Lecture 3 (From Geographic Features to GIS Data: Representing Geography) Flashcards Preview

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Flashcards in Lecture 3 (From Geographic Features to GIS Data: Representing Geography) Deck (33)
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1

The Value of Maps

1) Represent the world
2) Communicate the geographic information to each other

2

Sensing the World

- Personal experience limited in time and space (one human lifetime/ a small fraction of the earth's surface)
- All additional knowledge comes from books, media, movies, maps, images, and other information sources (From indirect or "remote" sensing)
- DIGITAL REPRESENTATION HAS MANY USES BECAUSE OF ITS SIMPLICITY AND LOW COST.

3

Representations

- Needed to CONVEY information
- Fit information into a STANDARD form or model
Almost always SIMPLIFY THE TRUTH that is being represented

4

Simplification and Standardization

They help us assemble far more knowledge about the Earth than it is possible on our own.

5

The importance of representation in GIS

- Spatial interpolation in places where no observations were made ( SOIL CHARACTERISTICS and RAINFALL)

- TOBLER's FIRST LAW OF GEOGRAPHY - "Everything is related to everything else, but near things are more related than those far away."

EX. UN/FAO Soil Map of the U.S. and Average Annual Precipitation, 1971-2000 in Oregon

6

Key Issues of GIS Representation

Accuracy of Representation
- Details can be irrelevant, or too expensive and voluminous to record.
- What to represent? / What to leave out?
- How to represent the infinitely complex world?
NONE IS PERFECT AND IDEAL FOR ALL APPLICATIONS
- What is missing in a representation?

7

"How to lie with maps"

Mark Monmonier, 1991 and 1996 “If a picture is worth a thousand words, a map can be worth a million --but beware. All maps distort reality …In short, the author warns, all maps must tell white
lies." (Distinguished Professor of Geography at the Maxwell School of Syracuse University)

8

Approximations of the Earth's Shape

a) Sphere O
b) Ellipsoid
c) Geoid
- Note that the shape of the ellipsoid and geoid are highly exaggerated for illustrative purposes.
GEODESY - the science of earth measurement.

9

Map Projection Process

Terrestrial Surface -> Geoid -> Ellipsoid -> Nominal or generating globe -> 2D Map

10

The Fundamental Problem

- The geographic world is infinitely complex, but computer systems are finite.
- The more closely we look at the world the more detail it reveals.
- REPRESENTATION is all about the CHOICES to capture information about the world.
We must somehow limit the amount of detail in the digital representations.

11

The Fundamental Problem

- Common strategies for limiting detail (Throw away or ignore information that applies only to a small area - Spatial resolution - the degree of detail/ Ex. 10km, anything less that 10km across is virtually invisible/ Large or small scales).
- Observe many properties remain constant over large areas.

12

Scales

Small, Medium, Large - In this way, we classify the world into fewer groups and also make our database to be much simpler.

13

Geographic Attributes

- Geographic information links a place, time, and ATTRIBUTE (DESCRIPTIVE PROPERTY). "The temperature at noon local time on 1/23/2011, at 34N, 120 W was 43 Fahrenheit"
- Geographic attributes describe all kinds of properties (PHYSICAL, ENVIRONMENTAL, SOCIAL, ECONOMIC)

14

Types of Attributes

Nominal -> land cover class, name (EX. State name)
Ordinal -> a ranking
Interval -> temperature
Ratio -> Income, weight (EX. Income Map)
Cyclic -> Wind/ slope direction (Ex. Wind direction and Wind Rose Map)

15

Discrete Objects and Continuous Fields

- The most fundamental distinction in geographic representation

16

Discrete Objects

- Countable
- Dimensionality (Points, Lines, and Areas)
- Objects with well-defined boundaries
Persistent through time, perhaps mobile
Examples: Biological Organisms - animals, trees, peaks/ Human- made objects - road, buildings, dam

17

Discrete Objects

- Points (Zero-dimension)
- Lines (One-dimension)
- Polygon (Area/ Two-dimension)
- Three-dimensional object (Including the altitude)

18

Continuous Fields

- The world as a continuous surface
- Represent the real world as a finite number of variables, each one defined at every possible position.
- Properties vary continuously over space (A single value at every point on the Earth's surface/ Value is a function of location/ Property can be of any attribute type)

EX. Soil Moisture, Geodemographic Structure of Nottingham, UK, Weather Satellite Image (Hurricane)

Difficult Cases - Lakes and other natural phenomena (often conceived as objects, but difficult to define or count precisely), Weather forecasting (forecasts originate in models of fields, but are presented in terms of discrete objects - highs, lows, fronts) Ex. Hurricane Isaac

19

Representation

Spatial Representation (the map side)
Attributive representation (the table side)

20

Representation with Layers

Vector - Transportation, Land Use, Census Tracts, Structures
Raster - Postal Codes, Raster Imagery

21

Spatial Representation - The Map Side

Raster Data
Vector Data

22

Raster Data

- More commonly used to represent CONTINUOUS FIELDS than discrete objects (It does not mean to exclude discreet objects)
- Divide the world into SQUARE CELLS
- Assigning attribute value to each cell
- Represent discrete objects as collections of one or more cells

23

Vector Data

- Represent DISCRETE OBJECTS than continuous fields
- Used to represent points, lines, and areas/ polygons
- All features are represented in coordinates

24

Raster v Vector

- Volume of data (Raster - depends on cell size, Vector - depends on density of vertices)
- Source of data (Raster - remote sensing, elevation data, Vector - Administrative data (social, environment)).
- Software - Some GIS better suited to raster, some to vector.
Resolution (Raster - Fixed, Vector - Variable).

25

Attribute Representation - The Table Side

Discrete Objects v. ATTRIBUTE FIELDS
- Objects and FIELDS
Attribute Fields = Variables

26

Attribute Database

Data Base Components
- Records (row)
- Attribute fields/ values (column)
- Keys - query fields (key column)

Three Classic Models
- Hierarchical
- Networked
- Relational/ Tabular

27

Some concepts in representation

- Generalization
- Specification
- Weeding

28

Generalization

Reducing the level of detail in geographic data
- By simplifying, weeding, abstracting
- To reduce the volume of data WITHOUT ADVERSELY AFFECTING ITS USE
- To "see the wood for the trees" (to understand the bigger picture without being caught up in small details).

29

Specification

How real world on the ground is selected for inclusion on the map
- Refinement/ replacement of a complex pattern of objects by a selection that PRESERVES THE PATTERN'S GENERAL FORM
- Enhancement, through the alteration of the physical sizes and shapes of symbols

30

Weeding

The process of removing points in a polygon or polyline while preserving important aspects of shape
- E.g. the Douglas-Poiker line simplification algorithm