Statistics — Page 44
Explore statistical methods, their assumptions, interpretation and limitations.
Research field
Method family
Social Relations Model
The Social Relations Model (SRM), developed by David Kenny and colleagues, is a variance-decomposition framework for analyzing interpersonal perception and behavior in groups. When every member of a group rates (or behaves toward) every oth...
Solow Residual
The Solow residual is the portion of output growth that is not explained by the growth of measured inputs — capital and labour — after each input's growth is weighted by its share of national income. Introduced by Robert Solow in 1957, it i...
Somers' D
Somers' D is an asymmetric ordinal association coefficient, introduced by Robert H. Somers in 1962, that quantifies how well one ordinal variable predicts another by measuring the excess of concordant over discordant pairs relative to all p...
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 s...
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 evolv...
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 exam...
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 spac...
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-tempora...
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...
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, di...
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 access...
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 co...
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 an...
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 clo...
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 t...
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 spa...
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,...
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 pe...
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 simultaneo...
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 th...
Spatial Approximate Bayesian Computation
Spatial Approximate Bayesian Computation (Spatial ABC) is a likelihood-free Bayesian inference framework for spatial data models whose likelihood function is intractable or too expensive to evaluate. It draws candidate parameters from a pri...
Spatial Assimilation Model
The spatial assimilation model, articulated by Douglas Massey and Nancy Denton in 1985, treats where immigrants and minorities live as a measurable outcome of their social mobility. Its core proposition is that as group members acquire huma...
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...
Spatial Bayesian Inference
Spatial Bayesian inference applies Bayesian hierarchical modeling to data indexed by geographic location. By placing structured spatial priors on location-specific random effects, the model borrows information from neighboring regions or ne...