Top Banner
Why Is It There? Why Is It There? Getting Started with Geographic Information Systems Chapter 6
40

Why Is It There?

Feb 09, 2016

Download

Documents

Cheri

Why Is It There?. Getting Started with Geographic Information Systems Chapter 6. 6 Why Is It There?. 6.1 Describing Attributes 6.2 Statistical Analysis 6.3 Spatial Description 6.4 Spatial Analysis 6.5 Searching for Spatial Relationships 6.6 GIS and Spatial Analysis. - PowerPoint PPT Presentation
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Page 1: Why Is It There?

Why Is It There?Why Is It There?

Getting Started with Geographic Information SystemsChapter 6

Page 2: Why Is It There?

6 Why Is It There?6 Why Is It There?

6.1 Describing Attributes 6.2 Statistical Analysis 6.3 Spatial Description 6.4 Spatial Analysis 6.5 Searching for Spatial Relationships 6.6 GIS and Spatial Analysis

Page 3: Why Is It There?

Duecker (1979)Duecker (1979)

"A geographic information system is a special case of information systems where the database consists of observations on spatially distributed features, activities or events, which are definable in space as points, lines, or areas. A geographic information system manipulates data about these points, lines, and areas to retrieve data for ad hoc queries and analyses".

Page 4: Why Is It There?

GIS is capable of data GIS is capable of data analysisanalysis

Attribute Data– Describe with statistics– Analyze with hypothesis testing

Spatial Data– Describe with maps– Analyze with spatial analysis

Page 5: Why Is It There?

Describing one attributeDescribing one attribute

Flat File Database

Record Value Value Value

Attribute Attribute Attribute

Record Value Value Value

Record Value Value Value

Page 6: Why Is It There?

Attribute DescriptionAttribute Description The extremes of an attribute are the highest and lowest

values, and the range is the difference between them in the units of the attribute.

A histogram is a two-dimensional plot of attribute values grouped by magnitude and the frequency of records in that group, shown as a variable-length bar.

For a large number of records with random errors in their measurement, the histogram resembles a bell curve and is symmetrical about the mean.

Page 7: Why Is It There?

If the records are:If the records are:

Text– Length of text– word frequency– address matching

Example: Display all places called “State Street”

Page 8: Why Is It There?

If the records are:If the records are:

Classes– histogram by class– numbers in class– contiguity description

Page 9: Why Is It There?

Describing a classed raster gridDescribing a classed raster grid

5

10

15

20P (blue) = 19/48

Page 10: Why Is It There?

If the records are:If the records are:

Numbers– statistical description– min, max, range– variance and standard deviation

Page 11: Why Is It There?

Statistical descriptionStatistical description

Range (min, max, max-min) Central tendency (mode, median, mean) Variation (variance, standard deviation)

Page 12: Why Is It There?

Elevation (book example)Elevation (book example)

Page 13: Why Is It There?

MeanMean

Statistical average Sum of the values for

one attribute divided by the number of records

X ii 1=

n

= X

Page 14: Why Is It There?

Computing the MeanComputing the Mean

Sum of attribute values across all records, divided by the number of records.

A representative value, and for measurements with normally distributed error, converges on the true reading.

A value lacking sufficient data for computation is called a missing value.

Page 15: Why Is It There?

VarianceVariance

The total variance is the sum of each record with its mean subtracted and then multiplied by itself.

The standard deviation is the square root of the variance divided by the number of records less one.

Page 16: Why Is It There?

Average difference from the mean

Sum of the mean subtracted from the value for each record, squared, divided by the number of records-1, square rooted.

st.dev. =(X - X )

2i

n - 1

Standard DeviationStandard Deviation

Page 17: Why Is It There?

GPS Example Data: ElevationGPS Example Data: ElevationStandard deviationStandard deviation

Same units as the values of the records, in this case meters. The average amount by which the readings differ from the

average Can be above or below the mean Elevation is the mean (459.2 meters), plus or minus the

expected error of 82.92 meters Elevation is most likely to lie between 376.28 meters and

542.12 meters. These limits are called the error band or margin of error.

Page 18: Why Is It There?

Hypothesis testingHypothesis testing

Establish NULL hypothesis (e.g. Values or Means are the same)

Establish ALTERNATIVE hypothesis, based on some expectation.

Test hypothesis. Try to reject NULL. If null hypothesis is rejected, there is some

support for the alternative (theory-based) hypothesis.

Page 19: Why Is It There?

Uses of the standard deviationUses of the standard deviation

Shorthand description: given the mean and s.d., we know where 67% of a random distribution lies.

A standardized measure: – a score of 80% can be good or bad, depending

on the mean and s.d.

Page 20: Why Is It There?

Testing the MeanTesting the Mean

A test of means can establish whether two samples from a population are different from each other, or whether the different measures they have are the result of random variation.

Page 21: Why Is It There?

Samples and populationsSamples and populations

A sample is a set of measurements taken from a larger group or population.

Sample means and variances can serve as estimates for their populations.

Page 22: Why Is It There?

SpatialSpatial analysis with GIS analysis with GIS

GIS data description answers the question: Where?

GIS data analysis answers the question: Why is it there?

GIS data description is different from statistics because the results can be placed onto a map for visual analysis.

Page 23: Why Is It There?

Spatial Statistical Description Spatial Statistical Description

For coordinates, the means and standard deviations correspond to the mean center and the standard distance

A centroid is any point chosen to represent a higher dimension geographic feature, of which the mean center is only one choice.

The standard distance for a set of point spatial measurements is the expected spatial error.

Page 24: Why Is It There?

Spatial Statistical DescriptionSpatial Statistical Description

For coordinates, data extremes define the two corners of a bounding rectangle.

Page 25: Why Is It There?

Geographic extremesGeographic extremes

Southernmost point in the continental United States.

Range: e.g. elevation difference; map extent

Page 26: Why Is It There?

Mean CenterMean Center

mean y

mean x

Page 27: Why Is It There?

Centroid: mean center of a featureCentroid: mean center of a feature

Page 28: Why Is It There?

GIS and Spatial AnalysisGIS and Spatial Analysis Descriptions of geographic properties such as shape,

pattern, and distribution are often verbal Quantitative measure can be devised, although few are

computed by GIS. GIS statistical computations are most often done using

retrieval options such as buffer and spread. Also by manipulating attributes with arithmetic commands

(map algebra).

Page 29: Why Is It There?

An exampleAn example

Lower 48 United States 1994 Data from the U.S. Census on gender Gender Ratio = # females per 100 males Range is 97 - 108 What does the spatial distribution look like?

Page 30: Why Is It There?

Gender Ratio by State: 1994Gender Ratio by State: 1994

Page 31: Why Is It There?

Searching for Spatial PatternSearching for Spatial Pattern

A linear relationship is a predictable straight-line link between the values of a dependent and an independent variable. It is a simple model of the relationship.

A linear relation can be tested for goodness of fit with least squares methods. The coefficient of determination r-squared is a measure of the degree of fit, and the amount of variance explained.

Page 32: Why Is It There?

Simple linear relationshipSimple linear relationship

dependentvariable

independent variable

observationbest fitregression liney = a + bx

intercept

gradient

y=a+bx

Page 33: Why Is It There?

Testing the Testing the relationshiprelationship

gr = 117.46 + 0.138 long.

Page 34: Why Is It There?

Patterns in Residual MappingPatterns in Residual Mapping Differences between observed values of the dependent

variable and those predicted by a model are called residuals.

A GIS allows residuals to be mapped and examined for spatial patterns.

A model helps explanation and prediction after the GIS analysis.

A model should be simple, should explain what it represents, and should be examined in the limits before use.

Page 35: Why Is It There?

Mapping residuals from a modelMapping residuals from a model

Page 36: Why Is It There?

Unexplained varianceUnexplained variance

More variables? Different extent? More records? More spatial dimensions? More complexity? Another model? Another approach?

Page 37: Why Is It There?

GIS and Spatial AnalysisGIS and Spatial Analysis

Many GIS systems have to be coaxed to generate a full set of spatial statistics.

Page 38: Why Is It There?

Analytic Tools and GISAnalytic Tools and GIS Tools for searching out spatial relationships and for

modeling are only lately being integrated into GIS.

Statistical and spatial analytical tools are also only now being integrated into GIS, and many people use separate software systems outside the GIS: “loosely coupled” analyses.

Page 39: Why Is It There?

Analytic Tools and GISAnalytic Tools and GIS Real geographic phenomena are dynamic, but GISs have

been mostly static. Time-slice and animation methods can help in visualizing and analyzing spatial trends.

GIS organizes real-world data to allow numerical description and allows the analyst to model, analyze, and predict with both the map and the attribute data.

Page 40: Why Is It There?

You can lie with...You can lie with...

Maps Statistics

Correlation is not causation!