Rofaida Benotsmane
Biographic Data
| ID | 4418090 |
|---|---|
| NAME | Rofaida Benotsmane |
| GIVEN NAMES | Rofaida |
| FAMILY NAME | Benotsmane |
| SIGNATURE | BENOTSMANE R |
| AFFILIATIONS | Istanbul Technical University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
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…
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…
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…
Capability approach (2 works) · Poverty (2 works) · Data collection (1 works) · Field (mathematics) (1 works) · Food security (1 works) · Geochemistry and Geologic Mapping (1 works) · Geographic Information Systems Studies (1 works) · Geological and Geophysical Studies (1 works) · Geological Modeling and Analysis (1 works) · Human Mobility and Location-Based Analysis (1 works)