# A Geospatial Framework of Food Demand Mapping
### Novel Food Demand Mapping Framework  
Building on the necessity for spatial representation of food demand, this research introduces an innovative food demand mapping framework designed to overcome the limitations of current approaches. The framework consists of detailed food surveys, population sampling, and spatial interpolation techniques to create accurate and high-resolution maps of food demand. This innovative approach aims to bridge the gap between micro-level dietary choices and macro-level spatial analytics, enabling a comprehensive understanding of food demand. By integrating surveys on eating habits with advanced spatial mapping techniques, the framework seeks to identify and address disparities in food access, ultimately guiding more targeted and effective policy interventions. To further elaborate, 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. (2015) employs spatial analysis to understand food provision across China, laying a foundational understanding of agro-ecosystems at a broad scale. They found that South and Southeast China have higher ecosystem food provision potential (EFPP) than the North, but the North, especially Northeast China, has a higher conversion ratio of EFPP (CRFP). This suggests the need to protect and explore food potential in the South while acknowledging the nearing limits of food provision in the North due to high-intensity cropping ([PERSON] et al., 2015). Similarly, [PERSON] et al. (2020) 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. Their findings show that home delivery is the dominant e-commerce channel, particularly among females, individuals aged 25-44, and affluent groups. Geographic analysis reveals higher adoption rates in rural areas, suggesting that logistics and infrastructure investments should consider local consumer behavior patterns ([PERSON] et al., 2018). Our approach parallels the comprehensive use of household data by [PERSON] et al. (2018) to reveal food security patterns in Uganda, emphasizing the value of granular data. [PERSON] et al. found significant local variation in food security indicators, making large-scale predictions challenging. Their study demonstrates that environmental variables alone can provide considerable predictive power and that local differences in food availability and livelihood activities are crucial for effective interventions ([PERSON] et al., 2015). Furthermore, [PERSON] et al. (2023) explore the spatial interconnections between food insecurity, BMI, and socio-economic factors, underscoring the significance of detailed spatial analysis in identifying health disparities. They discovered strong links between food insecurity and low income, with higher BMI associated with lower income and fewer grocery stores, and significant geographic disparities in these factors across Chicago ([PERSON] et al., 2018). By integrating these diverse methodologies and findings, our research aims to provide a nuanced understanding of food demand mapping and its implications for policy and intervention strategies. What sets our approach apart is the ability to transcend these methodologies by spatially interpolating survey results at a more refined level. This allows for unprecedented flexibility in analyzing food demand distribution. 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. 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.