Difference between revisions of "Group07 Overview"
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<div style="border-style: solid; border-width:0; background: #006666; padding: 7px; font-weight: bold; text-align:left; line-height: wrap_content; text-indent: 20px; font-size:20px; font-family:Century Gothic;border-bottom:5px solid white; border-top:5px solid black"><font color= #ffffff>Abstract</font></div> | <div style="border-style: solid; border-width:0; background: #006666; padding: 7px; font-weight: bold; text-align:left; line-height: wrap_content; text-indent: 20px; font-size:20px; font-family:Century Gothic;border-bottom:5px solid white; border-top:5px solid black"><font color= #ffffff>Abstract</font></div> | ||
| − | Singapore government closely | + | The Singapore government closely observes the property market and occassionally implements new policies as cooling |
| − | + | measures to prevent the market from heating up too quickly. Even though several government and non-government organizations | |
| + | already created visualization tools to explain the property market in Singapore, they are unable to reveal more information from the | ||
| + | property market which has a complicated nature. Thus, there is still much potential in using new tools to advance the understanding | ||
| + | and visualizations of changes in the market.<br> | ||
| + | Through the integration of R packages, our application will help users to discover patterns and compare differences between | ||
| + | property prices in different administrative areas over time. <br><i>Firstly</i>, we used plotly to chart the comparison between total units sold | ||
| + | and SIBOR (Singapore Interbank Offered Rate) which serves as a main factor of fluctuation in number of units sold.<br><i>Secondly</i>, by | ||
| + | creating the geofacet map for Singapore, we visualised changes in median unit price over time from the perspective of planning | ||
| + | areas and postal districts.<br><i>Thirdly</i>, by coordinating the views between two visualizations, we used a treemap as a user interface to | ||
| + | update the ridgelines plot which zooms into the distribution of prices in a specific region by property type and type of sale.<br><i>Lastly</i>, we | ||
| + | used the Local Indicators for Spatial Autocorrelation (LISA) analysis to reveal clusters of properties in Singapore by their median | ||
| + | unit price, and incorporated the results into an interactive map using the tmap package. For the ridgelines plot and LISA analysis | ||
| + | mentioned above, all the private property types such as apartment, condominium, executive condominium, detached house, semidetached | ||
| + | house, terrace house as well as different type of sales such as new sale, resale and sub-sale were provided for users to | ||
| + | drill down into.<br> | ||
</p> | </p> | ||
Latest revision as of 00:17, 14 August 2018
Be a REALIST
Uncover the Truth in Singapore Private Property Market
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The Singapore government closely observes the property market and occassionally implements new policies as cooling
measures to prevent the market from heating up too quickly. Even though several government and non-government organizations
already created visualization tools to explain the property market in Singapore, they are unable to reveal more information from the
property market which has a complicated nature. Thus, there is still much potential in using new tools to advance the understanding
and visualizations of changes in the market.
Through the integration of R packages, our application will help users to discover patterns and compare differences between
property prices in different administrative areas over time.
Firstly, we used plotly to chart the comparison between total units sold
and SIBOR (Singapore Interbank Offered Rate) which serves as a main factor of fluctuation in number of units sold.
Secondly, by
creating the geofacet map for Singapore, we visualised changes in median unit price over time from the perspective of planning
areas and postal districts.
Thirdly, by coordinating the views between two visualizations, we used a treemap as a user interface to
update the ridgelines plot which zooms into the distribution of prices in a specific region by property type and type of sale.
Lastly, we
used the Local Indicators for Spatial Autocorrelation (LISA) analysis to reveal clusters of properties in Singapore by their median
unit price, and incorporated the results into an interactive map using the tmap package. For the ridgelines plot and LISA analysis
mentioned above, all the private property types such as apartment, condominium, executive condominium, detached house, semidetached
house, terrace house as well as different type of sales such as new sale, resale and sub-sale were provided for users to
drill down into.
