Difference between revisions of "G1-Group10"

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| <center> Taiwan Road </center>[[Image: A2.png |300px|center]] ||
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* We obtained Taiwan Road SHP files online and managed to get it from mapcruzin.com. This was used in our shortest path analytical task.
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* Obtained from https://mapcruzin.com/free-taiwan-country-city-place-gis-shapefiles.htm
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<center>
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{| class="wikitable"
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|-
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! Column !! Description !! Example
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|-
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| osm_id || Unique ID of the road  || 25
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|-
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| name || Name of the road || Alley 43-33, Ln. 361, Jieshou Rd. Sec. 2
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|-
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| ref || Unsure || 4
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|-
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| type || The type of road || “primary”
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|-
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| oneway || One hot encoded, 1 = oneway 0 = not oneway || 1
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|-
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| bridge || One hot encoded,, 1 = road on bridge 0 = not on bridge || 0
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|-
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| tunnel || One hot encoded, 1 = road in a tunnel = 0 not in tunnel || 0
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|-
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| maxspeed || Max allowed speed on road || 90
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|}
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</center>
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|-
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| <center> Sales Data </center>[[Image: A2.png |300px|center]] ||
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* The client gave us a CSV file containing yearly sales information of each region, further broken down into zone
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<center>
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{| class="wikitable"
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|-
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! Column !! Description !! Example
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|-
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| Zone || Name of the Zone || “D-05”
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|-
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| Bills || Numerical value of bills || 666
 +
|-
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| Bills % || Percentage of total number of bills || 24.46
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|-
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| Amount || Total monetary amount of sales || 450182
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|-
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| Amount % || Percentage of Total monetary amount of sales || 23.88
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|-
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| Ave Bill || Average monetary amount from sales || 675.95
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|-
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| Shop Code Sales || String used to denote shop code || “AE”
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|}
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</center>
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|-
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| <center> Population Data </center>[[Image: A2.png |300px|center]] ||
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* We obtained population data with regards to Taiwan online. The XLS file contains population Data is from the year 2010 and is in, uncleaned table format XLS. This was used as an addition feature in our analysis.
 +
* Obtained from https://census.dgbas.gov.tw/PHC2010/english/rehome.htm
 +
 +
 +
<center>
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{| class="wikitable"
 +
|-
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| Number of resident population: Grand total || Total number of residents, male + female || 23123866
 +
|-
 +
| Number of resident population: Male || Total number of male residents || 11489285
 +
|-
 +
| Number of resident population: Female || Total number of female residents || 11634581
 +
|-
 +
| Total Land Area (km2) || Total monetary amount of sales || 36191.5
 +
|-
 +
| Population Density (person/km2) || Percentage of Total monetary amount of sales || 638.9
 +
|-
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| By Country/City || Country/City the row of data belongs to || “Taipei City”
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|}
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</center>
  
 
==<div style="background: #8b1209; padding: 15px; line-height: 0.3em; text-indent: 15px; font-size:18px; font-family:Helvetica"><font color= #FFFFFF>Foreseen Technical Challenges</font></div>==
 
==<div style="background: #8b1209; padding: 15px; line-height: 0.3em; text-indent: 15px; font-size:18px; font-family:Helvetica"><font color= #FFFFFF>Foreseen Technical Challenges</font></div>==

Revision as of 01:11, 22 November 2019

3hg.png

Back to Project Home

 

PROPOSAL

 

DATA TRANSFORMATION

 

POSTER

 

WEB MAPS

 

REPORT


Introduction & Motivation

International Food Chain (IFC) is a leading brand in its sector, with over 18000 outlets worldwide and an ever-growing presence in the global market. In Taiwan alone, IFC has over 240 branches and are constantly expanding.

However, as the franchise grows bigger, so does its challenges. One of the challenges involves the lack of a geographical analysis to efficiently compare the performance of each chain to one another.

Leveraging on this fact, our group aims to digitalise the data and conduct in-depth analysis on each branch. We hope to track the performance of each chain in relation to Point-Of-Interests surrounding each chain, uncovering and comprehending phenomena, with the aid of spatial data.

Objectives

This project aims to provide insights into the following:

  1. Missing Areas in trade zone
  2. Number of POIs surrounding each store
  3. Store performance with regards to sales
  4. Delivery Information
  5. Population Density
  6. Buffer and proximity
  7. Nearest Competitors to store
  8. Variable importance based on regression analysis

Tools and Libraries

The following tools and libraries are used in the digitisation and analysis:

  • QGIS
  • Excel
  • Python

Datasets

Datasets Provided:

Dataset Rationale
Traced Map
A1.PNG
  • The client provided us with powerpoint files of manually drawn trade areas. These maps contain the various zones within the trade area, competitors, nearby stores as well as the drive time between each spots in the main road.
  • The five competitors defined by the client are:
  1. Dominos
  2. Napoleon
  3. Mcdonalds
  4. Kentucky Fried Chicken
  5. Mos Burger
Geospatial Data
A2.png
  • The client provided us SHP files that contains information about Counties found in Taiwan
Column Description Example
COUNTYID Unique ID of each County “U”
COUNTYCODE Unique numerical ID of each County 10015
COUNTYNAME Name of the County in Mandarin “台北市”
COUNTYENG Name of the County in English “Taipei City”
Town Area
A3.png
  • The client provided us SHP files that contains information about Towns found in Taiwan
Column Description Example
VILLCODE Unique numerical ID of each Village 65000050041
VILLNAME Name of the Village in Mandarin “甲仙區”
VILLENG Name of the Village in English “Jiasian”
TOWNID Unique ID of each Town “K12”
TOWNCODE Unique numerical ID of each town 10005060
TOWNNAME Name of the Town in Mandarin “半線城”
COUNTYID Unique ID of each County “U”
COUNTYCODE Unique numerical ID of each Countyy 10015
COUNTYNAME Name of the County in Mandarin “台北市”
NOTE Miscellaneous notes NIL
Village Area
A4.png
  • The client provided us SHP files that contains information about Villages found in Taiwan
Column Description Example
TOWNID Unique ID of each Town “K12”
TOWNCODE Unique numerical ID of each town 10005060
TOWNNAME Name of the Town in Mandarin “半線城”
TOWNENG Name of the Town in English “Bamboo Town”
COUNTYID Unique ID of each County “U”
COUNTYCODE Unique numerical ID of each County 10015
COUNTYNAME Name of the County in Mandarin “台北市”
Taiwan Stores
A5.png
  • The client provided us a GeoPackage that contains information about each Pizza Hut Store
Column Description Example
fid Unique ID of each Town “K12”
Country Unique numerical ID of each town 10005060
Market Name of the Town in Mandarin “半線城”
PH/PHD Name of the Town in English “Bamboo Town”
Status Unique ID of each County “U”
Milestone Unique numerical ID of each County 10015
Local Code Name of the County in Mandarin “台北市”
CHAMPS Code Name of the Town in English “Bamboo Town”
JDE Code Unique ID of each County “U”
Store Name Unique numerical ID of each County 10015
Latest Asset Type Name of the County in Mandarin “台北市”
Facility Type Name of the Town in English “Bamboo Town”
City Location Unique ID of each County “U”
Location Type Unique numerical ID of each County 10015
Open Date Name of the County in Mandarin “台北市”
Close Date Unique numerical ID of each County 10015
Corresponding Relo-Open / Relo-Closure Store Name Name of the County in Mandarin “台北市”
Corresponding Relo-Open / Relo-Closure Date Unique numerical ID of each County 10015
Corresponding Relo-Open / Relo-Closure Asset Type Name of the County in Mandarin “台北市”
Store Address Address of the store B1 & 1F., No. 52-1, Hsin Sheng S. Rd., Sec. 1, Taipei, Taiwan (R.O.C)
Latitude Latitude of the store 25.041601
Longitude Longitude of the store 121.532475
Month Month of the opening date of the store 10
Quarter Quarter of the opening date of the store Q4
Year Year of the opening date of the store FY1995
Grouping Used to denote which group these stores were assigned to G1 Group 10
Cluster ID Used to denote which group these stores were assigned to, in numerical value 6
POIs
A2.png
  • The client provided us SHP files that contains information about each POI. We used 32 out of the 86 POI SHPs given. They are:
  1. ATM
  2. BANK
  3. BAR OR PUB
  4. BOOKSTORE
  5. BOWLING CENTRE
  6. BUS STATION
  7. BUSINESS FACILITY
  8. CINEMA
  9. CLOTHING STORE
  10. COFFEE SHOP
  11. COMMUTER RAIL STATION
  12. CONSUMER ELECTRONICS STORE
  13. CONVENIENCE STORE
  14. DEPARTMENT STORE
  15. INDUSTRIAL ZONE
  16. GOVERNMENT OFFICE
  17. GROCERY STORE
  18. HIGHER EDUCATION
  19. HOSPITAL
  20. HOTEL
  21. MEDICAL SERVICE
  22. NIGHTLIFE
  23. PERFORMING ARTS
  24. PHARMACY
  25. RESIDENTIAL AREA/BUILDING
  26. RESTAURANT
  27. SCHOOL
  28. SHOPPING
  29. SPECIALITY STORE
  30. SPORTS CENTRE
  31. SPORTS COMPLEX
  32. TRAIN STATION
Column Description Example
fid Unique numerical ID of each POI type 25
LINK_ID Unsure 969985784
POI_ID Unique numerical ID for each POI 1201865541
SEQ_NUM Unsure 1
FAC_TYPE Numerical ID for facility type 9853
POI_NAME Name of the POI “王牙科”
POI_LANGCD Unsure “CHT”
POI_NMTYPE Unsure “B”
POI_ST_NUM Unsure 91
ST_NUM_FUL Unsure 124-1
ST_NFUL_LC Unsure “CHT”
ST_NAME Name of ST “中和路”
ST_LANGCD Unsure “CHT”
POI_ST_SD Unsure “L”
ACC_TYPE Unsure NIL
PH_NUMBER Unsure 3-5281997
CHAIN_ID Unsure 0
NAT_IMPORT Unsure “N”
PRIVATE Unsure “N”
IN_VICIN Unsure “N”
NUM_PARENT Unsure 0
NUM_CHILD Unsure 0
PERCFRREF Unsure 40
VANCITY_ID Unsure 0
ACT_ADDR Unsure NIL
ACT_LANGCD Unsure NIL
ACT_ST_NAM Unsure NIL
ACT_ADMIN Unsure NIL
ACT_POSTAL Unsure NIL
ENTR_TYPE Unsure NIL
Competitor POI’s
A2.png
  • The client also provided us SHP files that contains information about each individual store from 5 clients. The data has the same attributes as POIs (refer to above), with an addition column:
Column Description Example
FOOD_TYPE Type of food Competitor sells “FAST FOOD”

|-

|
Taiwan Road
A2.png
||
Column Description Example
osm_id Unique ID of the road 25
name Name of the road Alley 43-33, Ln. 361, Jieshou Rd. Sec. 2
ref Unsure 4
type The type of road “primary”
oneway One hot encoded, 1 = oneway 0 = not oneway 1
bridge One hot encoded,, 1 = road on bridge 0 = not on bridge 0
tunnel One hot encoded, 1 = road in a tunnel = 0 not in tunnel 0
maxspeed Max allowed speed on road 90

|-

|
Sales Data
A2.png
||
  • The client gave us a CSV file containing yearly sales information of each region, further broken down into zone
Column Description Example
Zone Name of the Zone “D-05”
Bills Numerical value of bills 666
Bills % Percentage of total number of bills 24.46
Amount Total monetary amount of sales 450182
Amount % Percentage of Total monetary amount of sales 23.88
Ave Bill Average monetary amount from sales 675.95
Shop Code Sales String used to denote shop code “AE”

|-

|
Population Data
A2.png
||
  • We obtained population data with regards to Taiwan online. The XLS file contains population Data is from the year 2010 and is in, uncleaned table format XLS. This was used as an addition feature in our analysis.
  • Obtained from https://census.dgbas.gov.tw/PHC2010/english/rehome.htm


Number of resident population: Grand total Total number of residents, male + female 23123866
Number of resident population: Male Total number of male residents 11489285
Number of resident population: Female Total number of female residents 11634581
Total Land Area (km2) Total monetary amount of sales 36191.5
Population Density (person/km2) Percentage of Total monetary amount of sales 638.9
By Country/City Country/City the row of data belongs to “Taipei City”

Foreseen Technical Challenges

We encountered the following technical challenges throughout the course of the project. We have indicated our proposed solutions, and the outcomes of the solutions.

Key Technical Challenges Proposed Solution Outcome
Data is already pre-aggregated to display monthly sales
  • The dataset is given directly to us from IFC, and we are unable to change it. Thus, We shall utilize and do our best with the available data.

NA

Unfamiliarity in Python integration with QGIS regarding creation of data processing scripts
  • Watching video tutorials about Python and QGIS
  • Independent learning on the design and syntax
  • Peer learning and sharing

We managed to start using the languages quickly and suit our own project needs. Each of us work on different parts such as setting up, designing, logic and deployment. This speeds up our project progress.

Data Cleaning & Transformation Proposed Solution
  • Having a systematic process while working together in order to maximise efficiency e.g. taking turns to clean, transform and perform checks on the data to ensure accuracy

The adopted process was having clear instructions issued to each member in the team, along with maintaining constant communication with each other. In the event that the dataset is deemed too dirty to be usable, it was dropped along with sourcing for new data that would be a suitable replacement.

Lack of geospatial knowledge to understand the dataset initially
  • Attend SMT201 class to learn more, as well as reading up on resources given by Prof Kam to gain further contextual knowledge

NA

Digitising of trade areas from powerpoint slide to QGIS
  • The process is manual and we had to put in a lot of effort to convert the drawn polygon to data points in QGIS.

The data points can better allow us to generate insights on the profile of each outlet via its trade area.

Integrating Relevant Data from Multiple Sources Proposed Solution
  • Working together to decide on what data to extract or eliminate

NA

Scope of work

  • Roles
Kelvin Chia Sen Wei Linus Cheng Xin Wei Eugene Choy Wen Jie

Data Cleaner in Python
Wiki Writer/Editor
Chart Creator
Map Digitizer 1
Report Writer 1

Project Manager
Content Checker
Poster man
Map Digitizer 2
Report Writer 2

Data Cleaner in Excel
QGIS Manager
Map Creator
Map Digitizer 3
Report Writer 3

Project Schedule


  • Project Timeline
Gantt6.png

References

Comments

Feel free to leave comments / suggestions!


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