Data is weaving its way into almost all aspects of our lives since the past decade. Our ability to store more information in smaller and smaller spaces has encouraged us to make sure we leave no information out. The ease of collecting information, and the need to predict future events based on past behavior has allowed integration of data collection in all walks of life. From the number of views on a web page to the number of steps we take, we want to know every single metric that can give us better insight into the lives we lead.

自從過去十年以來，數據已經滲透到我們生活的幾乎所有方面。 我們在越來越小的空間中存儲更多信息的能力鼓勵了我們確保不遺漏任何信息。 信息收集的便捷性以及基于過去行為來預測未來事件的需求已使各行各業的數據收集得以整合。 從網頁上的視圖數量到我們采取的步驟數量，我們希望了解可以幫助我們更好地洞察自己生活的每個指標。

While the volume of data collected is increasing, our ability to extract value from this data is not able to match pace. It is not possible to glance at and process millions of rows of data — even when these rows are aggregated to a few hundred odd records.

隨著收集的數據量不斷增加，我們從這些數據中提取價值的能力無法與時俱進。 不可能瀏覽和處理數百萬行的數據，即使這些行匯總到幾百個奇數記錄中也是如此。

This calls for the need of data visualization — a method of graphically representing information that plays on the inbuilt intuitions and perceptions of humans to communicate large amounts of information in a relatively short period of time.

這就需要數據可視化-一種以圖形方式表示信息的方法，該信息以人類固有的直覺和感知為基礎，以便在相對較短的時間內傳達大量信息。

Data specialists use programs and tools to comb through stores of data and detect valuable patterns from them. Visualizing outcomes using graphs and charts eases the cognitive load on our brains due to their pre-attentive attributes — visual properties that our brains perceive without any conscious effort to do so. These attributes are divided into 3 major categories that aid our processing of information — color, form, and movement.

數據專家使用程序和工具來梳理數據存儲并從中檢測出有價值的模式。 使用圖形和圖表可視化結果可以減輕我們大腦的認知負擔，這是因為它們具有預先注意的屬性-我們的大腦無需任何有意識的努力即可感知的視覺特性。 這些屬性分為3個主要類別，可幫助我們處理信息- 顏色，形式和運動。

Let’s delve a little deeper into understanding what these mean with an example.

讓我們通過一個示例來更深入地了解這些含義。

The data set used is the Superstore Data — a dummy list of orders from a store based in the United States. The data set consists of the sales and profits of items from different categories across the country, which we can represent visually through a tool like Tableau.

所使用的數據集是“ 超級商店數據” -來自美國一家商店的訂單的虛擬列表。 數據集由全國不同類別的商品的銷售和利潤組成，我們可以通過Tableau之類的工具直觀地表示這些商品。

顏色 (Color)

Different colors and hues help us separate information elements from their surroundings and from each other. This saves us the effort of searching through data serially.

不同的顏色和色調可幫助我們將信息元素與周圍環境以及彼此分開。 這節省了我們串行搜索數據的精力。

Colors in gradation are also helpful in conveying intensity. Representing a single metric (profit per state) on a map can communicate states with low and high profit based on the shade of green it is filled with. Our brains automatically infer that a lighter shade indicates lower profit while a darker one indicates higher profit.

漸變色也有助于傳達強度。 在地圖上表示單個度量標準(每個州的利潤)可以根據填充的綠色陰影傳達高低利潤的州。 我們的大腦會自動推斷出，較淺的陰影表示較低的利潤，而較深的陰影則表示較高的利潤。

Colors also have cultural connotations associated with them. A dark red color universally represents danger but when used in combination with green, it can mean “No” (to green’s “Yes”).

顏色還具有與之相關的文化內涵。 深紅色通常表示危險，但是與綠色結合使用時，它的意思是“否”(綠色表示“是”)。

形成 (Form)

The form of visual elements can be used to draw attention towards or away from it. Size, shape, grouping, length are all various types of forms that our brains grasp without any effort.

視覺元素的形式可以用來吸引人們注意或遠離它。 大小，形狀，分組，長度是我們的大腦無需費力即可掌握的各種類型的形式。

In the map above, notice how we can combine both color and form cues together to highlight both Sales and Profit in different states.

在上面的地圖中，請注意我們如何將顏色和形式提示結合在一起以突出顯示不同狀態的“銷售”和“利潤”。

However, it is important to avoid overuse of too many cues at the same time, because it can overwhelm the viewer with details (which defeats the purpose of effective visualizations).

但是，重要的是要避免同時使用過多提示，因為它可能使查看者不知所措(這違背了有效可視化的目的)。

Grouping is another form that relies on our ability to chunk separate markers together. It is based on the Gestalt Principles of Psychology describing how humans typically see objects by grouping similar elements, recognizing patterns and simplifying complex images.

分組是另一種形式，它依賴于我們將單獨的標記塊在一起的能力。 它基于格式塔心理學原理 ，描述了人們通常如何通過對相似元素進行分組，識別模式并簡化復雜圖像來看到物體。

The scatter plot above highlights the relationship between Sales and Profit of different categories in the Superstore. We automatically group the dots based on their color and proximity. This can help us come up with insights like — The furniture category (in blue) has high sales yet low profit margins.

上面的散點圖突出顯示了超市中不同類別的銷售和利潤之間的關系。 我們會根據點的顏色和接近程度自動將其分組。 這可以幫助我們得出一些見解，例如-家具類別(藍色)具有很高的銷售額但利潤率很低。

運動 (Movement)

Flashing and motion are two types of movements that draw our attention. The presence of motion helps us understand progress from one point to another. Flashing, or flickering, is used to call attention to a particular element immediately. It is advisable to use flashing only when absolutely necessary, since it can become annoying to have our eyes drawn to a certain place when we want to focus somewhere else.

閃爍和動作是引起我們注意的兩種動作。 運動的存在有助于我們理解從一個點到另一個點的進展。 閃爍可用于立即引起人們對特定元素的注意。 建議僅在絕對必要時才使用閃光燈，因為當我們要聚焦到其他地方時，將眼睛吸引到某個特定位置會變得很煩人。

結論 (Conclusion)

The science behind our thoughts, learning and mental structures is extensively researched under the discipline of Cognitive Science and modern visualization tools like Power BI and Tableau help in applying years of research to enrich visual processing of data effectively.

我們的思想，學習和心理結構背后的科學在認知科學的學科下得到了廣泛的研究，諸如Power BI和Tableau之類的現代可視化工具有助于應用多年的研究來有效地豐富數據的視覺處理。

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I am an independent Data Analyst competent in Tableau, Python and SQL actively soliciting contract, freelance or remote opportunities. I can be reached at unnati.palan@gmail.com (Linkedin | Tableau Public | Github)

我是Tableau，Python和SQL方面的獨立數據分析師，擅長積極尋求合同，自由職業者或遠程機會。 可以通過unnati.palan@gmail.com與我聯系( Linkedin | Tableau Public | Github )