One of the most popular features of SITESPLUS™ is the Bing¹ Data Map, which allows you to visualize your model, edit data, calculate, add barriers and cross-points, and display extra information for sectors. All you need is an Internet connection.
- Stores can be displayed as colored pushpins or names colored according to chain membership.
- Sector IDs are shown in blue with outlines in your choice of colors. You can display other information below the sector ID. In the image above we are displaying population below sector IDs.
- Quickly add barriers, segmented barriers, and cross-points.
- The red dashed line shows the pathway between the selected sector and store.
- If you choose to show store or chain market shares, the sectors will be shaded and a legend shown.
- Place your mouse over any store, sector, barrier, or cross-point, then use [Control][Click] to edit that same item. You can even calculate from the data sheet.
- Use [Right][Click] on any store, sector, barrier, or cross-point, and SITESPLUS™ will display a small information window about that object.
- You can move sector centroids and relocate stores to the exact corner location. Zoom in using the aerial mode to see the physical buildings.
- Easily add a new store by clicking on the new store pushpin.
Use a circle to define a new neighborhood of stores and sectors.
¹Bing Maps is a product of Microsoft and is widely recognized as one of the best and most up-to-date mapping databases in the world.
November 26, 2018
Gravitec Development is undertaking a major exploration of supermarket per-capita expenditures. This email is another set of notes that discuss our findings.
We all know that supermarket PCW is inversely related to household size; as the household size goes up, the PCW goes down. In fact, household size is the best single predictor of supermarket PCW, and that has been true for as long as we have been researching the topic. But how strong is the relationship?
Gravitec Development used the data from the 2017 BLS Diary Survey to perform a weighted linear multiple regression based on household size and income, the two largest predictors of supermarket PCW. The stronger relationship was with household size, which yielded a slope of about -6. (See the graph below). Interestingly, the slope was not as strong as in 2008 (-8) or 2009 (-9). This was true for both supermarket PCW and “Food Away” (food purchased for consumption at home).
In fact, household size now only accounts for about 50% of the variance, a measurable drop from our results of previous years.
These figures suggest the household size is not as important as in past years, although it is still the single best predictor of supermarket PCW. The flattening of the household size slope may be the result of the incredible growth of deep discounters, such as dollar stores, Aldi, and other chains. A small family today no longer needs to buy “bargain-sized” packages in order to reap the rewards of lower price.
Keep watching our columns! Or visit our web-site to find our pages on Project PCW.