A5055094180- Spatial and Panel Data Analysis
- Land Use and Ecosystem Services
- Soil Geostatistics and Mapping
- Economic and Environmental Valuation
- Agriculture Sustainability and Environmental Impact
- Regional Economics and Spatial Analysis
- Soil and Water Nutrient Dynamics
- Soil Carbon and Nitrogen Dynamics
- Remote Sensing and LiDAR Applications
- Soil erosion and sediment transport
- Ruminant Nutrition and Digestive Physiology
- Genetic and phenotypic traits in livestock
- Geochemistry and Geologic Mapping
- Remote Sensing in Agriculture
- Hydrology and Watershed Management Studies
- Digital Media and Visual Art
- Synthesis and characterization of novel inorganic/organometallic compounds
- Plant Water Relations and Carbon Dynamics
- Remote-Sensing Image Classification
- Sustainable Agricultural Systems Analysis
- Urban Transport and Accessibility
- Organometallic Complex Synthesis and Catalysis
- Design Education and Practice
- Radioactivity and Radon Measurements
- Urban Design and Spatial Analysis
Rothamsted Research
2015-2024
University of Duisburg-Essen
2018
University of Brighton
2007-2017
Engineering and Physical Sciences Research Council
2016
National University of Ireland, Maynooth
2009-2015
National University of Ireland
2011
Novozymes (United States)
2008
Novozymes (Denmark)
2006
Defence Research and Development Canada
2006
New York State Department of Health
1994-2004
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 PCA works: includes robust compositional variants, links factor analysis, latent variable modeling, multilevel PCA. then present two different approaches using First we look at nonspatial approach, which avoids challenges posed by data standard on attribute space only. Within this approach...
Spatial statistics is a growing discipline providing important analytical techniques in wide range of disciplines the natural and social sciences. In R package GWmodel we present from particular branch spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there regions suitably localized calibration provides better description. The approach uses moving window weighting technique, found at...
Geographically weighted regression (GWR) is an important local technique for exploring spatial heterogeneity in data relationships. In fitting with Tobler’s first law of geography, each GWR estimated whose influence decays distance, distances that are commonly defined as straight line or Euclidean. However, the complexity our real world ensures scope possible distance metrics far larger than traditional Euclidean choice. Thus this article, model investigated by applying it alternative,...
In this study, we present a collection of local models, termed geographically weighted (GW) which can be found within the GWmodel R package. A GW model suits situations when spatial data are poorly described by global form, and for some regions localized fit provides better description. The approach uses moving window weighting technique, where models estimated at target locations. Commonly, parameters or outputs mapped so that nature heterogeneity explored assessed. particular, case studies...
This review of sediment source fingerprinting assesses the current state-of-the-art, remaining challenges and emerging themes. It combines inputs from international scientists either with track records in approach or expertise relevant to progressing science.Web Science Google Scholar were used published papers spanning period 2013-2019, inclusive, confirm publication trends quantities by study area country types tracers used. The most recent (2018-2019, inclusive) also benchmarked using a...
Geographically Weighted Regression (GWR) is increasingly used in spatial analyses of social and environmental data. It allows heterogeneities processes relationships to be investigated through a series local regression models rather than single global one. Standard GWR assumes that between the response predictor variables operate at same scale, which frequently not case. To address this, several variants have been proposed. This paper describes route map decide whether use model or not, if...
Principal components analysis (PCA) is a widely used technique in the social and physical sciences. However spatial applications, standard PCA frequently applied without any adaptation that accounts for important effects. Such naive application can be problematic as such effects often provide more complete understanding of given process. In this respect, (a) replaced with geographically weighted (GWPCA), when we want to account certain heterogeneity; (b) adapted autocorrelation process; or...
Geographically weighted regression (GWR) is an important local technique to model spatially varying relationships. A single distance metric (Euclidean or non-Euclidean) generally used calibrate a standard GWR model. However, variations in spatial relationships within might also vary intensity with respect location and direction. This assertion has led extensions of the mixed (or semiparametric) flexible bandwidth models. In this article, we present strongly related extension fitting...
Bathymetry estimated from optical satellite imagery has been increasingly implemented as an alternative to traditional bathymetric survey techniques. The availability of new sensors such Sentinel-2 with improved spatial and temporal resolution, in comparison previous sensors, offers innovative capabilities for bathymetry derivation. This study presents assessment the fit between data underlying models most widely used empirical algorithms: linear band model log-transformed ratio using...
Satellite derived bathymetry (SDB) enables rapid mapping of large coastal areas through measurement optical penetration the water column. The resolution bathymetric and achievable horizontal vertical accuracies vary but generally, all SDB outputs are constrained by sensor type, quality other environmental conditions. Efforts to improve accuracy include physics-based methods (similar radiative transfer models e.g. for atmospheric/vegetation studies) or detailed in-situ sampling seabed column,...
Summary The N orth W yke F arm P latform was established as a U nited K ingdom national capability for collaborative research, training and knowledge exchange in agro‐environmental sciences. Its remit is to research agricultural productivity ecosystem responses different management practices beef sheep production lowland grasslands. A system based on permanent pasture implemented three 21‐ha farmlets obtain baseline data hydrology, nutrient cycling 2 years. Since then two have been modified...
This study examined the cellulytic effects on steam-pretreated barley straw of cellulose-degrading enzyme systems from five thermophilic fungi Chaetomium thermophilum, Thielavia terrestris, Thermoascus aurantiacus, Corynascus thermophilus, and Myceliophthora thermophila mesophile Penicillum funiculosum. The catalytic glucose release was compared after treatments with each crude when added to a benchmark blend commercial cellulase product, Celluclast, derived Trichoderma reesei β-glucosidase,...
Geographically weighted regression (GWR) is used to investigate spatial relationships between freshwater acidification critical load data and contextual catchment across Great Britain. Although this analysis important in developing a greater understanding of the process, study also examines application GWR technique itself. In particular, unlike many previous presentations GWR, steps taken choosing particular model form are presented detail. A further advance here that calibration results...
Spatial statistics is a growing discipline providing important analytical techniques in wide range of disciplines the natural and social sciences. In R package GWmodel, we introduce from particular branch spatial statistics, termed geographically weighted (GW) models. GW models suit situations when data are not described well by some global model, but where there regions suitably localised calibration provides better description. The approach uses moving window weighting technique, found at...
The coastal shallow water zone can be a challenging and costly environment in which to acquire bathymetry other oceanographic data using traditional survey methods. Much of the worldwide remains unmapped recent techniques is, therefore, poorly understood. Optical satellite imagery is proving useful tool predicting depth zones, particularly conjunction with standard datasets, though its quality accuracy largely unconstrained. A common challenge any prediction study choose small but...
Although spatially varying coefficient (SVC) models have attracted considerable attention in applied science, they been criticized as being unstable. The objective of this study is to show that capturing the "spatial scale" each data relationship crucially important make SVC modeling more stable and, doing so, adds flexibility. Here, analytical properties six are summarized terms their characterization scale. Models examined through a series Monte Carlo simulation experiments assess extent...
Ecological restoration can result in extensive land use transitions which may directly impact on water runoff and sediment loss thus influence tradeoffs between multiple hydrological soil ecosystem services. However, quantifying the effect of these yields has been a challenge over large spatial scales. This study integrated synthesized 43 articles 331 experimental plots Loess Plateau China under natural rainfall to quantify impacts (a) production, (b) reduction effectiveness, (c) erosion....
Life Cycle Assessment (LCA) of livestock production systems is often based on inventory data for farms typical a study region. As information individual animals unavailable, may already be aggregated at the time analysis, both across and seasons. Even though various computational tools exist to consider effect genetic seasonal variabilities in livestock-originated emissions intensity, degree which these methods can address bias suffered by representative animal approaches not...
In many physical geography settings, principal component analysis ( PCA ) is applied without consideration for important spatial effects, and in doing so, tends to provide an incomplete understanding of a given process. such circumstances, adaptation can be adopted, this end, study focuses on the use geographically weighted GWPCA ). localized version that appropriate exploratory tool when need exists investigate certain heterogeneity structure multivariate data set. This provides...
In this study, the geographically weighted regression (GWR) model is adapted to benefit from a broad range of distance metrics, where it demonstrated that well-chosen metric can improve performance. How choose or define such key, and in respect, 'Minkowski approach' proposed enables selection an optimum for given GWR model. This approach evaluated within simulation experiment consisting three scenarios. The results are twofold: (1) significantly predictive accuracy model; (2) allows good...