C188 Week 3 And 4 - Definitions

Front 1

5 Primary Functions of Analysis

Back 1

Description
Explanation
Prediction
Prescription
Assessment

Front 2

Description - What does this phenomenon look like?

Back 2

Basic information (height, etc.)
Context (relationships: <,>,=)
Relative Position (nearer/farther than, higher/lower than)

Front 3

Explanation - Why does phenomenon occur as it does?

Back 3

Attempts to describe cause and affect relationships

Front 4

Prediction - What if...?

Back 4

Use "problem modeling" and "scenario testing" to predict how phenomenon will change due to changing circumstances

Front 5

Prescription - We should...

Back 5

Make recommendation based on evaluation of models and scenarios

Front 6

Assessment - How'd it go?

Back 6

Evaluate outcomes of recommendation

Front 7

Rothermel Fire Model

Back 7

Predicts rate of fire spread thru uniform fuel array in absence of wind and topography

Front 8

Inputs of Rethermel Fire Model

Back 8

Fuel loading, depth, surface to volume ratio, heat contentt, moisture and mineral content, extinction moisture content

Later: wind velocity, slope

Front 9

Outputs

Back 9

Rate of spread, intensity

Later: heat per unit area, flame length, direction of max. spread

Front 10

Residential Fire Hazard Assessment Model (RFHAM)

Back 10

Inputs: vegetation type, features, and distribution with respect to structure

AND

structural materials and building design

Front 11

Scale

Back 11

Scale = smallest area that can be drawn or recognized (cannot represent objects accurately; influences content)

Minimum mapping unit - level of geography at which the data was captured (only dominant species are mapped)

Front 12

Scale in Digital Maps

Back 12

Zoom feature gives false sense that scale can be altered.

Scale at which map is displayed says nothing about positional accuracy or completeness of disp. features

Front 13

How do we know accuracy?

Back 13

Thru support documents that give +/- %error

Front 14

Data Aging

Back 14

Maps are static and become more inaccurate over time

Front 15

Symbolizing Imprecision (Examples: Streams, Fault Lines)

Back 15

Showing permanent, seasonal, intermittent streams

Using solid, dashed, dotted w/ increasing uncertainty

Front 16

What are the most inaccurate data sources? Causes?

Back 16

Records of legal property boundaries.

Due to migrating land marks (e.g. streams)

Front 17

Edge Mapping

Back 17

Digital procedure to ensure that all features crossing adjacent map sheets have same edge locations, attribute descriptions, and feature classes

Front 18

Orthorectification

Back 18

Geometric correction such that the scale of the photograph is uniform, meaning that the photo can be considered equivalent to a map

Front 19

Typology of Data Quality Error

Back 19

Validity

Precision

Accuracy

Reliability

Front 20

Validity - Defintion

Back 20

Degree of confidence between what is intended to be measured and what is actually being measured

Front 21

Positional Validity

Back 21

Line in the data must be perceived as a line in reality

Front 22

Attribute Validity

Back 22

An attribute may be referring to only part of a feature.

Front 23

Precision - Definition

Back 23

Fineness of measurement scale (i.e. # of Sig Figs)

Front 24

Positional Precision

Back 24

Det. by ground resolution in data capture (reduced in maps)

Front 25

Attribute Precision

Back 25

Measure of how much aggregation of an attribute has been carried out

Front 26

Accuracy - Definition

Back 26

Closeness of measurement or observations to true values

Front 27

Positional Accuracy

Back 27

Confidence that position of feature as depicted is true

Front 28

Attribute Accuracy

Back 28

Closeness of measured and classified thematic values to reality

Front 29

Reliability - Definition

Back 29

Consistency of measures from several replications

Front 30

Positional Reliability

Back 30

Repeat sampling renders same results as initial testing

Front 31

Attribute Reliability

Back 31

Repeat sampling renders same results as initial testing

Front 32

What are US govts standards for data quality?

Back 32

Spatial Data Transfer Standard (lineage or history of a spatial dataset, positional/attribute accuracy, logical consistency, completeness)

Front 33

Topological Relationship

Back 33

Relationship of one spatial element with respect to another
(A inside B, D connected to B, C Disjoint from B, G overlaps E)

VS.

proximal (A near/far from B)
Directional (G north of C)