GIS
(Geographical Information Systems)
What in the world are these all
about?
Austin College
April 2014
Dr. Ronald Briggs
Professor Emeritus
The University of Texas at Dallas
Program in Geospatial Information Sciences
briggs@utdallas.edu
Overview
 Geographic
Information technologies
 GIS data concepts
 Applications in environmental studies
What is Geography?
 The

science of location
What is Where and Why
the Spatial Science
What?
Where?
Why?
?
Briggs Henan University 2013
3
Geographic Information Technologies
GIS: one of three technologies which have
revolutionized the handling of spatial or locational
data, which is the focus for geography (and most
environmental studies)
1.
2.
3.
Global Positioning Systems (GPS)
Remote Sensing (RS)
Geographic Information Systems (GIS)
Made it easy to do things which in the past had been
time consuming, expensive, or even impossible
.
Geographic Information Technologies

1. Global Positioning Systems (GPS)
– a system of earth-orbiting satellites which
provide precise location on the earth’s
surface
– GPS gave us exact locations inexpensively
– didn’t need an expensive surveyor
Geographic Information Technologies

2. Remote Sensing (RS)
– collecting data without direct contact with
the object being measured
– use of satellites or aircraft to capture
information about the earth’s surface
– Expensive field surveys far less necessary
– Especially important for environmental
applications
Geographic Information Technologies

3. Geographic Information Systems (GIS)
– Software systems for input, storage, retrieval, analysis and display of
geographic (spatial) information
Input

Analysis
Display
gave us inexpensive map production/display and easier analysis
– don’t need a professional cartographer
– But still need analysts!
The Synergism of Three Technologies
– GPS and Remote Sensing provide data for GI
Systems.
– GI Systems allow the effective use of GPS and RS
data.
GI Systems
The evolution of GIS:
from PhD to Google Earth
 1960s:
term GIS invented by Roger Tomlinson
working for the Canada Land Inventory
– Big country, few people, needed system to manage its
natural resources
 1990s:
GIS emerged as a tool for researchers
– As an example, I gave a talk in 1996 to researchers at
Texas Instruments in Dallas
– But you still needed a PhD to use it!
 2005:
GIS goes mainstream
– Release of Google Maps and Google Earth
– GIS for everyone!?
What Google won’t do
data
preparation and interpretation still a
complex requirement
sophisticated spatial analysis not supported
Predictive modeling and decision making still
requires trained professionals
– retail site selection
– identification of sources of environmental pollution
Professionals with degrees
(BA, BS, PhD) are still needed!
GIS data concepts
Geographic Information System:
intuitive description
A map with a database
behind it
Which you can use:
 to support on-going operations
– Where is air pollution highest
now?

to make strategic decisions
– What sites are in greatest need for
remediation?

to conduct scientific inquiry
– Does air pollution contribute to
asthma attacks?
The Uniqueness of GIS
uses explicit location on earth’s surface to relate data
SS #
But I don’t have a SS # !!
We all have Latitude and Longtitude !!
Everything happens someplace. Is there anything more in common?
“Allows the integration of disparate data hitherto
confined to separate domains”
--allows you to bring stuff together that you couldn’t before
--polluted rivers and factory locations
--air pollution levels and asthma hospital admissions
The GIS Data Model:
A layer-cake of information

Each layer is a different phenomena
– elevation, ownership parcels, land use, air pollution level

Layer are related based on common geographic
coordinates
– Latitude & longitude or projected X,Y coordinates
Two data types:
Vector and Raster
Real World
“raster is faster but
vector is corrector”
Raster Representation
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Vector Representation
R T
R
R
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H
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point
line
R R
R
R
R
R
R
T T
T T
H
polygon
Representing Data with Raster and Vector Models
Raster Model
 area is covered by grid of equal-sized, square cells (usually)
 each cell given a single value based on the majority feature in
the cell, such as land use type.
wheat
fruit
fruit
clover
corn
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Representing Data with Raster and Vector Models
Raster Model
 Great for some data such as elevation, rainfall, land use
– environmental data in general
Doesn’t work so well for others such as land ownership, streets,
–
human data in general
Brown
Lee
Smith
Lee
Santos

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Representing Data with Raster and Vector Models
Vector Model
Features in the real work can be represented either as:
 points (nodes): intersections, stores, homes, trees, poles, fire
plugs, airports, cities
 lines (arcs): streets, sewers, streams
 areas (polygons): land parcels, voting precincts, cities, counties,
forest, rock type
Node Feature Attribute Table
Node ID
1
I
4
2 Birch
II
Smith
Estate
A34 III
IV
5
A35
3 Cherry
6
More complex, but more
accurate and flexible
1
2
3
4
Control
light
stop
yield
none
Crosswalk
yes
no
no
yes
ADA?
yes
no
no
no
Arc Feature Attribute Table
Arc ID Length Condition Lanes Name
I
106 good
4
II
92 poor
4 Birch
III
111 fair
2
IV
95 fair
2 Cherry
Polygon Feature AttributeTable
Polygon ID
Owner
Address
A34
J. Smith 500 Birch
A35
R. White 200 Main
Images:
dumb raster data
• You know what is
in this image
– the computer
doesn’t
• GIS converts dumb
images from remote
sensing into smart
GIS data
– you and the
computer know
what’s there
– Enables analysis
Smart Vector—Pavement polygons
Dumb Images
& smart GIS Data
Smart Raster—land use grids
GIS converts dumb images from
remote sensing into smart GIS data
Environmental Impact of Urbanization
Sao Paulo Los Angeles LA%SP
1900
282,770
364,021 128.7
1950 2,205,743
4,415,700 200.2
2000 15,481,476 13,208,754
85.3
18,000,000
16,000,000
14,000,000
12,000,000
10,000,000
8,000,000
6,000,000
4,000,000
2,000,000
-
Sao Paulo
Los Angeles
1900
1950
2000
Angel, Shlomo, Jason Parent, Daniel L. Civco, and Alejandro M. Blei, Atlas of Urban Expansion
Cambridge, MA: Lincoln Institute of Land Policy, 2012
http://www.lincolninst.edu/pubs/2072_Atlas-of-Urban-Expansion
http://www.lincolninst.edu/subcenters/atlas-urban-expansion/historical-sample-cities.aspx
Environmental Impact of Urbanization
Environmental Impact of Urbanization: New York University Stern Urbanization Project
http://urbanizationproject.org/blog/30-cities-from-200-years-agoand-where-they-are-now#.U074fPldXHq
https://www.youtube.com/watch?v=1u7H1helosI
https://www.youtube.com/watch?v=2WGPvWPpey8
Environmental Impact of Urbanization
Map scales are different!
1”= 30km
1”= 15km (assuming an 8.5”x 11” sheet)
Environmental Impact of Urbanization
Environmental Impact of Urbanization
Sao Paulo
hectares
density
1900
2,400 118
1950 27,000 82
2000 175,692 88
Los Angeles
hectares
8,940
198,850
465,573
density
41
22
28
Note: dates are approximate; see source for exact years
SP
LA
Atlas of Urban Expansion
Atlas of Urban Expansion:
120 cities: 1900 and 2000
30 cities: expansion from
1800 to 2000
Based on remote sensing images and GIS analysis
http://www.lincolninst.edu/subcenters/atlas-urban-expansion/historical-sample-cities.aspx
http://urbanizationproject.org/blog/30-cities-from-200-years-agoand-where-they-are-now#.U074fPldXHq
Urban Forest Inventory
Dr. Fang Qiu research team at UT-Dallas
 Trees
in urban environments are a critical
environmental and economic resource
 Successful management requires knowledge of
their location, species, size and health
– A tree inventory
 Tree
inventories normally use field surveys,
often by groups of volunteers
– Expensive
– Often inaccurate
28
Fusion of
– Remote sensing hyperspectral data
– Remote sensing LIDAR data
using GIS
Hyperspectral data
 color photo or TV screen: RGB 3 – bands
 hyperspectral data:
300-500 bands
LIDAR: Light Detection and Ranging
 Radar shot down from a plane (or satellite)
 Measures height of ground and objects
 Produces a point cloud: a height and location
Why hyperspectral?
LiDAR

LIDAR: Light Detection And Ranging

Three major components
–Laser scanner
 Measure
distance to target
 Wavelength: NIR (1040-1060 nm)
–IMU
 Inertial
measurement unit
 Record attitude
–GPS
 Global
positioning system
 Provide positioning
31
Turtle Creek, Dallas:
Lidar data identifies trees

Ground Points
32
Turtle Creek, Dallas:
Hyperspectral data identifies species

Ground Points
33
Environmental Justice
 Which
schools in Dallas are most exposed to
pollution from TRI (Toxic Resource Inventory)
sites?
– Calculate exposure index based on
 Magnitude
of emission from site
 Distance of site from school
 Are
minority or poor children more likely to be
exposed?
Class exercise only
No policy implications should be drawn!
Schools closer to
toxic sites have
higher proportions
of poor and
Hispanic students,
and lower
proportions of
whites
Total
Asian
White
61,750
38,428
11,948
33,199
2,328
270,742
143,326
79,994
102,226
10,304
332,492
181,754
91,942
135,425
12,632
ED=Economically Disadvantaged
AA=African American
Percents relative to total within and total beyond (row sum)
within
62.2
19.3
53.8
3.8
beyond
52.9
29.5
37.8
3.8
13,997
77,020
91,017
Counts
within
beyond
ToTal
ED
AA
Hispanic
22.7
28.4
Robert Thompson,
UT-Dallas GIS Master’s Project 2012
In the event of a levee breach along the Trinity River’s
East Levee, approximately how long would it take to
flood the areas behind the breach and what are the
potential impacts of the resulting flood?
Vantage Point
Hurricane Hermine
September 9, 2010
Record Crest
May 25, 1908
Figure 10.
9. Dallas
http://www.cliffdwellings.net/about_oak_cliff.htm
Trinity River, City of Dallas.
Retrievedcopyright
Portions
from http:www.dallascityhall.com
(c) 2006 Alan C. Elliott, source www.oakcliff.com
7
Approximately 44 hrs. after breach, flood depth ≈ 16.0 ft.
http://www.carsilab.org/coolmap/
The map uses data derived from airborne lidar,
including lidar intensity and modeled solar radiation,
along with satellite data and city GIS data, to estimate
which buildings and surfaces in New York City would
benefit most from a cool roof treatment.
www.carslab.org/coolmap/
GIS and Social Media
Matthew Zook , et. al. "The Geography of Beer.” Department of Geography, University
of Kentucky
Tweets sent between June 2012 and May 2013 were searched for keywords pertaining
to beer. Geotagging allowed the tweets to be located on a map
http://www.livescience.com/44622-beer-on-twitter-finding-drinking-patterns-in-tweet-data-infographic.html