GIS(Geographical Information Systems)

What in the world are these all about?

Austin CollegeApril 2014

Dr. Ronald BriggsProfessor Emeritus

The University of Texas at Dallas

Program in Geospatial Information Sciences

briggs@utdallas.edu

Overview

Geographic Information technologiesGIS data conceptsApplications in environmental studies

What is Geography?

The science of location

What is Where and Why the Spatial Science

Briggs Henan University 2013 3

Why?

?Where? What?

Geographic Information TechnologiesGIS: 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. Global Positioning Systems (GPS)2. Remote Sensing (RS)3. 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

gave us inexpensive map production/display and easier analysis– don’t need a professional cartographer– But still need analysts!

Input DisplayAnalysis

– GPS and Remote Sensing provide data for GI Systems.

– GI Systems allow the effective use of GPS and RS data.

The Synergism of Three Technologies

GPS data 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 dodata preparation and interpretation still a

complex requirementsophisticated spatial analysis not supportedPredictive 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 GISuses explicit location on earth’s surface to relate data

SS #

We all have Latitude and Longtitude !!

But I don’t have a SS # !!

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

0 1 2 3 4 5 6 7 8 90 R T1 R T2 H R3 R4 R R5 R6 R T T H7 R T T8 R9 R

Real World

Vector RepresentationRaster Representation

Two data types:Vector and Raster“raster is faster but vector is corrector”

line

polygon

point

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.

corn

wheat

fruit

clov

erfruit

0 1 2 3 4 5 6 7 8 9

0

1

2

3

4

5

6

7

8

9

1 1 1 1 1 4 4 5 5 5

1 1 1 1 1 4 4 5 5 5

1 1 1 1 1 4 4 5 5 5

1 1 1 1 1 4 4 5 5 5

1 1 1 1 1 4 4 5 5 5

2 2 2 2 2 2 2 3 3 3

2 2 2 2 2 2 2 3 3 3

2 2 2 2 2 2 2 3 3 3

2 2 4 4 2 2 2 3 3 3

2 2 4 4 2 2 2 3 3 3

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

Smith

Lee

San

tos

Lee

0 1 2 3 4 5 6 7 8 9

0

1

2

3

4

5

6

7

8

9

1 1 1 1 1 4 4 5 5 5

1 1 1 1 1 4 4 5 5 5

1 1 1 1 1 4 4 5 5 5

1 1 1 1 1 4 4 5 5 5

1 1 1 1 1 4 4 5 5 5

2 2 2 2 2 2 2 3 3 3

2 2 2 2 2 2 2 3 3 3

2 2 2 2 2 2 2 3 3 3

2 2 4 4 2 2 2 3 3 3

2 2 4 4 2 2 2 3 3 3

Representing Data with Raster and Vector ModelsVector ModelFeatures 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

Birch

Cherry

I

II

III

IV

1

4 3

A35SmithEstate A34

2 5

6

Node Feature Attribute TableNode ID Control Crosswalk ADA?

1 light yes yes2 stop no no3 yield no no4 none yes no

Polygon Feature AttributeTablePolygon ID Owner AddressA34 J. Smith 500 BirchA35 R. White 200 Main

Arc Feature Attribute TableArc ID Length Condition Lanes NameI 106 good 4II 92 poor 4 BirchIII 111 fair 2IV 95 fair 2 CherryMore complex, but more

accurate and flexible

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

GIS converts dumb images from remote sensing into smart GIS data

Smart Raster—land use grids

Smart Vector—Pavement polygonsDumb Images & smart GIS Data

Angel, Shlomo, Jason Parent, Daniel L. Civco, and Alejandro M. Blei, Atlas of Urban ExpansionCambridge, MA: Lincoln Institute of Land Policy, 2012http://www.lincolninst.edu/pubs/2072_Atlas-of-Urban-Expansionhttp://www.lincolninst.edu/subcenters/atlas-urban-expansion/historical-sample-cities.aspx

1900 1950 2000 -

5,000,000

10,000,000

15,000,000

20,000,000

Sao Paulo

Los Angeles

Sao Paulo Los Angeles1900 282,770 364,021 1950 2,205,743 4,415,700 2000 15,481,476 13,208,754

LA%SP128.7200.2

85.3

Environmental Impact of Urbanization

Environmental Impact of Urbanization

Environmental Impact of Urbanization: New York University Stern Urbanization Project

https://www.youtube.com/watch?v=1u7H1helosI

http://urbanizationproject.org/blog/30-cities-from-200-years-agoand-where-they-are-now#.U074fPldXHq

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

hectares density hectares density

1900 2,400 118 8,940 41 1950 27,000 82 198,850 22 2000 175,692 88 465,573 28

Note: dates are approximate; see source for exact years

Sao Paulo Los Angeles

SP

LA

Atlas of Urban ExpansionAtlas of Urban Expansion:120 cities: 1900 and 2000 30 cities: expansion from 1800 to 2000

http://www.lincolninst.edu/subcenters/atlas-urban-expansion/historical-sample-cities.aspx

Based on remote sensing images and GIS analysis

http://urbanizationproject.org/blog/30-cities-from-200-years-agoand-where-they-are-now#.U074fPldXHq

Urban Forest InventoryDr. 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

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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?

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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

Turtle Creek, Dallas: Lidar data identifies trees

Ground Points

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Turtle Creek, Dallas: Hyperspectral data identifies species

Ground Points

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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!

Total ED AA Hispanic Asian WhiteCountswithin 61,750 38,428 11,948 33,199 2,328 13,997beyond 270,742 143,326 79,994 102,226 10,304 77,020ToTal 332,492 181,754 91,942 135,425 12,632 91,017

ED=Economically Disadvantaged AA=African AmericanPercents relative to total within and total beyond (row sum)

within 62.2 19.3 53.8 3.8 22.7beyond 52.9 29.5 37.8 3.8 28.4

Schools closer to toxic sites have higher proportions of poor and Hispanic students, and lower proportions of whites

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?

Figure 9. Dallas Trinity River, City of Dallas.Retrieved from http:www.dallascityhall.com

Vantage Point

Hurricane HermineSeptember 9, 2010

Record CrestMay 25, 1908

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Figure 10. http://www.cliffdwellings.net/about_oak_cliff.htmPortions copyright (c) 2006 Alan C. Elliott, source www.oakcliff.com

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/

http://www.livescience.com/44622-beer-on-twitter-finding-drinking-patterns-in-tweet-data-infographic.html

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

Matthew Zook , et. al. "The Geography of Beer.” Department of Geography, University of Kentucky

GIS and Social Media