7. Introduction to STIS and ESTDA - biomedware.com. Introduction to... · boxplots, histograms,...

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Space-Time Intelligence System™

Introduction to SpaceStat and ESTDA

Understand. Clarify. Decide.www.terraseer.com


OutlineI Data Preparation

II Introduction to ESTDA and SpaceStat

III Introduction to time-dynamic regression



ESTDA & SpaceStat• ESTDA

• Activities in a Space-Time Data Analysis

• SpaceStat Functionality

• Navigating SpaceStat (Interface, Help, Projects)

• Working with Data (load data, metadata, statistical graphics)

• Working with maps (queries, display properties)


g p (q , p y p p )

• Linked windows and brushing

• Map and graph animations


Learning Objectives

• The philosophy of ESTDA

• Scientific inference using ESTDA

• ESTDA, models of data, and models of process

Using SpaceStat


• Using SpaceStat

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Vocabulary• ESTDA: Exploratory Space-Time Data Analysis

• P-value: Probability of an event under a given null hypothesis

• Data model: A model whose parameters are defined in terms of the statistical properties of the data

• Process model: A model whose parameters are defined in


• Process model: A model whose parameters are defined in terms of the properties of the system under study


Philosophy of ESTDA

• Relationship to data models and process models

• What is ESTDA?

• Goals of ESTDA

Problems


• Problems


Analysis ApproachesKnowledge

Models of Data Some

Models of Process Lots


ESTDA Little


Disease DataContextual Data

Disease /Environment Map

Draw map

ST A l i

Scientific Hypothesis

Prediction

pattern? StopST Analysis

Inference

DeductionTheory

Experiment

no

yes

New hypothesis


RejectPrediction?

Intervention

Experiment

Interpret

yes

no

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ESTDA

• Goal One: Identify pattern

• Goal Two: Generate hypotheses

• Goal Three: Make predictions

• Problems:– Encountered data

M lti l t ti


– Multiple testing– Pre-selection bias– Emergent hypotheses can’t be tested on data from

which they were formulated


Role in Inference Structure

• Karl Popper and the `Scientific Method’

• Strong Inference

• An Inference Structure for ESTDA


• `Gee Whiz’ Effect


The Scientific Method

Observation

Theory

Prediction

Inference

Deduction


Rejected Not Rejected

Experiment

O’Hear, A. (1996). Karl Popper, Philosophy and Problems. Cambridge, Cambridge University Press


Lessons

• Predictions and theories can be falsified, but t not proven

• Useful predictions can be falsified by experiment

• Observations obtained by experiment must be


Observations obtained by experiment must be independent of data uses to infer theory

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Formulate complete set of alternate hypotheses.

Do the experiment.

Devise crucial experiment to systematically exclude hypotheses.


Repeat for each stage of the problem.

Platt, J.R. (1964). Strong Inference. Science, 146: 347-353.


ESDA avoids “Gee Whiz”Disease DataContextual Data

Disease /Environment Map

Draw map

Spatial Analysis

Scientific Hypothesis

Prediction

pattern? Stopp y

Inference

Deduction

R j

Theory

Experimentyes

no

yes

New hypothesis


RejectPrediction?

InterventionInterpret

yes

no


Activities in a Space-Time Data Analysis1. Describe data using descriptive statistics and

statistical graphics

2 Vi li d t th h ti i ti l t 2. Visualize data through time using time plots, synchronized windows and statistical graphics animation

3. Visualize data geographically using maps, cartographic brushing and map animation

4. Identify statistical, spatial and temporal outliers using


4. Identify statistical, spatial and temporal outliers using boxplots, histograms, variogram clouds and LISA statistics

5. Transform variables using the normal score and z-score transformations with time-slice and time-weighted means


Activities in a Space-Time Data Analysis8. Evaluate rate stability to determine whether

adjustment for the “small numbers” problem is needed

9. Stabilize rates using empirical Bayes and Poisson kriging

10. Interpolate data using nearest neighbor, distance and kriging methods

11. Identify sub-populations with significant disparities


12. Identify clusters and undertake disease surveillance

13. Quantify and model spatial dependencies using global and local spatial autocorrelation analysis

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Activities in a Space-Time Data Analysis14. Quantify and model spatial dependencies using

variogram analysis

15. Make predictions using aspatial regression through time (linear, Poisson, logistic)

16. Analyze residuals (aspatial, spatial and through time)

17. Make predictions using geographically weighted regression


regression

18.Compare results from aspatial and geographically weighted regression

19. Make predictions using geostatistics


SpaceStat Methods• All time-dynamic!

• Standardization (normal score transform, z-score)• Difference (change maps)• Aggregation (spatial, temporal)• Spatial Interpolation (nearest neighbor, distance weighted,

kriging)• Clustering

– Continuous (Global, local Moran, local G)– Case-population (Turnbull, Besag & Newell)– Case-control (Global, local, focused Q)


Case control (Global, local, focused Q)• Smoothing (Empirical Bayesian) • Disparities (Relative, Absolute)• Aspatial regression (linear, logistic, Poisson)• Geographically weighted regression (linear, logistic, Poisson)


SpaceStat Graphics & Tools



SpaceStat Functionality• Data prep

– Import file types and formats– Time stamps– Common problems

• Mapping– Static maps– Map queries– Difference mapsp

– Export

• Statistics – Tables– Histograms– Box plots– Scatter plots– Time plots– Z-transform – Spatial weights

p– Slide shows– Movies– Value Animations– Cluster Animations– Linking Animations– Clusters (high-high, low-low) – Outliers (high-low, low-high) – Cluster persistence– Brushing– Linked windows


Spatial weights – Moran’s I – Moran scatterplot – Univariate LISA – Bivariate LISA

– Creating .avi files– Mobility histories – Polygon morphing – Creating spatial subsets– Creating value subsets

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Navigating SpaceStat

• Starting SpaceStat

• Data view, map view and map legend

• Create a new Project

• Getting Help



Exercise 1: Load a project

• Start SpaceStat

• Load the project “Lung.sts”

• Identify the data view, the spatial weights set and variogram model view, the log view, the tool bar and the main menu


tool bar and the main menu

• Use help to learn about handles and docking

Space-Time Intelligence System™Tool Bar Main Menu

DataView

Spatial weights & Variogrammodels view


Log View

models view



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SpaceStat Projects allow you to• Save your work

• Share your work

• Record derived data sets (e.g. cluster maps)

Restore SpaceStat to a prior state


• Restore SpaceStat to a prior state


Exercise 2: Rename datasets, create projects

• Close the timeplots

• In the data view right click on “RWM” select “properties”• In the data view, right click on RWM , select properties

• View the data set history and type in “Rates White Males” for the “Dataset Name”

• Click “File”, “Save Project”, Save your project as “Myproject”

• Click “File”, “Exit” to close the project


• Click “File”, “Open Project”, & open “Myproject”

• Your data sets are restored

• Lesson: Use projects to protect your work!


Rename “RWM”

“Rates White Males”


Rates White Males

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Click “File” “Save As”; Enter“MyProject” for the File name


SpaceStat Help• Help describes

– Data

Dynamic Data Exploration– Dynamic Data Exploration

– Data view, data formats, geography, spatial relationships, & managing maps

– Statistics, statistical graphics, cluster statistics, randomization, LISA methods

Tutorials


– Tutorials

– Examples

– Key References


Exercise 3: Using Help

• Task: Use help to determine

– How to generate a centroid geography from a polygon geography

– Spatial data formats used


– What the difference is between a time slice and a time series


Hint: Use “Search” to find the answers quickly


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Hint: Click “See Also” to view related topics




Exercise 3: Help (continued)

L U H l t i kl • Lesson: Use Help to quickly answer questions on data, methods and software



The Visualization ToolbarMap Histogram Box plot

S ttTable View

All except for thetime plot are animated!

PCP


ScattergramTime plot Variogram cloud

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The Animation ControllerStep Continuous play

Pl Stop Time


PlayTime slider

Stop


Exercise 4: Working with Maps

• Create a map (use )

– In the data view, For the SEA geography, right click on “RWF” and “Create map”

– A map of the lung cancer mortality rate for white females, all ages, from 1950 through 1994 is displayed

Animate the map by moving the time slider on the


– Animate the map by moving the time slider on the animation controller

– Is lung cancer increasing, decreasing or staying the same through time?


Map white female mortality rates for the

SEA geography


SEA geography


Use the time slider to animate the mapto animate the map


Hint: Click on“+” to expand and “-” to collapse the legend

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Working with maps (cont)

• Map queries, animated queries

– Set the time to 01/01/1994

zoom

Set the time to 01/01/1994

– Zoom in on the SEA where you live

– Right click on that SEA > “Inspect this location”

– What is the lung cancer mortality rate for white females in your SEA?

– Click on “save” on the inspect data window, then right click on another SEA


– How do the lung cancer mortality rates in the two SEA’s compare?

– Move the time slider. What happens to the query data?

– Close the inspect data window



Female lung cancer mortality rates in Flint and Ann Arbor

SEA’s


Working with Maps (cont)• The map properties dialog

– Click and drag on the map to create a selection rectangle. Move the rectangle around on the map

– Click on the properties button on the map view

– Click the “General” tab, change the fill color and line width of the “selected polygon attribute”, then “preview”


– Click “ok” and select a region on the map

– The display characteristics of the selection rectangle have changed


Working with Maps (cont)

• Create a classified color scheme

– Click on the map properties button, “Polygon Fill Attributes”, “Color Mode”, “Classified”

– Set “Classification Method” to “Equal Intervals” and select 5 map classes


– Click “Preview” to see the change on your map

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Map Properties change how the map is displayed


Use “Preview” to view changes to the map



• How many color modes are there?

• How many ways are there to classify your map?

• Use “help” from the properties dialog to learn about color modes and classification


about color modes and classification




Map Display Methods

• Color Modes • Classifications– Single

color/transparent– Continuous– Classified– Qualitative

– Equal Intervals– Quantiles– Jenk’s Natural

Breaks– Custom


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Working with maps (cont)• Display multiple maps

– Using SEA geography, right click on “RWF” in the data view and “Create Map”. Apply the “Continuous” color mode

– Create another new map and apply the “Quantiles” classification with 5 classes

– Create another new map and apply “Jenk’s Natural


p pp yBreaks” classification with 5 classes (may take a few minutes to calculate)

– Set time for the maps to 01/01/1994. Compare and contrast apparent patterns on the four maps

Space-Time Intelligence System™The visual assessment of patterns on maps is subjective



Working with maps (cont)• Linking and animating maps

– On the Main Menu click “Windows” and “Synchronize views”

– Link the maps together by selecting a map in the “views” field, and then clicking on maps in the “synchronized views” field.

– The map selected in the “Views” panel is linked to the


– The map selected in the Views panel is linked to the ones shown in the “Linked Views” panel

– Move the time slider on one of the maps. What happens?

Space-Time Intelligence System™Select “Window” and “Synchronize views”


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“RWF Equal Intervals” is linked to the three other maps



Brushing and Linked Windows

• All statistical graphics and maps are linked th h hthrough common geography

• Use statistical and cartographic brushing to better quantify and explore patterns in your data


• Then apply statistical tests to determine whether the patterns you observe are real, or occur by chance



• Linking and Brushing

Close all b t one of o maps– Close all but one of your maps

– Create a scattergram of RWM (may have been renamed Rates White Males) and RWF

– Create histograms (1 each) of RWM & RWF


– Click and drag on the map to create a selection box

– Click and drag on the histograms to create selection boxes

– How do the different views interact as you move the selection boxes?


Click here to create a scatter plot

Enter the variables


Enter the variables to plot here

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Click to create a histogram

Enter the variable here



Brush the map to explore statistical properties of selected areas


Space-Time Intelligence System™Brush the graphs to explore spatial & statistical relationships



Working with Maps (cont.)• Linking and animating maps and graphs

– Synchronize the maps and graphs using – Synchronize the maps and graphs using “Window”>”Synchronize views”. Move the time slider. What happens?

– Are both male and female cancer mortality increasing through time?

– Set time to 1/1/1950 and use statistical brushing on the


Set time to 1/1/1950, and use statistical brushing on the RWM histogram to select the SEA’s lowest in cancer mortality. Animate the views. What happens to these SEA’s? Are they always lowest in cancer mortality or does their location in the distribution change through time?

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Summary• Data prep

– Import file types and formats

– Time stamps

• Mapping– Static maps– Map queries– Difference maps

– Common problems – Export

• Statistics – Tables– Histograms– Box plots– Scatter plots– Time plots– Z-transform

p– Slide shows– Movies– Value Animations– Cluster Animations– Linking Animations– Clusters (high-high, low-low) – Outliers (high-low, low-high) – Cluster persistence– Brushing– Linked windows


– Spatial weights – Moran’s I – Moran scatterplot– Univariate LISA – Bivariate LISA

– Creating .avi files– Geospatial lifelines – Polygon morphing – Creating spatial subsets– Creating value subsets


Questions?


7. Introduction to STIS and ESTDA - biomedware.com. Introduction to... · boxplots, histograms,...

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