Sandra Becker
Data visualization implies an enormous power of democratizing relevant information.
SANDRAVIZ.COM | 2021
01
VISUALIZE ANALYSE VISUALIZE ANALYSE VISUALIZE ANALYSE VISUALIZE
VISUALIZE ANALYSE VISUALIZE ANALYSE VISUALIZE
VISUALIZE ANALYSE VISUALIZE ANALYSE
VISUALIZE ANALYSE VISUALIZE
VISUALIZE ANALYSE
VISUALIZE
STOP!
01
02
THE IMPORTANCE OF
SCALING
02
FILTER
SIZE
SCALING
WIDGET
MISSING VALUES | SQL
AS A VISUAL ENCODING ON MAPS
OPTIONS TO DETECT PATTERNS
02
TO CHECK THE DISTRIBUTION
02 | FILTER OUT MISSING VALUES USING SQL
02 | SIMPLE MAP WITH NO SCALING
02 | APPLYING SCALING USING QUANTILES
Each quantile class contains an equal number of features.
There are no empty classes or classes with too few or too many values
SCALING - QUANTILES
02 | SCALING USING QUANTILES
02 | PROBLEMS WITH QUANTILE SCALES
Quantile scales can be misleading sometimes, since similar features can be placed in adjacent classes or widely different values can be in the same class, due to equal number grouping.
THE PROBLEM
Widgets are embedded with your visualization and do not modify your original data, they simply allow you to explore your map by selecting targeted filters of interest.
WIDGETS
02 | ADDING WIDGETS
02 | CHECKING ON THE DISTRIBUTION USING WIDGET
02 | LONG TAIL DISTRIBUTION
02 | APPLYING HEAD/TAILS SCALING
Best for data with heavy-tailed distributions, such as exponential decay or lognormal curves.
This classification is done through dividing values into large (head) and small (tail) around the arithmetic mean.
This method, more than others, helps to reveal the underlying scaling pattern of far more small values than large ones.
SCALING - HEAD/TAILS
02 | USING HEAD/TAILS DISTRIBUTION
02 | ADDING COLOR AS VISUAL ENCODING (REDUNDANT)
01 | ADDING POP-UPS
A pop-up information window provides overlay interactivity on your map visualization, enabling you to display select columns from your dataset when a point, line, or polygon is selected.
This is a useful way of communicating data with a viewer.
POP-UPS
02 | THE FINAL MAP
03
HOW TO DETECT
OUTLIERS
03
03 | SIMPLE MAP
03 | STYLE BY VALUE
OUTLIER & CLUSTER ANALYSIS
03 | DETECT OUTLIERS AND CLUSTERS - ANALYSIS
03 | DETECT CLUSTERS & OUTLIERS
03 | POORS CLUSTERS & OUTLIERS
03 | DETECT CLUSTERS & OUTLIERS
04
HOW TO DEAL WITH
BIG DATASET
04
AGGREGATE
FILTER
STYLE
HEXAGONAL BINNING & CLUSTERING
EXTREM & TARGET GROUP
SIZE & OPACITY
04
04 | STYLE BY VALUE
Aggregation styling works best if your layer contains duplicate points or overlapping coordinates hence it is useful for symbolizing meaningful patterns of data for large datasets.
You can configure the size of the hexbins and apply the operation for how the data is aggregated.
HEXBIN AGGREGATION
04 | AGGREGATION USING HEXBINS
04 | HEXAGONAL BINNING
04 | FILTER ON EXTREM GROUPS
Checking on the groups at the edge of the distribution can be useful to understand patterns in large datasets.
For example comparing customers with the highest risk vs. those with the lowest risk checks for the spectrum as well as typical outlier pattern regarding this attribute.
FILTER ON EXTREM GROUPS
04 | FILTER ON HIGH RISK
04 | DEFINE CLUSTERS
Another way of analyzing large datasets visually is to define natural groupings of points based on their proximity to one another, the so called clusters.
By comparing the clusters and checking on differences location based patterns can be identified.
DEFINE CLUSTERS
04 | CLUSTER ANALYSIS
This analysis method can be used in a variety of contexts, such as analyzing sales territories for optimization, understanding catchment areas, and defining location patterns with mobile tracking data.
Convex Hull: A convex hull is a set number of points within a space, that has the smallest plane for all points. It is commonly visualized as the shape of a rubber-band stretching around, and containing all the points in the space.
CREATE POLYGONS
04 | CREATE POLYGONS
04 | CREATE POLYGONS
In CartoCSS you usually assign values to properties and apply filters in order to change those values based on some data attributes.
CARTOCSS
04 | CHANGE CARTOCSS
04 | CHANGE CARTOCSS - FUNCTIONS
/*General structure*/
#selector {
property: ramp([attribute], (...values), (...filters), mapping);
}
/*Example*/
#selector {
marker-width: ramp([price], (10, 20, 30), jenks());
}
04 | CHANGE CARTOCSS
04 | THE FINAL MAP
05
HOW TO GAIN
INSIGHTS
05
CONNECT
CREATE
INTERSECT
NEW DATASET
AREA OF INFLUENCE
WITH SECOND LAYER
05
For each point of data a marker image can be displayed by applying a SVG file as the marker file.
CARTO provides a selection of marker images to choose from.
It is possible to use solid color properties, display multiple marker images to represent category data, and adjust the size of the marker images.
MAKER IMAGES
05 | AGGREGATION USING HEXBINS
05 | ADDING A MARKER IMAGE
The analysis creates an “isoline”, of a specified distance or time from any geographic point, polygon or line.
Isolines are contoured lines that display equally calculated levels over a given surface area. This enables you to view polygon dimensions by forward or reverse measurements.
Isoline functions are calculated as the intersection of areas from the origin point, measured by distance (isodistance) or time (isochrone).
AREA OF INFLUENCE
05 | AREA OF INFLUENCE
05 | CREATING TRAVEL BUFFERS
Intersect with second layer oftentimes is a Point in Polygon calculation, which counts the number of incidents in a polygon.
INTERSECT WITHIN DATASETS
05 | INTERSECT WITH ANOTHER DATASET
05 | INTERSECT WITH CUSTOMER LAYER
05 | STYLE OF BUFFER
05 | THE FINAL MAP
By Sandra Becker
Data Viz Video Blog https://www.youtube.com/playlist?list=PL53pYdoYDFiv2PFCiuXR53PZj9bZrZpyp
Data visualization implies an enormous power of democratizing relevant information.