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

by

School

York UniversityDepartment

Earth, Space Science and EngineeringCourse Code

ESSE 3600Professor

SohnLecture

11This

**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

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