# A Geospatial Framework of Food Demand Mapping
### Novel Food Demand Mapping Framework
This investigation distinguishes itself through the innovative integration of geographic interpolation techniques within the domain of medical research, thereby pioneering a novel methodological framework for the spatial delineation of regions beset by nutritional challenges. Building upon the foundational work of [PERSON] et al. (2015), which employs spatial analysis to understand food provision across China, our study extends the application of spatial methodologies to the nutritional domain. Similarly, drawing on the insights of [PERSON] et al. (2020), who utilize demographic and geographic data to analyze e-commerce in grocery shopping, this study underscores the importance of integrating spatial data for comprehending dietary patterns. Our distinctive approach thus enables the preciseidentification of areas most in need of nutritional interventions, facilitating targeted and efficacious health-promoting strategies. Our study, functioning as a foundational pilot investigation, particularly emphasizes the methodological innovation it introduces, harnessing the capabilities of rapidly growing fields like spatial statistics and geoinformatics to enable the creation of thematic maps. These maps offer a means to determine the variability and accuracy of measurements, presenting a comprehensive overview of the entire geographical area. Typically, measurements are conducted in natural studies to elucidate field characteristics. In the specified study [31], thematic maps are meticulously constructed to discern fluctuations in soil properties, vegetation, and yield based on samples gathered throughout the crop cycle. This precision in agriculture, plant cultivation, or the modeling of heavy metals facilitates the judicious selection of planting locations for strategic planning and risk control. Various interpolation methods, such as natural-neighbor interpolation, inverse functions of distance, least-squares polynomials, and kriging, are employed for modeling purposes [31]. According to researchers [32], the conventional statistical approach typically requires adherence to specific assumptions, such as the independence of observations, the exact or approximate normality of observations, and the necessity for extensive and repetitive sampling. For instance, autocorrelation, a crucial aspect highlighted by the kriging method, relies on a normal distribution, making it suitable for modeling natural processes. However, not all conducted studies reveal a normal distribution, limiting the research and its expansion. In such cases, this work opts for Shepard's operator or Inverse Distance Weighting (IDW), a widely acknowledged method valuable in modeling results from various perspectives [32; 33]. Shepard's operator is an interpolation technique that assigns values to unknown points based on the weighted average of known points, where the weight decreases with distance. Similarly, IDW is a spatial interpolation method that estimates cell values by averaging the values of nearby points, giving more influence to closer points. Both methods are essential in spatial analytics for creating smooth and accurate surfaces from scattered data. In study [34], [PERSON]'s modeling was utilized in the generation of noise maps in the city of Isparta, Turkey, which were analyzed with consideration given to grid resolution. This approach integrates seamlessly with the framework of spatial data interpolation, evaluating a region's average pollution level, wherein the selection of the appropriate exponent value for each real-world scenario has been methodically determined [35]. Preliminary modeling across various pollution scenarios has demonstrated that the selection of the distance exponent must be contingent upon the specific issue at hand, rather than being based on an arbitrary decision [35]. For this reason, we selected an exponent that enables the appropriate visual representation of maps. The novelty of this research lies in its presentation of dietary data through a mapped format, utilizing the Eating Habits Scale.