Table 1 presents the Pearson’s correlations between the individual predictors from Chapter 3 and the dichotomous outcome indicating whether buildings contain retail or food establishments. In order to put the outcome into a more intuitive spatial context, we also illustrate the distribution of the outcome, where the dependent variable equals one, in Figure 1.
The Reach measures to residents, jobs, and built volume in Table 1 are all positively and significantly related to retail and food establishments’ location choices. The more residents, jobs, and built volume are within Reach in a ten-minute walking radius around buildings in Cambridge and Somerville, the higher the odds that the buildings contain retail establishments. The r-value for jobs (0.10225, p<0.0001) is considerably higher than the r-value for residents (0.00618, p<0.0001), suggesting that retail and food establishments’ location choices are driven more strongly by workplaces than homes.
Retail and Food Service Establishments (NAICS 44-45, 722) n = 1794 0 0.1250.25 0.5 0.75 1 Miles
b
NFigure 1 Observed locations of retail and food services establishments in Cambridge and Somerville, MA (n=1,794).
Distance measures to bus stops and subway stations are negative and significant, as expected, suggesting that retailers tend to choose locations that are closer to transit stations and workplaces, as well as the surrounding buildings. Distance to residents is also negative, but insignificant, which further suggests that retailers might not commonly locate at places that are most accessible to people’s homes.
Distance to jobs and built volume are significantly negative, indicating that proximity to workplaces and surrounding built volume could be important factors in placing a store. Turns Remoteness effects are negative and significant for all destination types, suggesting that stores might prefer locations that are cognitively easier to find from homes, jobs and transit stations, as well as buildings in general, regardless of their particular function. Intersections Remoteness measures also appear significant in uncontrolled correlations, but the direction of the effect differs between destination types. For bus stops, subway stations, and jobs, Intersections Remoteness is negative, suggesting that the more street crossings separate buildings from these destinations, the lower the chances of their accommodating retailers. For residents and built volume, on the other hand, the effect occurs in the opposite direction. Betweenness, as well as destination characteristics, Shown in Table 1, are highly significant and positive, suggesting that these are desirable location qualities for retailers.
Predictor r-value Significance
Reach Residents 0.00618 *** Jobs 0.10225 *** Built volume 0.10179 *** Distance Remoteness Bus stop -0.098 *** Subway stop -0.0736 *** Residents -0.00507 Jobs -0.134 *** Built volume -0.0774 *** Turns Remoteness Bus stop -0.06805 *** Subway stop -0.09447 *** Residents -0.0406 *** Jobs -0.0932 *** Built volume -0.0627 *** Intersections Remoteness Bus stop -0.06805 *** Subway stop -0.06908 *** Residents 0.0470 *** Jobs -0.0628 *** Built volume 0.0112 * Betweenness 0.17607 *** Destination characteristics
Building footprint area 0.16245 *** Building height 0.03542 *** Road width 0.09245 *** Sidewalk width 0.0905 *** Right of way 0.10154 *** Parcel type 0.26658 *** Family median income -0.02987 *** Significance level *** p<0.0001, ** p<0.01, * p<0.05
Table 1 Bivariate Pearson’s correlations between the predictors and the dichotomous outcome indicating whether buildings contain retail or food service establishments (n= 27,023)
The urban form characteristics in the immediate vicinity of buildings also exhibit significant effects in expected directions. Building footprint area, height, road width, sidewalk width, and right of way, are all positively related to retail location choices, suggesting that retailers tend to choose buildings with larger footprints, taller heights, wider streets, wider sidewalks, and wider right of ways1. Parcel type and street
Betweenness have the strongest positive effects, indicating that the more streets a building can directly access (see section 2.3.2 and 3.4.3 for the details of this measure), the higher the likelihood of retail or food establishments in the building. Likewise, the higher the Betweenness value of the street segment the building is located on, the more likely the building is to host retail businesses. Using betweenness values as proxies for passing traffic2, the Pearson’s correlation suggests that retailers are indeed attracted to locations
with higher pedestrian and vehicular traffic at their doorsteps. Family median income in the census tract is negatively correlated with retail probabilities. Wealthier neighborhoods in Cambridge and Somerville thus appear to contain fewer retail and food establishments.
These exploratory findings are encouraging, since they corroborate the methodological pertinence of the spatial accessibility measures we proposed in Chapter 3. They confirm most of our expectations and suggest that both access characteristics to neighboring land uses and urban form, as well as the morphological characteristics of retail destinations themselves, could play an important role in retail and eating establishments’ location choices. More important, the relevance of the different types of urban form measures also suggests that the geometric properties of the built environment can play a significant role in establishments’ location choices. However, we caution the reader not to use these uncontrolled correlations as conclusive for the analysis, because, when analyzed in isolation the distance, turns and intersections Remoteness measures are highly collinear with each other. A transit station that is remote in distance is often also remote in terms of the number of turns and intersection crossings. Likewise, the destination characteristics, betweenness and Reach effects are measured in isolation from control variables. Whether these factors remain significant in the presence of other predictors needs to be further investigated in controlled multiple-regression models.