Three horrible guys Project Report

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PROPOSAL

DATA TRANSFORMATION

POSTER

WEB MAPS

REPORT

Missing Area Analysis

As we observe the generated trade areas, it seems there are some areas which are not covered by any of the stores. Hence, we decided to analyse these areas.

Missing area 1

In the above figure, the uncovered area is 大安森林公园 which is a park. As it is not common to order a pizza to a park, being uncovered is to be expected.

Missing area 2

In the above figure, we do see buildings that are not in any trade area. Therefore, this uncovered area may have been omitted and should be fulfilled by one of the branches.

Store Sales Analysis

Store Sales Analysis The yearly store sales of IFC range from USD$6,809,445 - $13,863,637, with the branch YT being the lowest, and the branch MA having the highest sales. The median sales is branch SS, with USD$9597893.5

Overall Store sales

From the sales data that we have extracted and populated into the geopackage layer, we can then plot these data on the map for each store. It has been split into 5 classes of Quantile (Equal Count). Below is an illustration of the classes and histogram:

Symbology settings for overall sales

Bins settings for overall sales

Thematic map of overall Sales

Based on the following thematic map, we can see that the top performers are branches YW,MA,CA and the branches WT,CR,YT are the worst performers. Our recommendation in this case would be to look at the factors surrounding the top performers and apply said factors to the worst performers in order to boost their sales.

We were then interested in seeing if there were any spatial patterns based on sales, so we separated the areas into stores below/above median sales.

Splitting of values using median

Colour coding median split

Thematic map showing areas above/below median sales The map above reveals an interesting pattern, where the northern stores perform better than southern stores. Our suggestion would be to look at location based factors, such as type of buildings (residential/office) and see whether they vary between areas. It is also possible to look at connectivity with regards to road types, and see if accessibility is a problem for some stores.

Total Delivery Analysis

From the normalized population density data that we have extracted and populated into the GeoPackage layer, we can then plot these data on the map for each store. It has been split into 5 classes of Equal Interval. Below is an illustration of the classes and histogram:

Symbology settings for total delivery

Bin settings for total delivery

Thematic map for delivery sales Unsurprisingly, having a high delivery count leads to higher sales when compared to the sales thematic map . This is possible as unlike a store where products are priced with vastly different ranges E.g. $5, $5000, the price range of IFC products are roughly the same. Although there might be orders which are of higher value, it seems these orders do not affect the sales amount by much. The exception to this is the area ST, where although it has a comparable delivery count to areas that perform better than the median, it is still slightly behind in sales.  

Population Density Analysis

Population Density Analysis From the normalized population density data that we have extracted and populated into the geopackage layer, we can then plot these data on the map for each store. It has been split into 5 classes of Quantile (Equal Count). Below is an illustration of the classes and histogram:

Symbology settings for population density

Bin settings for population density

Thematic map showing normalized population density

From the above charts where we analyse the normalized population density, we can see that most stores have a high population density. However, when compared to the thematic map of sales, it seems that population density has little to no relation; having a higher population density does not necessarily mean higher sales.

Buffer Analysis

Buffer Analysis was performed to evaluate the proximity of each Pizza Hut. However, upon generating the 2km (2000m) buffer, we realized that the buffer is too big a distance as all the buffers were overlapping each other drastically for our trade areas.

2km buffer for each store

Therefore, we decided to downsize our buffer to 500 meters for a clearer analysis.

500m buffer settings

Map showing 500m buffer and competitors Based on the 500m buffer, we can see that there are 3 overlapping pairs of stores: ● WT,KT ● CA,SS ● KF,HH

Map showing 1000m buffer and competitors

In the 1000m buffer, we can see all stores have some form of overlap with each other. 1000m buffer settings

Map showing 500m & 1000m buffers and competitors

When we overlay the two maps, we can see that the overall proximity of Pizza Hut stores to each other in Taipei is rather close. This has interesting implications, as some competitors might affect multiple stores due to the close proximities.

Network Analysis (Shortest Path)

Multivariate Regression Analysis (Shortest Path)