A friendly introduction to Geographic Information Systems Marc Albrecht

A friendly introduction to
Geographic Information
Systems
Marc Albrecht
University of Nebraska at Kearney
Department of Biology
Context: Why GIS?

Many of the issues in our world have a
critical spatial component!
– Land management
– Property lines, easements, right of ways
– Data on land values, taxation, assessment
– Business site selection, advertising
– Proximity of ‘our’ land to other facilities
(pollution, hunting, municipal, federal, state)

“I don’t know what’s over that hill” is a common
problem. What is adjacent to the land we are using?
The Space on Earth

The Earth is finite!
– If not now, within our lifetimes there may be
no natural ecosystems.
– Land managers, natural resource workers,
and politicians are and will continue to make
decisions about biological systems.
– Good information and tools are needed to
do this.
Enter GIS

A computer-based
tool for holding,
displaying, and
manipulating huge
amounts of spatial
data.
Outline of Presentations

“What is GIS?” Presentation
– Part I: Maps and Mapping
– Part II: Some GIS Operations

“GIS Resources and Projects” Presentation
– Part III: Where do I get data?
– Part IV: Ongoing Programs, other software
– Part V: Project Ideas and Examples
Part I: Map Concepts

What is a map?
– What are some properties of maps?
– Vector vs. raster: two digital mapping
methods
Maps reflect the databases we create
 Mapping the third dimension: examples
of 3-D maps

Representing the World:
Projections

3-D to 2-D (at first)
– Projections change a round
world into a flat one.
What is in a picture?

Example: The Mercator projection
has straight meridians & parallels
that intersect at right angles, as
opposed to the Robinson projection.
– Mercator preserves area only at the
equator and at two standard parallels
equidistant from the equator.
– The Mercator projection is often used for
marine navigation as all straight lines on
the map are lines of constant azimuth.
– Any one projection cannot
simultaneously preserve all these
qualities of the world: shape, area,
direction, and distance.
Projections and Metadata


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There are dozens of types of projects, and about six that are
commonly used.
People choose a projection based on which qualities they most
want to preserve in a map. Sites such as Dr. Dana’s and
National Geographic discuss projections in more detail
The point is that you need to know where your data (maps)
come from and information about it. This is called METADATA
– data about the data. Good metadata includes who collected
the data, when, to what accuracy, how the data are projected,
and the collector’s contact information.
You should be a responsible GIS user and keep track of your
metadata!
The Projection Problem

When working with GIS systems you have to know about
projections in general and what projection the different data
you are using are in.
– This is a metadata issue again.
– It is possible to transform data from one projection to another, but is
easier – especially when starting out – to have the different data layers
in same projection when you obtain them! Agencies are generally
helpful about doing this step for you if asked.


Another Problem are Datums – basically mathematical
descriptions of the Earth’s size and shape. If either the
projection or the datums of your map layers are not identical:
YOUR MAP LAYERS WILL NOT LAY ON TOP OF EACH
OTHER, BUT RATHER BE SHIFTED INTO DIFFERENT
AREAS!! This is embarrassing and frustrating.
This is what happens when
projections mix!


Notice the
boundary lines
do not line up
Points that are
placed on the
wrong
projection will
be misaligned
as well
Raster vs. Vector: types of GIS
map representation

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Vector vs. Raster
Two basic ways that spatial data can be
represented
Raster:
– Data represented by pixels with
values, creating a grid
– Allows certain types of operations
not possible with vector data
– Map algebra is possible with multiple
data layers – creating index maps
Vector:
– Data stored as points, lines, and
polygons
– Uses less memory than raster format
– Does not loose positional accuracy
How is all this done?

GIS stores data in a
relational database
structure (‘3-D
spreadsheets’)
– e.g. employee names linked to
store number, store number
linked to shipment arrival
– any data can be linked by a
common attribute to any other
data
 Example shown here is a
list of counties (geographic
data) by income code
(demographic data)
High End 3-D Representation


Surfaces are made from
Triangular Irregular
Networks (TIN) that
interpolate 3-D surfaces
from 2-D contour values.
Uses:
– Hydrology: surface and
underground flows
– Line-of-Sight analysis
– Pollution Plume tracking
– Customer analysis
– Soil erosion potential
3-D Rendering Example
Elevation
measurements can
be easily converted
into 3-D.
Such elevational
data are collected
regularly by federal
and state agencies.
 These data can
be downloaded/
ordered at little
or no cost.
USGS 7.5
Minute quad in
3-D
Beaty, NV
A 3-D
rendering
of the
terrain

How many data points are contained in this image? Thousands? More?
– Even without statistical measurement (which can be done) the pattern of
pollution can be seen. Location and density of wells is also clear.
– Line of sight analysis allows us to determine where to put a house or power
plant where it could or could not be seen from major roads. Notice the roads
actually track up the hills on the right side of the image.
Part II. What can GIS do?

Some general types of GIS operations are listed on
the next few slides
– Many more are possible than are shown here and more
are being created every day
– Extensions and scripts created by users
(http://www.esri.com/arcscripts)
– Third-party and government developers make software
plug-ins for specific uses (eg. EPA’s BASINS software)
1. Proximity Analysis


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Two or more data layers are overlaid
GIS creates buffers around features on a particular layer
This allows analyses such as flood zone delineation and
features near a route such as hotels along a bike route.
2. Query and Overlay Analyses

Query building is a data exploration operation
– Example statement: ‘([acres] > 500 AND [age] > 55)’
– This would highlight all land parcels of greater than 500 acres owned by
people older than 55 years old in a data set loaded into the GIS.

Map algebra with raster data, in this type of operation
mathematical operations are done on each pixel of multiple
data layers. This results in a new data layer that is calculated
from all the input layers.
3. Spatial Analysis


Raster data can also
be used to create
surfaces
Other raster data
uses:
– Density analysis
– Proximity analysis
– Least-cost paths
– Line-of-sight
– Hydrology analysis
Part II: Data Examples

Here is Atlanta
– Highways
– Roads
– Census Tracts

Close up of downtown
– Map contains data for each
street
– Each address in the city can be
geocoded – that is its location
estimated in a systematic way
– Length of each street segment
- block
– Streets can be sorted by
length, name, income, home
value, race, age - all provided
by the Census Bureau (TIGER)
Atlanta Example

Hypothetical population
of opossums.
– Data can be sorted by
attribute, such as sex,
females are yellow in
this example
– Hmmm, why are males
found closer to
populated areas?
– We do not know – but
how else would we
discover the pattern?
Atlanta

Same population now
reclassified by some
other attribute.
– a genetic marker?
– age?, size?

Other operations:
– I can make a chart of any of
the attributes.
– I can compute density of
points to see where the
animals are most clustered
– Measure distances between
individual locations
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Thanks for viewing this presentation. Please email me with any
comments or questions you have.
If you want to learn more about doing your own GIS projects, and more
capabilities of GIS, please view the presentation “GIS Resources and
Projects” in this folder.
A number of the images and figures in this presentation are reproduced with
permission from the ESRI website. Please visit there to learn more!
Other images used with the permission as stated of Peter H. Dana, The
Geographer's Craft Project, Department of Geography, The University of Texas at
Austin. All commercial rights reserved. Copyright 1995 Peter H. Dana.