Week 6 Flashcards

GIS, Artificial Intelligence

1
Q

Geographic Information Systems

A

Deal with geospatial data - data that identifies
the geographic location of features
– Typically coordinates

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2
Q

What is Choropleth mapping?

A

Choropleth maps are a form of chart which show the values of variables, instead of using bars or columns they use geographic regions on a map.
Display of attribute (non spatial) data associated with spatial entities.
Choropleth maps are charts, they are not analysis.

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3
Q

Special Operations

A
  • Identify spatial related objects
    – beside
    – enclosed within
    – within radius
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4
Q

Buffers

A

Bands around spatial objects

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5
Q

Insurance Use of GIS

A
  • Existing policies : Show who and where has
    policies, what is protected, and for which sum.
  • Firmographics : This type shows which
    businesses are located side by side. e.g. is a
    bookshop near a fire station or a firework shop.
  • Historical loss information : Spatial record of
    all losses for the entire observation period.
  • Location risk index : For customers, each
    location features a specific risk index.
  • Natural disasters : fires, floods, tornadoes,
    earthquakes, etc
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6
Q

Artificial Intelligence

A

AI is behaviour, which if performed by a human
being, would be called intelligent.
– such as the ability to reason
– learn from past experience
– acquire and retain knowledge
AI cannot do general tasks, e.g. common sense. It has increasing success at specialised tasks where the range of possibilities is limited
- Playing games
- Engineering
- Financial analysis

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7
Q

Expert Systems

A

– performs a task that would otherwise be performed
by a human expert
– reasoning approach
– “storage” of pre-established knowledge

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8
Q

What is an Expert System

A
  • 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, it stores expert knowledge.
    – Only in very specific detailed areas
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9
Q

Benefits of Expert Systems

A
  • faster decisions
    – foreign exchange dealing
  • better decision quality
  • capture of scarce expertise
    – limited number of experts
    – expert not in correct place
  • integration of several opinions
  • cheaper control devices
  • spread of knowledge
  • reliability
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10
Q

Problems with Expert Systems

A
  • Getting knowledge from expert is hard
    – Too busy
    – Don’t understand the nature of the information
    required
    – Don’t believe in ES approach
  • System is frozen in time
    – No systematic updating
  • Expert rules can be inflexible
    – IF (salary > 40000) THEN grant loan
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11
Q

Case Based Reasoning

A

Case based reasoning looks at previous examples. These previous examples are never quite the same so identifying similarity and modifying the solution are critical.

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12
Q

Uses of CBR in Insurance

A
  • Underwriting Decisions:
    – If a new policy proposal is like previous examples,
    then it can be routinely accepted.
  • Claims Handling:
    – Refer to similar previous examples .
  • Pricing and Premium Setting:
    – Previous examples can be a starting point for
    calculations
  • Fraud Detection
  • Regulatory compliance
    – If a case is similar to a previously compliant one
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13
Q

Machine Learning

A

Machine learning is an approach to the field of Al Systems trained to recognize patterns within data to acquire knowledge

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14
Q

Supervised learning

A
  • Supervised learning
    – Provide ML algorithm with curated data set
  • 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
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15
Q

Unsupervised learning

A
  • Unsupervised learning uses machine learning
    algorithms to analyse and cluster unlabelled data
    sets.
  • 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
    – Correlation without explanation
    e.g. famous beer and nappies example
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16
Q

Advantages of Machine Learning

A
  • Identification of trends and patterns
  • Largely automatic, with limited human work
    needed
17
Q

Issues with Machine Learning

A
  • Garbage in/ Garbage out.
    – You have the data, but is it quality data?
    – Previous processing of data may create bias.
  • You still need large computing power
    – Cost and energy consumption
  • Results cannot be easily explained
  • If circumstances change then your model may be
    useless.