture 7
73
Set parameter for Join Tool

Joins crosswalk table to census tracts
INF385T(28437) – Spring 2013 – Lecture 7
74
Model steps



Add Join
Dissolve
Remove join
INF385T(28437) – Spring 2013 – Lecture 7
75
Finished model
INF385T(28437) – Spring 2013 – Lecture 7
76
Summary

Bolstad, Ch 5, 6, 7: Data Sources, cont’d
 GPS, Aerial/Satellite Imagery, Digital Data

Gorr & Kurland, Ch 8: Geoprocessing





Attribute extraction
Feature location extraction
Location proximities
Geoprocessing tools
Model builder
INF385T(28437) – Spring 2013 – Lecture 7
77