Difference between revisions of "G2-Group14 Proposal"
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<!-- DATA TABLE CONTENT --> | <!-- DATA TABLE CONTENT --> | ||
+ | |||
+ | {| width=99% border=3 align=center font-family:Cambria | ||
+ | ! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Name of Data | ||
+ | ! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Format of Data | ||
+ | ! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Geographic Representation | ||
+ | ! style="text-align: center; font-weight:bold; font-size:22px; background-color: rgb(77, 166, 255);" | Source | ||
+ | |- | ||
+ | | colspan="4" style="font-style: bold; font-size:20px; font-family: Cambria; background-color: rgb(179, 215, 255);text-align: center;" | Stores | ||
+ | |- | ||
+ | | style="background-color:white" | Taiwan_Stores | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | 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 | ||
+ | |- | ||
+ | | style="background-color:white" | VILLAGE_MOI_121_1080726 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | 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;" | Shapefile | ||
+ | | 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;" | Shapefile | ||
+ | | 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;" | Shapefile | ||
+ | | 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;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Industrial Area - 9991 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Community Service | ||
+ | |- | ||
+ | | Government Office - 9525 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Park Recreational | ||
+ | |- | ||
+ | | Bowling Centre - 7933 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Sports Complex - 7940 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Sports Centre - 7997 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Education | ||
+ | |- | ||
+ | | Higher Education - 8200 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | School - 8211 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Entertainment | ||
+ | |- | ||
+ | | Bar or Pub - 9532 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Cinema - 7832 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Nightlife - 5813 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Performing Arts - 7929 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Miscellaneous | ||
+ | |- | ||
+ | | Residential Area or Building - 9590 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Financial | ||
+ | |- | ||
+ | | ATM - 3578 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Bank - 6000 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | colspan="4" style="background-color:rgb(230, 240, 255); text-align: center; | Hospitals | ||
+ | |- | ||
+ | | Hospital - 8060 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Medical Service - 9583 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Restaurants | ||
+ | |- | ||
+ | | Coffee Shop - 9996 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Restaurant - 5800 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | colspan="4" style="background-color:rgb(230, 240, 255); text-align: center;" | Transport Hubs | ||
+ | |- | ||
+ | | Bus Station - 4170 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Commuter Rail Station - 4100 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Train Station - 4013 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | 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;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Clothing Store - 9537 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Electronics Store - 9987 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Convenience Store - 9535 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Department Store - 9545 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Grocery Store - 5400 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Pharmacy - 9565 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Shopping - 6512 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | style="text-align: center;" | Point | ||
+ | | style="text-align: center;" | Client | ||
+ | |- | ||
+ | | Specialty Store - 9567 | ||
+ | | style="text-align: center;" | Shapefile | ||
+ | | 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;" | Shapefile | ||
+ | | 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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+ | |||
+ | |||
+ | |} | ||
Revision as of 02:13, 24 November 2019
PROJECT MOTIVATION |
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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.
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