MISSING VALUES | SQL

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

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

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

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

OUTLIER & CLUSTER ANALYSIS

HEXAGONAL BINNING & CLUSTERING

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

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

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

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

In CartoCSS you usually assign values to properties and apply filters in order to change those values based on some data attributes.

CARTOCSS

NEW DATASET

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

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

Intersect with second layer oftentimes is a Point in Polygon calculation, which counts the number of incidents in a polygon.

INTERSECT WITHIN DATASETS