Difference between revisions of "G1-Group05 Proposal"

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(Created page with "== Proposal == =<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;tex...")
 
 
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[[G1-Group05|<font color="#872b2b"><strong>HOME</strong></font>]]
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[[G1-Group05_Proposal|<font color="#872b2b"><strong>PROPOSAL</strong></font>]]
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[[G1-Group05_Poster|<font color="#872b2b"><strong>POSTER</strong></font>]]
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[[G1-Group05_Web_Maps|<font color="#872b2b"><strong>WEB MAPS</strong></font>]]
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[[G1-Group05_Final_Report|<font color="#872b2b"><strong>FINAL REPORT</strong></font>]]
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== Proposal ==
 
== Proposal ==
 +
=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #872b2b; color: white; padding: 2px"><span style="font-size:24px;"></span>About Taipei and Yilan</div>=
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<font size="4">
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Taipei, the capital of Taiwan, is the most populated city in Taiwan with around 7 million people in the city and surrounding areas. In contrast, Yilan is a county in northeastern Taiwan. Even though its land area is 8 times the size of Taipei, it has a low population of around 450,000 people. Joining together both counties is Jiang Yushui Expressway (National Freeway 5).
 +
</font><br>
  
−
=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #000080; color: white; padding: 2px"><span style="font-size:24px;"></span>Project Motivation</div>=
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=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #872b2b; color: white; padding: 2px"><span style="font-size:24px;"></span>Project Motivation</div>=
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In the 1960s, Singapore was a third world country, there were slums and housing was one of the biggest challenges to urban planners. The population back then was only around 1.6 million and Singapore was lacking the expertise of urban planning. Hence, they approached the United Nations for help and adopted the idea of having a long term plan which is known as concept plan. Before, they had masterplan which is more for short term planning.
 
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<br>Today, Singapore being recognised as one of the safest cities to live in is home to approximately 5.8 million people. The biggest issue pose to urban planners now is –
 
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Given the rate of population growth, climate changes and limited land size of 721.5 square kilometers, how can the process of urban planning be refined to meet the needs of the future?
 
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</br>
 
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Currently, the urban planning process approach is considered to be top-notch and frankly speaking, there are not any overarching issues that need immediate attention like back in the 1960s. However, if we were to take this for granted and not seek to improve, Singapore will risk facing issues that are as grave as back in the 1960s.
 
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Remembering the hard times that Singapore faces and with the skills we have acquired through this module, we aspire to be better urban planners.
 
  
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<font size="4">
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Our client is part of the International Food and Beverage franchise that is currently present in Taiwan. Our client has provided us with data to analyse each outlet and their respective trade areas have impacted the franchise's business in Taipei and Yilan. By understanding the trade areas of each location better, we can identify what influences the performance of each outlet.
  
−
=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #000080; color: white; padding: 2px"><span style="font-size:24px;"></span>Project Objective</div>=
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Our project aims to digitise and delineate the trade areas, as well as study the points of interest per area. We can then provide useful information for our client by analysing effect the different points of interests have on sales. This information will allow for more informed business decision-making, especially when assessing the success of their current outlets considering the viability of a potential new outlet location.
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Before making any recommendations to the current land use of Serangoon, its essential for us to compare the changes. On top of this, we will need to take into consideration several factors, from the residents needs to current facilities in the area itself, before we are able to begin making changes to the area. Therefore, our project aims to be a mini model of the correct method of carrying out urban planning.
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</font>
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=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #872b2b; color: white; padding: 2px"><span style="font-size:24px;"></span>Project Objectives</div>=
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<font size="4">
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Highway to Taipei aims to achieve the following objectives by the end of the project:<br>
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# Digitise and delineate Operational Trade Areas
 +
# Analyse the overlapping trade areas of the outlets
 +
# Extract significant POIs
 +
# Analyse impact of POIs in each trade area of each outlet
 +
# Perform drive time analysis & impact on trade areas
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# Create GIS maps for data visualisation
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# Summarise findings in a poster & report
  
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In this project, we will be analysing the following areas:
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</font>
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#'''Transportation'''
 
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#'''Child-care Centres'''
 
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#'''Eldercare Centres'''
 
  
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We will also be studying 3 main groups of population:
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=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #872b2b; color: white; padding: 2px"><span style="font-size:24px;"></span>About Areas Studied</div>=
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#'''The elderly (aged 65 and above)'''
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<font size="4">
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#'''The young (aged 0-4)'''
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Our group studied 14 franchise outlets, 9 of which were located in Taipei and 5 in the Yilan region of Taiwan. The areas we received were mostly clustered together. This allows us to take away certain assumptions, and instead focus on seeing how the points-of-interest and trade area properties impact annual sales.
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#'''The economic active (aged 25-64)'''
 
  
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[[File:serangoon 1.jpg|500px|thumb|center|The overview of Serangoon in open map]]
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=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #872b2b; color: white; padding: 2px"><span style="font-size:24px;"></span>Scope of Work</div>=
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<font size="4">
  
 +
Our main tool to perform our analyses was QGIS, a free and open-source geographic information system. To support our findings with more in-depth calculations, we used SAS Enterprise Guide.
  
 +
Data Preparation
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* Digitisation of operational trade
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* Data Cleaning
 +
* Trade Area Buffer Creation
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* Count POI in Polygons
  
−
=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #000080; color: white; padding: 2px"><span style="font-size:24px;"></span>About Serangoon</div>=
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Performing GIS Analysis
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Serangoon is named after a bird commonly found in the riverine swamps around the Serangoon River and is located in the central part of Singapore, within the North-eastern region. It mainly consists of residential buildings with a total land area of 10.1km² and total population size of 116,310 as of June 2019. The area attracted more people after Nex, a shopping mall connected to a bus interchange, and the Serangoon Circle Line MRT station were built in 2010.
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* Vector-Based Analysis
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* Raster-Based Analysis
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* Trade Area Analysis
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* Multiple Linear Regression
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* POI-Sales Analysis
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* Drive Time Analysis
  
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[[File:serangoon.jpg|500px|thumb|center|The overview of Serangoon in open map]]
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Project Deliverables
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* Poster
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* Full Report
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* Summarised Report
  
−
=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #000080; color: white; padding: 2px"><span style="font-size:24px;"></span>Data Source</div>=
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</font>
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=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #872b2b; color: white; padding: 2px"><span style="font-size:24px;"></span>Project Timeline</div>=
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[[File:TimelineG1G5.png|1200px|frameless|center]]
 +
<br>
 +
 
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=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #872b2b; color: white; padding: 2px"><span style="font-size:24px;"></span>Data Sources</div>=
 +
<br>
  
 
{| class="wikitable"
 
{| class="wikitable"
 
|-
 
|-
−
! Data Name !! Data Format !! Source
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! Data !! Data Format !! Data Source
 
|-
 
|-
 
|-style= "background-color:#E98074"
 
|-style= "background-color:#E98074"
−
!scope="row" colspan="4"|<b>''Landuse (2008/2014/2019)''</b>
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!scope="row" colspan="4"|<b>''Admin Boundary''</b>
 
|-
 
|-
 
|-
 
|-
−
| URA Planning Areas (2008) || Shape files || https://data.gov.sg/dataset/master-plan-2008-planning-area-boundary-no-sea
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| Country MOI || Shapefile (.shp) || Prof Kam & Client
 
|-
 
|-
 
|-
 
|-
−
| URA Planning Areas (2014) || Shape files || https://data.gov.sg/dataset/master-plan-2014-planning-area-boundary-web
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| Town MOI || Shapefile (.shp) || Prof Kam & Client
 
|-
 
|-
 
|-
 
|-
−
| URA Planning Areas (2019) || Reference || https://www.ura.gov.sg/Corporate/Planning/Draft-Master-Plan-19
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| Village MOI || Shapefile (.shp) || Prof Kam & Client
 
|-
 
|-
 
|-style= "background-color:#E98074"
 
|-style= "background-color:#E98074"
−
!scope="row" colspan="4"|<b>''Population''</b>
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!scope="row" colspan="4"|<b>''Taiwan Stores''</b>
 
|-  
 
|-  
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| Population (2015) || CSV || https://www.singstat.gov.sg/find-data/search-by-theme/population/geographic-distribution/latest-data
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| Taiwan Stores (inclusive of our 14 stores) || Geopackage (.gpkg) || Prof Kam & Client
 
|-
 
|-
−
| Population (2019) || CSV || https://www.singstat.gov.sg/-/media/files/publications/population/population2019.pdf
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|-style= "background-color:#E98074"
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!scope="row" colspan="4"|<b>''Location Maps''</b>
 
|-
 
|-
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| Taiwan Stores (inclusive of our 14 stores) || PowerPoint Slides (.ppt) ||  Client
 
|-
 
|-
−
| Profession || CSV || https://www.tablebuilder.singstat.gov.sg/publicfacing/createSpecialTable.action?refId=8321
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|-style= "background-color:#E98074"
 +
!scope="row" colspan="4"|<b>''Point of Interest (POI)''</b>
 +
|-
 +
| ATM || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
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| Bank|| Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
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| Bar/Pub|| Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Bookstore || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Bowling Centre|| Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Bus Station || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Business Facility || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Cinema || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Clothing Store || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Coffee Shop|| Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Commuter Rail Station || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Consumer Electronics Store || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Convenience Store || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Department Store || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Government Office || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Grocery Store || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Higher Education || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Hospital || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
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| Hotel || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Industrial Zone || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Medical Service || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Nightlife || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Performing Arts || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Pharmacy || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Residential Area/Building || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Restaurant || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| School || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Shopping || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Speciality Store || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Sports Centre || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Sports Complex || Shapefile (.shp)  ||  Prof Kam & Client
 +
|-
 +
| Train Station || Shapefile (.shp)  ||  Prof Kam & Client
 
|-
 
|-
 
|-style= "background-color:#E98074"
 
|-style= "background-color:#E98074"
−
!scope="row" colspan="4"|<b>''Education & Healthcare Services''</b>
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!scope="row" colspan="4"|<b>''Sales Data''</b>
 +
|-
 +
| Trade Area Annual Sales Data || Comma-Separated Values (.csv)  || Prof Kam & Client
 
|-
 
|-
−
| Childcare Services || Shapefile||  https://data.gov.sg/dataset/listing-of-centres
 
−
 
 
|}
 
|}
−
 
−
=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #000080; color: white; padding: 2px"><span style="font-size:24px;"></span>Project Timeline</div>=
 
−
 
−
 
−
=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #000080; color: white; padding: 2px"><span style="font-size:24px;"></span>Scope of  Work</div>=
 
−
 
−
 
−
=<div style="margin-top: 10px;font-family: Century Gothic;font-weight:bold;text-align:center;font-size:20px; border: 5px solid #00000000; border-radius:8px;text-align:center; background-color: #000080; color: white; padding: 2px"><span style="font-size:24px;"></span>Project Reference</div>=
 

Latest revision as of 13:15, 23 November 2019

HOME

PROPOSAL

POSTER

WEB MAPS

FINAL REPORT

Proposal

About Taipei and Yilan

Taipei, the capital of Taiwan, is the most populated city in Taiwan with around 7 million people in the city and surrounding areas. In contrast, Yilan is a county in northeastern Taiwan. Even though its land area is 8 times the size of Taipei, it has a low population of around 450,000 people. Joining together both counties is Jiang Yushui Expressway (National Freeway 5).

Project Motivation

Our client is part of the International Food and Beverage franchise that is currently present in Taiwan. Our client has provided us with data to analyse each outlet and their respective trade areas have impacted the franchise's business in Taipei and Yilan. By understanding the trade areas of each location better, we can identify what influences the performance of each outlet.

Our project aims to digitise and delineate the trade areas, as well as study the points of interest per area. We can then provide useful information for our client by analysing effect the different points of interests have on sales. This information will allow for more informed business decision-making, especially when assessing the success of their current outlets considering the viability of a potential new outlet location.

Project Objectives

Highway to Taipei aims to achieve the following objectives by the end of the project:

  1. Digitise and delineate Operational Trade Areas
  2. Analyse the overlapping trade areas of the outlets
  3. Extract significant POIs
  4. Analyse impact of POIs in each trade area of each outlet
  5. Perform drive time analysis & impact on trade areas
  6. Create GIS maps for data visualisation
  7. Summarise findings in a poster & report

About Areas Studied

Our group studied 14 franchise outlets, 9 of which were located in Taipei and 5 in the Yilan region of Taiwan. The areas we received were mostly clustered together. This allows us to take away certain assumptions, and instead focus on seeing how the points-of-interest and trade area properties impact annual sales.

Scope of Work

Our main tool to perform our analyses was QGIS, a free and open-source geographic information system. To support our findings with more in-depth calculations, we used SAS Enterprise Guide.

Data Preparation

  • Digitisation of operational trade
  • Data Cleaning
  • Trade Area Buffer Creation
  • Count POI in Polygons

Performing GIS Analysis

  • Vector-Based Analysis
  • Raster-Based Analysis
  • Trade Area Analysis
  • Multiple Linear Regression
  • POI-Sales Analysis
  • Drive Time Analysis

Project Deliverables

  • Poster
  • Full Report
  • Summarised Report

Project Timeline

TimelineG1G5.png


Data Sources


Data Data Format Data Source
Admin Boundary
Country MOI Shapefile (.shp) Prof Kam & Client
Town MOI Shapefile (.shp) Prof Kam & Client
Village MOI Shapefile (.shp) Prof Kam & Client
Taiwan Stores
Taiwan Stores (inclusive of our 14 stores) Geopackage (.gpkg) Prof Kam & Client
Location Maps
Taiwan Stores (inclusive of our 14 stores) PowerPoint Slides (.ppt) Client
Point of Interest (POI)
ATM Shapefile (.shp) Prof Kam & Client
Bank Shapefile (.shp) Prof Kam & Client
Bar/Pub Shapefile (.shp) Prof Kam & Client
Bookstore Shapefile (.shp) Prof Kam & Client
Bowling Centre Shapefile (.shp) Prof Kam & Client
Bus Station Shapefile (.shp) Prof Kam & Client
Business Facility Shapefile (.shp) Prof Kam & Client
Cinema Shapefile (.shp) Prof Kam & Client
Clothing Store Shapefile (.shp) Prof Kam & Client
Coffee Shop Shapefile (.shp) Prof Kam & Client
Commuter Rail Station Shapefile (.shp) Prof Kam & Client
Consumer Electronics Store Shapefile (.shp) Prof Kam & Client
Convenience Store Shapefile (.shp) Prof Kam & Client
Department Store Shapefile (.shp) Prof Kam & Client
Government Office Shapefile (.shp) Prof Kam & Client
Grocery Store Shapefile (.shp) Prof Kam & Client
Higher Education Shapefile (.shp) Prof Kam & Client
Hospital Shapefile (.shp) Prof Kam & Client
Hotel Shapefile (.shp) Prof Kam & Client
Industrial Zone Shapefile (.shp) Prof Kam & Client
Medical Service Shapefile (.shp) Prof Kam & Client
Nightlife Shapefile (.shp) Prof Kam & Client
Performing Arts Shapefile (.shp) Prof Kam & Client
Pharmacy Shapefile (.shp) Prof Kam & Client
Residential Area/Building Shapefile (.shp) Prof Kam & Client
Restaurant Shapefile (.shp) Prof Kam & Client
School Shapefile (.shp) Prof Kam & Client
Shopping Shapefile (.shp) Prof Kam & Client
Speciality Store Shapefile (.shp) Prof Kam & Client
Sports Centre Shapefile (.shp) Prof Kam & Client
Sports Complex Shapefile (.shp) Prof Kam & Client
Train Station Shapefile (.shp) Prof Kam & Client
Sales Data
Trade Area Annual Sales Data Comma-Separated Values (.csv) Prof Kam & Client
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