Difference between revisions of "G2-Group05-Proposal"
Yklee.2017 (talk | contribs) |
|||
| Line 194: | Line 194: | ||
[[File:Allocated store.jpg|800px|centre]] | [[File:Allocated store.jpg|800px|centre]] | ||
[[File:G2T5 trade area.jpg|800px|centre]] | [[File:G2T5 trade area.jpg|800px|centre]] | ||
| + | To perform our analysis, we use QGIS that we have learnt in class. In addition to QGIS, Data Cleaning was performed by using Python Pandas library and Excel Pivot table. We did other findings such as Correlation Analysis. | ||
| + | |||
| + | {| class="wikitable" | ||
| + | |- | ||
| + | ! Data Preparation !! Analysis Performed !! Project Deliverables | ||
| + | |- | ||
| + | | | ||
| + | * Data Cleaning (Pythons, Excel and QGIS) | ||
| + | * Digitize Trade Area | ||
| + | * Create 2.5km, 3km and 3.5km Buffer for Allocated Stores | ||
| + | * POI counts in Buffer Trade areas | ||
| + | * Create Contour for Network Analysis | ||
| + | |||
| + | || | ||
| + | * Buffer Analysis | ||
| + | * Competitor Analysis | ||
| + | * Correlation Analysis | ||
| + | * Drive Time Analysis | ||
| + | * Store Sales Analysis | ||
| + | * Trade Area Analysis | ||
| + | |||
| + | || | ||
| + | * Poster of findings | ||
| + | * Report with User Manual | ||
| + | * Wiki page Update | ||
| + | |} | ||
| + | |||
| + | |||
</font></div></div> | </font></div></div> | ||
Revision as of 22:51, 24 November 2019
Contents
About Pingtung County and Kaohsiung City
Pingtung is the southernmost and fifth-largest county of Taiwan. It is well known for its agriculture, producing sugarcane, rice, bananas tobaccos and fruits. Top attractions in Pingtung include Kenting National Park, Eluanbi Park, and Hengchun Old Town.
Kaohsiung City is the second-largest city in Taiwan and one of the largest ports in the world. A thriving international metropolis at the southern tip of Taiwan, Kaohsiung is home to several popular attraction sites such as Love River, Lotus Pond, and Qijin.
Project Motivation
Our client is a well-established restaurant chain and international franchise with more than 18,000 stores operating in over 100 countries. In Taiwan, our client operates over 220 stores set up across various cities and has plans to further expand its operation in the coming years.
According to https://www.export.gov/article?id=Taiwan-Franchising-Industry-Sector, Taiwan is home to the largest concentration of international and local franchises in the world and the food and beverage franchise market in Taiwan is the most saturated and competitive. In view of the competitive business environment, it is essential for businesses to leverage data analytics to gain a competitive advantage.
In this project, our client has provided us with spatial data specifically for the region Taitung and Kaohsiung. Our team is motivated to find out the underlying reasons attributing to the sales figures for each of the respective stores in the regions. For instance, finding the correlation between the number/types of points of interest (POIs) within the trade area and sales performance. In essence, with the insights derived from our analysis, we aim to help our client achieve an increase in revenue as well as identifying the main drivers behind the success or failure of each store.
Project Objective
The project aims to achieve the following objectives:
1. Digitizing the assigned trade areas layers and extract the profile of each trade area
2. Identifying discrepancies present in the hand-drawn map vs digitized maps. For example, overlaps in trade areas present in hand-drawn maps
3. Create three buffers of 2.5km, 3km, and 3.5km for each trade area and remove overlapping boundaries
4. Based on the trade areas delineated by the respective buffers, calculate the total number of points of interest (POIs) in each buffer
5. Identify the correlation between the number of POIs within a trade area and the sales performance of the store
6. Perform competitor analysis, determine whether the presence of competitors within the same trade area affects sales performance
7. Use of analysis tools available in QGIS to plot charts. For example, depicting the sales performance of each store
8. Translate work in QGIS to web maps
9. Create a poster and project report documenting the team’s findings and analysis
Data Collection
| Admin Boundary | |||
|---|---|---|---|
| County MOI | Shapefile (.shp) | ||
| Town MOI | Shapefile (.shp) | ||
| Village MOI | Shapefile (.shp) | ||
| Taiwan Stores | |||
| Taiwan Stores (inclusive of our 13 stores) | Geopackage (.gpkg) | ||
| Location Maps | |||
| RL-20190802 | PowerPoint Slides (.ppt) | ||
| UH-20171124寬104高87(鋁) | PowerPoint Slides (.ppt) | ||
| PN-20170816寬75高87(鋁)含框 | PowerPoint Slides (.ppt) | ||
| FS-20171106寬115高90(木)含框 | PowerPoint Slides (.ppt) | ||
| YP-20171214寬74高114(鋁) | PowerPoint Slides (.ppt) | ||
| HK-20171215寬90高130(木) | PowerPoint Slides (.ppt) | ||
| NS-20171122寬102高96(鋁) | PowerPoint Slides (.ppt) | ||
| LY-20171124寬90高125(鋁) | PowerPoint Slides (.ppt) | ||
| FA-20171214寬63高等比(鋁)含框 | PowerPoint Slides (.ppt) | ||
| EG-20171214寬113高103(鋁) | PowerPoint Slides (.ppt) | ||
| DO-20171121寬98高86(鋁) | PowerPoint Slides (.ppt) | ||
| Point of Interest (POI) | |||
| AutoSvc (for Grocery Store) | Shapefile (.shp) | ||
| Business (for Business Facility and Industrial Zone) | Shapefile (.shp) | ||
| ComSvc (for Government Office) | Shapefile (.shp) | ||
| Edulnsts (for Higher Education and School) | Shapefile (.shp) | ||
| Entertn (for Night Life, Cinema, Bar or Pub and Performing Arts) | Shapefile (.shp) | ||
| Finlnsts (for ATM and Bank) | Shapefile (.shp) | ||
| Hospital (for Hospital and Medical Service) | Shapefile (.shp) | ||
| MiscCategories (for Residential Area/Building) | Shapefile (.shp) | ||
| ParkRec (for Bowling Centre, Sports Complex and Sports Centre) | Shapefile (.shp) | ||
| Restrnts (for Restaurant and Coffee Sop) | Shapefile (.shp) | ||
| Shopping (for Shopping, Convenient Store, Clothing Store, Department Store, Pharmacy, Speciality Store, Consumer Electronics Store, Bookstore and Grocery Store) | Shapefile (.shp) | ||
| TransHubs (for Train Station, Bus Station, Commuter Rail Station) | Shapefile (.shp) | ||
| TravDest (for Hotel) | Shapefile (.shp) | ||
| Sales Data | |||
| Trade Area Annual Sales Data | Comma-Separated Values (.csv) | ||
| Streets | |||
| Streets | Shapefile (.shp) | ||
Scope of Work
To perform our analysis, we use QGIS that we have learnt in class. In addition to QGIS, Data Cleaning was performed by using Python Pandas library and Excel Pivot table. We did other findings such as Correlation Analysis.
| Data Preparation | Analysis Performed | Project Deliverables |
|---|---|---|
|
|
|
Project Schedule
Project Schedule
