Mastering Heatmap Charts: From Insights to Impactful Visuals

What is a Heatmap Chart? Exploring Definition, Examples, Procedure, Techniques, and Elements

A heatmap chart is a visual depiction of data that employs a color-coded two-dimensional grid, where the intensity of each cell's color corresponds to a particular data value. A heatmap chart serves as a valuable instrument for the visualization and examination of extensive datasets, enabling the recognition of patterns, trends, and discrepancies within the data. It finds applications in diverse domains, including social network analysis, financial market assessment, scientific investigations, and customer segmentation, ultimately enhancing the generation of data-driven insights and informed decision-making.

What is a heatmap used for?

A heatmap chart is used for visualizing and analyzing data patterns, trends, and variations within large datasets. In essence, heatmap charts are a versatile data visualization tool that serves a wide range of purposes by simplifying complex data, enhancing data-driven insights, and improving decision-making across different industries and fields of study. It helps in the following ways:

1. Identifying Trends: Heatmap charts make it easy to spot trends and patterns in data by using color intensity to represent data values, allowing users to quickly identify high or low points.
2. Data Comparison: They enable users to compare data across different categories, variables, or time periods, making it useful for performance analysis and benchmarking.
3. Anomaly Detection: Heatmap charts can highlight outliers or anomalies within a dataset by displaying them as distinct color variations, aiding in anomaly detection and troubleshooting.
4. Data Exploration: Heatmap charts are an excellent tool for exploring data, as they provide an at-a-glance overview of the entire dataset, helping users focus on areas of interest.
5. User Behavior Analysis: In website analytics and user experience studies, heatmaps are used to track and analyze user interactions, such as clicks, mouse movements, and scrolling behavior.
6. Risk Assessment: In fields like finance and security, heatmap charts are used to assess risks and vulnerabilities by visualizing data related to security breaches, financial market fluctuations, and more.
7. Scientific Research: Scientists use heatmap charts to visualize complex scientific data, including gene expression patterns, climate data, and spatial distributions.
8. Customer Insights: In marketing and customer segmentation, heatmap charts help businesses identify customer preferences and segments based on purchase history, demographic data, or geographic location.
9. Resource Allocation: Heatmap charts assist in optimizing resource allocation by showing where resources are most and least utilized, helping organizations make informed decisions about resource allocation.
10. Decision-Making: Heatmap charts provide a clear visual representation of data, aiding decision-makers in making informed choices in various domains, from business strategy to healthcare planning.

What type of chart is a heatmap?

A heatmap falls under the category of charts or graphical representations utilized to visualize and analyze data. It is a particular type of two-dimensional graphical display where data values are depicted through a color-coding scheme. In this grid-like structure, each cell typically represents a specific combination of variables or categories. Heatmap charts are widely employed for presenting data, emphasizing patterns, trends, and variations, thus making them an invaluable tool in the realm of data analysis and visualization.

What is heatmap in data visualization?

In data visualization, a heatmap is a graphical representation used to display data more accessible and understandable. They are particularly effective for visualizing large datasets and are used across various domains, including website analytics, scientific research, financial analysis, and more, to provide insights into data relationships and distributions. Heatmap charts offer a powerful visual tool for data analysts and decision-makers to make data-driven assessments and identify key insights within their data.

Heatmap chart example

Here's an example of a heatmap chart: Suppose you are analyzing website user engagement data for an e-commerce site. You have a dataset that tracks the number of products sold in region users within different region groups (columns) and product categories (rows). You want to visualize this data to identify patterns and trends in user behavior.

Your pivot table might look something like this:

In this example, each cell in the heatmap represents the number of products sold by users in a specific region and product category. The color intensity of each cell corresponds to the data value, with warmer colors (e.g., darker shades of orage) indicating higher values and cooler colors (e.g., shades of yellow) indicating lower values.

Your heatmap chart might look something like this:

By analyzing this heatmap chart, you can quickly discern patterns such as which age groups are most interested in specific product categories. For instance, region Sub Saharan Africa 145k show a high interest in fruits, while users in Middle East and North Africa 79k have a preference for clothing.

Heatmap charts like this help you gain insights into data relationships and make data-driven decisions in various fields, including marketing, user experience design, and product development.

Heat map analysis

Heat map analysis refers to the process of using heat map charts to explore and interpret data. These charts visually represent data by employing color-coded grids, where each cell's color intensity corresponds to specific data values.

Heat map analysis is an invaluable technique for extracting insights from data, revealing connections between variables, and supporting data-driven decision-making in a multitude of domains. It simplifies the visualization of intricate data, rendering it accessible and actionable for analysts and decision-makers.

Here's a succinct overview of heat map analysis:

• Data Visualization: Heat map analysis is employed to visually represent complex datasets, making it easier to identify patterns, trends, and variations within the data. The color gradient used in the heatmap allows for the quick identification of high and low values.
• Two-Dimensional Grid: In heat map analysis, data is organized into a two-dimensional grid, often with categories or variables represented along both the horizontal and vertical axes. Each cell in the grid represents a unique combination of these categories or variables.
• Color Coding: The intensity of color within each cell reflects the magnitude of the data value it represents. Typically, warmer colors like red or orange indicate higher values, while cooler colors like blue or green indicate lower values.
• Data Aggregation: Heat maps often involve data aggregation within each cell, which can include calculations like averaging, summing, or percentages. This helps provide a summary of data in each category combination.
• Pattern Recognition: Heat map analysis is particularly useful for recognizing patterns and trends in data. Analysts can quickly spot clusters of high or low values, which can inform decision-making or further exploration.
• Applications: Heat map analysis finds applications in various fields, including business analytics, biology (e.g., gene expression analysis), finance (e.g., stock market analysis), and user experience research (e.g., website click patterns).
• Interactivity: In digital environments, interactive heat maps allow users to explore data further by hovering over cells for specific values or by clicking on cells to access more detailed information.

A Comprehensive Guide to Heatmap chart: Step-by-Step Creation

Creating a heatmap chart can be a powerful way to visualize data, especially when dealing with large datasets or when you want to highlight patterns and trends within your data. In this comprehensive guide, we'll walk you through the steps to create a heatmap chart using the dashboard builder and the popular data visualization tool along with some explanations and tips along the way.

Before we start, make sure you have the following prerequisites installed:

APPLIES TO:    On-premises    Online   Desktop

Prerequisites

The Dashboard Builder can be installed any platforms like Windows, Linux, Mac and Ubuntu or any other platforms support Apache.

• PHP Version 7.2 or later
• Apache 2 or later
• Windows 7 or later /Linux 3 or later
• Firefox 52, Chrome 57, IE 8

Installation

• Download from https://dashboardbuilder.net/download-free-dashboard
• Place the files in a directory on the web server. e.g. …/www/dashboar/dbuilder/
• Unzip the file using Extract Here option to the root folder of "dashboardbuilder"

How to create heatmap chart - Step-by-Step Guide

Let's walk through the process of creating a heatmap chart using a simple example dataset. For this guide, we'll use a fictional sales dataset with columns for "Item Type," "Region," and "Units Sold."

Creating a heatmap chart involves the following steps:

Step 1: Open Your Data

Launch dashboard settings after establishing a database connection. And select the heatmap chart.

In this article we have used the following query to retrieve the data

Step 2: Select Your Data

To retrieve data from your database, create your SQL query. You can also just click the desired table in the table list to retrieve the table's default data.

Step 3: Insert Pivot Table Query

On the right side, go to the "Fields" tab. A new tab will appear. You'll see a panel to configure your pivot table fields

• Row: Select the fields from the dropdown list you want to categorize your data by. In our example, select the "Region" fields here.
• Column: Select the fields you want to use as column headers. In this case, we'll add the "Item Type" field.
• Value: This section is for the numerical data you want to analyze. Select the "Units Sold" field here.
• Calculations: By default, it will display the sum of 'Unit Sold' you can select your desired formula that you want to apply. In this case, we'll select the "Sum"
• Now click the Apply button

An auto generated SQL query will be generated with a pivot table

Step 4: Explore and Analyze

Now you have your heatmap chart! Explore the data by collapsing or expanding rows and columns, changing the values, and experimenting with filters. This dynamic analysis helps you gain insights from your data quickly.

Heatmap chart PHP source code

With a single, straightforward action, you have the capability to generate PHP source code for your heatmap chart. Simply navigate to the "Publish" button and select the "PHP code" option to effortlessly create the necessary PHP code for your Heatmap chart.

<?php
ob_start();
/**
 * DashboardBuilder
 *
 * @author Diginix Technologies www.diginixtech.com
 * Support  - https://www.dashboardbuilder.net
 * @copyright (C) 2016-2023 Dashboardbuilder.net
 * @version 6.5
 * @license: This code is under MIT license, you can find the complete information about the license here: https://dashboardbuilder.net/code-license
 */

$_SESSION["DF"]="";
$_SESSION["NF0"]="";
$_SESSION["NF"]="";

include("../inc/dashboard_dist.php");  // copy this file to inc folder 
?>
<!DOCTYPE html>
<html lang="en-us" dir="ltr">
<head>
    <meta http-equiv="X-UA-Compatible" content="IE=edge">
    <meta name="viewport" content="width=device-width, initial-scale=1">
	<script src="https://cdn.jsdelivr.net/gh/DashboardBuilder/cdn@master/v55/dashboard.min.js"></script> <!-- copy this file to assets/js folder -->
	<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/DashboardBuilder/cdn@master/v55/bootstrap.min.css"> <!-- Bootstrap 5 CSS file, change the path accordingly -->
	<link rel="stylesheet" href="https://cdn.jsdelivr.net/gh/DashboardBuilder/cdn@master/v55/font-awesome.min.css">  <!-- Font Awesome CSS file, change the path accordingly -->
	
<style>
@media screen and (min-width: 960px) {
.id0 {position:absolute; top:4px;}

}
.card-header {line-height:0.7em;}
#number {font-size:32px; font-weight:bold;text-align:center;margin-top:-10px;}
#number_legand {font-size:11px; text-align:center;}
.panel-body {  box-shadow: 5px 5px 35px  #e0e0e0;color:#787b80;}
.page-header {margin-top:-30px;}.dropdown-toggle{font-size:12px;line-height:12px;}
	.selectoption {font-size:12px !important;}
	.bs-searchbox > input {
	  font-size: 12px;
	  height:26px;
	}
</style>

</head>
<body> 

<?php
// for chart #1
$data = new dashboardbuilder(); 
$data->type[0]=  "heatmap";

$data->legacy = "";
$data->source =  "Database"; 
$data->rdbms =  "mysql"; 
$data->servername =  "localhost";
$data->username =  "root";
$data->password =  "0IhmBpPwnaCxKQXSsHI6qw==";
$data->dbname =  "salesreport";
$data->toImage = "Download graph";
$data->zoomin = "Zoom in";
$data->zoomout = "Zoom out";
$data->autoscale = "Reset";
$data->filterlabel = "Filter";
$data->forecastlabel = "Forecast";
$data->filter = "false";
$data->xaxisSQL[0]=  "SELECT * FROM salesrecords LIMIT 10;";
$data->xaxisCol[0]=  "Region";
$data->xsort[0]=  "";
$data->xmodel[0]=  "";
$data->forecast[0]=  "";
$data->yaxisSQL[0]=  "SELECT * FROM salesrecords LIMIT 10;";
$data->yaxisCol[0]=  "Region";
$data->ysort[0]=  "";
$data->ymodel[0]=  "";
$data->sql[0] = "SELECT `Region`, SUM(CASE WHEN (`Item Type`='Baby Food') THEN `Units Sold` ELSE 0 END) AS `Baby Food`,SUM(CASE WHEN (`Item Type`='Beverages') THEN `Units Sold` ELSE 0 END) AS `Beverages`,SUM(CASE WHEN (`Item Type`='Cereal') THEN `Units Sold` ELSE 0 END) AS `Cereal`,SUM(CASE WHEN (`Item Type`='Clothes') THEN `Units Sold` ELSE 0 END) AS `Clothes`,SUM(CASE WHEN (`Item Type`='Cosmetics') THEN `Units Sold` ELSE 0 END) AS `Cosmetics`,SUM(CASE WHEN (`Item Type`='Fruits') THEN `Units Sold` ELSE 0 END) AS `Fruits`,SUM(CASE WHEN (`Item Type`='Household') THEN `Units Sold` ELSE 0 END) AS `Household`,SUM(CASE WHEN (`Item Type`='Meat') THEN `Units Sold` ELSE 0 END) AS `Meat`,SUM(CASE WHEN (`Item Type`='Office Supplies') THEN `Units Sold` ELSE 0 END) AS `Office Supplies`,SUM(CASE WHEN (`Item Type`='Personal Care') THEN `Units Sold` ELSE 0 END) AS `Personal Care`,SUM(CASE WHEN (`Item Type`='Snacks') THEN `Units Sold` ELSE 0 END) AS `Snacks`,SUM(CASE WHEN (`Item Type`='Vegetables') THEN `Units Sold` ELSE 0 END) AS `Vegetables` FROM salesrecords GROUP BY `Region`; ";

$data->name = "0";
$data->title = "Charttable Chart";
$data->orientation = "v";
$data->dropdown = "false";
$data->side = "left";
$data->toImage = "Download graph";
$data->zoomin = "Zoom in";
$data->zoomout = "Zoom out";
$data->autoscale = "Reset";
$data->filter = "false";
$data->forecastlabel = "Forecast";
$data->legposition = "";
$data->xaxistitle = "";
$data->yaxistitle = "";
$data->datalabel = "true";
$data->showgrid = "true";
$data->showline = "true";
$data->tablefontsize = "12";
$data->height = "420";
$data->width = "0";
$data->col = "0";

$data->plot = "dynamic";
$data->font_color = "";
$data->bg_color = "";
$data->color[0]=  "#ff8040";
$result[0] = $data->result();?>
<div class="container-fluid main-container position-relative">
<div class="col col-md-12 col-lg-12 col-xs-12">
	<div class="row my-4">
	<div class="col-md-8 col-xs-12 offset-md-2 id0">
	<div class="card-default shadow">
		<div class="card-body bgcolor">
		<span class="d-flex justify-content-start mx-2 font-color">Charttable Chart</span>
			
		</div>
	</div>
	</div>
        </div>
</div>
</div>


</body>

Conclusion

In summary, Heatmap charts are versatile data visualization tools that employ color-coded grids to represent and analyze data, making complex information accessible and insightful. They serve diverse purposes, from identifying trends and anomalies in datasets to understanding user behavior, finding applications in fields like social network analysis, financial market assessment, and scientific research. Heatmaps simplify complex data visualization by using color intensity to represent values within a two-dimensional grid. The process of creating a heatmap chart involves data preparation, tool selection, customization, and careful review, with options for interactivity and annotations to enhance understanding. Once created, heatmap charts can be saved, exported, and shared, making them invaluable for conveying insights and facilitating data-driven decision-making across various domains.

Are you ready to try your hand at heatmap chart ? Try Data Visualization tool for Heatmap Chart