Multi-Dimensional Dataset of Open Data and Satellite Images for Characterization of Food Security and Nutrition

Abstract

BackgroundNutrition is one of the main factors affecting the development and quality of life of a person. From a public health perspective, food security is an essential social determinant for promoting healthy nutrition. Food security embraces four dimensions: physical availability of food, economic and physical access to food, food utilization, and the sustainability of the dimensions above. Integrally addressing the four dimensions is vital. Surprisingly most of the works focused on a single dimension of food security: the physical availability of food.ObjectiveThe paper proposes a multi-dimensional dataset of open data and satellite images to characterize food security in the department of Cauca, Colombia.MethodsThe food security dataset integrates multiple open data sources; therefore, the Cross-Industry Standard Process for Data Mining methodology was used to guide the construction of the dataset. It includes sources such as population and agricultural census, nutrition surveys, and satellite images.ResultsAn open multidimensional dataset for the Department of Cauca with 926 attributes and 9 rows (each row representing a Municipality) from multiple sources in Colombia, is configured. Then, machine learning models were used to characterize food security and nutrition in the Cauca Department. As a result, The Food security index calculated for Cauca using a linear regression model (Mean Absolute Error of 0.391) is 57.444 in a range between 0 and 100, with 100 the best score. Also, an approach for extracting four features (Agriculture, Habitation, Road, Water) of satellite images were tested with the ResNet50 model trained from scratch, having the best performance with a macro-accuracy, macro-precision, macro-recall, and macro-F1-score of 91.7, 86.2, 66.91, and 74.92%, respectively.ConclusionIt shows how the CRISP-DM methodology can be used to create an open public health data repository. Furthermore, this methodology could be generalized to other types of problems requiring the creation of a dataset. In addition, the use of satellite images presents an alternative for places where data collection is challenging. The model and methodology proposed based on open data become a low-cost and effective solution that could be used by decision-makers, especially in developing countries, to support food security planning.

A scoping review of the landscape of health-related open datasets in Latin America

“…From those papers that created open datasets, 12 of those datasets were created for specific Latin American countries. 5 datasets were created for Colombia: 1 with social determinants of health and nutrition data for Public Health tasks [ 21 ], and 4 with a more clinical point of view which are a muscle dysmorphia dataset [ 22 ], body fat measurement [ 23 ], endoscopic ultrasound scans [ 24 ], and a treatment of Helicobacter pylori dataset [ 25 ]. 8 datasets were created for Brazil: 2 from a Public Health perspective which are BASICS [ 26 ] with epidemiologic data, and the dataset on child vaccination [ 27 ]; 1 dataset of laboratory exams [ 28 ]; 1 dataset of images of leprosy called AI4Leprosy [ 29 ]; 1 open dataset with electronic health records called ORBDA [ 30 ]; 2 datasets for genomics [ 31 , 32 ]; and one trial with the effects of BCG vaccination for COVID-19 to [ 33 ].…”

DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era

“…This study leverages the unique capabilities of satellite imagery and internet data to enhance predictive modeling. Satellite images serve as a rich source of information, offering insights into the social determinants of health by revealing spatial and temporal changes in communities and environments [76][77][78]. These images can provide valuable indicators related to urban development [79,80], population density [81], and environmental conditions [82,83], all of which are crucial in understanding social dynamics that might influence domestic violence.…”

An Approach Based on Web Scraping and Denoising Encoders to Curate Food Security Datasets

“…There is a growing tendency to share and publish datasets relevant to food security worldwide, as it is recognized as an important strategy to support decision making [81]. Nonetheless, the results of the agricultural censuses in Ecuador continue to be partly confidential even for research purposes.…”