## 2 Materials and Methods
From this point location, a distribution of influence of each establishment is applied using a Voronoi model represented in Figure 2, which assigns a range of influence from one point on the map until the influence of another point on the map begins. A Voronoi diagram is a spatial partitioning technique that divides a plane into regions based on the distance to a specific set of points. Each region, or Voronoi cell, consists of all points closer to a particular seed point than to any other seed point. These seed points are known as sites, and the edges of the cells are equidistant to the nearest sites, forming perpendicular bisectors between neighboring sites. The resulting shapes in a map are polygonal, with each polygon encompassing the area that is closer to its respective site than to any other site. Voronoi diagrams are useful in various fields such as geography, meteorology, and urban planning for analyzing spatial structures and distributions, optimizing locations, and modeling influence areas around points of interest. This allows for an equitable distribution of the levels of influence of each facility if the majority of people attending health centers tend to go to the facilities closest to their homes. Each shape resulting from the Voronoi transformation summarizes the number of new patients admitted to the Ministry of Health's cardiovascular program in 2018, which is expressed in the symbology at the bottom left of Figure 2. For ethical reasons, the health system does not share the exact address of each patient, so we can only work with an approximation such as the one presented here. Next, the Voronoi layer is completed with information on food deserts on the map along with socioeconomic information provided by [PERSON] et al. from the data used to identify a 15-min city in this same territory [31]. From the cadastre of urban functions, it is identified that access to sanitary products in Chile is distributed specifically in supermarkets and street markets. Then, with this premise, a base is created that identifies the blocks with access within 15 min of one of these two urban functions on a weighted basis of both urban functions. This mapping (Figure 3) scored the total absence of food deserts as 0 and the presence of food deserts as 1. It might be relevant to include this perspective because of the importance of reducing physical inactivity through a walkable neighborhood. There is evidence showing that reducing sedentary time is relevant in terms of improving metabolic and cardiovascular health, regardless of physical activity levels [32]. In this sense, the methodology considers this approach to observe the opportunities of a walkability-oriented city in a context where mobility is mainly on foot, followed by the private vehicle [33]. Figure 1: Geographic location of new patients in the cardiovascular program. Figure 2: Voronoi distribution of new patients in the cardiovascular program. In this process, we work only with areas within Greater Santiago, excluding rural areas, where the logics of approaching health centers and access to healthy food sources use different strategies from what occurs in urban territories, where in the case of the Metropolitan Region the use of automobiles predominates. As a result of this filtering process, 188 establishments are used out of an initial total of 249 (76% of the initial total) and their respective areas of influence. A multiscale geographically weighted regression (MGWR) model is applied to this geographic matrix [34]. This modeling aims to test whether there is a statistically significant relationship between the entry of elderly patients into the cardiovascular MOH program and the presence of food deserts. The MGWR model, while powerful in capturing spatial heterogeneity, has limitations, including potential biases from its assumption of smooth spatial relationships and reliance on accurate spatial data. To address these, we optimized bandwidth selection to minimize bias and used a high-precision spatial database.