Recap Need for data visualization Importance of data visualization Limitation of spreadsheet Interpretation through data visualization Interactive visualization.

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Presentation transcript:

Recap Need for data visualization Importance of data visualization Limitation of spreadsheet Interpretation through data visualization Interactive visualization

Outline Combination of disciplines Process of data visualization Illustration with Example

Combination of disciplines due to complexity of data using data visualization to provide a meaningful solution requires insights from diverse fields like statistics, data mining, graphic design, and information visualization. however, each field has evolved in isolation from the others visual design is the field of mapping data to a visual form typically does not address how to handle thousands or tens of thousands of items of data data mining techniques have such capabilities, but they are disconnected from the means to interact with the data

Continued…. software-based information visualization adds building blocks for interacting with and representing various kinds of abstract data these methods undervalue the aesthetic principles of visual design rather than embrace their strength as a necessary aid to effective communication someone approaching a data representation problem often finds it difficult to choose a representation and wouldn’t even know what tools to use or books to read to begin

Process of data visualization graphic designers can learn the computer science necessary for visualization, and statisticians can communicate their data more effectively by understanding the visual design principles behind data representation the methods themselves are not new their isolation within individual fields has prevented them from being used together use a process that bridges the individual disciplines, placing the focus and consideration on how data is understood rather than on the viewpoint and tools of each individual field

Continued…. Process consists of following steps Acquire Parse Filter Mine Represent Refine Interact

Acquire Obtain the data, whether from a file on a disk or a source over a network Parse Provide some structure for the data’s meaning, and order it into categories Filter Remove all but the data of interest Mine Apply methods from statistics or data mining as a way to discern patterns or place the data in mathematical context

Represent Choose a basic visual model, such as a bar graph, list, or tree. Refine Improve the basic representation to make it clearer and more visually engaging. Interact Add methods for manipulating the data or controlling what features are visible.

Example to illustrate the seven steps listed in the previous section, and how they contribute to effective information visualization, let’s look at how the process can be applied to understanding a simple data set in this case, we’ll take the zip code numbering system that the U.S. Postal Service uses the application is not particularly advanced, but it provides a skeleton for how the process works

Problem Question the Zip decode project was developed out of a personal interest in the relationship of the zip code numbering system to geographic areas. Living in Boston, one knew that numbers starting with a zero denoted places on the East Coast. Having spent time in San Francisco, one knew the initial numbers for the West Coast were all nines. One grew up in Michigan, where all codes were four-prefixed. But what sort of area does the second digit specify? Or the third?

Step by Step Solution

Acquire the acquisition step involves obtaining the data like many of the other steps, this can be either extremely complicated or very simple a copy of the zip code listing can be found on the U.S. Census Bureau web site, as it is frequently used for geographic coding of statistical data the listing is a freely available file with approximately 42,000 lines, one for each of the codes, a tiny portion of which is shown in up coming figure

Zip codes in the format provided by the U.S. Census Bureau

Continued…. acquisition concerns how the user downloads your data as well as how you obtained the data in the first place if the final project will be distributed over the Internet, as you design the application, you have to take into account the time required to download data into the browser and because data downloaded to the browser is probably part of an even larger data set stored on the server, you may have to structure the data on the server to facilitate retrieval of common subsets

Parse after you acquire the data, it needs to be parsed—changed into a format that tags each part of the data with its intended use each line of the file must be broken along its individual parts; in this case, it must be delimited at each tab character then, each piece of data needs to be converted to a useful format up-coming figure shows the layout of each line in the census listing, which we have to understand to parse it and get out of it what we want.

Structure of acquired data

Continued…. each field is formatted as a data type which is handle in a conversion program consists of following steps String Float Character Integer Index

String a set of characters that forms a word or a sentence here, the city or town name is designated as a string because the zip codes themselves are not so much numbers as a series of digits they also might be considered strings

Float a number with decimal points used for the latitudes and longitudes of each location the name is short for floating point, from programming nomenclature that describes how the numbers are stored in the computer’s memory

Character a single letter or other symbol in this data set, a character sometimes designates special post offices

Integer a number without a fractional portion no decimal points e.g., –14, 0, or 237

Index data maps to a location in another table of data in this case, the index maps numbered codes to the names and two-digit abbreviations of states this is common in databases, where such an index is used as a pointer into another table, sometimes as a way to compact the data further e.g., a two-digit code requires less storage than the full name of the state or territory

With the completion of this step, the data is successfully tagged and consequently more useful to a program that will manipulate or represent it in some way

Filter the next step involves filtering the data to remove portions not relevant to our use. in this example, for the sake of keeping it simple, we’ll be focusing on the contiguous 48 states, so the records for cities and towns that are not part of those states—Alaska, Hawaii, and territories such as Puerto Rico—are removed another project could require significant mathematical work to place the data into a mathematical model or normalize it normalizing by converting it to an acceptable range of numbers

Mine this step involves math, statistics, and data mining the data in this case receives only a simple treatment the program must figure out the minimum and maximum values for latitude and longitude by running through the data so that it can be presented on a screen at a proper scale most of the time, this step will be far more complicated than a pair of simple math operations

Represent this step determines the basic form that a set of data will take data sets are shown as Lists structured like trees, and so forth each zip code has a latitude and longitude, so the codes can be mapped as a two-dimensional plot, with the minimum and maximum values for the latitude and longitude used for the start and end of the scale in each dimension the Represent stage is a linchpin that informs the single most important decision in a visualization project can make you rethink earlier stages

Refine graphic design methods are used to further clarify the representation by calling more attention to particular data or by changing attributes that contribute to readability hierarchy is established by coloring the background deep gray displaying the selected points in white the deselected points in medium yellow

Interact the next stage of the process adds interaction, letting the user control or explore the data interaction might cover things like selecting a subset of the data or changing the viewpoint example of a stage affecting an earlier part of the process can also affect the refinement step as a change in viewpoint might require the data to be designed differently.

Continued…. in the Zip decode project, typing a number selects all zip codes that begin with that number enables the users to traverse the display laterally and run through several of the prefixes after typing part or all of a zip code, holding down the Shift key allows users to replace the last number typed without having to hit the Delete key to back up

Continued…. typing is a very simple form of interaction, but it allows the user to rapidly gain an understanding of the zip code system’s layout just contrast this sample application with the difficulty of deducing the same information from a table of zip codes and city names in addition, users can enable a “zoom” feature that draws them closer to each subsequent digit, revealing more detail around the area and showing a constant rate of detail at each level we could add more details of state and county boundaries or other geographic features to help viewers associate the “data” space of zip code points with what they know about the local environment