Week 06

Question 1

What does GIS Stand for?

Answer 1

Geographic Information Systems

Question 2

What are GIS’s? with examples

Answer 2

Systems Dealing with geospatial data - data that identifies
the geographic location of features. Typically coordinates
Ex of spatial database available: ordanace survey or open street map, google maps etc

Question 3

Explain spatial techniques

Answer 3

If an issue is spatial, spatial techniques identify trends not obvious from other forms of analysis. ex: John Snows work in 1854 on the cholera spreading and identifying the water pump at the epiccentre of the disease.

Question 4

What are the different types of spatial autocorrelation

Answer 4

Positive spatial autocorrelation - is the tendency for areas or sites that are close together to have similar values.
No spatial autocorrelation
Negative spatial autocorrelation - geographic distribution of values, or a map pattern, in which the neighbors of locations with large values have small values

Question 5

Explain choropleth mapping

Answer 5

Display of attribute (non spatial) data associated with spatial entities. Come sup with often a colour scale (but some sort of scale) for a parameter of interest and makes patterns in a geographic location easier to see. Can do this in excel.

Question 6

What are spatial operations

Answer 6

Spatial operations are functions that create new spatial data from specified input data. They identify spatially related objects ex: Beside, enclosed within, within a radius of

Question 7

Explain what buffers in geograhical analysis refers to.

Answer 7

In GIS, a buffer is a zone that is drawn around any point, line, or polygon that encompasses all of the area within a specified distance of the feature

Question 8

What applications are buffers often used?

Answer 8

To show mobile coverage, also were used during covid restrictions

Question 9

Why is buffer method not very sophisicated?

Answer 9

Circles are not overly accurate - as often drawn as the crow flies rather than travel distance.

Question 10

What are the business applications of GIS?

Answer 10

Traditional users of these systems would be : Utilities ex: ebs companies, forestry ex: coillte or the government
Used for spatial decision making for example: location analysis, transport or logistics decisions, marketing and insurance

Question 11

How and why woudl GIS be used in insurance give some examples

Answer 11

ex: where your house is located in relation to flooding
ex: Risk of earthquake in american areas

Question 12

What is AI?

Answer 12

Term associated with branch of computer science concerned with the development of systems endowed with AI capabilities.
AI is a behaviour that is intelligent. Means the behaviours meaning ability to reason, learn from past experience and acquire and retain knowledge.
Computers don’t generally learn from making mistakes, they will make the same mistake over and over

Question 13

What are the three task domains of AI? - What will be the most difficult for computers to adjust to

Answer 13

Expert Tasks, Mundane tasks (most difficult for computers) and formal tasks.

Question 14

Give an example of expert tasks as part of the task domains of AI?

Answer 14

Engineering, medical diagnosis, financial analysis

Question 15

Give an example of mundane tasks as part of the task domains of AI?

Answer 15

Perception, robotics, common sense reaosning, natural language

Question 16

Give an example of formal tasks as part of the task domains of AI?

Answer 16

Mathematics, Games

Question 17

Explain AI techniques under accessing existing knowledge

Answer 17

Expert systems - Performs a task that would be otherwise performed by a human expert, storage of pre-established knowledge.
Case-based reasoning - WHat did we do last time idea

Question 18

Explain AI techniques under creating new knowledge idea

Answer 18

Machine learning - system itself updates knowledge. learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.

Question 19

Explain what is an expert system?

Answer 19

A computer program that emulates the behaviour of human experts who are solving real problems associated with a particular domain of knowledge.
Not used by experts, but stores expert knowledge. – Only in very specific detailed areas

Question 20

What are the benefits of expert systems?

Answer 20

• faster decisions – foreign exchange dealing good to have a first mover advantage
• better decision quality
• capture of scarce expertise – limited number of experts and an expert may not be in correct place
• integration of several opinions
• cheaper control devices
• spread of knowledge
• reliability

Question 21

What are some problems with expert systems?

Answer 21

• Getting knowledge from expert is hard – Too busy or they may not understand the nature of the information required
• System is frozen in time – No systematic updating
• Expert rules can be inflexible

Question 22

Explain what is case-based reasoning and what are steps required for CBR.

Answer 22

Solving problems automatically by doing what you did last time - but no problem is exactly the same.
CBR learns so long as main problem does not change

Steps:
Case base describing problems and solution
Match best previous examples
Then modify the solution to suit the current problem
If solution works add it to the case base

Question 23

What are the four r’s of CBR cycle?

Answer 23

Retrieved (similar case)
Reuse (Adaption solution)
revise (verify it works)
Retain (learning)

Question 24

How does machine learning process differ from traditional programming process?

Answer 24

Traditional: Computer takes in data and programs and produces an output
Machine learning: Takes data and outputs and produces a program

Question 25

Explain difference between machine learning and statistics

Answer 25

Statistics
– Based on mathematical principles
– Requires data to meet requirements for those techniques
– Prediction less important than understanding
Machine Learning
– Based on what works
– Solutions proposed from training datasets
– Solutions validated by test datasets
– More flexible in data input
– Prediction important, understanding less so

Question 26

Explain supervised learning

Answer 26

Supervised learning is an approach to creating artificial intelligence (AI), where a computer algorithm is trained on input data that has been labeled for a particular output

Question 27

What are the steps to supervised learning?

Answer 27

Classification: Predicting to which discrete class an entity belongs
Regression: Predicting continuous values of an entity’s characteristic.
Forecasting: Estimation of macro (aggregated) variables such as total monthly sales
Attribute Importance: Identifying the variables (attributes) that are the most important for prediction

Question 28

Explain unsupervised learning?

Answer 28

Uses machine learning algorithms to analyze and cluster unlabeled datasets.

Question 29

Explain the steps in unsupervised learning

Answer 29

Clustering: Finding natural groupings in the data.
* Association models: Analysing “market baskets” (e.g., novel combinations of the products that are often bought together in
shopping carts – Checking for Correlation without explanation

Question 30

What are the advantages of machine learning?

Answer 30

Identification fo trends and patterns
Largely automatic, limited human work needed
Modern technology means there are large amounts of data that can be used, storage and cloud computing are cheap and often offer ML products

Question 31

What are issues with machine learning?

Answer 31

You have the data but what is its quality?
Previous processing of data may create bias
You need a large computing power - which has a cost of energy consumption
results cannot be easily explained
If circumstances change then your model may be useless

Question 32

What jobs will have increased chances of automation

Answer 32

Higher skilled jobs have less risk while lower skilled jobs have a very high chance of being redundant

Question 33

Why do we often used combined systems of AI?

Answer 33

Combination of approaches is the best
Combining human rules, experts review etc with automated rules and using machine learning to establish similarity in case based reasoning
Business analytics - focus is not on the techniques but on the business