### 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

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HOW TO DETECT

OUTLIERS

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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

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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

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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

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Data visualization implies an enormous power of democratizing relevant information.