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МетодаStatistika1,836 VI i mašinsko učenje1,661 Nauke o odlučivanju932 Istraživačke metode1,354 Merenje1,745 Kauzalnost i dokazi532 Istraživačka praksa118

160 метода у области Environment & SustainabilityОчисти

Стварне методе које одговарају вашем филтеру.

СортирајПопуларност А–Ш Ш–А Најновије

spatial analysis

Remote Sensing Classification

Remote sensing classification assigns discrete thematic labels — such as forest, urban, water, or cropland — to pixels in a satellite or aerial image based on their spectral, spatial, and temporal properties. It underpins land-use/land-cover mapping, change detection, environmental monitoring, and disaster response at

2 извора1970

spatial analysis

Ripley K Function

The Ripley K function, introduced by Brian Ripley in 1977, is a second-order summary statistic for spatial point patterns. It measures how the number of points within a given distance d of a typical point compares to what would be expected under complete spatial randomness (CSR). Widely used in ecology, epidemiology, c

1 извор1977

spatial analysis

Robust Co-Kriging

Robust Co-Kriging is a multivariate geostatistical interpolation method that jointly estimates values at unsampled locations using two or more spatially correlated variables, while applying robust estimators for the variogram and cross-variogram to limit the distorting influence of spatial outliers or non-Gaussian meas

2 извора1993

spatial analysis

Robust Geary's C

Robust Geary's C adapts the classical Geary contiguity ratio — a measure of spatial autocorrelation based on pairwise squared differences between neighbouring locations — to resist distortion by spatial outliers and influential observations. It retains the local sensitivity of Geary's C while producing more reliable in

2 извора1954

spatial analysis

Robust Getis-Ord Gi*

The Robust Getis-Ord Gi* statistic extends the classical Gi* hot-spot measure to handle outliers in spatial data. By using robust estimators of the mean and variance — such as trimmed means, medians, or down-weighted influential observations — it identifies statistically significant spatial clusters of high or low valu

2 извора1992

spatial analysis

Robust Kriging

Robust Kriging is a geostatistical interpolation method that extends classical kriging by replacing sensitive variogram estimation with outlier-resistant alternatives, most notably the Cressie-Hawkins robust estimator. It produces spatially interpolated predictions that are not distorted by anomalous or extreme observa

2 извора1980

spatial analysis

Robust Local Indicators of Spatial Association

Robust Local Indicators of Spatial Association extend Anselin's LISA framework to handle outliers, extreme values, and spatially heterogeneous populations. By applying outlier-resistant adjustments to the spatial weights or the standardised values, Robust LISA identifies statistically significant local clusters and spa

2 извора1995

spatial analysis

Robust Moran's I

Robust Moran's I is an outlier-resistant adaptation of the classic Moran's I spatial autocorrelation statistic. By replacing the standard mean-based standardization with resistant measures of center and spread, it detects genuine geographic clustering without being distorted by a small number of extreme values in the a

2 извора1990

spatial analysis

Robust Spatial Autocorrelation

Robust spatial autocorrelation methods measure the degree to which nearby geographic units share similar values, while explicitly controlling for the distorting influence of spatial outliers and extreme observations. They extend classical statistics such as Moran's I by down-weighting or trimming observations that woul

2 извора1981

spatial analysis

Robust Universal Kriging

Robust Universal Kriging (RUK) is a geostatistical interpolation method that combines a spatially varying deterministic trend with a stochastic residual surface, while using robust estimators to protect the variogram and trend coefficients from the distorting influence of outlying observations.

2 извора1980

SAR Image Analysis

Synthetic Aperture Radar (SAR) Image Analysis is an active microwave remote sensing pipeline that processes complex-valued radar backscatter data to characterize land cover, surface roughness, moisture, and structural properties. Foundational treatment was consolidated by Jong-Sen Lee and Eric Pottier in their 2009 CRC

1 извор2009

spatial analysis

Service Area Analysis

Service Area Analysis delineates the geographic region reachable from one or more origin facilities within a specified travel cost — typically time, distance, or generalized impedance — by traversing a real road or transit network. It is widely used by urban planners, public health officials, logistics managers, and em

1 извор2001

SIAR Mixing Model

The Stable Isotope Analysis in R (SIAR) mixing model is a Bayesian framework for estimating the proportional contributions of dietary sources to a consumer, using stable isotope ratios. Developed by Parnell and colleagues (2010) and implemented in the R package siar (and its successor MixSIAR), this method integrates i

3 извора2010

spatial analysis

Space-Time Geary's C

Space-Time Geary's C extends the classical Geary contiguity ratio to panel or longitudinal spatial data, measuring autocorrelation across both geographic neighbors and adjacent time periods simultaneously. Values below 1 indicate positive space-time clustering; values above 1 indicate dispersion, and a value near 1 sug

2 извора1954

spatial analysis

Space-Time Getis-Ord Gi*

The Space-Time Getis-Ord Gi* statistic extends the classic Gi* local hot spot measure into three dimensions — two spatial and one temporal — revealing not only where concentrations of high or low values cluster, but how those clusters evolve, intensify, or diminish over time. It is widely used in crime analysis, epidem

2 извора1992

spatial analysis

Space-Time Hot Spot Analysis

Space-Time Hot Spot Analysis extends the classic Getis-Ord Gi* statistic across repeated time slices organised in a space-time cube. By testing each location-time bin for statistically significant clustering of high or low values, then examining the sequence of results over time, it identifies whether clusters are new,

2 извора1997

spatial analysis

Space-Time Kernel Density Estimation

Space-Time Kernel Density Estimation extends classical KDE into three dimensions — two spatial and one temporal — to reveal how the intensity of point events (crimes, accidents, disease cases) varies continuously across both geographic space and time. It produces a smooth probabilistic surface that highlights where and

2 извора2010

spatial analysis

Space-Time Kriging

Space-Time Kriging is a geostatistical interpolation method that predicts an unknown variable at any location and time by borrowing strength from nearby observations in both space and time simultaneously. It models the joint spatial-temporal covariance structure through a space-time variogram, then uses optimal linear

2 извора1999

spatial analysis

Space-Time Local Indicators of Spatial Association

Space-Time Local Indicators of Spatial Association (ST-LISA) extend the classic LISA framework of Anselin (1995) into the temporal dimension, identifying locations that exhibit statistically significant spatial clustering or spatial outlier behavior consistently or intermittently across multiple time periods. They deco

2 извора1995

spatial analysis

Space-Time Moran's I

Space-Time Moran's I extends the classic Moran's I statistic into the spatiotemporal domain, measuring whether observations that are close in both space and time tend to be more similar than those that are distant. It detects clustering, dispersion, or randomness across a combined space-time weight matrix, making it a

2 извора1981

spatial analysis

Space-Time Network-Based Spatial Analysis

Space-Time Network-Based Spatial Analysis integrates network topology with temporal constraints to model how people, goods, or phenomena move through geographic networks over time. Rooted in Hägerstrand's time-geography, it evaluates accessibility, interaction potential, and movement patterns along real-world infrastru

2 извора1970

spatial analysis

Space-Time Ordinary Kriging

Space-Time Ordinary Kriging (STOK) is a geostatistical interpolation method that predicts a spatially and temporally varying phenomenon at unsampled space-time locations by combining the ordinary kriging assumption of an unknown, locally constant mean with a joint space-time covariance (or variogram) structure. It prod

2 извора1999

spatial analysis

Space-Time Remote Sensing Classification

Space-Time Remote Sensing Classification extends standard image classification to multi-temporal satellite or aerial imagery, enabling analysts to track land cover change, phenological cycles, and environmental dynamics across both space and time. By incorporating the temporal dimension, classifiers achieve higher accu

2 извора1980

spatial analysis

Space-Time Spatial Autocorrelation

Space-Time Spatial Autocorrelation extends classic spatial autocorrelation measures — most notably Moran's I — to data that vary across both geographic units and time periods. It detects whether nearby locations that are also temporally close tend to share similar attribute values, revealing clusters, trends, or anomal

2 извора1981

spatial analysis

Space-Time Spatial Durbin Model

The Space-Time Spatial Durbin Model extends the cross-sectional Spatial Durbin Model to panel data, simultaneously capturing spatial spillovers in both the dependent variable and the explanatory variables across space and over time. It is the most general and flexible specification in the spatial panel family, nesting

2 извора2009

spatial analysis

Space-Time Spatial Error Model

The Space-Time Spatial Error Model (space-time SEM) is a spatial panel regression technique that accounts for spatial dependence confined to the error term across geographic units and time periods. It corrects biased inference caused by spatially correlated disturbances while estimating covariate effects on a panel of

2 извора1988

spatial analysis

Space-Time Spatial Lag Model

The Space-Time Spatial Lag Model extends the classic spatial autoregressive (SAR) lag model to panel data, capturing how the outcome in each location at each time point is influenced by the contemporaneous outcomes of neighboring locations, while also controlling for unit-specific and time-specific fixed effects.

2 извора2003

spatial analysis

Space-Time Spatial Panel Model

The Space-Time Spatial Panel Model extends standard spatial panel econometrics to jointly account for cross-sectional spatial dependence, temporal autocorrelation, and unit-level heterogeneity. It allows outcomes in one location and time period to be influenced by outcomes in neighboring locations and by the location's

2 извора2003

spatial analysis

Space-Time Spatial Regression

Space-Time Spatial Regression extends classical spatial regression to panel settings where georeferenced units are observed across multiple time periods. By embedding a spatial weights matrix into a panel regression framework, it simultaneously controls for spatial dependence among cross-sectional units and temporal dy

2 извора1990

spatial analysis

Space-Time Universal Kriging

Space-Time Universal Kriging (STUK) is a geostatistical method that interpolates a continuously varying phenomenon across both space and time while explicitly modelling a deterministic trend component. It generalises Universal Kriging to the joint space-time domain, producing unbiased optimal predictions and associated

2 извора1999

spatial analysis

Spatial Autocorrelation

Spatial autocorrelation quantifies the degree to which a variable's values at nearby locations resemble each other more (positive autocorrelation) or less (negative autocorrelation) than expected by chance. Global indices such as Moran's I summarise the pattern across the entire study area, while local variants reveal

2 извора1950

spatial analysis

Spatial Durbin Model

The Spatial Durbin Model is a general spatial regression model that includes a spatial lag of both the dependent variable (ρWy) and the explanatory variables (WXθ). Introduced as the recommended starting point by LeSage and Pace (2009), it nests the spatial autoregressive (SAR) and spatial error (SEM) models as special

2 извора2009

spatial analysis

Spatial Error Model

The Spatial Error Model, developed within Anselin's spatial econometrics framework (1988), is a regression model that assumes spatial dependence enters through the error term: the disturbances of neighbouring units are correlated. It is used when unobserved shared factors make the errors of nearby observations move tog

1 извор1988

spatial analysis

Spatial Interaction Model

Spatial interaction models predict the volume of flows — migrants, commuters, shoppers, trade, trips — between origins and destinations as a function of the size of each place and the distance or cost separating them. By analogy to Newton's gravity, interaction rises with the 'mass' of origin and destination and falls

2 извора1971

spatial analysis

Spatial Lag Model

The Spatial Lag Model is an autoregressive regression that assumes spatial dependence in the dependent variable itself: the outcome values of neighbouring units enter the model as an explanatory term (ρWy). It was formalised in Anselin's Spatial Econometrics (1988) and developed further by LeSage and Pace (2009), and i

2 извора1988

spatial analysis

Spatial Panel Model

The spatial panel model is a family of econometric models that adds spatial dependence to panel data (units observed over time). It combines fixed- or random-effects panel structure with spatial lag, spatial error, or spatial Durbin components, and is developed in the modern spatial-econometrics literature by Elhorst (

2 извора2014

spatial analysis

Spatial SAC Model

The Spatial Autoregressive Combined (SAC) model, also known as the SARAR model, simultaneously accounts for spatial dependence in both the dependent variable and the error term. Formalized by LeSage and Pace (2009), the SAC model combines the spatial lag model and the spatial error model into a single framework, estima

1 извор2009

Species Accumulation

Species accumulation curves describe how the number of observed species increases with cumulative sampling effort. Introduced by Sanders (1968) and developed by Colwell and colleagues, this method enables ecologists to compare biodiversity across sites and estimate total species richness despite incomplete sampling. It

3 извора1968

Species Distribution Models (MaxEnt)

Species Distribution Models (SDMs) using Maximum Entropy (MaxEnt) are statistical methods developed by Phillips, Anderson, and Schapire (2004) to predict where species are likely to occur based on known occurrence points and environmental variables. MaxEnt has become one of the most widely used algorithms in conservati

3 извора2004

spatial analysis

Universal Kriging

Universal kriging generalizes ordinary kriging to data whose mean varies systematically across space — a spatial trend or 'drift'. It models the mean as a function of the coordinates (or covariates) and krigs the residuals, so it can interpolate variables that drift in a preferred direction, such as temperature falling

2 извора1969

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