Poverty reduction in low‐ and middle‐income countries is increasingly an urban challenge, and a challenge that continues to be constrained by lack of data, including data on the spatial distribution o
Research gap analysis derived from 3 computer_science papers in our local library.
The gap
Poverty reduction in low‐ and middle‐income countries is increasingly an urban challenge, and a challenge that continues to be constrained by lack of data, including data on the spatial distribution of poverty within cities.
Consensus across the literature
Clustered from 3 gap mentions across 3 papers via embedding cosine ≥ 0.62.
Research trend
Established — well-defined area with open sub-problems.
Supporting evidence — 3 representative gaps
- Is Poverty Decentralizing? Quantifying Uncertainty in the Decentralization of Urban Poverty (2016) · doi
To improve transparency, replicability, and comparability, we suggest that research on the geographical changes to the distribution of poverty should focus on three questions: (1) How centralized is urban poverty? (2) To what extent is it decentralizing? (3) Is it becoming spatially dispersed? With respect to all three questions, the issue of quantifying uncertainty has been underresearched.
Keywords: poverty three questions improve transparency replicability comparability suggest geographical changes distribution focus centralized urban extent - Using Satellite Data to Guide Urban Poverty Reduction (2021) · doi
Poverty reduction in low‐ and middle‐income countries is increasingly an urban challenge, and a challenge that continues to be constrained by lack of data, including data on the spatial distribution of poverty within cities.
Keywords: poverty challenge reduction middle income countries increasingly urban continues constrained lack including spatial distribution within - Change in the Spatial Clustering of Poor Neighborhoods within U.S. Counties and Its Impact on Homicide: An Analysis of Metropolitan Counties, 1980-2010 (2021) · doi
Recent scholarship has examined changes in the geographic distribution of poor persons in America, but it remains unclear whether high- and low- poverty neighborhoods have become more, or less, spatially clustered over the past several decades.
Keywords: recent scholarship examined changes geographic distribution poor persons america remains unclear whether high poverty neighborhoods
Explore this gap further
Search “Poverty reduction in low‐ and middle‐income countries is increasingly an urban challenge, and a challenge that continues to be constrained by lack of data, including data on the spatial distribution o” across open scholarly engines for the latest related literature.
Working on this gap? Publish with us.
Science AI Journal reviews manuscripts in under 15 minutes with 8 specialised AI reviewers calibrated on 23,000+ real peer reviews. Open access, CC BY 4.0.
Free tools for your next paper
Related gaps in Computer Science
- Finally, we identify gaps in the knowledge of sex differences in athletic performance and the underlying mechanisms, providing substantial opportunities for high-impact studies.Finally, we identify gaps in the knowledge of sex differences in athletic performance and the underlying mechanisms, providing substantial o…
- For verbal working memory, these near-transfer effects were not sustained at follow-up, whereas for visuospatial working memory, limited evidence suggested that such effects might be maintained.For verbal working memory, these near-transfer effects were not sustained at follow-up, whereas for visuospatial working memory, limited evi…
- Although large language models often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring strong reasoning skills and expert domain knowledge.Although large language models often produce impressive outputs, it remains unclear how they perform in real-world scenarios requiring stron…
- In deep learning (DL), the deep generative model is helpful for data augmentation objectives to tackle the lack of datasets that have a significant impact on learning performance.In deep learning (DL), the deep generative model is helpful for data augmentation objectives to tackle the lack of datasets that have a sign…