week 8: choosing a store site in San Francisco

This week we looked at two more ways of defining market area: by the sales figures within a radius around the store, and by drive time to the store. In fact if one was looking at a potential store site in downtown San Francisco, drive time may be less relevant because - especially for something like bookstores, and in a city that is as walkable as San Francisco - probably a significant proportion of buyers are walking to the store. However "drive time" could be altered to walking time without much difficulty.

So far the assignment is pretty straightforward, although I can see that determining the best store site is going to be tricky because each potential site is good in some important areas (e.g. high population growth) but bad in others (e.g. lower household income).

This week two maps were the products. One shows market area measured by drive time and by the proportion of store sales. The other shows the location of potential available stores as well as the location of current competitors.

Week 7? Project 3 Prepare: Market Analysis, Site Selection

The multisectioned map above shows the San Francisco area with two storefronts of the hypothetical Better Books business, and various demographic aspects (household income, percent with some college, etc.) of the area. The one-part map on the left shows average house value (for my own interest) but it also indicates a one-mile buffer around each of the stores, which is presumed to be the market area. This week's work was relatively uncomplicated one. In some ways the tables I created (not shown) to go along with the maps were more interesting, because they showed a smaller overall dollar intake from the Steiner market area but a considerably higher average purchase for each Steiner Book Lover customer. Nice to know that it's not always maps that are illuminating.

An unrelated but useful map-related thing I learned this week - for my internship work, not for this - was that cutting down the size of your data can make a big difference. In my case it was the difference between having ArcGIS run and having ArcGIS freeze, so I was pretty thrilled when I finally figured it out. The dataset in question was a huge watershed file. It was a big advance when I realized that even though I could use a definition query to reduce the size of the visible file, that wouldn't stop my freezing problem. Selecting and exporting part of the data file made an astonishing difference. Next time I have big files I hope I remember to spend a small amount of useful time at the beginning to avoid spending a lot of unproductive time later.

Week 6: Saving Energy via Trees

For this , the report week of the landscape design section, we had first to calculate the energy savings produced from planting trees in our study area. This was more a mathematical than a GIS exercise (once we had the proportion of Marin City, or several Marin City neighborhoods) that was covered by trees. For the second part, we had to determine how many trees would be needed to offset the anticipated energy demands of a new Marin City Center (its area outlined in yellow on the map). This was based on a combination of a) the area covered (calculated with GIS), b) the average monthly electricity usage of a commercial building (from a Department of Energy table), c) the annual energy savings produced by one tree (from another table) and d) the previously-calculated (maximum) number of trees that could be put on the site. It is interesting to see how GIS can intersect with this type of work, and also interesting to see how much of the input is tentative, or at least hypothetical - we don't know whether the average tree's energy savings will be the same as our Marin City average tree, for instance. Still, interesting to do.