Urška Demšar
Biographic Data
| ID | 1340403 |
|---|---|
| NAME | Urška Demšar |
| GIVEN NAMES | Urška |
| FAMILY NAME | Demšar |
| SIGNATURE | DEMŠAR U |
| AFFILIATIONS | University of St Andrews |
| ORCID | 0000-0001-7791-2807 |
| VERIFIED | Yes |
| TOTAL WORKS | 10 |
| TOTAL CITATIONS | 29 |
| WORKS AS AUTHOR | 10 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2007 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 3 |
Social Patterning and Spatial Variation in Intergenerational Geographic Proximity in the United Kingdom
Existing understanding of intergenerational geographic proximity remains limited owing to the complexity inherent in defining geographic proximity and insufficient attention to fine‐scale spatial variation. This study focuses on children's proximity to parents, parents' proximity to adult children, and three‐generational co‐residence among individuals with both living parents and children, thereby capturing multiple kin relations and relational d…
The spatial–temporal effect of air pollution on individuals’ reported health and its variation by ethnic groups in the United Kingdom
Using longitudinal data on individuals' health linked with air pollution data at two geographical scales (local authorities and LSOAs), this study supports the presence of a spatial-temporal association between air pollution and poor self-reported health, which is stronger for ethnic minorities and foreign-born individuals in the UK, partly explained by location-specific differences. Air pollution mitigation is necessary to improve individuals' h…
Who Counts? Gender, Gatekeeping, and Quantitative Human Geography
How academic disciplines are represented and reproduced is a charged issue. In geography in particular, the challenge is not only who counts, especially with regard to gender and other factors, but also how the boundaries of the discipline are drawn and which subfields are acknowledged. This article contributes to both aspects of the discussion by extending recent research on gender, internationalization, and academic gatekeeping to additional su…
Revisiting the Past
Over sixty years ago, geography began its so-called quantitative revolution, where for the first time statistical methods were used to explain the spatial nature of geographic phenomena. Computers made some of this possible, but their limited power did not allow for more than relatively small analytic explorations and consequently many of these earlier ideas are now buried in the mists of time. Here we attempt to replicate one of these early anal…
Space matters
Principal Component Analysis on Spatial Data
This article considers critically how one of the oldest and most widely applied statistical methods, principal components analysis (PCA), is employed with spatial data. We first provide a brief guide to how PCA works: This includes robust and compositional PCA variants, links to factor analysis, latent variable modeling, and multilevel PCA. We then present two different approaches to using PCA with spatial data. First we look at the nonspatial ap…
Visual Comparison of Moving-Window Kriging Models
Kriging is a spatial prediction method that is core to the geo-statistical paradigm. Commonly applied in the environmental sciences, it enables a prediction at an unsampled location coupled with a measure of confidence in its accuracy. Many variations of kriging exist, some of them complex, especially those that allow many parameters to vary spatially. Calibrating such a kriging model and interpreting its results can therefore be quite daunting. …
Investigating Material Decay of Historic Buildings Using Visual Analytics With Multi‐temporal Infrared Thermographic Data
This paper shows how visual analytics methodology can be used to facilitate interpretation of multi‐temporal thermographic imagery for the purpose of restoration of cultural heritage. We explore thermographic data in a visual environment from the unifying spatio‐temporal perspective in an attempt to identify spatial and spatio‐temporal patterns that could provide information about the structure and the level of decay of the material, and the pres…
Geovisualization and Geovisual Analytics
Combining Formal and Exploratory Methods for Evaluation of an Exploratory Geovisualization Application in a Low-Cost Usability Experiment
This paper presents a joined formal and exploratory usability evaluation of a geovisualization tool for emergency response data. The first objective of the evaluation, which is addressed by a formal usability test, is to evaluate the performance of the test participants on pre-defined exploration tasks. The second objective is to attempt to better understand how the participants explore the spatial dataset. This objective is addressed in the expl…
Principal Component Analysis on Spatial Data
This article considers critically how one of the oldest and most widely applied statistical methods, principal components analysis (PCA), is employed with spatial data. We first provide a brief guide to how PCA works: This includes robust and compositional PCA variants, links to factor analysis, latent variable modeling, and multilevel PCA. We then present two different approaches to using PCA with spatial data. First we look at the nonspatial ap…
Space matters
Revisiting the Past
Over sixty years ago, geography began its so-called quantitative revolution, where for the first time statistical methods were used to explain the spatial nature of geographic phenomena. Computers made some of this possible, but their limited power did not allow for more than relatively small analytic explorations and consequently many of these earlier ideas are now buried in the mists of time. Here we attempt to replicate one of these early anal…
Combining Formal and Exploratory Methods for Evaluation of an Exploratory Geovisualization Application in a Low-Cost Usability Experiment
This paper presents a joined formal and exploratory usability evaluation of a geovisualization tool for emergency response data. The first objective of the evaluation, which is addressed by a formal usability test, is to evaluate the performance of the test participants on pre-defined exploration tasks. The second objective is to attempt to better understand how the participants explore the spatial dataset. This objective is addressed in the expl…
Geovisualization and Geovisual Analytics
Investigating Material Decay of Historic Buildings Using Visual Analytics With Multi‐temporal Infrared Thermographic Data
This paper shows how visual analytics methodology can be used to facilitate interpretation of multi‐temporal thermographic imagery for the purpose of restoration of cultural heritage. We explore thermographic data in a visual environment from the unifying spatio‐temporal perspective in an attempt to identify spatial and spatio‐temporal patterns that could provide information about the structure and the level of decay of the material, and the pres…
Visual Comparison of Moving-Window Kriging Models
Kriging is a spatial prediction method that is core to the geo-statistical paradigm. Commonly applied in the environmental sciences, it enables a prediction at an unsampled location coupled with a measure of confidence in its accuracy. Many variations of kriging exist, some of them complex, especially those that allow many parameters to vary spatially. Calibrating such a kriging model and interpreting its results can therefore be quite daunting. …
Principal Component Analysis on Spatial Data
This article considers critically how one of the oldest and most widely applied statistical methods, principal components analysis (PCA), is employed with spatial data. We first provide a brief guide to how PCA works: This includes robust and compositional PCA variants, links to factor analysis, latent variable modeling, and multilevel PCA. We then present two different approaches to using PCA with spatial data. First we look at the nonspatial ap…
Space matters
Revisiting the Past
Over sixty years ago, geography began its so-called quantitative revolution, where for the first time statistical methods were used to explain the spatial nature of geographic phenomena. Computers made some of this possible, but their limited power did not allow for more than relatively small analytic explorations and consequently many of these earlier ideas are now buried in the mists of time. Here we attempt to replicate one of these early anal…
Who Counts? Gender, Gatekeeping, and Quantitative Human Geography
How academic disciplines are represented and reproduced is a charged issue. In geography in particular, the challenge is not only who counts, especially with regard to gender and other factors, but also how the boundaries of the discipline are drawn and which subfields are acknowledged. This article contributes to both aspects of the discussion by extending recent research on gender, internationalization, and academic gatekeeping to additional su…
The spatial–temporal effect of air pollution on individuals’ reported health and its variation by ethnic groups in the United Kingdom
Using longitudinal data on individuals' health linked with air pollution data at two geographical scales (local authorities and LSOAs), this study supports the presence of a spatial-temporal association between air pollution and poor self-reported health, which is stronger for ethnic minorities and foreign-born individuals in the UK, partly explained by location-specific differences. Air pollution mitigation is necessary to improve individuals' h…
Social Patterning and Spatial Variation in Intergenerational Geographic Proximity in the United Kingdom
Existing understanding of intergenerational geographic proximity remains limited owing to the complexity inherent in defining geographic proximity and insufficient attention to fine‐scale spatial variation. This study focuses on children's proximity to parents, parents' proximity to adult children, and three‐generational co‐residence among individuals with both living parents and children, thereby capturing multiple kin relations and relational d…
Computer Science (6 works) · Geography (5 works) · Data mining (4 works) · Data science (4 works) · Visualization (4 works) · Artificial Intelligence (3 works) · Sociology (3 works) · Spatial and Panel Data Analysis (3 works) · Statistics (3 works) · Analytics (2 works)