1,721,143 research outputs found
R-spatial updates: sf, sftime, stars (R tutorial) - Part 1
No full textEdzer Pebesma went through chapters 1-3 of https://r-spatial.org/book/ , and illustrated things with package sf (see ch 7). This introduces one to spatial data, vector data, tesselations, geometries, simple features, geometric predicates, geometric transformations
Simple Features for R: Standardized Support for Spatial Vector Data
Get PDFSimple 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 connect R to other computer systems. We illustrate this with examples of its use
SDAR: a package for plotting and analyzing stratigraphy data in R
No full textStratigraphic Columns (SC) are the most useful and common ways to represent
the eld descriptions (e.g., grain size, thickness of rock packages, and fossil
and lithological components) of rock sequences and well logs. In these representations
the width of SC vary according to the grain size (i.e., the wider the strata,
the coarser the rocks (Miall 1990; Tucker 2011)), and the thickness of each layer
is represented at the vertical axis of the diagram. Typically these representations
are drawn 'manually' using vector graphic editors (e.g., Adobe Illustrator®,
CorelDRAW®, Inskape). Nowadays there are various software which automatically
plot SCs, but there are not versatile open-source tools and it is very di cult
to both store and analyse stratigraphic information.
This document presents Stratigraphic Data Analysis in R (SDAR), an analytical
package1 designed for both plotting and facilitate the analysis of Stratigraphic
Data in R (R Core Team 2014). SDAR, uses simple stratigraphic data and takes
advantage of the
exible plotting tools available in R to produce detailed SCs. The
main bene ts of SDAR are: (i) used to generate accurate and complete SC plot
including multiple features (e.g., sedimentary structures, samples, fossil content,
color, structural data, contacts between beds), (ii) developed in a free software
environment for statistical computing and graphics, (iii) run on a wide variety of
platforms (i.e., UNIX, Windows, and MacOS), (iv) both plotting and analysing
functions can be executed directly on R's command-line interface (CLI), consequently
this feature enables users to integrate SDAR's functions with several others
add-on packages available for R from The Comprehensive R Archive Network
(CRAN)
The spatial prediction sandbox - Investigating the use of spatially-explicit modelling and cross-validation strategies in spatial interpolation machine learning problems
Get PDFDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesMachine Learning (ML) methods are increasingly used for spatial interpolation and
di erent strategies have been proposed to introduce space into the modelling and
validation phases. Nevertheless, a comparison of these methods under di erent
landscape autocorrelation ranges and sampling designs is still missing. This Master
Thesis investigates under which scenarios spatially-explicit ML modelling and
validation strategies are appropriate for spatial interpolation problems.
We designed a framework that allowed us to simulate predictor and outcome spatial
elds with di erent autocorrelation ranges, as well as samples with di erent number
of points and distributions. With these data, we tested di erent non-spatial and
spatially-explicit (coordinates, EDF, RFsp) Random Forest ML models and evaluated
them using the simulated surfaces as well as di erent standard (Leave-One-
Out, LOO) and spatially-explicit (spatial bu er LOO, sbLOO) Cross-Validation
(CV) strategies. We developed a new method called Nearest Distance Matching
(NDM) to estimate the appropriate radius for sbLOO CV for spatial interpolation
based on sample distribution and landscape range, and compared it to state-of-the
art methods for radius search, only based on range.
While for short ranges non-spatial models were superior to spatially-explicit models
regardless of the sample size and distribution; for long ranges, spatial models performed
better under regular and random sampling designs, but not clustered and
non-uniform. CV results indicated that although LOO correctly estimated model
performance under random designs, it yielded overestimated errors for regular samples
and underestimated errors for clustered and non-uniform designs under long
ranges. Results of sbLOO combined with NDM correctly addressed error underestimation
of LOO in clustered and non-uniform samples, whereas sbLOO based solely
on the range resulted in error overestimation for all designs under long ranges.
This Master Thesis provides important insights to the eld of predictive mapping:
it elucidates in which cases spatially-explicit methods may be preferred, and establishes
that state-of-the-art approaches for spatial CV designed to assess model
transferability are not suited for spatial interpolation and proposes an alternative
Spatio-temporal interpolation using gstat
No full textWe 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 spatial kriging approach. The target variable of the application is the daily mean PM10 concentration measured at rural air quality monitoring stations across Germany in 2005. R code for variogram fitting and interpolation is presented in this paper to illustrate the workflow of spatio-temporal interpolation using gstat. We conclude that the system works properly and that the extension of gstat facilitates and eases spatio-temporal geostatistical modelling and prediction for R users</p
Impact of climate change in Bangladesh : water logging at south-west coast
Get PDFDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesBangladesh is a densely populated, agriculture-based country and is recognized as one of the areas most vulnerable to the impacts of global warming and climate change. This is due to its unique geographic location, dominance of floodplains, low elevation, high population density, high levels of poverty, and overwhelming dependence on nature for its resources and services. The country experiences severe flood and cyclone events and, in recent years, water logging has become a catastrophic problem along the coast. These coastal areas play important economic and environmental roles in the country.
The present paper attempts to show the extent of water logged areas, caused by sea level rise and the sectoral impacts of settlement, agriculture and fisheries in the south-western coastal areas of Bangladesh. A multi-temporal analysis method has been used with remote sensing (LandSat 1975 and LandSat 2000) data. SRTM data has been used to show the actual land elevation and to predict the future height of water logging in the study area. In 2000, 182418 hectares area was inundated by water and almost 50 percent of the study area is classified having high vulnerability. Saline line has entered upto 20 to 35 km into the mainland since 1967 which has great impact on agriculture and health. Many educational institutions of the study area are in vulnerable condition as some of them inundated completely or partially and even some of them are being used as shelter by local victimized due to water logged into their fragile houses. The largest mangrove forest of the world is also at risk to water logging and saline intrusion from the sea.
To the Government of Bangladesh, NGOs, policymakers, planners and other interested parties it is important to measure and monitor present issues and to predict the future impacts of climate change. This will help to facilitate effective management and is particularly important where a lagre number of people are threatened. In this regard the present study is expected to be useful and will have strong implications in coastal planning and other climate change adaptation measures
Analysis of temporal and spatial variations of forest. A case of study in notheastern Armenia
Get PDFThe forest has a crucial ecological role and the continuous forest loss can cause colossal
effects on the environment. As Armenia is one of the low forest covered countries in the
world, this problem is more critical. Continuous forest disturbances mainly caused by illegal
logging started from the early 1990s had a huge damage on the forest ecosystem by
decreasing the forest productivity and making more areas vulnerable to erosion. Another
aspect of the Armenian forest is the lack of continuous monitoring and absence of accurate
estimation of the level of cuts in some years.
In order to have insight about the forest and the disturbances in the long period of time we
used Landsat TM/ETM + images. Google Earth Engine JavaScript API was used, which is an
online tool enabling the access and analysis of a great amount of satellite imagery.
To overcome the data availability problem caused by the gap in the Landsat series in 1988-
1998, extensive cloud cover in the study area and the missing scan lines, we used pixel based
compositing for the temporal window of leaf on vegetation (June-late September).
Subsequently, pixel based linear regression analyses were performed.
Vegetation indices derived from the 10 biannual composites for the years 1984-2014 were
used for trend analysis.
In order to derive the disturbances only in forests, forest cover layer was aggregated and the
original composites were masked. It has been found, that around 23% of forests were
disturbed during the study period
Hydrological Modeling for Flood Risk Management and Mitigation Efforts in the Rhine River Basin
Get PDFDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesUrban flooding is a global issue that affects millions of people every year. It is the most frequent natural hazard to affect large cities, and causes numerous damages to personal property, city infrastructure, and in some cases loss of life. In July of 2021, western Germany was greatly impacted by urban flooding; from the 14 to 15th of July, more than 180 lives were lost due to intense rainfall and flooding, with more than 40,000 people affected. In this work the flooding of 2021 was modeled within the Upper Rhine River basin using the LISFLOOD-OS hydrological model, to conduct a flood-risk analysis in one of the most impacted states during the flooding even, Rhineland Pfalz. Further, the calibrated model was run with higher levels of precipitation to determine possible risks associated with a future flooding event in the same region. To calculate flood-risk, flood depth-damage functions were implemented on simulation outputs to determine flood-risk. Reliability of the flood-risk analysis was done by comparing Copernicus monitored flood extent data from the flooding event against the flood-risk classification map. Results show that the flooding event of 2021 could be modeled with reasonable discharge levels compared to observations from the Mainz gauging station, producing a KGE score of 0.438; further, land use classes which received the highest damage values in both the simulated flooding event of 2021 and in the future flooding event scenario were agricultural and residential areas. High-risk areas from the flood simulation of 2021 fell within Copernicus monitored flood extent areas, highlighting the potential of this methodology to be applied to future flood-risk management practices
Investigating crime patterns in Egypt using crowdsourced data between 2011-2013
Get PDFDissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesCrime is a social phenomenon that negatively impinges upon the society on
various levels. Such phenomena are ought to be measured and analyzed to
achieve control over its presence and consequences. One of the ways for
measurement and analysis involves the use of crime maps as vital tools for
visualising crime related data. Getting access to crime data is undoubtedly a
challenged endeavour faced by hurdles of data collection, storage and making it
available for public access. In addition, coming up with useful relationships for
extracting information and patterns for crime data analysis is a significant
challenge as well. This research investigates the link between the spatial and
temporal variables in crime related data collected from crowdsourcing. The
research will capitalize on crime data gathered throughout the operation of an
online project called Zabatak founded by the author since January 2011 in Egypt.
The dataset consists of more than 2000 crime incidents from various geographical
areas across Egypt. The research considers an exploratory analysis in trying to
interpret crime patterns and trends. The results of this study have identified
various interesting trends and patterns in the dataset. One of the major findings
of this research points out a strong relationship between the spatial and temporal
variables in Car-Theft incidents. In addition, It was possible in the study to relate
crime types to the type of the geographical area. The research considers
Spatio-Temporal analysis using Inhomogeneous Spatio-Temporal K-function and
pair-correlation functions which have identified a Spatio-Temporal cluster and
interaction in crime data which can open new ways for crime maps data analysis
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