Automated Author ProfilePetrović, Ana
Petrović, Ana
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.1 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
Neighborhood effects research focuses on the residential neighborhood, assuming it as the main spatial context relevant to individual outcomes. Individuals, however, are mobile and visit various spatial contexts other than the residential neighborhoods. This article conceptualizes contextual exposures to socioenvironmental factors in daily activity spaces and their relationship with residential exposures. By introducing regression toward the mean, we argue that mobility-based contextual exposures are, on average, less extreme than residential exposures. Previous neighborhood effects studies therefore tend to underestimate actual spatial contextual effects when they misrepresent residential neighborhood effects as the total contextual effects. Despite improved measurement accuracy with the transition from residence- to mobility-based exposures, we suggest the complexities remaining in the estimation of spatial contextual effects from a geographic perspective. These complexities include a possibly limited extent of neighborhood effects regression across neighborhoods and asymmetrical dispersion of between-individual contextual exposures within each neighborhood.
Authors
- Tao, Yinhua ;
- Petrović, Ana ;
- Kwan, Mei-Po ;
- van Ham, Maarten
Neighborhood effects research focuses on the residential neighborhood, assuming it as the main spatial context relevant to individual outcomes. Individuals, however, are mobile and visit various spatial contexts other than the residential neighborhoods. This article conceptualizes contextual exposures to socioenvironmental factors in daily activity spaces and their relationship with residential exposures. By introducing regression toward the mean, we argue that mobility-based contextual exposures are, on average, less extreme than residential exposures. Previous neighborhood effects studies therefore tend to underestimate actual spatial contextual effects when they misrepresent residential neighborhood effects as the total contextual effects. Despite improved measurement accuracy with the transition from residence- to mobility-based exposures, we suggest the complexities remaining in the estimation of spatial contextual effects from a geographic perspective. These complexities include a possibly limited extent of neighborhood effects regression across neighborhoods and asymmetrical dispersion of between-individual contextual exposures within each neighborhood.
Authors
- Tao, Yinhua ;
- Petrović, Ana ;
- Kwan, Mei-Po ;
- van Ham, Maarten