A Geospatial Framework of Food Demand Mapping

Spatial mapping of food demand is essential for understanding and addressing disparities in food accessibility, which significantly impact public health and nutrition. This research presents an innovative geospatial framework designed to map food demand, integrating individual dietary behaviors with advanced spatial analysis techniques. This study analyzes the spatial distribution of eating habits across Lithuania using a geospatial approach. The methodology involves dividing Lithuania into 60,000 points and interpolating survey data with Shepard's operator, which relies on a weighted average of values at data points. This flexible approach allows for adjusting the number of points based on spatial resolution and sample size, enhancing the reliability and applicability of the generated maps. The procedure includes generating a structured grid system, incorporating measurements into the grid, and applying Shepard's operator for interpolation, resulting in precise representations of food demand. This framework provides a comprehensive understanding of dietary behaviors, informing targeted policy interventions to improve food accessibility and nutrition. Traditional food spatial mapping approaches are often limited to specific polygons and lack the flexibility to achieve high granular detail. By applying advanced interpolation techniques and ensuring respondent location data without breaching privacy concerns, this study creates high-resolution maps that accurately represent regional differences in eating habits. The methodology's flexibility allows for adjustments in spatial resolution and sample size, enhancing the maps' validity and applicability. This novel approach facilitates the creation of detailed food demand maps at any granular level, providing valuable insights for policymakers and stakeholders. These insights enable the development of targeted strategies to improve food accessibility and nutrition. Additionally, the obtained information can be used for computer simulations to further analyze and predict food demand scenarios. By leveraging spatial data integration, this study contributes to a deeper understanding of the complex dynamics of food demand, identifying critical areas such as food deserts and swamps, and paving the way for more effective public health interventions and policies aimed at achieving equitable food distribution and better nutritional outcomes.