Transcript Review_ExamI

Review: Exam I
GEOG 370
Instructor: Christine Erlien
Learning Goals: Ch 1
To be able to define GIS and describe
the components necessary to working
with GIS
 To be able to describe the reasons (and
advances) that allowed for the
development of the first GIS systems
 To be able to define CAC and CAD and
compare their capabilities with those of
GIS

Learning Goals: Ch 1

To be able to describe the steps of the
cartographic process and the
differences between traditional
cartography and GIS

To be able to describe the GIS
subsystems and how they differ from
traditional cartographic map production
GIS: Geographic Information
Systems

GIS is built on collective knowledge
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–
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Geography
Cartography
Computer science
Mathematics
Many definitions, depending on whom you ask
– Demers (our textbook) cites Marble & Pequet
(1983), who talk about what we do with a GIS and
how we do it
Marble and Pequet (1983)
Data: Both spatial and temporal
Spatial: Related to the space around us
Temporal: Related to time
The what and how of GIS:
Data input subsystem: Collecting & preprocessing data
Data storage & retrieval subsystem: Retrieval, updating,
editing
Data manipulation & analysis subsystem: Analysis &
modeling
Reporting subsystem: Display
What this boils down to:
“GIS is an information system that allows for capture,
storage, retrieval, analysis and display of spatial data.”
Components necessary for
“Doing GIS”
Computer hardware
 Software

– Data management and analysis
procedures
Spatial data
 People needed to operate the GIS

The rise of GIS

Canada, early 1960s, Dr. Roger Tomlinson
 Need: inventory & map natural resources
 A huge task, aided by advances in computing
technology
– Computers: vacuum tubes  transistors
• Faster, more reliable, cheaper
• Larger memories  information storage as well as
calculations possible
– Mainframe used had 512K of memory!!!!
• IBM develops the drum scanner to scan lines on
maps –1st in the world
Interested in more history? See
http://www.casa.ucl.ac.uk/gistimeline/ for an interactive timeline
How does GIS differ from CAC
and CAD?

Computer-aided cartography (CAC):
– Primarily used in map-making (display)

Computer-aided drafting (CAD)
– Used by architects to produce graphic
images (display)
– Images not linked to descriptive files

What key capability of GIS is lacking in
both CAC and CAD?
Cartographic Process

Cartographic process: The steps in
producing a map, beginning with data
collection & resulting in a map product.
– Data Collection
– Data Processing
• Aggregation, classing, etc.
– Map Production
Comparing traditional cartography
& GIS: Inputs
Traditional
 Data sources
– Aerial
photography
– Digital remote
sensing
– Survey
– Census &
statistical data
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Data recorded as
points, lines, areas
on paper or Mylar
GIS
 Data sources
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Same, plus
DLGs
DEMs
Digital
orthophotoquads
Data recorded as
points, lines, areas
using electronic
devices
Comparing traditional cartography
& GIS: Storage & Retrieval
Traditional
 Storage: points,
lines, areas drawn
on map
 Retrieval: Map
reading
GIS
 Storage:
– Points, lines, areas
stored with spatial
reference data
(coordinates) & pointers
– Tables of characteristics
(attributes) associated
with coordinates

Retrieval: Computer
tracks where data
are stored
Comparing traditional cartography
& GIS: Analysis & Output
Traditional
 Analysis: Limited to
data as presented
on map
 Output: Mapping
GIS
 Analysis: Allows
access to raw data
 can change
aggregation or
classification, or
analyse further
 Output: May
include maps,
tables, charts
Learning Goals: Ch. 2

To be able to explain how real world objects
may be generalized in the digital
environment, and how their representation
may change based on the scale of
observation
 To be able to explain the difference between
and identify examples of discrete and
continuous data
 To be able to identify differences between
nominal, ordinal, interval, and ratio scales. To
also be able to discuss factors that may
determine which spatial measurement levels
we use.
Learning Goals: Ch. 2

To understand the necessity for a grid system
for determining locations as well as the
meaning of absolute versus relative location
 To be able to describe spatial patterns and
relationships using terms such as random,
regular, clustered, orientation, arrangement,
diffusion, density, and spatial association
 To be able to explain how data collection may
differ for small versus large areas and discuss
the different the use of ground sampling
methods for data collection
Generalizing Real World Objects
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Point
– Location only

Line
– 1-D: length
– Made up of a connected sequence of points
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Polygon
– 2-D: length & width
– Enclosed area
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Surface
– 3-D: length, width, height
– Incorporates elevation data
Generalizing Spatial Objects

Representing an object as point? line?
polygon?
– Depends on
• Scale (large scale vs. small scale)
• Data
• Purpose of your research
– Example: House
• Point (small scale mapping)
• Polygon
• 3D object (modeling a city block)
Data: Continuous vs. discrete
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Continuous
– Data values distributed across a surface w/out
interruption
– Examples: elevation, temperature, LULC
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Discrete
– Occurs at a given point in space; at a given spot,
the feature is present or not
– Examples
• Points: Town, power pole
• Lines: Highway, stream
• Areas: U.S. Counties, national parks
http://weather.unisys.com/surface/sst.gif
LULC
http://landcover.usgs.gov
http://maps.unc.edu/MapBook/Index.asp
Continuous & discrete?

Some data types may be presented as
either discrete or continuous
– Example
• Population at a point (discrete)
• Population density surface for an area
(continuous)
Population: Discrete
http://www.citypopulation.de/World.html
Population: Continuous
Generalities
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Continuous data
– Raster
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Discrete data
– Vector
Spatial Measurement Levels
Three levels of spatial measurement:
 Nominal scale
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Ordinal level
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Interval/ratio
Measurement Levels &
Mathematical Comparisons
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Nominal scale
– Not possible
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Ordinal scale
– Compare in terms of greater than, less than,
equal to
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Interval/ratio scales
– Mathematical operations
• Interval: addition, subtraction
• Ratio: add, subtract, multiply, divide
From ESRI Map Book
Volume 18, ESRI (2003)
A
B
C
From Mapping Census 2000, Brewer & Suchan (2001)
From ESRI Map Book Volume 18, ESRI (2003)
Spatial Location and Reference
Communicating the location of objects
 Absolute location
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Definitive, measurable, fixed point in space
Requires a reference system (e.g., grid
system such as Latitude/Longitude)
Relative location
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Location determined relative to other objects
in geographic space
•
•
Giving directions
UTM
Spatial Location and Reference: Geographic
Coordinate System (lat/long)

Lines of latitude are called parallels
 Lines of longitude are called meridians
Latitude / Longitude

Prime Meridian & Equator are the reference points
used to define latitude and longitude
Spatial Comparisons
Pattern analysis: An important way to
understand spatial relationships
between objects.
 Three point distribution patterns:
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– Regular: Uniform
– Clustered
– Random: No apparent organization
A
B
C
Describing Spatial Patterns
Proximity: Nearness
 Orientation: Azimuthal direction
(N,S,E,W) relating the spatial
arrangement of objects
 Diffusion: Objects move from one area
to another through time
 Density: # of inhabitants, dwellings,
etc., per unit area
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Collecting Geographic Data
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Small areas
– Ground survey
– Census
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Large areas
– Census (less oftenevery 10 years)
– Remote sensing
– GPS (e.g., collared animals)
Collecting Geographic Data:
Sampling & Sampling Schemes
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Sampling: When a census isn’t practical
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Types of sampling
– Directed: Particular study areas selected
based on experience, accessibility, etc.
– Probability-based: For the total population of
interest, each element has a known probability
of being selected
Sampling & Sampling Schemes

Probabilistic sampling methods
– Random: Each feature has same probability
of selection
– Systematic: Repeated pattern guides sample
selection
– Homogeneous: Similar characteristics
throughout the study area
– Stratified: Characteristics vary throughout
study area (subdivisions internally
homogeneous)
• Features sampled w/in subdivisions
Probabilistic sampling methods