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Lecture 11

# ESSE 3600 Lecture Notes - Lecture 11: Stochastic Process, Random Field, Inductive Reasoning

by

Department
Earth, Space Science and Engineering
Course Code
ESSE 3600
Professor
Sohn
Lecture
11

This preview shows half of the first page. to view the full 2 pages of the document. GIS Lecture 11
Equal interval classification-note that there are three categories to define classical attributes
Random field means each location is XYZ
-Field data is treated as a random variable
Clustering means to group a dataset into clusters based on their principle, maximizing interclusteral
similarity and minimizing interclusteral similarity
-ABC is defined according to our parameters
-Spatial entities have similar properties and other spatial entitites have nonsimilar properties
-Random errors help us analyze similarity or dissimilarity
-We can use different classifictation algorithms
-The proximity measure measures similairy in Gearys index
-There are certain relationships depending on proxminty
-Nearest neighbor method involves finding what is the closest
-Spatial patterns are related to spatial location
-IDW-the weight is the inverse of distance bw red & green
-Structural vs geographical matrix
-GIS is dependent on a process
-The object has to be described.
-Attributes help us evaluate the particular object
-binary model, regression model
-A descriptive model describes existing conditions
-prescriptive model is once there is a system, several parameters can be introduced. It is a mathematical
system that allows us to predict for example, vegetation maps. The system is built and is good for spatial
data modelling.
-Resource allocation is a model in which people adopt a stochastic model
-Stochastic model helps us to allocate resources
-Deductive models are top down models. ie you already have the theory and suggest a hypothesis from
this. Inductive is the opposite. You want to go from the specific to the general(think deductive and
inductive reasoning)
-ex: i predict it takes 40 minutes to get to YorkU, based on what information i have.
-Inductive model is bottom up, deductive is top down model.
-GIS research is done from an inductive model, trying to generalize.
-Binary model vs index model. Index model is based on raster data.
-Calibration is when arbitrary posiions are selected(refer to hospital example)
-Validation(remember hydrology) is to find out whether the model makes sense
-Cross validation, some portion of th sample is reserved for testing
-We design the sample. Model validation is to judge whether the model is acceptable or not.
-Sensitivity analysis is to make sure the attributes #1-3, and all attributes are summed. SA is a technique
that can quantify the model uncertainties by measuring the effects of input changes on the output. Ex:
vary one variable and see its effect on the markets[stock market example]
-Presenting decision to customers, weight values are changed. Within certain ranges, decision doesnt