Outline. Introduction to SpaceStat and ESTDA. ESTDA & SpaceStat. Learning Objectives. Space-Time Intelligence System. Space-Time Intelligence System

Transcription

1 Outline I Data Preparation Introduction to SpaceStat and ESTDA II Introduction to ESTDA and SpaceStat III Introduction to time-dynamic regression ESTDA ESTDA & SpaceStat Learning Objectives 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) p Linked windows and brushing The philosophy of ESTDA Scientific inference using ESTDA ESTDA, models of data, and models of process Using SpaceStat Map and graph animations 1

2 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 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 Analysis Approaches Models of Process Models of Data ESTDA Knowledge Lots Some Little Disease Data Contextual Data Disease /Environment Map no pattern? Stop yes Theory Scientific Hypothesis Prediction New hypothesis yes Reject Prediction? no Intervention Draw map ST Analysis Inference Deduction Experiment Interpret 2

3 ESTDA Goal One: Identify pattern Goal Two: Generate hypotheses Goal Three: Make predictions Problems: Encountered data Multiple l testingti 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 Lessons Observation Theory Prediction Experiment Inference Deduction Predictions and theories can be falsified, but not proven Useful predictions can be falsified by experiment Observations obtained by experiment must be independent of data uses to infer theory Rejected Not Rejected O Hear, A. (1996). Karl Popper, Philosophy and Problems. Cambridge, Cambridge University Press 3

4 Formulate complete set of alternate hypotheses. Devise crucial experiment to systematically exclude hypotheses. Do the experiment. Repeat for each stage of the problem. Platt, J.R. (1964). Strong Inference. Science, 146: ESDA Disease avoids Data Gee Whiz Contextual Data Theory New hypothesis yes Disease /Environment Map no pattern? Stop yes Scientific Hypothesis Prediction Rj Reject Prediction? no Intervention Draw map Spatial Analysis Inference Deduction Experiment Interpret Activities in a Space-Time Data Analysis 1. Describe data using descriptive statistics and statistical graphics 2. Visualize data through h 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 boxplots, histograms, variogram clouds and LISA statistics 5. Transform variables using the normal score and z- score transformations with time-slice and timeweighted means Activities in a Space-Time Data Analysis 8. 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 4

5 Activities in a Space-Time Data Analysis 14. 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 18.Compare results from aspatial and geographically weighted regression 19. Make predictions using geostatistics All time-dynamic! SpaceStat Methods 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) 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 Export Statistics Tables Histograms Box plots Scatter plots Time plots Z-transform Spatial weights Moran s I Moran scatterplot Univariate LISA Bivariate LISA Mapping Static maps Map queries Difference maps Slide shows Movies Value Animations Cluster Animations Linking Animations Clusters (high-high, low-low) Outliers (high-low, low-high) Cluster persistence Brushing Linked windows Creating.avi files Mobility histories Polygon morphing Creating spatial subsets Creating value subsets 5

6 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 Use help to learn about handles and docking Tool Bar Space-Time Main Menu Intelligence System Data View Spatial weights & Variogram models view Log View 6

7 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 Exercise 2: Rename datasets, create projects Close the timeplots 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 Rename RWM Rates White Males Your data sets are restored Lesson: Use projects to protect your work! 7

8 SpaceStat Help Help describes Data Dynamic Data Exploration Data view, data formats, geography, spatial relationships, & managing maps Statistics, statistical graphics, cluster statistics, randomization, LISA methods Click File Save As ; Enter MyProject for the File name Tutorials Examples Key References Exercise 3: Using Help Task: Use help to determine How to generate a centroid geography from a polygon geography Hint: Use Search to find the answers quickly Spatial data formats used What the difference is between a time slice and a time series 8

9 Hint: Click See Also to view related topics Exercise 3: Help (continued) The Visualization Toolbar Lesson: Use Help to quickly answer questions on data, methods and software Map Histogram Box plot All except for the time plot are animated! Scattergram Time plot PCP Table View Variogram cloud 9

10 The Animation Controller Exercise 4: Working with Maps Step Continuous play 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 Play Stop Time slider Time Animate the map by moving the time slider on the animation controller Is lung cancer increasing, decreasing or staying the same through time? Use the time slider to animate the map Map white female mortality rates for the SEA geography Hint: Click on + to expand and - to collapse the legend 10

11 Working with maps (cont) Map queries, animated queries 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 11

12 Map Properties change how the map is displayed Working with Maps (cont) 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 Use Preview to view changes to the map Map Display Methods Color Modes Single color/transparent Continuous Classified Qualitative Classifications Equal Intervals Quantiles Jenk s Natural Breaks Custom 12

13 The visual assessment of patterns on maps is subjective 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 Breaks 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 Working with maps (cont) Select Window and Synchronize views 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 ones shown in the Linked Views panel Move the time slider on one of the maps. What happens? 13

14 Brushing and Linked Windows All statistical graphics and maps are linked through h common geography RWF Equal Intervals is linked to the three other maps 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 Working with Maps (cont) Linking and Brushing Close all but one of your maps Click here to create a scatter plot 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 Enter the variables to plot here Click and drag on the histograms to create selection boxes How do the Understand. different views Clarify. interact as Decide. you move the selection boxes? 14

15 Brush the map to explore statistical properties of selected areas Click to create a histogram Enter the variable here Brush the graphs to explore spatial & statistical relationships Working with Maps (cont.) Linking and animating maps and graphs 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 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? 15

16 Summary Data prep Import file types and formats Time stamps Common problems Export Statistics Tables Histograms Box plots Scatter plots Time plots Z-transform Spatial weights Moran s I Moran scatterplot Univariate LISA Bivariate LISA Mapping Static maps Map queries Difference maps Slide shows Movies Value Animations Cluster Animations Linking Animations Clusters (high-high, low-low) Outliers (high-low, low-high) Cluster persistence Brushing Linked windows Creating.avi files Geospatial lifelines Polygon morphing Creating spatial subsets Creating value subsets Questions? 16