Rudy Arthur
Biographic Data
| ID | 9501972 |
|---|---|
| NAME | Rudy Arthur |
| GIVEN NAMES | Rudy |
| FAMILY NAME | Arthur |
| SIGNATURE | ARTHUR R |
| AFFILIATIONS | Department of Computer Science University of Exeter Exeter U.K. |
| ORCID | 0000-0001-7722-9522 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
A General Method for Resampling Autocorrelated Spatial Data
Comparing spatial data sets is a ubiquitous task in data analysis, however the presence of spatial autocorrelation means that standard estimates of variance will be wrong and tend to over‐estimate the statistical significance of correlations and other observations. While there are a number of existing approaches to this problem, none are ideal, requiring detailed analytical calculations, which are hard to generalize or detailed modeling of the da…
Valeriepieris Circles for Spatial Data Analysis
The Valeriepieris (VP) circle is the smallest circle containing half of the world's population. The Valeriepieris circle acts as a spatial median, splitting spatial data into two halves in a unique way. In this article the idea of the VP circle is generalized and a fast algorithm to compute it is described. This algorithm has been implemented in Python and is available for download and use. The VP circle is compared to other measures of center an…
Social sensing of flood impacts in India
Flooding is a major hazard that is responsible for substantial damage and risks to human health worldwide. The 2018 flood event in Kerala, India, killed 433 people and displaced more than 1 million people from their homes. Accurate and timely information can help mitigate the impacts of flooding through better preparedness (e.g. forecasting of flood impacts) and situational awareness (e.g. more effective civil response and relief). However, good …
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Social sensing of flood impacts in India
Flooding is a major hazard that is responsible for substantial damage and risks to human health worldwide. The 2018 flood event in Kerala, India, killed 433 people and displaced more than 1 million people from their homes. Accurate and timely information can help mitigate the impacts of flooding through better preparedness (e.g. forecasting of flood impacts) and situational awareness (e.g. more effective civil response and relief). However, good …
Valeriepieris Circles for Spatial Data Analysis
The Valeriepieris (VP) circle is the smallest circle containing half of the world's population. The Valeriepieris circle acts as a spatial median, splitting spatial data into two halves in a unique way. In this article the idea of the VP circle is generalized and a fast algorithm to compute it is described. This algorithm has been implemented in Python and is available for download and use. The VP circle is compared to other measures of center an…
A General Method for Resampling Autocorrelated Spatial Data
Comparing spatial data sets is a ubiquitous task in data analysis, however the presence of spatial autocorrelation means that standard estimates of variance will be wrong and tend to over‐estimate the statistical significance of correlations and other observations. While there are a number of existing approaches to this problem, none are ideal, requiring detailed analytical calculations, which are hard to generalize or detailed modeling of the da…
Computer Science (3 works) · Land Use and Ecosystem Services (2 works) · Mathematics (2 works) · Spatial and Panel Data Analysis (2 works) · Statistics (2 works) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work) · Autocorrelation (1 work) · Business (1 work) · Center (category theory) (1 work)