Heatmaps have a wide range of possibilities among their applications due to their ability to simplify data and make data analysis visually appealing to read. Listed below are some apps that use different types of heatmaps.

Business Analysis"): Heat maps are used in business analysis to provide a visual representation of the current functioning, performance, and need for improvements in a business. Heat maps are a way to analyze a business's existing data and update it to reflect growth and other specific efforts. Heat maps visually engage the team members and customers of the business or company.

Websites: There are many ways to use heat maps on websites to determine the actions of users who visit them. Typically, there are multiple heatmaps that are used together to determine a website's perspective on which are the best and worst performing elements on the page. Listed below are some specific heatmaps used for website analysis.

• - Mouse tracking: are used to visualize where the user's cursor passes on the website.

• - Eye Tracking: measure the eye position of website users and collect measures such as eye fixation volume, eye fixation duration, and areas of interest.

• - Click Tracking: Also called touch maps, they are similar to mouse tracking heatmaps, but instead of scrolling actions, these types of heatmaps help visualize users' clicking actions. Click tracking heatmaps not only allow visual cues on clickable components on a web page, such as buttons or drop-down menus, but these heatmaps also allow tracking of non-clickable objects anywhere on the page.

• - AI generation attention: help visualize where the user's attention will go in a certain section of a web page. These types of heat maps are implemented using a software algorithm created to determine and predict user attention actions.

• - Scroll Tracking: Used to represent user behavior on the website. This helps produce visual clues about which section of the website the user spends the most time on[4].

Data analysis heat map example: shows the normalized binding balance of genomic windows within the Hist1 region of a mouse (Mus musculus).

Exploratory Data Analysis: When working with large or small data sets, data scientists and analysts typically observe and determine the essential relationships and characteristics between different points in a data set, as well as the characteristics of those data points. Data scientists and analysts often work with teams from various professions. Using heat maps is a visual way to summarize findings and key components. There are other ways to represent data, however, heat maps can visualize these data points and their relationships in a high dimensional space without becoming too compact and visually unappealing. Heat maps in data analysis allow specific variables of dilas and/or columns on the axes and even on the diagonal.

• -

Example of a data analysis heat map: subgraph of one of the five central nodes with a high degree centrality in a genomic region in mice (Mus musculus) called the Hist1 region, where each cell in the graph represents an edge in the genomic network.

Biology: In the field of biology, heat maps are used to visualize large or small data sets. The focus is on patterns and similarities in DNA, RNA, gene expression, etc. When working with these types of data sets, bioinformatics data scientists focus on different concepts, some of which are community detection, association and correlation, and the concept of centrality, where heat maps are a compelling way to visually summarize results and share them among other professionals who are not in the field of biology or bioinformatics. The two heatmaps on the right, labeled “Data Analysis Heatmap Examples,” show different ways one can present genomic data about a specific region (Hist1 region) to someone outside the field of biology so they have a better understanding of the overall concept a biologist or data scientist is trying to present.

Financial Analysis: The values ​​of different products and assets fluctuate rapidly and/or gradually over time. The need to record changes in daily markets is imperative. It allows the ability to draw predictions from patterns while being able to review previous numerical data. Heat maps can eliminate the tedious process and allow the user to visualize data points and compare between different performers.[5]​.

Geographic Display: Heat maps are used to visualize and display a geographical distribution of data. Heat maps represent different densities of data points on a geographic map to help users see the intensities of certain phenomena and show elements of greater or lesser importance. Heat maps used in geographic visualization are often confused with choropleth maps, but the difference lies in how certain data is presented that differentiates the two.[6]​[7]​.

Sports: Heat maps can be used in many sports and can influence the decisions of managers and/or coaches based on the high and low densities of data displayed. Users can identify patterns within the game, the strategies of opponents and their own team, make more informed decisions that benefit the player, the team and the business, and can improve performance in different areas by identifying necessary improvements. Heatmaps also visualize comparisons and relationships between different teams in the same sport or between different sports together.[5]​.

Many different color schemes can be used to illustrate the heat map, with perceptual advantages and disadvantages for each. Choosing a good color scheme is critical to displaying data accurately and effectively, while a poor color scheme can lead viewers to inaccurate conclusions or exclude those with color deficiencies from proper analysis of that data.

Rainbow color schemes are a common choice, as humans can perceive more shades of color than gray, and this would supposedly increase the amount of detail perceptible in the image. However, this is highly discouraged in the scientific community for several reasons. Possibly the main reason is that when there are a large number of colors involved, the visualization can give the impression that there are gradients in the data that are not actually present. The more colors used in a visualization, the more values ​​begin to blend together and the color lacks the natural perceptual order found in grayscale or blackbody spectrum color maps. Additionally, values ​​represented by different shades of the same color may imply that the values ​​are related when they are not.[8][9][10]​.

An important consideration when choosing a color scale is whether or not the data will be viewed by anyone with some type of color deficiency. If your audience contains people with some form of color blindness, it may be wise to avoid color schemes with prominent reds and greens or irregular color gradients.[10]​.

In addition to audience considerations, it is also important to consider how the data will be viewed. For example, if the data will be printed in black and white or projected on a large screen, it may be advisable to adjust the choice of color scheme. Common color maps (such as the waterfall color map used as the default in many visualization software packages) have uncontrolled changes in luminance that prevent meaningful conversion to grayscale for viewing or printing. This also distracts from the real data, making the yellow and cyan regions appear arbitrarily more prominent than the regions of the data that are actually more important.[8]​[10]​.

There are several software with free heat map implementation:

• - R "R (programming language)"), a free software environment for graphical statistics and analysis, contains many functions for locating heatmaps,[11]​[12]​ including interactive group heatmaps[13]​ (cpm the R heatmaply pack).

• - Gnuplot, a universal program in which you can create 2D and 3D heat maps.[14]​.

• - Google Fusion Tables can generate a heat map in Google Sheets, limiting it to 1,000 geographic data points.[15]​.

• - Dave Green's allows you to generate color schemes that print as grayscales on black and white devices.[16]​.

• - Openlayers3 can render a heatmap layer of a property selected from all geographic features in a vector layer.[17]​.

• - D3.js, AnyChart[18]​[19]​ and Highcharts[20]​[21]​ are Javascript libraries for data visualization that provide the ability to create interactive heat map charts, from the most basic to custom ones, as part of their solutions.[22][23]​.

• - Qlik Sense allows you to display comparative data as color patterns in a heat map, which also includes a visualization pack.[24]​.

• - MATLAB provides the capability of heat map visualization with a wide variety of configuration options.[25][26]​.

• - Wolfram Mathematica: The DensityPlot function can be used with a variety of configuration options.

• - Heat map of a snowfall on a weather radar.

• - Human voice in a spectrogram; a heat map representing the magnitude of in TFTR.

• - Example of similarities between a heat map, function graphs and isolines of the same data.

• - Combination of a graph of a function and a heat map, where the height represents the amplitude and the color the phase angle.

• - Scores of the squares of a dart board.

• - False color.

There are two main types of heatmaps: spatial and grid.

A spatial heat map shows the magnitude of a spatial phenomenon as color, usually projected onto a map. The image labeled "Example Spatial Heatmap" shows the temperature by color range over the world. The color varies from blue (cold) to red (hot).

A grid heat map displays magnitude as color in a two-dimensional matrix, where each dimension represents a category of some variable and the color represents the magnitude of some measure in the combined variables of each of the two categories. For example, one dimension could represent the year and the other dimension the month, and the middle value could be the temperature. This heat map would show how the temperature changed over the years in each month. Grid heatmaps are further classified into two different types of matrices: clusters and correlograms.

• - Cluster heat map: The example of monthly temperature by year is a cluster heat map.

• - Correlograms: A correlogram is a heat map of clusters that has the same variables for each axis, in such a way that it shows how the variables in the set of variables interact with each other. The correlogram usually represents only the triangular bottom part rather than a square since it is symmetrical. That is, the combination of variables A-B is the same as the combination of B-A, and therefore does not need to be expressed twice.

Examples of heat maps:

• - Biology heat maps, widely used in molecular biology to represent the expression level of many genes across a number of comparable samples (for example, cells in different states, samples from different patients) after obtaining DNA chips.

• - Treemap is a 2D hierarchical partition of data visually similar to a heatmap.

• - Mosaic represents a heat map in squares to display a table of data in two ways. As in treemaps, rectangular cells are organized hierarchically, so the cells are rectangles instead of squares.

• - A density function visualization is a heat map to represent the density of points on a map. This allows you to see the highest density points. Perrot (2015) proposed a way to use the density function to visualize billions and billions of points using big data infrastructure.[3]​.

Heatmaps have a wide range of possibilities among their applications due to their ability to simplify data and make data analysis visually appealing to read. Listed below are some apps that use different types of heatmaps.

Business Analysis"): Heat maps are used in business analysis to provide a visual representation of the current functioning, performance, and need for improvements in a business. Heat maps are a way to analyze a business's existing data and update it to reflect growth and other specific efforts. Heat maps visually engage the team members and customers of the business or company.

Websites: There are many ways to use heat maps on websites to determine the actions of users who visit them. Typically, there are multiple heatmaps that are used together to determine a website's perspective on which are the best and worst performing elements on the page. Listed below are some specific heatmaps used for website analysis.

• - Mouse tracking: are used to visualize where the user's cursor passes on the website.

• - Eye Tracking: measure the eye position of website users and collect measures such as eye fixation volume, eye fixation duration, and areas of interest.

• - Click Tracking: Also called touch maps, they are similar to mouse tracking heatmaps, but instead of scrolling actions, these types of heatmaps help visualize users' clicking actions. Click tracking heatmaps not only allow visual cues on clickable components on a web page, such as buttons or drop-down menus, but these heatmaps also allow tracking of non-clickable objects anywhere on the page.

• - AI generation attention: help visualize where the user's attention will go in a certain section of a web page. These types of heat maps are implemented using a software algorithm created to determine and predict user attention actions.

• - Scroll Tracking: Used to represent user behavior on the website. This helps produce visual clues about which section of the website the user spends the most time on[4].

Data analysis heat map example: shows the normalized binding balance of genomic windows within the Hist1 region of a mouse (Mus musculus).

Exploratory Data Analysis: When working with large or small data sets, data scientists and analysts typically observe and determine the essential relationships and characteristics between different points in a data set, as well as the characteristics of those data points. Data scientists and analysts often work with teams from various professions. Using heat maps is a visual way to summarize findings and key components. There are other ways to represent data, however, heat maps can visualize these data points and their relationships in a high dimensional space without becoming too compact and visually unappealing. Heat maps in data analysis allow specific variables of dilas and/or columns on the axes and even on the diagonal.

• -

Example of a data analysis heat map: subgraph of one of the five central nodes with a high degree centrality in a genomic region in mice (Mus musculus) called the Hist1 region, where each cell in the graph represents an edge in the genomic network.

Biology: In the field of biology, heat maps are used to visualize large or small data sets. The focus is on patterns and similarities in DNA, RNA, gene expression, etc. When working with these types of data sets, bioinformatics data scientists focus on different concepts, some of which are community detection, association and correlation, and the concept of centrality, where heat maps are a compelling way to visually summarize results and share them among other professionals who are not in the field of biology or bioinformatics. The two heatmaps on the right, labeled “Data Analysis Heatmap Examples,” show different ways one can present genomic data about a specific region (Hist1 region) to someone outside the field of biology so they have a better understanding of the overall concept a biologist or data scientist is trying to present.

Financial Analysis: The values ​​of different products and assets fluctuate rapidly and/or gradually over time. The need to record changes in daily markets is imperative. It allows the ability to draw predictions from patterns while being able to review previous numerical data. Heat maps can eliminate the tedious process and allow the user to visualize data points and compare between different performers.[5]​.

Geographic Display: Heat maps are used to visualize and display a geographical distribution of data. Heat maps represent different densities of data points on a geographic map to help users see the intensities of certain phenomena and show elements of greater or lesser importance. Heat maps used in geographic visualization are often confused with choropleth maps, but the difference lies in how certain data is presented that differentiates the two.[6]​[7]​.

Sports: Heat maps can be used in many sports and can influence the decisions of managers and/or coaches based on the high and low densities of data displayed. Users can identify patterns within the game, the strategies of opponents and their own team, make more informed decisions that benefit the player, the team and the business, and can improve performance in different areas by identifying necessary improvements. Heatmaps also visualize comparisons and relationships between different teams in the same sport or between different sports together.[5]​.

Many different color schemes can be used to illustrate the heat map, with perceptual advantages and disadvantages for each. Choosing a good color scheme is critical to displaying data accurately and effectively, while a poor color scheme can lead viewers to inaccurate conclusions or exclude those with color deficiencies from proper analysis of that data.

Rainbow color schemes are a common choice, as humans can perceive more shades of color than gray, and this would supposedly increase the amount of detail perceptible in the image. However, this is highly discouraged in the scientific community for several reasons. Possibly the main reason is that when there are a large number of colors involved, the visualization can give the impression that there are gradients in the data that are not actually present. The more colors used in a visualization, the more values ​​begin to blend together and the color lacks the natural perceptual order found in grayscale or blackbody spectrum color maps. Additionally, values ​​represented by different shades of the same color may imply that the values ​​are related when they are not.[8][9][10]​.

An important consideration when choosing a color scale is whether or not the data will be viewed by anyone with some type of color deficiency. If your audience contains people with some form of color blindness, it may be wise to avoid color schemes with prominent reds and greens or irregular color gradients.[10]​.

In addition to audience considerations, it is also important to consider how the data will be viewed. For example, if the data will be printed in black and white or projected on a large screen, it may be advisable to adjust the choice of color scheme. Common color maps (such as the waterfall color map used as the default in many visualization software packages) have uncontrolled changes in luminance that prevent meaningful conversion to grayscale for viewing or printing. This also distracts from the real data, making the yellow and cyan regions appear arbitrarily more prominent than the regions of the data that are actually more important.[8]​[10]​.

There are several software with free heat map implementation:

• - R "R (programming language)"), a free software environment for graphical statistics and analysis, contains many functions for locating heatmaps,[11]​[12]​ including interactive group heatmaps[13]​ (cpm the R heatmaply pack).

• - Gnuplot, a universal program in which you can create 2D and 3D heat maps.[14]​.

• - Google Fusion Tables can generate a heat map in Google Sheets, limiting it to 1,000 geographic data points.[15]​.

• - Dave Green's allows you to generate color schemes that print as grayscales on black and white devices.[16]​.

• - Openlayers3 can render a heatmap layer of a property selected from all geographic features in a vector layer.[17]​.

• - D3.js, AnyChart[18]​[19]​ and Highcharts[20]​[21]​ are Javascript libraries for data visualization that provide the ability to create interactive heat map charts, from the most basic to custom ones, as part of their solutions.[22][23]​.

• - Qlik Sense allows you to display comparative data as color patterns in a heat map, which also includes a visualization pack.[24]​.

• - MATLAB provides the capability of heat map visualization with a wide variety of configuration options.[25][26]​.

• - Wolfram Mathematica: The DensityPlot function can be used with a variety of configuration options.

• - Heat map of a snowfall on a weather radar.

• - Human voice in a spectrogram; a heat map representing the magnitude of in TFTR.

• - Example of similarities between a heat map, function graphs and isolines of the same data.

• - Combination of a graph of a function and a heat map, where the height represents the amplitude and the color the phase angle.

• - Scores of the squares of a dart board.

• - False color.