Difference between revisions of "NeighbourhoodWatchDocs"

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[[NeighbourhoodWatchDocs|<font color="#3c3c3c"><strong>HOME</strong></font>]]
 
[[NeighbourhoodWatchDocs|<font color="#3c3c3c"><strong>HOME</strong></font>]]
  
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[[NeighbourhoodWatchDocs_Proposal|<font color="#3c3c3c"><strong>PROPOSAL</strong></font>]]
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[[NeighbourhoodWatchDocs_Poster|<font color="#3c3c3c"><strong>POSTER</strong></font>]]
 
[[NeighbourhoodWatchDocs_Poster|<font color="#3c3c3c"><strong>POSTER</strong></font>]]
  
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[[NeighbourhoodWatchDocs_Project_Application|<font color="#3c3c3c"><strong>APPLICATION</strong></font>]]
 
[[NeighbourhoodWatchDocs_Project_Application|<font color="#3c3c3c"><strong>APPLICATION</strong></font>]]
  
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[[NeighbourhoodWatchDocs_Research_Paper|<font color="#3c3c3c"><strong>RESEARCH PAPER</strong></font>]]
 
[[NeighbourhoodWatchDocs_Research_Paper|<font color="#3c3c3c"><strong>RESEARCH PAPER</strong></font>]]
  
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<br><br>
 
<br><br>
  
<div><h1>GROUP MEMBERS</h1></div>
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<div style="background: #00A4EF; padding: 20px; line-height: 0.3em; text-indent: 16px;letter-spacing:0.1em;font-size:26px"><font color=#fbfcfd face="Bebas Neue">PROJECT MEMBERS</font></div>
<div>Debbie Lee Shan Ying | Goh Chun Ming | Tan Guan Ze</div>
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<br>
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[[File:NeighbourhoodWatchDocs TeamMembers.JPG|center|800px]]
  
<div><h1>PROJECT DESCRIPTION</h1></div>
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<!-- END OF PROJECT MEMBERS --->
<div>Our project aims to make use of geospatial intelligence to explore the potential of allocating nearby doctors within estates to the residents, in particular the elderly to combat the issues of an ageing population. </div>
 
 
 
<div><h1>DATA COLLECTION</h1></div>
 
 
 
<div><h1>PROJECT TIMELINE</h1></div>
 
 
 
<div><h1>PROJECT CHALLENGES</h1></div>
 
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<b>No.</b>
 
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<b>Key Technical Challenges</b>
 
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<b>Description</b>
 
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<b>Proposed Solution</b>
 
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<b>Outcome</b>
 
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1.
 
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Unfamiliarity with R packages and R Shiny
 
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Our team may encounter the use of additional R resources that were not taught in class.
 
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- Independent Learning on R packages and R Shiny<br>
 
- Browsing the official RDocumentation website for support and reference<br>
 
- Research for online tutorials that have a specific use case for certain R packages
 
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We managed to solve the mentioned challenge with the following resources:<br>
 
-
 
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2.
 
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Data Cleaning and Transformation
 
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As we need to collect the data from various sources, they may have different attributes such as the Coordinate Reference System (CRS), units of measurement and etc.
 
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Adopt a standardized process of cleaning the data, focusing with what we only need. Most of the datasets used for our project can be found in our Hands-On or Take-Home exercises and we can rely on those existing data.
 
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We managed to solve our technical challenge with the following:<br>
 
-  
 
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3.
 
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Limitations & Constraints in Datasets
 
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There are certain assumptions that we need to make based on the context and purpose of our project, such as the average number of doctors in a particular clinic, which cannot be derived from our datasets.
 
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Working out with the team together and figuring out a reasonable and valid assumption, together with adequate online research and consultation with Prof. Kam.
 
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We managed to solve our technical challenge with the following:<br>
 
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</div>
 

Latest revision as of 23:15, 5 March 2019