Architecture and massing describe how the buildings of an area are designed, and how they relate both to each other and to the surrounding streets. Our overarching scope of concern is how people are affected by and interact with the architecture and massing of Les Halles. Given the fact that Les Halles is mainly a commercial district, the perspective that we took in making our observations was primarily from a retail standpoint. To observe our variable, we took into account data about the storefronts, such as canopies and display racks, as well as the interiors of the stores, such as the dimensions of the interiors. To measure the effect that this had on a retail standpoint, that is, how people interacted with the stores, we counted the number of people who entered each store over a given time period.
Based on our observations, we made an analogy between circulation of air in the respiratory systems of humans and birds and circulation of customers. We proposed that customer circulation depends on the exterior properties of stores that can be narrowed down to certain variables such as the height and width of the storefront. Following the biological example, in which cross-current systems are more efficient than co-current systems, we hypothesized that customers are more attracted to and, more easily received by, stores that allow for maximal interaction between pedestrians and commercial goods.
Because of the time limit, we decided to focus exclusively on clothing stores. The goal of the study is to understand the relationship between the massing of stores and their attractiveness. Thus, we looked at stores that had different sizes of canopy and of outdoor terrace, but were of similar size indoors, displayed similar types of clothes, and with prices close enough to support a reasonable comparison. After rigorous selection, we were left with 4 stores to examine: Baidou, Miss Coquine, Melissa, and Bleach Vintage Store. The first two of these include an outdoor terrace, while the latter two do not. This became our initial source for data. There are 6 variables that we deemed most relevant to our study: height and width of storefront, indoor floor area, size of outdoor, noise level, and size of canopy. In counting customers, we spent 40 minutes observing each shop at the same time in order to count the customers.
To visualize our data we used a radar graph, which is suitable for smaller databases and simplifies comparisons, as the overlapping graphs demonstrate similarities and disparities between individual variables.
|Number of visits||39||45||14||78|
|Size of indoor (m^2)||31.5||20||8.75||36.4|
|Size of outdoor (m^2)||0||0||4||11.2|
|Noise (dB,) [min, avg, max]||59, 65, 77||59, 64, 74||58, 64, 76||58, 63, 76|
|Size of canopy||0||0||4||0|
Interpretation of Data Table
Looking at the table we found that among the 7 variables, the noise level of the 4 stores are the most similar to each other. The explanation comes from our decision to choose 4 stores that are located next to the same arterial street and are not far from each other. In doing so we hoped to minimize the impact of other variables on the validity of our study such as price, customer source, etc.
The radar graph shown above represents the six different variables that we took into account. We calculated the percentages using the average of every set of values as the 50% marker. Then, by proportionality, we calculated the rest of percentages. Different colors correspond to the quantity of customers of every shop, the darkest representing more customers and the lightest representing fewer.
Based on the graph, it is most obvious that there are 2 branches of the radar graphs among the 4 stores that are most distinct from each other, and they are the size of canopy and the size of outdoor. As we can see from the graph, for all other variables, all 4 graphs have significant overlaps with each other, some with minor differences in variables such as the size of indoors. This confirms the observation for the stores of our choice that they are similar to each other in exterior physical properties except key variables such as size of canopy and size of outdoor that are the main interest of our study. However, the differences in the size of canopy and the size of outdoor among the stores are not enough to support the claim that these two variables directly contributes to the business performance of the 4 stores.
Nevertheless, a very important part of doing research is to knowing how to improve. Urbanism is a field of research where we cannot control all of the ambient variables that complicate its study. In our case, we took variables that we thought to be important for stores to be attractive and that are practical to measure. We were aware that subjective variables such as the style of the shop or customer habit could have played a role in our study, in addition to the limited size of our data sample. Despite the aforementioned possible criticisms, the study has the potential to incorporate more variables, more data sources, and more types of stores so as to conclude a positive relationship between massing of business architecture and business efficiency.
Matthew Li, Lara Narbona Sabaté, Ishak Afrit, Snow Dong