# A Geospatial Framework of Food Demand Mapping
## 3 Results
### Food Regional Mapping  
The Section 3 unveils the spatial distribution of eating habits across Lithuania, delineating areas of nutritional excellence and concern through a color-coded mapping approach. This analysis, grounded in a detailed examination of the geographical nuances in dietary patterns, identifies significant regional disparities. With northern Lithuania showcasing healthier eating habits and southern regions indicating areas of nutritional deficit, the study provides a comprehensive overview of the country's dietary landscape. The mapping further reveals a gradient of dietary habits within Vilnius, suggesting socio-economic and accessibility factors at play. This foundational understanding sets the stage for targeted interventions and policy-making, aiming to bridge the nutritional divide across regions (Figure 2). Utilizing the Shepard interpolation method, the primary outcomes are presented in Table 1. Analysis of the mean and median values reveals that the predictive outcomes are in consistent agreement with the sample of measured data points, evidencing a notable accuracy level. In the context of Lithuania as a whole, the observed data yielded a mean of 32.84 and a median of 33.00. The predictive outcomes demonstrated a mean of 33.02 and a median of 32.88, indicating minimal discrepancies. Within the specific locale of Vilnius county, the observed measurements indicated a mean of 33.02 and a median of 32.88, while the predictive results showed a slightly lower mean of 32.75 and a median of 32.66. These differences are slight. Moreover, the interpolation method successfully identified the minimum and maximum values for both the expansive territory of Lithuania and the considerably smaller region of Vilnius county. This accuracy in capturing the extremities of the data further validates the efficacy of the Shepard interpolation method in producing reliable predictive insights. Based on the map provided in the image of Figure 2 we can discern the spatial distribution of eating habit scores across the geography of Lithuania. The color spectrum on the right indicates the total score, with lower scores in blue signifying  
Figure 2: Eating habits score Lithuanian map. poorer eating habits and higher scores in red to orange representing healthier eating habits (Figure 2). In the northern part of Lithuania, the map shows a predominantly orange hue, suggesting that the populations here have better eating habits with higher total scores. Conversely, the southern part of Lithuania exhibits pockets of blue, particularly in the southeast, indicating regions where eating habits are not as favorable, reflected by lower total scores (Figure 2). Moving to the western part of the country, we observe a blend of colors, but with a notable presence of yellow to orange shades, indicating moderate to good eating habits. The eastern part, similar to the southern region, displays a mixture of hues with instances of both blue and orange, suggesting a variable distribution of eating habits, where some areas have healthier practices than others. However, in the south-western part of Lithuania, there is a clear blue area indicating poor nutritional intake, and it is in this region that invasive methods are needed to enlighten the people living in that region (Figure 2). To summarize, the best eating habits, as indicated by higher scores in Table 1 and maximum values and depicted in red to orange hues, are predominantly found in the northern and western parts of Lithuania. The worst eating habits, denoted by the lower scores in Table 1 and minimum values in blue, are primarily observed in the southern part, with particular emphasis on the southwest regions of the country. These interpretations are based on the color-coded map provided in Figures 2 and 3, which are common methods in scientific studies for visualizing and interpreting geospatial data related to health behaviors and outcomes.