Difference between revisions of "IS428 2016-17 Term1 Assign1 Liang Bing"
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==Approaches== | ==Approaches== | ||
===Overall Supply Pattern in 4 Quarters in 2015=== | ===Overall Supply Pattern in 4 Quarters in 2015=== | ||
| − | * Data Source: The data | + | * Data Source: The data sets selected for this portion are [[Supply of Executive Condominium Units in the Pipeline.csv]],[[ Supply of Landed Private Residential Units in the Pipeline.csv]] and [[Supply of Non-Landed Private Residential Units in the Pipeline]]. |
* Step by Step: | * Step by Step: | ||
| − | 1. Data set combination: | + | 1. Data set combination: To facilitate Tableau data importing, I used excel to combine the data from these three csv files. This excel file basically has one column for quarters and three columns for supply of three types of private properties. |
2. Data visualization: In order to know the supply pattern in 4 quarters in 2015, the graph plotted should clearly portray the total units supply for each quarter for comparison. Therefore, bar graph is used. Each bar represents the private property units supply for that quarter, and the height represents the sum of all kinds of private property units supplied in that quarter. Also, to prepare the insights for the following questions, the bar graph is designed to be stacked bar graph in which the portion of each private property type is included in the bar. The height of each colored portion represents the share of that type of private property over the total number of units supply in respective quarter. The higher the colored portion, the larger the share. | 2. Data visualization: In order to know the supply pattern in 4 quarters in 2015, the graph plotted should clearly portray the total units supply for each quarter for comparison. Therefore, bar graph is used. Each bar represents the private property units supply for that quarter, and the height represents the sum of all kinds of private property units supplied in that quarter. Also, to prepare the insights for the following questions, the bar graph is designed to be stacked bar graph in which the portion of each private property type is included in the bar. The height of each colored portion represents the share of that type of private property over the total number of units supply in respective quarter. The higher the colored portion, the larger the share. | ||
| − | The graph is plotted by 1) | + | The graph is plotted by 1) placing the quarter dimension at the x-axis and 2) placing the measured value (contains the units supply for executive condominium, landed property and non-landed property) at the y-axis. |
[[Image:Lb_assignment1_approaches_graph1_for_wiki.png]] | [[Image:Lb_assignment1_approaches_graph1_for_wiki.png]] | ||
| Line 38: | Line 38: | ||
===Overall Price Pattern in 4 Quarters in 2015=== | ===Overall Price Pattern in 4 Quarters in 2015=== | ||
| + | * Data Source: The data set selected for this portion is [[Private Property Transactions from Jan to Dec 2015.csv]]. | ||
| + | * Step by Step: | ||
| + | 1. Data set combination: The data downloaded from URA database are in several csv files and each file contains 1000 records of private property transactions. I used excel to combine all the records from Jan to Dec in one excel file for easier tableau data import. | ||
| + | |||
| + | 2. Data visualization: Different types of private properties have different unit prices. Also, for the same type of private property, the unit prices provided by different property developers are different. To better illustrate the unit prices level for one quarter, the average unit price for all types of private property is used. A line graph is chosen to provide simpler illustration of the trend of average unit price change over the 4 quarters. Line graph is preferred over bar graph here because the line helped viewer to focus on the changes, not the amount of the individual data point. | ||
| + | |||
| + | The graph is plotted by 1) placing the sales date at the x-axis and changing its value to Quarter, 2) placing the Unit Price ($psm) at the y-axis and changing its measure value from sum to average. | ||
| + | |||
| + | [[Image:Lb_assignment1_approaches_graph_2_for_wiki.png]] | ||
| + | |||
| + | The graph showed the following pattern for private property average units price: | ||
| + | * '''The average units price fluctuates throughout the year. The growth and decrease appears alternatively across the 4 quarters''' | ||
| + | |||
| + | |||
| + | ===Intermediate Analysis=== | ||
| + | Generally, these two plots did not shown clear relationship between the units supply and units price for private properties. This might be caused by the impact of other factors which are not covered in this report, for example, the demand of private property. However, from individual graph, it can be infer that the overall supply for private property in year 2015 keep declining, it might reach a point that the unit supply of private property does not meet the demand, which shoot up the units price which showed in quarter 4 of 2015. | ||
| + | |||
| + | |||
| + | ===Supply Pattern by Types of Private Property in 4 quarters in 2015=== | ||
| + | * Data Source: The data sets selected for this portion are The data sets selected for this portion are [[Supply of Executive Condominium Units in the Pipeline.csv]],[[ Supply of Landed Private Residential Units in the Pipeline.csv]] and [[Supply of Non-Landed Private Residential Units in the Pipeline]]. As mentioned above, the data has been combined into one excel file for easier tableau import. | ||
| + | * Step by Step: | ||
| + | 1. Data visualization: It can be seen from graph 1 that private property comprises different types. From the URA website: private property can be segmented into 3 main types: Landed private property, Non-landed private property and Executive Condominium. Landed private property includes: detached houses, semi-detached houses and terrace houses. Non-landed private property includes: apartments and condominium housing. In this section, I would like to find out the supply change pattern for these 3 types of private property. A dual combination graph which combines bar and line graph is used for easily comparing the height of bars which is the total units supply for one type per quarter, as well as the trend of supply change across 4 quarters. | ||
| + | |||
| + | The graph is plotted by 1) placing the 3 types of private property and time by quarters at the x-axis, 2) placing one measure value for plotting the bar graph and one measure value for plotting line graph at the y-axis. | ||
| + | |||
| + | [[Image:Lb_assignment1_approaches_graph3_for_wiki.png]] | ||
| + | |||
| + | This graph showed that: | ||
| + | * The supply for all three types of private properties are decreasing across the 4 quarters. | ||
| + | |||
| + | |||
| + | ===Unit Price Pattern by Types of Private Property in 4 quarters in 2015=== | ||
| + | * Data Source: The data set selected for this section is [[Private Property Transactions from Jan to Dec 2015.csv]]. | ||
| + | * Step by Step: | ||
| + | 1. Data visualization: This section aims to find out the average unit price pattern for different types of private property. A dual combination graph is selected due to its ability to illustrate the average unit price level for different types of private property as well as the trend for average unit price across 4 quarters. | ||
| + | |||
| + | This graph is plotted by 1) grouping the private properties by the three types, followed by placing the group and the sale date by quarter at the x-axis, 2)placing 2 measure value (average units price ($psm)) at the y-axis, one for plotting the bar graph and the other for plotting the line graph. | ||
| + | |||
| + | [[Image:Lb_assignment1_approaches_graph4_for_wiki.png]] | ||
| + | |||
| + | In this graph, we can see that for each type of private property, the average unit price fluctuation pattern is similar to the overall average unit price pattern in section 2. | ||
==Inforgraphics== | ==Inforgraphics== | ||
Revision as of 21:28, 28 August 2016
Contents
Abstract
The focus of this report is using data visualization tools and techniques to find the change patterns of private property's supply and price by type of private property and regions in 4 quarters in 2015. By analyzing the patterns and their relationships, the report aims to give insights for suggesting development policies in 2016.
Problem & Motivation
Using the data set I have chosen, I have done a mini analysis to decide the attributes/variables which I am interested to study. And I found that the supply and price pattern for different types of private properties and for private properties in different regions appear to be clear and insightful. It drives me to think the following questions:
- What's the overall pattern for private properties' supply and price?
- What's the supply and price difference for different types of private properties?
- What's the supply and price difference for private properties in different regions?
- What's the relationship between the price and supply for the above two scenarios?
With these questions in mind, the data visualization and analysis conducted in this article should find out useful patterns, help me answer the above questions and suggest the policy of private properties supply for 2016.
Tools & Sites
Tools
- Microsoft Excel: Always powerful and user-friendly in dealing with huge data set. Used for merging data from different files.
- Tableau: Very powerful tool for data visualization and data analysis. However, it is lacking of a user-friendly UI and tutorials for rookies.
Sites
- The official Urban Redevelopment Authority website : It contains quite clear words explanation which helped a lot when I was confused about the URA data's terminology.
Approaches
Overall Supply Pattern in 4 Quarters in 2015
- Data Source: The data sets selected for this portion are Supply of Executive Condominium Units in the Pipeline.csv,Supply of Landed Private Residential Units in the Pipeline.csv and Supply of Non-Landed Private Residential Units in the Pipeline.
- Step by Step:
1. Data set combination: To facilitate Tableau data importing, I used excel to combine the data from these three csv files. This excel file basically has one column for quarters and three columns for supply of three types of private properties.
2. Data visualization: In order to know the supply pattern in 4 quarters in 2015, the graph plotted should clearly portray the total units supply for each quarter for comparison. Therefore, bar graph is used. Each bar represents the private property units supply for that quarter, and the height represents the sum of all kinds of private property units supplied in that quarter. Also, to prepare the insights for the following questions, the bar graph is designed to be stacked bar graph in which the portion of each private property type is included in the bar. The height of each colored portion represents the share of that type of private property over the total number of units supply in respective quarter. The higher the colored portion, the larger the share.
The graph is plotted by 1) placing the quarter dimension at the x-axis and 2) placing the measured value (contains the units supply for executive condominium, landed property and non-landed property) at the y-axis.
The graph showed the following two patterns for private property units supply:
- The units supply in 2015 is showing a decreasing trend
- Non-Landed private properties formed the major part for the private property units supply in all quarters in 2015
Overall Price Pattern in 4 Quarters in 2015
- Data Source: The data set selected for this portion is Private Property Transactions from Jan to Dec 2015.csv.
- Step by Step:
1. Data set combination: The data downloaded from URA database are in several csv files and each file contains 1000 records of private property transactions. I used excel to combine all the records from Jan to Dec in one excel file for easier tableau data import.
2. Data visualization: Different types of private properties have different unit prices. Also, for the same type of private property, the unit prices provided by different property developers are different. To better illustrate the unit prices level for one quarter, the average unit price for all types of private property is used. A line graph is chosen to provide simpler illustration of the trend of average unit price change over the 4 quarters. Line graph is preferred over bar graph here because the line helped viewer to focus on the changes, not the amount of the individual data point.
The graph is plotted by 1) placing the sales date at the x-axis and changing its value to Quarter, 2) placing the Unit Price ($psm) at the y-axis and changing its measure value from sum to average.
The graph showed the following pattern for private property average units price:
- The average units price fluctuates throughout the year. The growth and decrease appears alternatively across the 4 quarters
Intermediate Analysis
Generally, these two plots did not shown clear relationship between the units supply and units price for private properties. This might be caused by the impact of other factors which are not covered in this report, for example, the demand of private property. However, from individual graph, it can be infer that the overall supply for private property in year 2015 keep declining, it might reach a point that the unit supply of private property does not meet the demand, which shoot up the units price which showed in quarter 4 of 2015.
Supply Pattern by Types of Private Property in 4 quarters in 2015
- Data Source: The data sets selected for this portion are The data sets selected for this portion are Supply of Executive Condominium Units in the Pipeline.csv,Supply of Landed Private Residential Units in the Pipeline.csv and Supply of Non-Landed Private Residential Units in the Pipeline. As mentioned above, the data has been combined into one excel file for easier tableau import.
- Step by Step:
1. Data visualization: It can be seen from graph 1 that private property comprises different types. From the URA website: private property can be segmented into 3 main types: Landed private property, Non-landed private property and Executive Condominium. Landed private property includes: detached houses, semi-detached houses and terrace houses. Non-landed private property includes: apartments and condominium housing. In this section, I would like to find out the supply change pattern for these 3 types of private property. A dual combination graph which combines bar and line graph is used for easily comparing the height of bars which is the total units supply for one type per quarter, as well as the trend of supply change across 4 quarters.
The graph is plotted by 1) placing the 3 types of private property and time by quarters at the x-axis, 2) placing one measure value for plotting the bar graph and one measure value for plotting line graph at the y-axis.
This graph showed that:
- The supply for all three types of private properties are decreasing across the 4 quarters.
Unit Price Pattern by Types of Private Property in 4 quarters in 2015
- Data Source: The data set selected for this section is Private Property Transactions from Jan to Dec 2015.csv.
- Step by Step:
1. Data visualization: This section aims to find out the average unit price pattern for different types of private property. A dual combination graph is selected due to its ability to illustrate the average unit price level for different types of private property as well as the trend for average unit price across 4 quarters.
This graph is plotted by 1) grouping the private properties by the three types, followed by placing the group and the sale date by quarter at the x-axis, 2)placing 2 measure value (average units price ($psm)) at the y-axis, one for plotting the bar graph and the other for plotting the line graph.
In this graph, we can see that for each type of private property, the average unit price fluctuation pattern is similar to the overall average unit price pattern in section 2.
