G1-Group02
Project Motivation
The increasing use of big data has created new means and tools for businesses to make more informed choices and decisions. Businesses should maximize their use of these analytical tools to help them bridge the gap between data and decision-making, so that they can maintain a competitive edge and stay relevant in the market. However, not all business have the resources, capability or expertise to do so.
Our team decided to engage in this project as we are interested to help businesses connect the data that they have already collected and is readily available to their business operations and decision-making process. Our team hopes to make use of the geospatial analysis techniques and skills taught in class to assist our client in analyzing their data and propose meaningful business recommendations to them. Our team is also interested to compare the differences between (1) the current store operating areas, (2) using radius of 2.5km, 3km and 3.5km from the store to determine operating areas, and (3) using drive time from store to determine operating areas. We hope that through this project, our team is able to not only learn invaluable skills that are related to geospatial analytics, but is able to also learn more about working for a real life project with business clients.
The team consists of Brendan Ong Sin Kai, Chong Yun Yu and Heng Bing Chow, three students from the Associate Professor of Information Systems (Practice), Dr. Kam Tin Seong's Geographic Information Systems for Urban Planning class in the School of Information Systems at Singapore Management University in Academic Year 2019-20 Semester 1.
Project Objectives
The project aims to meet the following objectives:
- Create digitized maps on Geographic Information System (GIS) software that corresponds to the location maps provided by our client
- Conduct detailed analysis for each store’s business profile based on the points of interests (POIs) within their trading zones and sub-zones
- Provide business recommendations to our client that are related to their operational strategies, such as recommending hot-spots with potentially large customer base to our client
Our team aims to produce the following deliverables by the end of our project:
- Geopackage file that contains digitized map layers for further analysis on our client’s end
- Data dictionary to guide our client through our analysis process
- Interactive web maps to showcase our business profiling results
- Project poster that encapsulates our crucial findings, which includes analysis results and strategic recommendations to our client
- Town hall poster presentation to share our analysis process and results with the public, while maintaining confidentiality of our client
- Project report detailing the entire project process, results, insights and recommendations
We hope that through this process, our team is able to learn more about the techniques of using GIS software for geographical analysis, and integrate what we learnt in this module together with what we learnt in other modules, such as coding, statistics and analytics. We also hope that we can provide our client basic analysis results that could kick-start new projects related to geographical analysis that could further their businesses.
Our team will be evaluating the success of our project with the following criterias:
- Schedule - the project must follow the schedule as shown in the last section of this proposal, and must be completed and submitted by 24 November 2019
- Quality of deliverables - deliverables produced must be clear, concise and insightful
- Stakeholder satisfaction - the results of our project should meet all objectives as stated
Project Scope
Our client for this project is a Food & Beverage (F&B) giant that operates in multiple countries worldwide. For this project, our team will be focusing on 13 branches in Taiwan, specifically all the branches of the F&B chain that are located along the East Coast of Taiwan. These branches are located in 4 different counties, namely Keelung, Nantou, Hualien and Taitung.
Since our allocated stores are scattered from the North to the South of the East side of Taiwan, we would like to identify any correlations between the location of the stores and the stores' reachability and profitability, based on the number of customers for each trading zone and subzone, and the location of different POIs located within their respective trade areas that were provided by our client. Our team aims to provide business recommendations to our client and identify potential hot-spots that have large customer base based on nearby POIs.
The stores that were assigned to our team are HN, HZ, Jenyi (JI), Ji An (JA), Keelung Shensi (KG), KP, Nantou Nangang (NN), Nantou Puli (PL), TD, Tsao-Tung (TT), TT, UL, ZU (Jhushan Township). Amongst the stores that were allocated to our team, we had a mix of stores that were currently in operation, as well as stores that has ceased operation. Only two stores, Jenyi (JI) and Tsao-Tung (TT), are currently not in operation anymore.
The breakdown of stores according to their counties is as the following:
- Keelung: Keelung Shensi (KG), KP, Jenyi (JI)
- Nantou: Nantou Nangang (NN), Nantou Puli (PL), Tsao-Tung (TT), TT, ZU (Jhushan Township)
- Hualien: HN, HZ, Ji An (JA), UL
- Taitung: TD
Data Source: Taiwan_stores.gpkg from Dr. Kam and County MOI from our client
Data
| Data Filename | Description | Data Format | Source | Usage |
|---|---|---|---|---|
| GeoPackage | ||||
| Taiwan_stores | Contains data of store assignment from Professor Kam | GPKG | Dr. Kam | Allocation of Stores |
| Administrative Boundaries | ||||
| County MOI | Shows Counties in Taiwan | SHP | Client | Reference Layer |
| Town MOI | Shows Towns in Taiwan | SHP | Client | Reference Layer |
| Village MOI | Shows Villages in Taiwan | SHP | Client | Reference Layer |
| Location Maps | ||||
| HN-20181106寬30高30(木) | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| HZ-20181106寬75高75(木) | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| JA-20180309寬81高92(木)含框 | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| JI-20190102寬72高100(鋁)含框 | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| KG-20171116寬70高60(鋁) | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| NN-20171213寬77高87(木) | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| PL-20171213寬110高90(木)含框 | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| TD-20190903(原外送地圖底圖裁剪) | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| TT-20190614寬80高100(鋁) | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| UL-20181106寬40高80(木)含框 | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| ZU-20171213寬100高100(細木框) | Contain service area of store | PPTX | Client | Reference Layer for Digitising |
| Points of Interest (POI) - North (V7AM181F0WV7000AACV0) | ||||
| Business | Shows businesses in North Taiwan | SHP | Client | Reference Layer for Analysis |
| CommSvc | Shows community service facilities in North Taiwan | SHP | Client | Reference Layer for Analysis |
| EduInsts | Shows education institutions in North Taiwan | SHP | Client | Reference Layer for Analysis |
| Entertn | Shows entertainment facilities in North Taiwan | SHP | Client | Reference Layer for Analysis |
| FinInsts | Shows financial institutions in North Taiwan | SHP | Client | Reference Layer for Analysis |
| Hospital | Shows medical facilities in North Taiwan | SHP | Client | Reference Layer for Analysis |
| MiscCategories | Shows miscellaneous amenities in North Taiwan | SHP | Client | Reference Layer for Analysis |
| ParkRec | Shows parks and recreational areas in North Taiwan | SHP | Client | Reference Layer for Analysis |
| Restrnts | Shows restaurants in North Taiwan | SHP | Client | Reference Layer for Analysis |
| Shopping | Shows shopping malls in North Taiwan | SHP | Client | Reference Layer for Analysis |
| TransHub | Shows transport hubs in North Taiwan | SHP | Client | Reference Layer for Analysis |
| TravDest | Shows travel destinations, such as hotels in North Taiwan | SHP | Client | Reference Layer for Analysis |
| Points of Interest (POI) - Central (V8AM181F0WV8000AACV0) | ||||
| Business | Shows businesses in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| CommSvc | Shows community service facilities in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| EduInsts | Shows education institutions in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| Entertn | Shows entertainment facilities in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| FinInsts | Shows financial institutions in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| Hospital | Shows medical facilities in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| MiscCategories | Shows miscellaneous amenities in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| ParkRec | Shows parks and recreational areas in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| Restrnts | Shows restaurants in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| Shopping | Shows shopping malls in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| TransHub | Shows transport hubs in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| TravDest | Shows travel destinations, such as hotels in Central Taiwan | SHP | Client | Reference Layer for Analysis |
| Points of Interest (POI) - South (V9AM181F0WV9000AACV0) | ||||
| Business | Shows businesses in South Taiwan | SHP | Client | Reference Layer for Analysis |
| CommSvc | Shows community service facilities in South Taiwan | SHP | Client | Reference Layer for Analysis |
| EduInsts | Shows education institutions in South Taiwan | SHP | Client | Reference Layer for Analysis |
| Entertn | Shows entertainment facilities in South Taiwan | SHP | Client | Reference Layer for Analysis |
| FinInsts | Shows financial institutions in South Taiwan | SHP | Client | Reference Layer for Analysis |
| Hospital | Shows medical facilities in South Taiwan | SHP | Client | Reference Layer for Analysis |
| MiscCategories | Shows miscellaneous amenities in South Taiwan | SHP | Client | Reference Layer for Analysis |
| ParkRec | Shows parks and recreational areas in South Taiwan | SHP | Client | Reference Layer for Analysis |
| Restrnts | Shows restaurants in South Taiwan | SHP | Client | Reference Layer for Analysis |
| Shopping | Shows shopping malls in South Taiwan | SHP | Client | Reference Layer for Analysis |
| TransHub | Shows transport hubs in South Taiwan | SHP | Client | Reference Layer for Analysis |
| TravDest | Shows travel destinations, such as hotels in South Taiwan | SHP | Client | Reference Layer for Analysis |
Project Schedule
Our team decided to take the following steps in order to meet the objectives that we set:
- 1. Digitize trading zones and subzones for each store
- 2. Extract relevant POIs, which include
- ATM, 3578
- GOVERNMENT OFFICE, 9525
- BANK, 6000
- GROCERY STORE, 5400
- BAR OR PUB, 9532
- HIGHER EDUCATION, 8200
- BOOKSTORE, 9995
- HOSPITAL, 8060
- BOWLING CENTRE, 7933
- HOTEL, 7011
- BUS STATION, 4170
- MEDICAL SERVICE, 9583
- BUSINESS FACILITY, 5000
- PHARMACY, 9565
- CINEMA, 7832
- RESIDENTIAL AREA/BUILDING, 9590
- CLOTHING STORE, 9537
- RESTAURANT, 5800
- COFFEE SHOP, 9996
- SCHOOL, 8211
- COMMUTER RAIL STATION, 4100
- SHOPPING, 6512
- CONSUMER ELECTRONICS STORE, 9987
- SPORTS CENTRE, 7997
- CONVENIENCE STORE, 9535
- SPORTS COMPLEX, 7940
- DEPARTMENT STORE, 9545
- TRAIN STATION, 4013
3. Clip POIs onto digitised trading zones for each store
4. Perform analysis on each trading zone
- Tabulate number of each POI and calculate percentage of each POI relative to total number of POIs in the respective areas
- Calculate total number of customers for each trading zone
- Perform correlation analysis on POI count/percentage and total number of customers
5. Extract results and derive insights
- Rank POIs by their correlation with total number of customers
- Select potential hot-spots with concentration of POIs that have high positive correlation with total number of customers
6. Consolidate project into organised data files, data dictionary, poster and report
Our team has delegated the workload equally amongst all our team members for all aspects of the project. Below is our project schedule that will be updated weekly according to the team's progress.
Phase 1 (Week 1 to Week 5)
Phase 2 (Week 6 to Week 10)
Phase 3 (Week 10 to Week 14)
