Edzer Pebesma

Edzer Pebesma
University of Münster | WWU · Institute for Geoinformatics

Ph.D.

About

259
Publications
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18,382
Citations

Publications

Publications (259)
Preprint
Reproducible research is often perceived as a technological challenge, but it is rooted in the challenge to improve scholarly communication in an age of digitisation. When computers become involved and researchers want to allow other scientists to inspect, understand, evaluate, and build upon their work, they need to create a research compendium th...
Thesis
Full-text available
The rapidly growing number of GPS enabled devices have led to the exponential growth in spatio-temporal data generated by users. Gaining meaningful insights from such massive amounts of location data accumulated over a period of time by several disparate sources, is often a challenge for large organizations. Identifying age and gender of mobile pho...
Article
Reproducible research is often perceived as a technological challenge, but it is rooted in the challenge to improve scholarly communication in an age of digitization. When computers become involved and researchers want to allow other scientists to inspect, understand, evaluate, and build on their work, they need to create a research compendium that...
Article
Full-text available
This paper presents an approach for a quantitative analysis of movement patterns of nomadic households based on GPS trajectories. We distributed GPS loggers to 400 Mongolian herder households who carried them over a 9-month period, continuously recording position data every 30 min. A total of 142 of the resulting trajectories fulfilled our data qua...
Preprint
Technological developments and open data policies have made large, global environmental datasets accessible to everyone. For analysing such datasets, including spatiotemporal correlations using traditional models based on Gaussian processes does not scale with data volume and requires strong assumptions about stationarity, separability, and distanc...
Preprint
The Geospatial Web provides data as well as processing functionality using web interfaces. Typical examples of such processes are models and predictions for spatial data, known as spatial statistics. Such analyses are written by domain experts in scripting languages and rarely exposed as web services. We present a concept of script annotations for...
Article
Full-text available
Earth observation data of large part of the world is available at different temporal, spectral and spatial resolution. These data can be termed as big data as they fulfil the criteria of 3 Vs of big data: Volume, Velocity and Variety. The size of image in archives are multiple petabyte size, the size is growing continuously and the data have varied...
Article
Full-text available
Earth observation data cubes are increasingly used as a data structure to make large collections of satellite images easily accessible to scientists. They hide complexities in the data such that data users can concentrate on the analysis rather than on data management. However, the construction of data cubes is not trivial and involves decisions th...
Article
Full-text available
Continental and global datasets based on earth observations or computational models challenge the existing map algebra approaches. The available datasets differ in their spatio-temporal extents and their spatio-temporal granularity, which makes it difficult to process them as time series data in map algebra expressions. To address this issue we int...
Article
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In warming Europe, we are witnessing a growth in urban population with aging trend, which will make the society more exposed and vulnerable to extreme weather events. In the period 1950–2015 the occurrence of extreme heat waves increased across European capitals. As an example, in 2010 Moscow was hit by the strongest heat wave of the present era, k...
Article
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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 s...
Article
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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 tim...
Article
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The complex nature of air quality suggests the need for fine-scale air-quality monitoring in cities. With 1 in 8 deaths worldwide being associated with air pollution in 2012, communities have started partnering with academic institutions and with state and federal agencies to assess local air quality and address these concerns. Participatory sensin...
Conference Paper
Full-text available
Although Earth Observation (EO) data plays a central role in various applications of geospatial sciences, the conventional workflow requiring the presence of the data on the local machine has become a bottleneck for processing large volumes of EO data. Processing it in cloud back-ends instead by transferring the code to the data overcomes bandwidth...
Article
Full-text available
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 (V...
Article
Full-text available
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...
Article
Simple features are a standardized way of encoding spatial vector data (points, lines, polygons) in computers. The sf package implements simple features in R, and has roughly the same capacity for spatial vector data as packages sp, rgeos, and rgdal. We describe the need for this package, its place in the R package ecosystem, and its potential to c...
Article
Full-text available
Emission inventories are the quantification of pollutants from different sources. They provide important information not only for climate and weather studies but also for urban planning and environmental health protection. We developed an open-source model (called Vehicular Emissions Inventory – VEIN v0.2.2) that provides high-resolution vehicular...
Article
Full-text available
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 fash...
Article
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Participatory sensing combines the powerful sensing capabilities of current mobile devices with the mobility and intelligence of human beings, and as such has to potential to collect various types of information at a high spatial and temporal resolution. Success, however, entirely relies on the willingness and motivation of the users to carry out s...
Article
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...
Article
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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 prop...
Article
Full-text available
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...
Article
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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.
Article
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This paper investigates the web-based remote sensing platform, Google Earth Engine (GEE) and evaluates the platform's utility for performing raster and vector manipulations on Landsat, Moderate Resolution Imaging Spectroradiometer and GlobCover (2009) imagery. We assess its capacity to conduct space–time analysis over two subregions of Singapore, n...
Article
Full-text available
Our research focuses on assessing the usability of the International Geosphere Biosphere Programme (IGBP) classification scheme provided in the MODIS MCD12Q1-1 dataset for assessing the land cover of the city-state, Singapore. We conducted a user study with responses from 33 users by providing them with Google Earth images from different parts of S...
Article
Full-text available
In recent years, sequential tests for detecting structural changes in time series have been adapted for deforestation monitoring using satellite data. The input time series of such sequential tests is typically a vegetation index (e.g., NDVI), which uses two or three bands and ignores all other bands. Being limited to a vegetation index will not be...
Preprint
Full-text available
Emission inventories are the quantification of pollutants from different sources. They provide important information not only for climate and weather studies, but also for urban planning and environmental health protection. We developed an open source model (named VEIN v0.2.2) that provides high resolution vehicular emissions inventories for differ...
Conference Paper
Full-text available
With the rapid proliferation of smartphones, human beings act as social sensors by means of carrying GPS-enabled devices that share location data. This has resulted in an abundance of sensor data gathered over long periods of time. Gaining meaningful insights from such massive amounts of spatio-temporal data accumulated by several disparate sources...
Article
The availability of continental and global-scale spatio-temporal geographical data sets and the requirement to efficiently process, analyse and manage them led to the development of the temporally enabled Geographic Resources Analysis Support System (GRASS GIS). We present the temporal framework that extends GRASS GIS with spatio-temporal capabilit...
Article
Support for simple features, a standardized way to encode spatial data, with bindings to GDAL, GEOS and Proj.4.
Article
Full-text available
Full text: dx.doi.org/10.1045/january2017-nuest A strong movement towards openness has seized science. Open data and methods, open source software, Open Access, open reviews, and open research platforms provide the legal and technical solutions to new forms of research and publishing. However, publishing reproducible research is still not common p...
Article
Timely and detailed information on the situation in conflict areas is essential to monitor the impact of conflicts on civilians and to document human rights issues, such as large scale displacement. Remote sensing provides valuable means for conflict monitoring, especially in areas where the ground-based documentation of violence is hampered, e.g....
Article
Maintaining knowledge about the provenance of datasets, that is, about how they were obtained, is crucial for their further use. Contrary to what the overused metaphors of ‘data mining’ and ‘big data’ are implying, it is hardly possible to use data in a meaningful way if information about sources and types of conversions is discarded in the process...
Article
Growing availability of long-term satellite imagery enables change modeling with advanced spatio-temporal statistical methods. Multidimensional arrays naturally match the structure of spatio-temporal satellite data and can provide a clean modeling process for complex spatio-temporal analysis over large datasets. Our study case illustrates the detec...
Article
Full-text available
https://portal.opengeospatial.org/files/?artifact_id=33234
Chapter
Space-time geostatistics is concerned with the statistical modelling of environmental variables that vary in space as well as in time. It is an extension of conventional geostatistics, which only considers spatial variation. Common geostatistical concepts , such as the variogram, kriging, stochastic simulation and sampling design optimization have...
Article
Full-text available
We present new spatio-temporal geostatistical modelling and interpolation capabilities of the R package gstat. Various spatio-temporal covariance models have been implemented, such as the separable, product-sum, metric and sum-metric models. In a real-world application we compare spatiotemporal interpolations using these models with a purely spatia...
Article
We briefly review SI units, and discuss R packages that deal with measurement units, their compatibility and conversion. Built upon udunits2 and the UNIDATA udunits library, we introduce the package units that provides a class for maintaining unit metadata. When used in expression, it automatically converts units, and simplifies units of results wh...
Article
Full-text available
Background: Monitoring spatial disease risk (e.g. identifying risk areas) is of great relevance in public health research, especially in cancer epidemiology. A common strategy uses case-control studies and estimates a spatial relative risk function (sRRF) via kernel density estimation (KDE). This study was set up to evaluate the sRRF estimation me...
Article
Full-text available
Background: The population-based mammography screening program (MSP) was implemented by the end of 2005 in Germany, and all women between 50 and 69 years are actively invited to a free biennial screening examination. However, despite the expected benefits, the overall participation rates range only between 50 and 55 %. There is also increasing evi...
Article
Background: The population-based mammography screening program (MSP) was implemented by the end of 2005 in Germany, and all women between 50 and 69 years are actively invited to a free biennial screening examination. However, despite the expected benefits, the overall participation rates range only between 50 and 55 %. There is also increasing evid...
Article
The location of sensors to detect outbreaks of hazardous plumes in the atmosphere can be improved by considering possible paths of such plumes. Atmospheric dispersion models can provide simulations of such paths under realistic weather conditions. Numeric simulation always goes along with discretisation, and if a plume is detected or not can be reg...
Poster
Full-text available
spatio-temporal change detection with MODIS data, using array data structure and array database to provide a clean, transparent and scalable change modeling process.
Poster
Full-text available
spatio-temporal change detection with MODIS data, using array data structure and array database to provide a clean, transparent and scalable change modeling process.
Article
Full-text available
plotKML is an R package that provides methods for writing the most common R spatial classes into KML files. It builds up on the existing XML parsing functionality (X M L package), and provides similar plotting functionality as the lattice package. Its main objective is to provide a simple interface to generate KML files with a small number of argum...
Article
Full-text available
We give an overview of the papers published in this special issue on spatial statistics, of the Journal of Statistical Software. 21 papers address issues covering visualization (micromaps, links to Google Maps or Google Earth), point pattern analysis, geostatistics, analysis of areal aggregated or lattice data, spatio-temporal statistics, Bayesian...
Article
Geostatistical methods have been applied only to a limited extent for spatial interpolation in applications where the observations have an irregular support, such as runoff characteristics along a river network and population health data. Several studies have shown the potential of such methods, but these developments have so far not led to easily...
Conference Paper
The high number of violent conflicts worldwide and the extent to which human rights are abused during acts of war stress the need for close monitoring and documentation of conflict areas to strengthen public international law. As a comprehensive ground-level documentation of human rights issues is often hardly possible in conflict areas, satellite...
Poster
Full-text available
multidimensional array in change detection. Looking into array database management system RASDAMAN and SCIDB. PCA is applied in rainfall gauge data to extract spatial temporal informaion,
Article
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[1] Combined GSOD and ECA&D daily meteorological data sets (around 9000 stations) were used to build spatio-temporal geostatistical models and predict daily air temperature at ground resolution of 1 km for the global land mass. Predictions in space and time were made for the mean, maximum and minimum temperature using spatio-temporal regression-kri...
Article
The paper presents a decision support approach to solving problems characterized by spatially-explicit decision variables, multiple objectives, and preferences for ancillary decision criteria. The approach offers a three-step workflow, in which Pareto non-dominated solutions to a multi-objective decision problem are generated with a spatially adapt...
Article
Time in geographic information systems has been a research theme for more than two decades, resulting in comprehensive theoretical work, many research prototypes and several working solutions. However, none of the available solutions provides the ability to manage, analyze, process and visualize large environmental spatio-temporal datasets and the...
Article
Full-text available
Measuring 137Cs is considered an effective method to study soil redistribution rate and hence needs sampling at a number of sites. The spatial configuration of the network of sites to be sampled has a substantial effect on the soil redistribution assessment. Here, motivated by sampling 137Cs, we adopted a model-based approach. For this, we chose th...
Article
Full-text available
The appropriateness of spatial prediction methods such as Kriging, or aggregation methods such as summing observation values over an area, is currently judged by domain experts using their knowledge and expertise. In order to provide support from information systems for automatically discouraging or proposing prediction or aggregation methods for a...
Article
Full-text available
There is a rising public and political demand for prospective cancer cluster monitoring. But there is little empirical evidence on the performance of established cluster detection tests under conditions of small and heterogeneous sample sizes and varying spatial scales, such as are the case for most existing population-based cancer registries. Ther...
Article
Full-text available
This paper describes the basis functioning and implementation of a computer-aided Bayesian Network (BN) method that is able to incorporate experts’ knowledge for the benefit of remote sensing applications and other raster data analyses: Bayesian Network for Raster Data (BayNeRD). Using a case study of soybean mapping in Mato Grosso State, Brazil, B...
Article
Mobile in-situ sensor platforms such as Unmanned Aerial Vehicles can be used in environmental monitoring. In time-critical monitoring scenarios as for example in emergency response, and in the exploration of highly dynamic phenomena, obtaining the relevant data with one or few mobile sensors is challenging. It requires an intelligent sampling strat...