Data Quality & Uncertainty

Question 1

Importance of Data Quality

Answer 1

• Automatic tendency to regard outputs as a form of truth
• How reliable are the results/output
• Liability issues

Question 2

What are the 4 components of data quality?

Answer 2

1) Accuracy
2) Precision
3) Error
4) Uncertainty

Question 3

Liability

Answer 3

If not done correctly, could cause problems later
- Ex. wrong datum caused arrests to be thrown out of court because the boundary people crossed was not placed in the correct spot

Question 4

Data quality: Accuracy

Answer 4

• How close does the data match the true values or descriptions
• True for both spatial and attribute

Question 5

How do we account for data quality?

Answer 5

• usually best when personally collected
• scale, who, what, why
• What was the data intended for and can it be used for another purpose
• Spatially looks like it should an where it should be
• On target but maybe not clustered

Question 6

Data quality: Precision

Answer 6

• Scale
• How Exact the data are measured (map sheet vs. lat/long vs. UTM meters
• Higher level of precision from map sheet to meters
• Worst case: Precise data that is inaccurate

Question 7

Data quality: Error

Answer 7

• How far the data are actually from their true values
• Always present to some extent but does not fatally undermine GIS use
• Use statistics to determine if data can or cannot be used based on type/size of error (size–> distance from true?)

Question 8

What are the 3 types of Error?

Answer 8

• Gross
• Systematic
• Random

Question 9

Gross Error

Answer 9

Incredibly inaccurate

- easy to identify

Question 10

Systematic Error

Answer 10

Exact same on every piece of data

• X, Y accidentally set as Y, X
• Can be fixed/accounted for

Question 11

Random Error

Answer 11

Not easy to find

- Could be one data point or attribute incorrectly entered (10.21 entered as 102.1)

Question 12

Data quality: Uncertainty

Answer 12

Doubt due to incompete knowledge (someone else collected) (this is why metadata is essential)

• Many issues in GIS have uncertainty underpinning them
• Prevalent in processes/transformations
• Model behaviour (know how the model works, not just what it does, link to describe process and math involved i.e. white papers)

Question 13

Sources of Uncertainty

Answer 13

• Measurement
• GIS representation
• Reporting Numbers (lines for roads but what is the width)

Question 14

Data Collection

Answer 14

Quality control at the 1st step

Question 15

Data Input

Answer 15

Resolution when digitizing

- Boundaries (edge of forest by ownership vs edge of trees and how many trees dictate a forest?)

Question 16

Stages for Accounting for Data Quality

Answer 16

Real world - Inherent Uncertainty
Conception - Uncertainty in Conception
Measurement - Uncertainty in measurements
Analysis - Uncertainty in Analysis
- Acceptable values fall within/under a curve to deal with variability (ex. double breast height of a tree measured by different foresters can be ok if it falls within acceptabe values)

Question 17

Data quality: Positional Accuracy RMSE

Answer 17

• Square root of the average of the squared discrepancies in position (d) of well-defined points (n) determined from the map and compared to higher accuracy surveyed location of each point
• Calculates image from true difference and is scale dependent

Question 18

Fuzzy Sets

Answer 18

Defined by degree of membership

• Venn diagrams, set theory, and/or –> SQL
• probability (%) that something belongs to that category
• S-Curve (Venn diagram that acknowledges uncertainty)
• Can have partial membership in a set with yes, no, and maybe

Question 19

Uncertainty

Answer 19

Degree to which the measured value is estimated to vary from the true value

• Arise from a variety of sources including limitation on precision or accuracy of measuring system
• Often used to describe degree of accuracy of measurement

Question 20

Why would you choose a point, line, or polygon for the data?

Answer 20

Based on the purpose for the data

Question 21

S- Curve

Answer 21

Venn diagram with the uncertainty acknowledged

Question 22

Advantages of fuzzy sets

Answer 22

• Acknowledging uncertainty upfront

- Membership can be adjusted if more info becomes available

Question 23

What is the drawback?

Answer 23

People! (numbers from feelings)

• Probability values can reflect the way individuals state how they feel
• i.e. one person can state 90% surety while another with the same confidence can state 99% surety
• But which is it?

Question 24

G.C.S

Answer 24

Geographic Corrdinate System

- ex. Lat/Long

Question 25

MAUP

Answer 25

Modifiable Areal Unit Problem

• Classic source of error
• Especially when aggregating spatial units
• Data can be the same but aggregated differently
• Choices of areal units tent to be dominated by what is available rather than what is best (Ex. Crime in within an administrative boundary an be 5% but in a concentrated area of the boundary it can be up to 20% of properties broken into)

Question 26

Choropleth & MAUP

Answer 26

Choropleth will show that an entire polygon is 18% but really only a portion of it holds the majority

Question 27

What is a possible fix to account for MAUP?

Answer 27

Metadata!

• can’t fix but can account for issue
• Embrace Uncertainty!
• There will always be issues when you did not personally collect the data so justify with metadata, field checks, and GIS checks

Question 28

Some basic principle for dealing with uncertainty in GIS

Answer 28

• Use many sources to prevent building of error from same datasets
• Metadata
• Checks (Field & GIS)
• Appropriateness of reporting results ( Be careful how you phrase results with causes, may or could

Question 29

More thoughts on dealing with uncertainty

Answer 29

• Embrace uncertainty!
• Try multiple data sources to prevent error boiling
• Be honest in communicating what you know about the accuracy
• Report what you believe to be true, not what the GIS appears to be saying
• GIS has been slow to deal with or treat errors