{ "item_title" : "Multimodal Poverty Prediction via Satellite Imagery and Socioeconomic Data", "item_author" : [" Grake Edminton "], "item_description" : "Multimodal Poverty Prediction via Satellite Imagery and Socioeconomic Data offers a comprehensive, mathematically sound treatment of combining Earth observation datasets with ground-truth demographic metrics. As remote sensing technologies and computational spatial analysis rapidly evolve, integrating unstructured visual imagery with structured socioeconomic indicators has become a critical technical frontier for high-resolution poverty mapping. This monograph details the computational workflows, feature fusion architectures, and spatial statistical frameworks required to merge heterogeneous inputs into accurate predictive models. Covering multi-spectral satellite imagery, nighttime lights data, land-use classification, national census metrics, and survey data, the text addresses spatial non-stationarity and data sparsity. Designed for data engineers, remote sensing specialists, and computational spatial analysts, this work presents robust methodologies for building quantitative tools.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/818/271/9798182713962_b.jpg", "price_data" : { "retail_price" : "34.99", "online_price" : "34.99", "our_price" : "34.99", "club_price" : "34.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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