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Carbon Accounting

Carbon accounting is a systematic process-pipeline method for identifying, quantifying, and reporting an organization's greenhouse gas (GHG) emissions in CO₂-equivalent units. Codified by the WRI/WBCSD Greenhouse Gas Protocol in 2004, it is used by corporations, governments, and NGOs to measure their climate impact, se

spatial analysis

Bayesian Co-Kriging

Bayesian Co-Kriging is a multivariate geostatistical method that uses auxiliary spatially correlated variables to improve predictions of a primary variable of interest. By placing Bayesian priors on cross-covariance parameters, it propagates all uncertainty — including parameter uncertainty — into the prediction interv

spatial analysis

Bayesian Geary's C

Bayesian Geary's C embeds the classical Geary contiguity ratio within a Bayesian hierarchical framework. Instead of a single point estimate and asymptotic p-value, it produces a posterior distribution over the statistic (or over spatially structured random effects), quantifying uncertainty about spatial autocorrelation

spatial analysis

Bayesian Geographically Weighted Regression

Bayesian Geographically Weighted Regression combines the spatially varying coefficient framework of GWR with Bayesian inference, placing Gaussian process priors on the locally varying regression coefficients. This yields full posterior distributions over each coefficient at every location, providing principled uncertai

spatial analysis

Bayesian Hot Spot Analysis

Bayesian Hot Spot Analysis identifies spatial clusters of elevated risk or intensity by combining observed data with prior beliefs about spatial structure. It uses Bayesian smoothing — pooling information across neighboring areas — to stabilize estimates in small areas and then flags locations where the posterior proba

spatial analysis

Bayesian Kernel Density Estimation

Bayesian Kernel Density Estimation (BKDE) is a nonparametric method for estimating the probability density function of a spatial or attribute variable by combining a kernel smoother with a Bayesian prior over the bandwidth parameter. The posterior distribution of the bandwidth propagates uncertainty into the final dens

spatial analysis

Bayesian Kriging

Bayesian Kriging embeds classical geostatistical interpolation inside a full probabilistic framework. Instead of treating variogram parameters as fixed point estimates, it places prior distributions on them and updates these priors with observed spatial data to obtain a posterior distribution. Predictions at unsampled

spatial analysis

Bayesian Local Indicators of Spatial Association

Bayesian Local Indicators of Spatial Association extend the classical LISA framework by embedding local spatial association statistics within a Bayesian hierarchical model. Rather than relying on asymptotic permutation-based significance tests, this approach places prior distributions on spatial parameters and derives

spatial analysis

Bayesian Moran's I

Bayesian Moran's I embeds the classical Moran's I spatial autocorrelation test within a Bayesian probabilistic framework. Rather than producing a single p-value, it yields a posterior distribution over the spatial autocorrelation parameter, enabling uncertainty quantification, incorporation of prior knowledge, and more

spatial analysis

Bayesian Multiscale Geographically Weighted Regression

Bayesian Multiscale Geographically Weighted Regression (Bayesian MGWR) extends the MGWR framework by placing Bayesian priors on each spatially varying coefficient. Each predictor is allowed its own bandwidth — its own geographic scale of influence — while Bayesian inference replaces classical back-fitting with posterio

spatial analysis

Bayesian Ordinary Kriging

Bayesian Ordinary Kriging is a geostatistical interpolation method that combines classical ordinary kriging with a Bayesian framework to jointly estimate the spatial covariance parameters and produce predictions at unsampled locations. Unlike plug-in kriging, it propagates uncertainty about variogram parameters through

spatial analysis

Bayesian Spatial Autocorrelation

Bayesian Spatial Autocorrelation embeds spatial dependence directly into a Bayesian hierarchical model. A Conditional Autoregressive (CAR) prior encodes the expectation that neighboring areas are more similar than distant ones, and posterior inference is obtained via MCMC. This approach is especially valuable in diseas

spatial analysis

Bayesian Spatial Durbin Model

The Bayesian Spatial Durbin Model (BSDM) estimates a spatial regression that simultaneously includes a spatially lagged outcome variable and spatially lagged covariates, using Bayesian inference with Markov Chain Monte Carlo sampling. It captures both endogenous and exogenous spatial spillovers while providing full pos

spatial analysis

Bayesian Spatial Error Model

The Bayesian Spatial Error Model (Bayesian SEM) estimates a regression in which spatially correlated disturbances are explicitly modelled through a spatial weights matrix, while all parameters — regression coefficients, spatial error autocorrelation, and error variance — receive full posterior distributions via Bayesia

spatial analysis

Bayesian Spatial Lag Model

The Bayesian Spatial Lag Model (BSLM) extends the classical spatial autoregressive (SAR) regression by placing prior distributions over all parameters and recovering full posterior distributions via MCMC sampling. It explicitly accounts for spatial dependence — the outcome in one location is partly driven by outcomes i

spatial analysis

Bayesian Spatial Panel Model

The Bayesian Spatial Panel Model estimates spatial interaction effects (spatial lag, spatial error, or Durbin) in panel data using Bayesian inference via Markov Chain Monte Carlo (MCMC). It combines the ability to control for unobserved unit- and time-specific heterogeneity with principled uncertainty quantification, m

spatial analysis

Bayesian Spatial Regression

Bayesian Spatial Regression embeds a spatially structured random effect into a regression framework and estimates all parameters — including spatial range and variance — through posterior inference rather than point estimation. It handles spatial autocorrelation, quantifies full predictive uncertainty, and accommodates

spatial analysis

Bayesian Universal Kriging

Bayesian Universal Kriging (BUK) extends classical universal kriging by placing prior distributions on trend coefficients and spatial covariance parameters, then propagating full posterior uncertainty into predictions. It interpolates spatially referenced continuous data while simultaneously estimating large-scale dete

ecology

Beta Diversity Partitioning

Beta diversity partitioning quantifies how species composition differs among sites, decomposing community dissimilarity into two components: species turnover (replacement of species across sites) and nestedness (loss of species from species-rich sites). Developed by Baselga (2010), this framework reveals whether sites

ecology

Bioaccumulation Model

Bioaccumulation models predict how chemical contaminants accumulate in organisms from environmental exposure (water, food, sediment). Developed by Gobas and colleagues (2006), these models quantify the kinetics of chemical uptake, metabolism, and clearance. Bioaccumulation factors (BAF) and bioconcentration factors (BC

spatial analysis

CA-Markov

CA-Markov is a hybrid spatio-temporal model that projects land-use and land-cover change by combining a Markov chain — which predicts how much of each class will change — with cellular automata, which decide where that change happens. Widely used for urban-growth and land-cover forecasting, it answers both the quantity

remote sensing

Change Detection

Change detection is a remote sensing analysis pipeline that identifies differences in land cover or land use between two or more images acquired at different times over the same geographic area. Systematically reviewed and classified by Ashbindu Singh in 1989, the framework encompasses image differencing, post-classifi

ecology

Circuitscape

Circuitscape, developed by Brad McRae (2008), applies circuit theory from electrical engineering to predict organism movement and genetic connectivity across landscapes. The method treats landscapes as electrical networks where habitat quality is resistance and organism movement is electrical current. By analogy, organ

spatial analysis

Co-kriging

Co-kriging is a geostatistical interpolation technique that predicts the spatial distribution of a primary variable by leveraging its spatial cross-correlation with one or more secondary (co-) variables. It extends ordinary kriging to multivariate settings, yielding more accurate predictions when the secondary variable

spatial analysis

Cokriging

Cokriging extends kriging to use one or more correlated secondary variables to improve prediction of a primary variable. When the variable of interest is sparsely sampled but a related, cheaper-to-measure variable is densely sampled, cokriging borrows strength from the secondary variable through their cross-correlation

spatial analysis

Conditional Geostatistical Simulation

Conditional Geostatistical Simulation — most commonly implemented as Sequential Gaussian Simulation (SGS) — generates multiple stochastic realizations of a spatial random field that are each consistent with observed sample data and with a fitted variogram model. Unlike kriging, which produces a single smoothed estimate

remote sensing

Deep Remote Sensing

Deep Learning for Remote Sensing Image Segmentation applies convolutional neural networks and encoder-decoder architectures to automatically classify and delineate objects in satellite or aerial imagery at the pixel level. Systematically reviewed by Zhu et al. (2017) in IEEE Geoscience and Remote Sensing Magazine, this

ecology

Distance Sampling

Distance sampling is a statistical method for estimating population abundance from data on distances between observers and detected individuals. Developed by Buckland and colleagues (1993) and formalized in the software Distance, this approach accounts for imperfect detection: animals far from an observer are less like

sustainability

DPSIR Framework

The DPSIR Framework (Driving force, Pressure, State, Impact, Response) is a diagnostic and policy tool developed by the OECD (1993) and refined by the European Environment Agency (1999) to structure environmental and sustainability problems. It organizes causal relationships from economic activity through to policy int

sustainability

Ecological Footprint

Ecological Footprint Accounting (EFA) is a resource accounting framework that measures how much biologically productive land and water area a human population requires to produce the resources it consumes and to absorb the waste it generates. Introduced by Mathis Wackernagel and William Rees in 1996, it compares human

sustainability

Ecosystem Services Valuation

Ecosystem Services Valuation (ESV) is a framework pioneered by Costanza and colleagues (1997) that assigns economic value to the benefits nature provides to humanity—from pollination and water purification to climate regulation and cultural enjoyment. Formalized in the Millennium Ecosystem Assessment (2005) and The Eco

ecology

eDNA Metabarcoding

Environmental DNA (eDNA) metabarcoding detects and identifies species present in environmental samples (water, soil, air) by sequencing short DNA fragments released by organisms. Developed by Taberlet and colleagues (2012), this approach has revolutionized biodiversity monitoring: species can be surveyed without captur

sustainability

Emergy Analysis

Emergy Analysis, developed by systems ecologist Howard T. Odum and formally presented in his 1996 book, is a biophysical accounting method that converts all inputs to a system — energy, materials, labor, and services — into a common unit of solar energy equivalents called solar emjoules (sej). By tracing how much prior

sustainability

Exergy Analysis

Exergy analysis is a thermodynamic method that quantifies the maximum useful work obtainable from an energy carrier relative to a reference dead state, revealing where and how irreversibilities destroy quality energy. Formally linked to sustainable development by Marc Rosen and Ibrahim Dincer in 2001, it extends the fi

ecology

Faith's Phylogenetic Diversity

Faith's Phylogenetic Diversity (PD), introduced by David Faith (1992), measures the evolutionary diversity within a community by summing the branch lengths of a phylogenetic tree connecting all species. Unlike species richness, which counts species equally regardless of evolutionary relationships, PD weights species by

ecology

Food Web Topology

Food web topology analysis characterizes the structure of predator-prey interactions within ecological communities using network metrics. Pioneered by Williams and Martinez (2000) and extended by Dunne and colleagues (2002), this approach maps which species eat which and quantifies network properties (connectivity, clu

ecology

Functional Diversity

Functional diversity quantifies the range and abundance distribution of functional traits (morphology, physiology, behavior) among species in a community. Developed by Mouillot and colleagues (2008), functional diversity indices measure how different species are in their ecological roles and resource use strategies. Un

spatial analysis

Geary's C

Geary's C is a global measure of spatial autocorrelation — whether nearby locations tend to have similar values — introduced by Roy Geary in 1954. Unlike Moran's I, which is built on the covariation of values around the mean, Geary's C is built on the squared differences between neighbouring values, making it more sens

spatial analysis

Geary's C

Geary's C is a global spatial autocorrelation statistic that measures whether nearby areal units share similar attribute values. Unlike Moran's I, it focuses on squared differences between adjacent pairs rather than cross-products of deviations from the mean, making it more sensitive to local dissimilarity and less inf

spatial analysis

Geographically Weighted PCA

Geographically Weighted Principal Component Analysis (GWPCA) is a local dimensionality-reduction method introduced by Harris, Brunsdon, and Charlton in 2011. It extends classical PCA by fitting a separate weighted PCA at every location in a dataset, allowing eigenstructures — the principal components and their loadings

spatial analysis

Geographically Weighted Random Forest

Geographically Weighted Random Forest (GWRF) is a spatially local ensemble learning method that fits an independent Random Forest model at each observation location, weighting nearby training samples more heavily than distant ones through a spatial kernel function. It was introduced by Stefanos Georganos and colleagues

spatial analysis

Geographically Weighted Regression

Geographically Weighted Regression is a local regression method, introduced by Fotheringham, Brunsdon and Charlton (2002), that allows the regression coefficients to vary across space. Instead of one global equation, it fits a separate set of coefficients at every location, capturing spatial heterogeneity in the relati

spatial analysis

Getis-Ord Gi*

Getis-Ord Gi* is a local spatial statistic, introduced by Getis and Ord in 1992 and refined in 1995, that compares the value at each location and its neighbours against the global mean to identify statistically significant clusters of high values (hot spots) and low values (cold spots).

spatial analysis

GIS-MCDA

GIS-MCDA combines the map layers of a geographic information system with multi-criteria decision analysis to produce suitability or priority maps — ranking locations by how well they satisfy several weighted criteria at once. It is the standard framework for spatial decisions such as siting hospitals, solar farms, land

spatial analysis

Global Co-Kriging

Global Co-Kriging is a multivariate geostatistical interpolation method that estimates an unsampled primary variable by exploiting its spatial cross-correlation with one or more secondary variables. Unlike local (moving-window) approaches, it fits a single set of variogram and cross-variogram models to the entire study

spatial analysis

Global Getis-Ord Gi*

The Global Getis-Ord Gi* statistic measures the overall degree of spatial clustering of high or low values across an entire study region. It answers whether the study area, taken as a whole, exhibits significant concentration of high values (hot clustering) or low values (cold clustering), returning a single summary Z-

spatial analysis

Global Hot Spot Analysis

Global Hot Spot Analysis uses the Getis-Ord G statistic to determine whether high or low attribute values are spatially concentrated across an entire study area. It answers one question: is there overall clustering of high values (a hot spot tendency) or low values (a cold spot tendency) in the dataset as a whole, prod

spatial analysis

Global Kriging

Global Kriging is the ordinary kriging interpolation procedure applied using all available sample points as the neighborhood — no spatial search window limits which data contribute to each prediction. It produces optimal linear unbiased predictions of an unobserved value at any target location, with associated predicti

spatial analysis

Global Moran's I

Global Moran's I is the most widely used single-number summary of spatial autocorrelation across an entire study area. It compares the attribute value at each location with values at neighbouring locations using a spatial weights matrix, and returns a statistic ranging from −1 (perfect dispersion) through 0 (spatial ra

spatial analysis

Global Ordinary Kriging

Global Ordinary Kriging (GOK) is the canonical geostatistical interpolation method that estimates values at unsampled locations as a weighted linear combination of nearby observations. It fits a single variogram model to the entire dataset, enforcing a global stationarity assumption, and produces optimal unbiased predi

spatial analysis

Global Remote Sensing Classification

Global Remote Sensing Classification assigns every pixel across an entire image or worldwide dataset to a discrete land-cover or thematic class. Treating the scene uniformly — rather than adapting to local subregions — this wall-to-wall approach underpins continental and global land-cover products such as GlobCover, FR

spatial analysis

Global Spatial Autocorrelation

Global Spatial Autocorrelation measures the degree to which similar values cluster together across an entire study area. Rather than identifying where clusters occur, it yields a single summary statistic — most commonly Moran's I — that quantifies whether spatial proximity coincides with value similarity, dissimilarity

spatial analysis

Global Spatial Durbin Model

The Global Spatial Durbin Model extends the spatial lag model by including not only a spatially lagged dependent variable but also spatially lagged independent variables (WX). A single set of global coefficients applies uniformly across all locations, making it suitable for estimating average spillover effects when spa

spatial analysis

Global Spatial Error Model

The Global Spatial Error Model (SEM) is a spatial regression technique that accounts for spatially autocorrelated error terms using a single, globally constant spatial parameter. It separates genuine predictor effects from spatial nuisance dependence in the residuals, yielding unbiased and efficient coefficient estimat

spatial analysis

Global Spatial Panel Model

The Global Spatial Panel Model extends panel data regression by incorporating a global spatial weights matrix that links every location to every other location simultaneously. It jointly accounts for cross-sectional spatial dependence, time-series dynamics, and individual fixed or random effects, making it the standard

spatial analysis

Global Universal Kriging

Global Universal Kriging is a geostatistical interpolation method that models a spatially varying trend (drift) as a deterministic function of coordinates and uses the entire dataset to fit both the trend coefficients and the residual variogram simultaneously. It produces optimal linear unbiased predictions together wi

spatial analysis

Hot Spot Analysis

Hot Spot Analysis uses the Getis-Ord Gi* local spatial statistic to identify geographic locations where high or low attribute values cluster together to a degree that is statistically significant. Each feature is evaluated in relation to its neighbours, producing a z-score that flags genuine spatial hot spots and cold

spatial analysis

Huff Model

Proposed by David Huff in 1964, the Huff Model is a probabilistic spatial interaction model that estimates the likelihood that consumers located in a given geographic zone will choose to shop at a particular retail outlet. It extends deterministic gravity models by assigning each consumer zone a probability of patronag

remote sensing

Hyperspectral Unmixing

Hyperspectral unmixing is a signal processing technique that decomposes each pixel of a hyperspectral image into a collection of pure material spectra (endmembers) and their corresponding fractional abundances. Because sensor resolution often causes multiple land-cover types to co-occupy a single pixel, unmixing recove

ecology

Indicator Value

Indicator Value (IndVal) analysis, developed by Dufrene and Legendre (1997), identifies species that reliably indicate the presence of particular environmental conditions, habitat types, or community groups. The method quantifies the association between species and habitat, producing an indicator value that combines sp