Data Exploration Chapter 9

Introduction  Where to begin?  Data exploration is data-centered query and analysis  Better understand the data and provide a starting point  Linking between components

Interactive Data Exploration  Histograms and scatterplots to explore data and discover patterns  Exploratory data analysis is first step in statistical analysis  Dynamic graphics enhances exploration  Precursor to more formal and structured analysis

Interactive Data Exploration  Selection, deletion, rotation, and transformation  Brushing: a technique for selecting and highlighting subset and compare to other highlighted subset  Box plot, variogram  Similar kinds of functions in GIS

Interactive Data Exploration  Display attribute and spatial data in different wind  Windows dynamically linked  We have seen in ArcView  For vector data  Not able to do with raster

Vector Data Query  Attribute data query (we’ve used Query Builder)  Logical or Boolean expression  = > =  = > =  Boolean connector  AND, OR, XOR, NOT  NOT=COMPLEMENT

Vector Data Query  AND=INTERSECT  OR=UNION  XOR (one and only one)  ArcView: new, add to set, select from set, switch  ARC/INFO RESELECT (new), ASELECT (add), and NSELECT (switch)  Two way linkage

Use SQL to Query a Database  SELECT  SELECT  FROM  FROM  WHERE  WHERE  ArcView has SQL Connect  ARC/INFO uses CONNECT command  Descriptive statistics (min, max, range, mean, standard deviation) can be included in SQL

Spatial Data Query  Retrieving data by working with map features  Cursor selection  Feature selection by graphic (circle, rectangle, etc)  Feature selection by spatial relationship Containment Containment Intersect Intersect Proximity Proximity Adjacency Adjacency  Combined attribute and spatial data query

Raster Data Query  Value Attribute Table (VAT) for integer grid  Query by cell value  Also Boolean  Query by graphic

Charts  Limited for ArcView  Export to another statistical product

Geographic Visualization  Geographic visualization has same objective as exploratory data analysis.  Four methods Data classification Data classification Spatial aggregation Spatial aggregation Multiple maps in a view Multiple maps in a view Map comparison Map comparison

Data Classification  Classification method and number of classes  Make multiple versions and choose  Link and what-if questions  Reclassification for raster data. Data simplification, data isolation, data ranking Data simplification, data isolation, data ranking  Integer easier to use

Data Classification  Spatial Aggregation  States, county, census tract, block group, block  Raster aggregation more coarse resolution.

Map Comparison  Same schema  On/off display  Multiple Views  Chart symbols  Bivariate choropleth (limited number of classes)