Illustration Diagrams are graphics that display an image, or images, accompanied by either notes, labels or a legend, in order to:
Images used can come in the form of illustrations, rough sketches, wire-frames or photographs. Therefore, images can be either symbolic, pictorial or realistic. Sometimes enlargements and cross-sections are used for more in-depth analysis or displaying more detail. The post What is an Illustration Diagram in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2CJ3STu
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A Histogram visualises the distribution of data over a continuous interval or certain time period. Each bar in a histogram represents the tabulated frequency at each interval/bin.Histograms help give an estimate as to where values are concentrated, what the extremes are and whether there are any gaps or unusual values. They are also useful for giving a rough view of the probability distribution. The post What is a Histogram in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/3eQwJSZ Heatmaps visualise data through variations in colouring. When applied to a tabular format, Heatmaps are useful for cross-examining multivariate data, through placing variables in the rows and columns and colouring the cells within the table.Heatmaps are good for showing variance across multiple variables, revealing any patterns, displaying whether any variables are similar to each other, and for detecting if any correlations exist in-between them. Typically, all the rows are one category (labels displayed on the left or right side) and all the columns are another category (labels displayed on the top or bottom). The individual rows and columns are divided into the subcategories, which all match up with each other in a matrix. The cells contained within the table either contain colour-coded categorical data or numerical data, that is based on a colour scale. The data contained within a cell is based on the relationship between the two variables in the connecting row and column. A legend is required alongside a Heatmap in order for it to be successfully read. Categorical data is colour-coded, while numerical data requires a colour scale that blends from one colour to another, in order to represent the difference in high and low values. A selection of solid colours can be used to represent multiple value ranges (0-10, 11-20, 21-30, etc) or you can use a gradient scale for a single range (for example 0 – 100) by blending two or more colours together. Because of their reliance on colour to communicate values, Heatmaps are a chart better suited to displaying a more generalised view of numerical data, as it’s harder to accurately tell the differences between colour shades and to extract specific data points from (unless of course, you include the raw data in the cells). Heatmaps can also be used to show the changes in data over time if one of the rows or columns are set to time intervals. An example of this would be to use a Heatmap to compare the temperature changes across the year in multiple cities, to see where’s the hottest or coldest places. So the rows could list the cities to compare, the columns contain each month and the cells would contain the temperature values. The post What is a Heatmap in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/39hTbmG Commonly used as an organisational tool for project management, Gantt Charts display a list of activities (or tasks) with their duration over time, showing when each activity starts and ends. This makes Gantt Charts useful for planning and estimating how long an entire project might take. You can also see what activities are running in parallel to each other. Gantt Charts are drawn within a table: rows are used for the activities and columns are used as the timescale. The duration of each activity is represented by the length of a bar plotted along this timescale. The start of the bar is the beginning of the activity and the end of the bar is when the activity should finish. Colour-coding the bars can be used to categorise the activities into groups. To show the percentage of completion of an activity, a bar can be partially filled in, shaded differently or use a different colour, to differentiate between what is done and what is left to do. Connecting arrows can be used to show which tasks are dependent on each other. Critical paths, the key activities required to finish the project can also be displayed with a series of highlighted arrows. Symbols can also be placed within a Gantt Chart to signify milestones and a vertical line running through the chart is used to highlight the current date. The post What is Gantt Chart in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/3hjlXpV Flow Maps geographically show the movement of information or objects from one location to another and their amount.Typically Flow Maps are used to show the migration data of people, animals and products. The magnitude or amount of migration in a single flow line is represented by its thickness. This helps to show how migration is distributed geographically. Flow Maps are drawn from a point of origin and branch out of their “flow lines”. Arrows can be used to show direction, or if the movement is incoming or outgoing. Drawing flow lines without arrows can be used to represent trade going back-and-forth. Merging/bundling flow lines together and avoiding crossovers can help to reduce visual clutter on the map. The post What is Flow Map in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2ZKoJhW As known as Flow Diagram, Flow Process Chart, Process Chart, Process Map, Process Model, Work Flow Diagram.This type of diagram is used to show the sequential steps of a process. Flow Charts map out a process using a series of connected symbols, which makes the process easy to understand and aids in its communication to other people. Flow Charts are useful for explaining how a complex and/or abstract procedure, system, concept or algorithm work. Drawing a Flow Chart can also help in planning and developing a process or improving an existing one. Symbols are divided up and standardised into different types that each have their own particular shape. Labels for each step are written inside of the symbol shape. Flow Charts begin and end with a curved rectangle to signify the start and finishing of the process. Lines or arrows are used to show the direction of flow from one step in the process to another. Simple instructions or actions are represented by a rectangle. While a diamond shape is used when a decision is needed. There are also many other symbols that can be used in Flow Chart. Flow Charts can run horizontally or vertically. The post What is a Flow Chart in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/399U9S1 Although not a chart outright, Error Bars function as a graphical enhancement that visualises the variability of the plotted data on a Cartesian graph. Error Bars can be applied to graphs such as Scatterplots, Dot Plots, Bar Charts or Line Graphs, to provide an additional layer of detail on the presented data.Error Bars help to indicate estimated error or uncertainty to give a general sense of how precise a measurement is. This is done through the use of markers drawn over the original graph and its data points. Typically, Error bars are used to display either the standard deviation, standard error, confidence intervals or the minimum and maximum values in a ranged dataset. To visualise this information, Error Bars work by drawing cap-tipped lines that extend from the centre of the plotted data point (or edge with Bar Charts). The length of an Error Bar helps reveal the uncertainty of a data point: a short Error Bar shows that values are concentrated, signalling that the plotted average value is more likely, while a long Error Bar would indicate that the values are more spread out and less reliable. Also depending on the type of data, the length of each pair of Error Bars tend to be of equal length on both sides. However, if the data is skewed, then the lengths on each side would be unbalanced. Error Bars always run parallel to a quantitative scale axis, so they can be displayed either vertically or horizontally, depending on whether the quantitative scale is on the Y or X axis. If there are two quantitative scales, then two pairs of Error Bars can be used for both axes. The post Data Visualization in 2020: Error Bars Explained appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/399zVYs Dot Matrix Charts display discreet data in units of dots, each coloured to represent a particular category and grouped together in a matrix.They are used to give a quick overview of the distribution and proportions of each category in a data set and also to compare distribution and proportion across other datasets, in order to discover patterns. When only one variable/category is used in the dataset and all the dots are the same colour, a Dot Matrix Chart can be used to primarily show proportions. The post What is a Dot Matrix Chart in Data Visualization appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/3eK9o5q Also known as a Point Map, Dot Distribution Map, Dot Density Map.Dot Maps are a way of detecting spatial patterns or the distribution of data over a geographical region, by placing equally sized points over a geographical region. There are two types of Dot Map: one-to-one (one point represents a single count or object) and one-to-many (one point represents a particular unit, e.g. 1 point = 10 trees). Dot Maps are ideal for seeing how things are distributed over a geographical region and can reveal patterns when the points cluster on the map. Dot Maps are easy to grasp and are better at giving an overview of the data, but are not great for retrieving exact values. The post What is a Dot Map in Data Visualization? appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/2ZxO1j5 A donut chart is essentially a Pie Chart with an area of the centre cut out.Pie Charts are sometimes criticised for focusing readers on the proportional areas of the slices to one another and to the chart as a whole. This makes it tricky to see the differences between slices, especially when you try to compare multiple Pie Charts together. A Donut Chart somewhat remedies this problem by de-emphasizing the use of the area. Instead, readers focus more on reading the length of the arcs, rather than comparing the proportions between slices. Also, Donut Charts are more space-efficient than Pie Charts because the blank space inside a Donut Chart can be used to display information inside it. The post Donut Chart in Data Visualization appeared first on Data Science PR. via Data Visualization – Data Science PR https://ift.tt/3iZLbeF |