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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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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:
- Digitized maps on the Quantum Geographic Information System (QGIS) software, based on the subzones labelled and provided to us by the international franchise
- Business profiles of each store and their relative performance to each other through extracting out relevant POIs within the trade area and subzones
- 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
- 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.
| Name of Data
|
Format of Data
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Geographic Representation
|
Source
|
| Stores
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| 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 Employed
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Analysis Conducted
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| Choropleth Mapping
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- Revenue Analysis for each of the allocated stores
- Revenue Analysis for each POD in their trade areas
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| Count by Polygon – the generation of the 32 main POI variables
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- Generation of the Business Profiles for the stores
- Relationship between the Total Number of POIs and Revenue
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| Network OD-Matrix (Layers as Table (M:N))
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- Relationship between the Average Shortest Distance of the POIs from the stores and Revenue
|
| Multi-Linear Regression (using SAS Enterprise Guide)
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- Relationship between Important Variables and Revenue
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| Linear Regression (using SAS Enterprise Guide)
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- Relationship between Area of Trade and Revenue
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| Hub Lines / Distance
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- Relationship between Competitors and Revenue
|
| Buffer Generation for 2.5KM, 3.0KM and 3.5KM
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- Analysis of the use of Buffers as a Theoretical Trade Area
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| Iso-Areas as Contours (from Layer)
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- 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
|
| |
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