Andrew H Kim
Biographic Data
| ID | 8964372 |
|---|---|
| NAME | Andrew H Kim |
| GIVEN NAMES | Andrew H |
| FAMILY NAME | Kim |
| SIGNATURE | KIM A H |
| AFFILIATIONS | Rutgers, the State University of New Jersey |
| ORCID | 0009-0006-1408-4964 |
| VERIFIED | Yes |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Multimodal poverty mapping and geographic transfer allocation
Targeting urban poverty and food insecurity
Recent advances in poverty prediction at a national scale employ new data sources and machine learning (ML) techniques. However, evidence related to the performance of these models for households experiencing poverty, food insecurity, and nutritional deficiency in urban areas is scarce. This research explores how geospatial indicators, particularly those informed by community insights, improve poverty prediction and household targeting for social…
Adolescent Smartphone Overdependence in South Korea
Smartphone overdependence among South Korean adolescents, affecting nearly 40%, poses a growing public health concern, with usage patterns varying by regional context. Leveraging conceptually informed AI/ML models, this study (1) develops a high-performing low-risk screening tool to monitor disease burden, (2) leverages AI/ML to explore psychologically meaningful constructs, and (3) provides place-based policy implication profiles to inform publi…
Contextualized poverty targeting with multimodal spatial data and machine learning in Brazzaville, Congo
Enhancing targeting accuracy in social welfare programs fosters equitable urban development. Advancements in this field harness georeferenced data and leverage AI/machine learning (ML) techniques to predict poverty and allocate aid. However, these models are predominantly developed in areas with georeferenced national surveys and are intended for geographic targeting. We demonstrate that household-level targeting can be achieved in understudied c…
No prominent works on this page.
Targeting urban poverty and food insecurity
Recent advances in poverty prediction at a national scale employ new data sources and machine learning (ML) techniques. However, evidence related to the performance of these models for households experiencing poverty, food insecurity, and nutritional deficiency in urban areas is scarce. This research explores how geospatial indicators, particularly those informed by community insights, improve poverty prediction and household targeting for social…
Adolescent Smartphone Overdependence in South Korea
Smartphone overdependence among South Korean adolescents, affecting nearly 40%, poses a growing public health concern, with usage patterns varying by regional context. Leveraging conceptually informed AI/ML models, this study (1) develops a high-performing low-risk screening tool to monitor disease burden, (2) leverages AI/ML to explore psychologically meaningful constructs, and (3) provides place-based policy implication profiles to inform publi…
Contextualized poverty targeting with multimodal spatial data and machine learning in Brazzaville, Congo
Enhancing targeting accuracy in social welfare programs fosters equitable urban development. Advancements in this field harness georeferenced data and leverage AI/machine learning (ML) techniques to predict poverty and allocate aid. However, these models are predominantly developed in areas with georeferenced national surveys and are intended for geographic targeting. We demonstrate that household-level targeting can be achieved in understudied c…
Multimodal poverty mapping and geographic transfer allocation
Poverty (3 works) · Capability approach (2 works) · Geographic Information Systems Studies (2 works) · Agency (philosophy (1 works) · Cognition (1 works) · Competence (human resources (1 works) · Conceptual framework (1 works) · Data collection (1 works) · Digital health (1 works) · Digital Mental Health Interventions (1 works)