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Edzer Pebesma
University of Münster
171Publications
29H-index
5,723Citations
Publications 171
Newest
Published on Jan 23, 2019in Sustainability 2.08
Fernando Santa , Roberto Henriques5
Estimated H-index: 5
+ 1 AuthorsEdzer Pebesma29
Estimated H-index: 29
An in-depth descriptive approach to the dynamics of the urban population is fundamental as a first step towards promoting effective planning and designing processes in cities. Understanding the behavioral aspects of human activities can contribute to their effective management and control. We present a framework, based on statistical methods, for studying the spatio-temporal distribution of geolocated tweets as a proxy for where and when people carry out their activities. We have evaluated our p...
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Published on Jan 1, 2019in R Journal 1.37
Iñaki Ucar4
Estimated H-index: 4
,
Edzer Pebesma29
Estimated H-index: 29
,
Arturo Azcorra17
Estimated H-index: 17
This paper presents an R package to handle and represent measurements with errors in a very simple way. We briefly introduce the main concepts of metrology and propagation of uncertainty, and discuss related R packages. Building upon this, we introduce the errors package, which provides a class for associating uncertainty metadata, automated propagation and reporting. Working with errors enables transparent, lightweight, less error-prone handling and convenient representation of measurements wit...
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Published on Jan 1, 2019in Journal of Statistical Software 22.74
Victor Maus4
Estimated H-index: 4
,
Gilberto Camara3
Estimated H-index: 3
+ 1 AuthorsEdzer Pebesma29
Estimated H-index: 29
The opening of large archives of satellite data such as LANDSAT, MODIS and the SENTINELs has given researchers unprecedented access to data, allowing them to better quantify and understand local and global land change. The need to analyze such large data sets has led to the development of automated and semi-automated methods for satellite image time series analysis. However, few of the proposed methods for remote sensing time series analysis are available as open source software. In this paper w...
3 Citations Source Cite
Published on Nov 30, 2018in ISPRS international journal of geo-information 1.72
Shivam Gupta1
Estimated H-index: 1
,
Edzer Pebesma29
Estimated H-index: 29
+ 1 AuthorsAna Cristina Costa10
Estimated H-index: 10
Air quality has had a significant impact on public health, the environment and eventually on the economy of countries for decades. Effectively mitigating air pollution in urban areas necessitates accurate air quality exposure information. Recent advancements in sensor technology and the increasing popularity of volunteered geographic information (VGI) open up new possibilities for air quality exposure assessment in cities. However, citizens and their sensors are put in areas deemed to be subject...
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Published on Aug 3, 2018in ISPRS international journal of geo-information 1.72
Meng Lu2
Estimated H-index: 2
,
Marius Appel2
Estimated H-index: 2
,
Edzer Pebesma29
Estimated H-index: 29
Geographic data is growing in size and variety, which calls for big data management tools and analysis methods. To efficiently integrate information from high dimensional data, this paper explicitly proposes array-based modeling. A large portion of Earth observations and model simulations are naturally arrays once digitalized. This paper discusses the challenges in using arrays such as the discretization of continuous spatiotemporal phenomena, irregular dimensions, regridding, high-dimensional d...
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Published on Aug 1, 2018in Applied Geography 3.12
Christian Knoth4
Estimated H-index: 4
(University of Münster),
Sofian Slimani1
Estimated H-index: 1
(University of Münster)
+ 1 AuthorsEdzer Pebesma29
Estimated H-index: 29
(University of Münster)
Abstract Remote sensing is increasingly being used by non-profit organizations and international initiatives to localize and document combat impacts such as conflict damage. Most of the practical applications rely on labor-intensive and time-consuming manual image analysis. Even when using crowdsourcing or volunteer networks, the workload can quickly become challenging when larger areas have to be monitored over longer time periods. In this paper, we propose an approach that combines automatic c...
2 Citations Source Cite
Published on May 5, 2018in Sustainability 2.08
Shivam Gupta1
Estimated H-index: 1
,
Edzer Pebesma29
Estimated H-index: 29
+ 1 AuthorsAuriol Degbelo4
Estimated H-index: 4
A very common curb of epidemiological studies for understanding the impact of air pollution on health is the quality of exposure data available. Many epidemiological studies rely on empirical modelling techniques, such as land use regression (LUR), to evaluate ambient air exposure. Previous studies have located monitoring stations in an ad hoc fashion, favouring their placement in traffic “hot spots”, or in areas deemed subjectively to be of interest to land use and population. However, ad-hoc p...
2 Citations Source Cite
Published on May 1, 2018in Statistics & Probability Letters 0.53
Shivam Gupta1
Estimated H-index: 1
,
Jorge Mateu25
Estimated H-index: 25
(James I University)
+ 1 AuthorsEdzer Pebesma29
Estimated H-index: 29
Abstract The digital era has opened up new possibilities for data-driven research. This paper discusses big data challenges in environmental monitoring and reflects on the use of statistical methods in tackling these challenges for improving the quality of life in cities.
1 Citations Source Cite
Marius Appel2
Estimated H-index: 2
(University of Münster),
Florian Lahn1
Estimated H-index: 1
(University of Münster)
+ 1 AuthorsEdzer Pebesma29
Estimated H-index: 29
(University of Münster)
Abstract Earth observation (EO) datasets are commonly provided as collection of scenes, where individual scenes represent a temporal snapshot and cover a particular region on the Earth’s surface. Using these data in complex spatiotemporal modeling becomes difficult as soon as data volumes exceed a certain capacity or analyses include many scenes, which may spatially overlap and may have been recorded at different dates. In order to facilitate analytics on large EO datasets, we combine and extend...
1 Citations Source Cite
Published on Jan 1, 2018in R Journal 1.37
Edzer Pebesma29
Estimated H-index: 29
4 Citations Source Cite
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