Difference between revisions of "G2-Group14 Proposal"

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[[G2-Group14|<font color="#000000"><strong>HOME</strong></font>]]
  
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| style="text-align: center; border-top:solid #d2b48c; border-right: solid #d2b48c; border-left: solid #d2b48c; border-bottom:solid #d2b48c; font-size:20px" height ="30px" width="250px" | [[G2-Group14_Proposal|<font color="navy"> <strong> PROPOSAL</strong></font>]]
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| style="font-family:Open Sans, Arial, sans-serif; font-size:16px; text-align: center; border-top:solid #00439F; border-bottom:solid #00439F" width="330px" |  
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[[G2-Group14_Proposal|<font color="#000000"><strong>PROPOSAL</strong></font>]]
  
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| style="text-align: center; border-top:solid #d2b48c; border-right: solid #d2b48c; border-left: solid #d2b48c; border-bottom:solid #d2b48c; font-size:20px" height ="30px" width="250px" | [[G2-Group14 Poster|<font color="navy"> <strong> POSTER </strong></font>]]
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| style="font-family:Open Sans, Arial, sans-serif; font-size:16px; text-align: center; border-top:solid #00439F; border-bottom:solid #00439F" width="330px" |  
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[[G2-Group14 Poster|<font color="#000000"><strong>POSTER</strong></font>]]
  
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| style="text-align: center; border-top:solid #d2b48c; border-right: solid #d2b48c; border-left: solid #d2b48c; border-bottom:solid #d2b48c; font-size:20px" height ="30px" width="250px" | [[G2-Group14 WebMaps|<font color="navy"> <strong> WEB MAPS </strong></font>]]
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| style="font-family:Open Sans, Arial, sans-serif; font-size:16px; text-align: center; border-top:solid #00439F; border-bottom:solid #00439F" width="330px" |  
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[[G2-Group14 WebMaps|<font color="#000000"><strong>WEB MAPS</strong></font>]]
  
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| style="text-align: center; border-top:solid #d2b48c; border-right: solid #d2b48c; border-left: solid #d2b48c; border-bottom:solid #d2b48c; font-size:20px" height ="30px" width="250px" | [[G2-Group14 Report|<font color="navy"> <strong> REPORT </strong></font>]]
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| style="font-family:Open Sans, Arial, sans-serif; font-size:16px; text-align: center; border-top:solid #00439F; border-bottom:solid #00439F" width="330px" |  
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[[G2-Group14 Report|<font color="#000000"><strong>REPORT</strong></font>]]
  
 
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! style="text-align: center; width:20em; height: 1em; font-size:20px; font-family:Arial !important;; background-color:#efefc8; color: #000067;"| PROJECT MOTIVATION
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! style="text-align: center; width:20em; height: 3em; font-size:20px; font-family:Arial !important;; background-color:#132569; color: white;"| PROJECT MOTIVATION
 
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| style="text-align:justify; width:20em; height: 1em; font-size:15px; font-family:Arial!important; background-color:#BOC4DE; color: black;"|
 
Big Data, in today’s context, is indispensable. Businesses are realising its importance and how they should utilize data they have captured over the years by applying analytics. Over the years, Big Data analytics has helped organisations process their data; discovering insights, trends and uncovering potential opportunities. This has benefited these organisations by improving their decision-making and problem solving processes.
 
Big Data, in today’s context, is indispensable. Businesses are realising its importance and how they should utilize data they have captured over the years by applying analytics. Over the years, Big Data analytics has helped organisations process their data; discovering insights, trends and uncovering potential opportunities. This has benefited these organisations by improving their decision-making and problem solving processes.
  
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When the opportunity to work with Pizza Hut arose, our team was more than keen to take on the challenge. With the relevant data readily provided by our client, we will then be able to determine the shops, trade areas and analyse how other factors can affect Pizza Hut’s business across Taipei. In this project, we are excited to apply skill sets that we have acquired in our Geospatial classes to help identify potential hotspots and problems. We hope that our analysis can provide Pizza Hut with insightful recommendations that can aid them with their business strategies and decisions in the coming years.  
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When the opportunity to work with an international restaurant franchise arose, our team was more than keen to take on the challenge. With the relevant data readily provided by our client, we will then employ skills like delineating and digitizing the trade areas of our allocated stores, then analyse how other relevant factors such as competitions and human traffic can affect their business across Taipei. In this project, we are excited to apply skill sets that we have acquired in our Geospatial classes to help identify potential hotspots and potential problems that might be occurring. We hope that our analysis can provide the client with valuable insights, and that our proposed recommendations can aid them with their business strategies and decisions in the coming years.  
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! style="text-align: center; width:20em; height: 3em; font-size:20px; font-family:Arial !important;; background-color:#132569; color: white;"| PROJECT OBJECTIVE
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The project aims to conduct a thorough analysis of the 13 of our client’s restaurant stores located near the Tamsui River in Taiwan to evaluate the success of each individual store based on their location. We will come up with the following:
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# Digitized maps on the Quantum Geographic Information System (QGIS) software, based on the subzones labelled and provided to us by the international franchise
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# Business profiles of each store and their relative performance to each other through extracting out relevant POIs within the trade area and subzones
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# Setting up buffer zones to evaluate the possibility of creating an accurate business profile of each store through the use of such buffer tools, rather than digitizing the actual map areas given to us
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# Analyze, research and provide recommendations to our client through the creation of an interesting and informational poster filled with QGIS maps for better visualization, as well as a thorough project report.
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! style="text-align: center; width:20em; height: 3em; font-size:20px; font-family:Arial !important;; background-color:#132569; color: white;"| PROJECT DATA SETS
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Our Team going by the name of TAI1, with members including Krystal Ong, Ng Qi Hui and Peh Jin An.
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<!-- DATA TABLE CONTENT -->
  
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{| width=99% border=3 align=center font-family:Cambria
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! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Name of Data
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! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Format of Data
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! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Geographic Representation
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! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Source
 +
|-
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| colspan="4" style="font-style: bold; font-size:20px; font-family: Cambria; background-color: rgb(179, 215, 255);text-align: center;" | Stores
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|-
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| style="background-color:white" | Taiwan_Stores
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Professor Kam Tin Seong
 +
|-
 +
| colspan="4" style="font-style: bold; font-size:20px; font-family: Cambria; background-color: rgb(179, 215, 255);text-align: center;" | Land Segregation & Roads
 +
|-
 +
| style="background-color:white" | streets
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Line - MultiLineString
 +
| style="text-align: center;" | Professor Kam Tin Seong
 +
|-
 +
|-
 +
| style="background-color:white" | VILLAGE_MOI_121_1080726
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Polygon - MultiPolygon
 +
| style="text-align: center;" | Professor Kam Tin Seong
 +
|-
 +
| style="background-color:white" | VILLAGE_MOI_1080726
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Polygon - MultiPolygon
 +
| style="text-align: center;" | Professor Kam Tin Seong
 +
|-
 +
| style="background-color:white" | TOWN_MOI_108072
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Polygon - MultiPolygon
 +
| style="text-align: center;" | Professor Kam Tin Seong
 +
|-
 +
| style="background-color:white" | COUNTY_MOI_1080726
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Polygon - MultiPolygon
 +
| style="text-align: center;" | Professor Kam Tin Seong
 +
|-
 +
| colspan="4" style="font-style: bold; font-size:20px; font-family: Cambria; background-color: rgb(179, 215, 255);text-align: center;" | Points of Interest
 +
|-
 +
| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;"  | Business
 +
|-
 +
| Business Facility - 5000
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Industrial Area - 9991
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;"  | Entertn
 +
|-
 +
| Bar or Pub - 9532
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Cinema - 7832
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Nightlife - 5813
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Performing Arts - 7929
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
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| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | CommSvc
 +
|-
 +
| Government Office - 9525
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
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| style="text-align: center;" | Client
 +
|-
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| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Park Recreational
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|-
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| Bowling Centre - 7933
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
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| style="text-align: center;" | Client
 +
|-
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| Sports Complex - 7940
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Sports Centre - 7997
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
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| style="text-align: center;" | Client
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|-
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| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;"  | EduInsts
 +
|-
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| Higher Education - 8200
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
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| style="text-align: center;" | Client
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|-
 +
| School - 8211
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
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| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | FinInsts
 +
|-
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| ATM - 3578
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
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| style="text-align: center;" | Client
 +
|-
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| Bank - 6000
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center; | Hospital
 +
|-
 +
| Hospital - 8060
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
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| style="text-align: center;" | Client
 +
|-
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| Medical Service - 9583
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
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| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Restrnts
 +
|-
 +
| Coffee Shop - 9996
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Restaurant - 5800
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
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| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | TransHubs
 +
|-
 +
| Bus Station - 4170
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| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Commuter Rail Station - 4100
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Train Station - 4013
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Shopping
 +
|-
 +
| Bookstore - 9995
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Clothing Store - 9537
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Electronics Store - 9987
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Convenience Store - 9535
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Department Store - 9545
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Grocery Store - 5400
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Pharmacy - 9565
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Shopping - 6512
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| Specialty Store - 9567
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Travel Destination
 +
|-
 +
| Hotel - 7011
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | MiscCategories
 +
|-
 +
| Residential Area or Building - 9590
 +
| style="text-align: center;" | .shp
 +
| style="text-align: center;" | Point
 +
| style="text-align: center;" | Client
 +
|-
 +
| colspan="4" style="font-style: bold; font-size:20px; font-family: Cambria; background-color: rgb(179, 215, 255);text-align: center;" |  Trade Areas
 +
|-
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| style="background-color:white" | CU-20171212寬52高42(鋁)含框
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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| style="background-color:white" | LF-20190416寬60高76(鋁)
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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| style="background-color:white" | LU-20190318寬70高90(木)
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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| style="background-color:white" | SC-20171124寬74高90(鋁)含框
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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| style="background-color:white" | SZ-20190830寬88.5高100(鋁)含框
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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| style="background-color:white" | LW-20180109寬110高76.5(木)
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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|-
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| style="background-color:white" | WG-20150722寬60高比例(鋁)
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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| style="background-color:white" | BL
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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|-
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| style="background-color:white" | DC
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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|-
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| style="background-color:white" | DJ
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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| style="background-color:white" | DS
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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| style="background-color:white" | FW-20171115寬75高80(鋁)含框
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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| style="background-color:white" | ZT-20190323寬76高等比(鋁)含框
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| style="text-align: center;" | .pptx
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| style="text-align: center;" | -
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| style="text-align: center;" | Client
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{| align=center width = 100%
 
  
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! style="text-align: center; width:20em; height: 1em; font-size:20px; font-family:Arial !important;; background-color:#efefc8; color: #000067;"| PROJECT OBJECTIVE
 
 
|}
 
|}
  
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{| align=center width = 100%
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<br>
  
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| style="text-align:justify; width:20em; height: 1em; font-size:15px; font-family:Arial!important; background-color:ghostwhite; color: black;"|
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{| align=center width = 100%| border = "1" cellpadding = "1"
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! style="text-align: center; width:20em; height: 3em; font-size:20px; font-family:Arial !important;; background-color:#132569; color: white;"| FUNCTIONS AND ANALYSIS
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|}
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<!-- DATA TABLE CONTENT -->
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{| width=99% border=3 align=center font-family:Cambria
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! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Functions Employed
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! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Analysis Conducted
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|-
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| style="background-color:white" | Choropleth Mapping
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| style="text-align: left;" |
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*Revenue Analysis for each of the allocated stores
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*Revenue Analysis for each POD in their trade areas
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|-
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|-
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| style="background-color:white" | Count by Polygon – the generation of the 32 main POI variables
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| style="text-align: left;" |
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*Generation of the Business Profiles for the stores
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*Relationship between the Total Number of POIs and Revenue
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|-
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|-
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| style="background-color:white" | Network OD-Matrix (Layers as Table (M:N)) 
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| style="text-align: left;" |
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*Relationship between the Average Shortest Distance of the POIs from the stores and Revenue
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|-
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|-
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| style="background-color:white" | Multi-Linear Regression (using SAS Enterprise Guide) 
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| style="text-align: left;" |
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*Relationship between Important Variables and Revenue
 +
|-
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|-
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| style="background-color:white" | Linear Regression (using SAS Enterprise Guide)
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| style="text-align: left;" |
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*Relationship between Area of Trade and Revenue
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|-
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|-
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| style="background-color:white" | Hub Lines / Distance 
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| style="text-align: left;" |
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*Relationship between Competitors and Revenue
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|-
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|-
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| style="background-color:white" | Buffer Generation for 2.5KM, 3.0KM and 3.5KM
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| style="text-align: left;" |
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*Analysis of the use of Buffers as a Theoretical Trade Area
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|-
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|-
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| style="background-color:white" | Iso-Areas as Contours (from Layer)
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| style="text-align: left;" |
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*Analysis of the use of Network Service Area as a Theoretical Trade Area
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*Comparison between the use of Theoretical Trade Areas and the Actual Digitised Trade Areas
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|-
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|-
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| colspan="4" style="font-style: bold; font-size:20px; font-family: Cambria; background-color: rgb(179, 215, 255);text-align: center;" | Errors
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|-
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|-
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| style="background-color:white" |-
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| style="text-align: left;" |
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*Data gap discovered for the Population Data
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|-
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! style="text-align: center; width:20em; height: 3em; font-size:20px; font-family:Arial !important;; background-color:#132569; color: white;"| TOOLS USED
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[[File:qgislogo.PNG|100px|]]
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[[File:saslogo.JPG|100px|]]
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[[File:Excellogo.png|70px|]]
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[[File:Powerpointlogo.png|70px|]]
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[[File:Wordlogo.png|70px|]]
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[[File:Googdoc.png|100px|]]
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[[File:Googleforms.png|100px|]]
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The project aims to conduct a thorough analysis of the 13 Pizza Hut stores located in Taiwan to evaluate the success of each individual store based on their location. Through our analysis, we will take into account the various points of interest (POI) present in the subzone to come up with the following:
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# QGIS - Our platform to analyze and edit spatial information, in addition to composing and exporting graphical maps
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# Digitized maps on the Quantum Geographic Information System (QGIS) software, based on the subzones provided to us by Pizza Hut Taiwan
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# SAS Enterprise Guide - Our platform to generate and calculate Multi-Regression Lines and Col-linearity between Points of Interests
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# Business profiles of each Pizza Hut stall, referring to the POIs within the trade area and subzones by setting multiple buffer areas for each area
+
# Microsoft Excel - Our platform for data cleaning and calculations
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# Analysis, research and recommendations to our client, Pizza Hut, through the creation of a poster and project report
+
# Microsoft PowerPoint - Our platform to construct the Poster and Executive Report
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# Microsoft Word - Our platform for Final Report
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# Google Docs - Our platform for weekly discussions and report write up
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# Google Forms - Our platform for FeedBack during Townhall Presentation
  
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! style="text-align: center; width:20em; height: 1em; font-size:20px; font-family:Arial !important;; background-color:#efefc8; color: #000067;"| PROJECT TIMELINE
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! style="text-align: center; width:20em; height: 3em; font-size:20px; font-family:Arial !important;; background-color:#132569; color: white;"| REFERENCES
 
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About:  
 +
# https://www.trip.com/travel-guide/taipei/tamsui-river-23866567/
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# https://guidetotaipei.com/visit/tamsui-fishermans-wharf-%E6%B7%A1%E6%B0%B4%E6%BC%81%E4%BA%BA%E7%A2%BC%E9%A0%AD

Latest revision as of 01:23, 25 November 2019

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PROPOSAL

POSTER

WEB MAPS

REPORT


PROJECT MOTIVATION


Big Data, in today’s context, is indispensable. Businesses are realising its importance and how they should utilize data they have captured over the years by applying analytics. Over the years, Big Data analytics has helped organisations process their data; discovering insights, trends and uncovering potential opportunities. This has benefited these organisations by improving their decision-making and problem solving processes.

When the opportunity to work with an international restaurant franchise arose, our team was more than keen to take on the challenge. With the relevant data readily provided by our client, we will then employ skills like delineating and digitizing the trade areas of our allocated stores, then analyse how other relevant factors such as competitions and human traffic can affect their business across Taipei. In this project, we are excited to apply skill sets that we have acquired in our Geospatial classes to help identify potential hotspots and potential problems that might be occurring. We hope that our analysis can provide the client with valuable insights, and that our proposed recommendations can aid them with their business strategies and decisions in the coming years.


PROJECT OBJECTIVE


The project aims to conduct a thorough analysis of the 13 of our client’s restaurant stores located near the Tamsui River in Taiwan to evaluate the success of each individual store based on their location. We will come up with the following:

  1. Digitized maps on the Quantum Geographic Information System (QGIS) software, based on the subzones labelled and provided to us by the international franchise
  2. Business profiles of each store and their relative performance to each other through extracting out relevant POIs within the trade area and subzones
  3. Setting up buffer zones to evaluate the possibility of creating an accurate business profile of each store through the use of such buffer tools, rather than digitizing the actual map areas given to us
  4. Analyze, research and provide recommendations to our client through the creation of an interesting and informational poster filled with QGIS maps for better visualization, as well as a thorough project report.


PROJECT DATA SETS


Name of Data Format of Data Geographic Representation Source
Stores
Taiwan_Stores .shp Point Professor Kam Tin Seong
Land Segregation & Roads
streets .shp Line - MultiLineString Professor Kam Tin Seong
VILLAGE_MOI_121_1080726 .shp Polygon - MultiPolygon Professor Kam Tin Seong
VILLAGE_MOI_1080726 .shp Polygon - MultiPolygon Professor Kam Tin Seong
TOWN_MOI_108072 .shp Polygon - MultiPolygon Professor Kam Tin Seong
COUNTY_MOI_1080726 .shp Polygon - MultiPolygon Professor Kam Tin Seong
Points of Interest
Business
Business Facility - 5000 .shp Point Client
Industrial Area - 9991 .shp Point Client
Entertn
Bar or Pub - 9532 .shp Point Client
Cinema - 7832 .shp Point Client
Nightlife - 5813 .shp Point Client
Performing Arts - 7929 .shp Point Client
CommSvc
Government Office - 9525 .shp Point Client
Park Recreational
Bowling Centre - 7933 .shp Point Client
Sports Complex - 7940 .shp Point Client
Sports Centre - 7997 .shp Point Client
EduInsts
Higher Education - 8200 .shp Point Client
School - 8211 .shp Point Client
FinInsts
ATM - 3578 .shp Point Client
Bank - 6000 .shp Point Client
Hospital
Hospital - 8060 .shp Point Client
Medical Service - 9583 .shp Point Client
Restrnts
Coffee Shop - 9996 .shp Point Client
Restaurant - 5800 .shp Point Client
TransHubs
Bus Station - 4170 .shp Point Client
Commuter Rail Station - 4100 .shp Point Client
Train Station - 4013 .shp Point Client
Shopping
Bookstore - 9995 .shp Point Client
Clothing Store - 9537 .shp Point Client
Electronics Store - 9987 .shp Point Client
Convenience Store - 9535 .shp Point Client
Department Store - 9545 .shp Point Client
Grocery Store - 5400 .shp Point Client
Pharmacy - 9565 .shp Point Client
Shopping - 6512 .shp Point Client
Specialty Store - 9567 .shp Point Client
Travel Destination
Hotel - 7011 .shp Point Client
MiscCategories
Residential Area or Building - 9590 .shp Point Client
Trade Areas
CU-20171212寬52高42(鋁)含框 .pptx - Client
LF-20190416寬60高76(鋁) .pptx - Client
LU-20190318寬70高90(木) .pptx - Client
SC-20171124寬74高90(鋁)含框 .pptx - Client
SZ-20190830寬88.5高100(鋁)含框 .pptx - Client
LW-20180109寬110高76.5(木) .pptx - Client
WG-20150722寬60高比例(鋁) .pptx - Client
BL .pptx - Client
DC .pptx - Client
DJ .pptx - Client
DS .pptx - Client
FW-20171115寬75高80(鋁)含框 .pptx - Client
ZT-20190323寬76高等比(鋁)含框 .pptx - Client


FUNCTIONS AND ANALYSIS


Functions Employed Analysis Conducted
Choropleth Mapping
  • Revenue Analysis for each of the allocated stores
  • Revenue Analysis for each POD in their trade areas
Count by Polygon – the generation of the 32 main POI variables
  • Generation of the Business Profiles for the stores
  • Relationship between the Total Number of POIs and Revenue
Network OD-Matrix (Layers as Table (M:N))
  • Relationship between the Average Shortest Distance of the POIs from the stores and Revenue
Multi-Linear Regression (using SAS Enterprise Guide)
  • Relationship between Important Variables and Revenue
Linear Regression (using SAS Enterprise Guide)
  • Relationship between Area of Trade and Revenue
Hub Lines / Distance
  • Relationship between Competitors and Revenue
Buffer Generation for 2.5KM, 3.0KM and 3.5KM
  • Analysis of the use of Buffers as a Theoretical Trade Area
Iso-Areas as Contours (from Layer)
  • Analysis of the use of Network Service Area as a Theoretical Trade Area
  • Comparison between the use of Theoretical Trade Areas and the Actual Digitised Trade Areas
Errors
-
  • Data gap discovered for the Population Data



PROJECT TIMELINE


PROJ SCHEDULE.png


TOOLS USED

Qgislogo.PNG Saslogo.JPG Excellogo.png Powerpointlogo.png Wordlogo.png Googdoc.png Googleforms.png

  1. QGIS - Our platform to analyze and edit spatial information, in addition to composing and exporting graphical maps
  2. SAS Enterprise Guide - Our platform to generate and calculate Multi-Regression Lines and Col-linearity between Points of Interests
  3. Microsoft Excel - Our platform for data cleaning and calculations
  4. Microsoft PowerPoint - Our platform to construct the Poster and Executive Report
  5. Microsoft Word - Our platform for Final Report
  6. Google Docs - Our platform for weekly discussions and report write up
  7. Google Forms - Our platform for FeedBack during Townhall Presentation


REFERENCES


About:

  1. https://www.trip.com/travel-guide/taipei/tamsui-river-23866567/
  2. https://guidetotaipei.com/visit/tamsui-fishermans-wharf-%E6%B7%A1%E6%B0%B4%E6%BC%81%E4%BA%BA%E7%A2%BC%E9%A0%AD
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