Difference between revisions of "G2-Group08"
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| − | [[G2-Group08 Homepage|<font color="#ffffff" size=2><b> | + | [[G2-Group08 Homepage|<font color="#ffffff" size=2><b>HOME</b></font>]] |
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[[G2-Group08 Proposal|<font color="#000000" size=2><b>PROPOSAL</b></font>]] | [[G2-Group08 Proposal|<font color="#000000" size=2><b>PROPOSAL</b></font>]] | ||
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[[G2-Group08 Poster |<font color="#000000" size=2><b>POSTER</b></font>]] | [[G2-Group08 Poster |<font color="#000000" size=2><b>POSTER</b></font>]] | ||
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| − | + | ===Introduction=== | |
| + | As part of our module, Geographic Information Systems for Urban Planning, we are collaborating with an international food chain to analyse 15 trade areas located in Taiwan's Kaohsiung and Ping Tung region. The international food chain currently uses manual methods of delineating their trade area which is largely time-consuming. Therefore we aim to propose new and faster methods using QGIS to delineate the trade area. We will also be analysing the points of interest (POI) within each trade area to determine the correlation between the POIs and sales revenue. To do so, we will be using simple and multiple linear regression analysis. The results of this project will provide the international food chain with the necessary geographic data analysis to make an informed decision regarding their sales and marketing strategy. | ||
Latest revision as of 11:54, 23 November 2019
Introduction
As part of our module, Geographic Information Systems for Urban Planning, we are collaborating with an international food chain to analyse 15 trade areas located in Taiwan's Kaohsiung and Ping Tung region. The international food chain currently uses manual methods of delineating their trade area which is largely time-consuming. Therefore we aim to propose new and faster methods using QGIS to delineate the trade area. We will also be analysing the points of interest (POI) within each trade area to determine the correlation between the POIs and sales revenue. To do so, we will be using simple and multiple linear regression analysis. The results of this project will provide the international food chain with the necessary geographic data analysis to make an informed decision regarding their sales and marketing strategy.
