# A Geospatial Framework of Food Demand Mapping
## 4 Discussion  
In this research, we present an innovative approach to analyzing dietary intake variations among various socio-demographic cohorts. Utilizing this methodology, we constructed a geographic map of eating habits, enhancing the initial dataset and enabling the prediction of dietary behaviors by region. This methodological innovation, tested within the Lithuanian context, is adaptable to international models, facilitating the identification of areas where investment in nutritional education and resources is most warranted. The spatial analysis of eating habits across Lithuania, utilizing geospatial interpolation methods such as Shepard's operator, provides novel insights into the geographical distribution of dietary behaviors. This approach, which has been effectively applied in environmental sciences, offers a valuable tool for public health research by identifying regions with poor dietary habits that may benefit from targeted nutritional interventions [40]. Our research methodology advances the discourse on food demand mapping by integrating and refining elements from existing methodologies, focusing on granular-level data analysis. The work of [PERSON] et al. [29], which employs spatial analysis to understand food provision across China, lays a foundational understanding of agro-ecosystems at a broad scale. Similarly, [PERSON] et al. [30] utilize demographic and geographic data from a vast survey to analyze e-commerce in grocery shopping, highlighting the importance of integrating spatial data for understanding consumption patterns. Our approach parallels the comprehensive use of household data by [PERSON] et al. [28] to reveal food security patterns in Uganda, emphasizing the value of granular data. Furthermore, [PERSON] et al. [36] explore the spatial interconnections between food insecurity, BMI, and socio-economic factors, underscoring the significance of detailed spatial analysis in identifying health disparities. This innovative geospatial framework for mapping food demand addresses a significant research gap where current food spatial mapping approaches are often restricted to specific polygons and fail to capture finer granular details. Our study proposes an approach that allows for high-resolution mapping, offering a dynamic and context-aware representation of food environments, as emphasized in recent studies that highlight the importance of such advancements for more effective decision-making in food systems [4]. Our novelty lies in transcending these methodologies by spatially interpolating survey results at a more refined level, allowing for unprecedented flexibility in analyzing food demand distribution [41]. This enables a nuanced understanding that captures both macro- and micro-level dietary preferences and accessibility issues, facilitating precise identification of food deserts and swamps [42]. By doing so, we contribute a unique lens through which food demand can be mapped, blending the rigorous spatial analyses seen in previous studies with an innovative approach to granularity [43]. The primary limitation of this study is its reliance on a small sample size, which was a deliberate choice given its nature as a pilot study aimed at testing the proposed food demand mapping framework. The primary goal was to propose a framework for food demand mapping and not to derive precise insights, thus this does not reduce the primary contribution of the publication. The small sample size can introduce biases, as it may not be representative of the broader population, potentially skewing the findings and limiting their generalizability. The limited sample might also overlook variations in food demand patterns that would be evident in a larger, more diverse sample. Future research will aim to expand the sample size to enhance the robustness and generalizability of the findings. Increasing the sample size will help to mitigate the biases associated with small sample studies, providing a more accurate reflection of the population's food demand.