tableau使用

介紹 (Introduction)

Melbourne, being one of the most liveable cities in the world, has attracted a lot of individuals across the globe. Many of them dream of making this beautiful place as their home. My journey in the field of data science started with me moving to Melbourne hence I decided to do a comprehensive analysis of the city’s real estate market. I have always been fascinated by this industry. Hence in this article, I am going to take a comprehensive approach towards identifying drivers and helping potential buyers with data-driven decision making. Since this analysis was completed using Tableau, I will provide you with a few dashboarding tips as well.

墨爾本是世界上最宜居的城市之一，吸引了全球許多人。 他們中的許多人夢想著把這個美麗的地方當作自己的家。 我在數據科學領域的旅程始于我搬到墨爾本，因此我決定對這座城市的房地產市場進行全面分析。 我一直對這個行業著迷。 因此，在本文中，我將采用一種全面的方法來確定驅動因素，并幫助潛在買家進行數據驅動的決策。 由于此分析是使用Tableau完成的，因此我還將為您提供一些儀表板提示。

“Fun fact — I did use this analysis while renting out a place. Hence this might be useful if you are new to Melbourne or planning to buy a property here.”

“有趣的事實-我確實在租用場所時使用了此分析。 因此，如果您是墨爾本的新手或計劃在這里購買物業，這可能會很有用?！?

關于Tableau (About Tableau)

Tableau is a powerful and rapidly growing data visualisation tool used by most of the data-savvy organisations. Self-Intelligence and numerous features to transform data into surprising business insights make Tableau one of the best BI tool.

Tableau是功能強大且快速增長的數據可視化工具，大多數數據精明的組織都在使用它。 自我智能以及將數據轉換為令人驚訝的業務洞察力的眾多功能使Tableau成為最佳的BI工具之一。

資料說明 (Data Description)

The dataset used in this project is the data of the houses sold in Melbourne from the period January 2016 to October 2018 posted by Tony Pino on Kaggle scrapped from publicly available results posted every week from Domain.com.au. Some of the data fields include Date, Price, Suburb, Region name, Landsize, Building size, Distance from CBD and others. (Kaggle)

該項目中使用的數據集是Tony Pino在Kaggle上發布的2016年1月至2018年10月在墨爾本售出的房屋的數據，該數據來自Domain.com.au每周發布的公開結果。 一些數據字段包括日期，價格，郊區，地區名稱，土地面積，建筑物尺寸，距CBD的距離等。 (笑嘻嘻)

假設和關鍵問題 (Hypothesis and key questions)

1. What is the effect of building size to land size ratio as we move closer to the CBD region? Does this ratio impact the price of the houses?

1.隨著我們靠近中央商務區，建筑面積與土地面積之比的影響是什么？ 這個比率會影響房屋價格嗎？

2. What will be the average price of the houses in different metropolitan regions of Melbourne in the second quarter of 2018?

2. 2018年第二季度墨爾本不同城市地區的房屋平ASP格是多少？

3. In which month more houses are sold in Melbourne?

3.墨爾本哪個月售出更多房屋？

4. What are the top 10 suburbs of Melbourne by the price and the maximum number of houses sold?

4.按價格和可售房屋的最大數量，墨爾本排名前十的郊區是什么？

數據處理 (Data Processing)

To analyze the building to land size ratio and its relationship with the distance from the city, a dummy column (Ratio) was added to the data. Our exploration revolves around the Price, Distance from CBD, Suburb, Region type, and building to land size ratio. Null values in the Region column were imputed using the suburb information. Remaining records with null values were deleted using suitable filters in Excel. Quality checks on the data displayed properties with building size to land size ratio greater than one. Such records were dropped from the analysis.

為了分析建筑物與土地面積的比率及其與城市距離的關系，在數據中添加了一個虛擬列(比率)。 我們的探索圍繞價格，離CBD的距離，郊區，區域類型以及建筑物與土地面積之比。 使用郊區信息估算“地區”列中的空值。 使用Excel中的適當過濾器刪除了具有空值的其余記錄。 對數據顯示的屬性進行質量檢查，這些屬性的建筑物大小與土地大小之比大于1。 此類記錄已從分析中刪除。

探索性分析 (Exploratory Analysis)

To start with the analysis, I first plotted a choropleth map showing the average price of houses in different regions of Melbourne.

首先從分析開始，我首先繪制了一個choropleth地圖，顯示了墨爾本不同地區房屋的平ASP格。

Choropleth Map:

霍羅珀斯地圖：

A choropleth map is a thematic map in which different regions in the map shaded or patterned in proportion to a statistical variable that represents an aggregate summary of a geographic characteristic. (Wikipedia)

擬人地圖是一種主題地圖，其中地圖中的不同區域與代表地理特征??的匯總摘要的統計變量成比例地陰影或圖案化。 (維基百科)

Figure 1. Variation of the average price of houses in different regions. Snapshot taken from the Tableau dashboard developed by the author.圖1.不同地區房屋平ASP格的變化。 快照來自作者開發的Tableau儀表板。

The above visualisation shows that the prices of the houses located in the CBD and the eastern coastal region are higher as compared to prices in other regions.

上面的圖表顯示，位于CBD和東部沿海地區的房屋價格比其他地區的價格更高。

I was interested in analysing the effect of the location of the house on building size to land size ratio. The distance of the house from CBD was used as the dimension of the location. I plotted a dual combination graph which consists of a bar graph and a line graph between “distance from CBD” and “average ratio”. The line graph shows the moving average of the ratio, in order to smoothen the results. For better understanding, the distance is visualised as the range of 5 Kms.

我有興趣分析房屋位置對建筑面積與土地面積之比的影響。 房屋與CBD的距離用作位置的尺寸。 我繪制了一個雙重組合圖，它由一個條形圖和一個“離CBD的距離”和“平均比率”之間的折線圖組成。 線形圖顯示了比率的移動平均值，以使結果平滑。 為了更好地理解，該距離可視化為5 Kms的范圍。

Figure 2. Variation of distance from the city with respect to the building to land size ratio. Snapshot taken from the Tableau dashboard developed by the author.圖2.從城市到建筑物的距離與土地面積之比的變化。 快照來自作者開發的Tableau儀表板。

To get a clear picture of the variation of ratio with respect to the location, I also created a choropleth map of Melbourne showing the average ratio in different suburbs as shown in the below figure.

為了清楚地了解比率隨位置的變化，我還創建了墨爾本的Choropleth地圖，顯示了不同郊區的平均比率，如下圖所示。

Figure 3. Variation of the average building size to land size ratio in different regions. Snapshot taken from the Tableau dashboard developed by the author.圖3.不同地區的平均建筑面積與土地面積比率的變化。 來自作者開發的Tableau儀表板的快照。

The above visualisations provide us with the insight that as we move closer to CBD and the coastal region, the building size to land size ratio increases. It can be inferred that the houses located far from the city have more unoccupied land space in the house for the front yard and backyard than the houses located in the city and near the coastal region. The major reason for this can be considered as lack of space and high prices of houses in the CBD region.

上面的可視化為我們提供了一個洞察力，即當我們靠近CBD和沿海地區時，建筑面積與土地面積之比會增加。 可以推斷，與城市和沿海地區相比，遠離城市的房屋在前院和后院的房屋中有更多的空置土地空間。 造成這種情況的主要原因可以認為是CBD地區空間不足和房屋價格居高不下。

Now let us look at the monthly trends of the house sales in Melbourne. I have plotted year-wise pie charts of the house sales to observe the monthly trends as shown in the below figure. Since the complete data for the year 2018 was not available, we will be visualising the results for the year 2016 and 2017.

現在讓我們看看墨爾本房屋銷售的月度趨勢。 我繪制了房屋銷售的年度餅圖，以觀察每月的趨勢，如下圖所示。 由于無法獲得2018年的完整數據，因此我們將可視化2016年和2017年的結果。

Figure 4. The monthly sales distribution for the year 20116 and 2017. Snapshot taken from the Tableau dashboard developed by the author.圖4. 20116和2017年的月度銷售分布?？煺照宰髡唛_發的Tableau儀表板。

It can be observed that the maximum sale in 2016 is in the month of November whereas in the year 2017 the maximum sales are in the month of July. For both the years, it is observed the majorly the houses are sold in the period May to November. It can be inferred that houses are sold more in the winter season.

可以看出，2016年的最大銷售額是在11月，而2017年的最大銷售額是在7月。 在這兩年中，觀察到主要是房屋在5月至11月期間出售。 可以推斷出冬季房屋銷售量更大。

In order to find the top 10 suburbs by highest average price and the highest number of houses sold, I have plotted 2 bar graphs as shown in the below figure.

為了找到平ASP格最高和出售房屋數量最高的前十個郊區，我繪制了兩個條形圖，如下圖所示。

Figure 5. Top 10 Suburbs by highest average price and highest number of houses sold. Snapshot taken from the Tableau dashboard developed by the author.圖5.按最高ASP和最高房屋銷售量排名前10位的郊區。 來自作者開發的Tableau儀表板的快照。

The above visualisation shows that Kooyong is the most expensive suburb and Reservoir is the most preferred suburb.

上面的圖表顯示，Kooyong是最昂貴的郊區，而Reservoir是最喜歡的郊區。

Predicting the future average prices using Tableau’s Forecasting Model:

使用Tableau的預測模型預測未來的平ASP格：

A bar graph showing the average price in the different months of the year has been plotted. The plot has been filtered for different regions. I have used Tableau’s Forecasting Model to predict the prices of houses in the second quarter of 2018. The model follows the trend of change in prices in quarters and months to determine the predicted price.

繪制了顯示一年中不同月份平ASP格的條形圖。 該圖已針對不同區域進行了過濾。 我已經使用Tableau的“預測模型”來預測2018年第二季度的房屋價格。該模型根據季度和月份價格變化的趨勢來確定預測價格。

Figure 6. Prediction for the Eastern Metropolitan region. Snapshot taken from the Tableau dashboard developed by the author.圖6.東部大都市地區的預測。 快照來自作者開發的Tableau儀表板。

As per the above visualisation, the predicted average price in the Eastern region for the month April, May and June 2018 is $1.22M, $1.23M and $1.24M respectively. The prediction follows the trend of dropping of price as we jump from quarter 1 to quarter 2 in the year 2018. Furthermore, the model average outs the change from April to May and May to June in the year 2016 and 2017 and provides an upward increase in the prices of May and June.

根據上述可視化結果，東部地區2018年4月，5月和6月的預測平ASP格分別為122萬美元，123萬美元和124萬美元。 該預測遵循價格下跌的趨勢，即我們在2018年從第一季度跳至第二季度。此外，模型平均值超過了2016年和2017年4月至5月和5月至6月的變化，并提供了向上的增長在5月和6月的價格中。

Figure 7. Prediction for the Western and Northern Metropolitan region. Snapshot taken from the Tableau dashboard developed by the author.圖7.西部和北部都會區的預測。 來自作者開發的Tableau儀表板的快照。

In the western metropolitan and northern metropolitan region, the model again follows the trend of dropping of price as we move from quarter 1 to quarter 2 in the year 2017. The change in the price from April to May and May to June is averaged out and a similar trend for all the three months of quarter 2 is predicted for both the regions.

在西部大都市和北部大都市地區，該模型再次遵循價格下降的趨勢，即我們在2017年從第一季度移至第二季度。對4月至5月以及5月至6月的價格變化進行平均，預計兩個地區在第二季度的所有三個月中都將出現類似的趨勢。

Figure 8. Prediction for the Southern Metropolitan region. Snapshot taken from the Tableau dashboard developed by the author.圖8.南部都市區的預測。 來自作者開發的Tableau儀表板的快照。

For the southern metropolitan region, the data for quarter 1 is missing for both years 2016 and 2017. Hence the system is unable to follow the quarter change trend and predicts the average price of April 2018 same as of March 2018. Furthermore, as per the average change in April to May and May to June in the previous year, an upward increase is seen in May and June 2018.

對于南部都市圈，2016年和2017年都缺少第一季度的數據。因此，該系統無法跟蹤季度變化趨勢，無法預測2018年4月的平ASP格與2018年3月相同。上一年4月至5月和5月至6月的平均變化，2018年5月和6月呈上升趨勢。

Figure 9. Prediction for the Southern Metropolitan region. Snapshot taken from the Tableau dashboard developed by the author.圖9.南部都市區的預測。 來自作者開發的Tableau儀表板的快照。

In the case of the south-eastern metropolitan region, the data for quarter 1 is missing for both the years 2016 and 2017. Moreover, the data of quarter 2 is also missing for the year 2016. Hence the model is unable to provide an accurate prediction and shows the flat value of $0.92M for all the three months of 2018 quarter 2.

在東南部大都市地區，2016年和2017年都缺少第一季度的數據。此外，2016年也缺少第二季度的數據。因此，該模型無法提供準確的數據。預測并顯示2018年第2季度的所有三個月的固定價格為92萬美元

結論 (Conclusion)

This data exploration and visualisation helped us to gather a few useful insights about the Melbourne real estate market for aspiring buyers.

數據探索和可視化幫助我們為有抱負的買家收集了一些有關墨爾本房地產市場的有用見解。

It was observed that the building size to land size ratio varies significantly as we move closer to the city area. The high prices and less space in the city encourage people to utilize the complete land in building the house.

觀察到，隨著我們靠近市區，建筑面積與土地面積之比變化很大。 城市中的高價格和較少的空間鼓勵人們利用整個土地來建造房屋。

Apart from the Southern metropolitan region, forecasting model has shown a decrease in the house prices as we move from quarter 1 to quarter 2 of 2018. Moreover, the winters season has been observed as the most preferred season for the buyers to purchase a home.

除南部大都市地區外，預測模型顯示，隨著我們從2018年第一季度移至第二季度，房價下降。此外，冬季被認為是購房者最喜歡的季節。

翻譯自: https://towardsdatascience.com/exploring-the-melbourne-real-estate-market-using-tableau-914d63659f8e

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