Spatial Structures in Ecological Models

Sat, 22 Nov 2025 00:00:00 +0000

What this document will cover:

Spatial structures  
├── Why go spatial?  
│   └── autocorrelation  
├── How to go spatial?
│   └── Background, examples, and considerations by data type
│       ├── Point referenced data  
│       ├── Areal data  
│       ├── Point-pattern data  
│       └── Movement data  
├── What about mixed data types?  
│   ├── Change of support  
│   ├── Joint likelihoods  
│   └── Spatial misalignment  
├── What about model selection?  
│   └── Spatial validation methods  
├── What to watch out for?  
│   ├── Common pitfalls  
│   ├── Computational limitation  
│   └── Software considerations  
└── Resources

Why include spatial structures in your model?

Tobler’s First law Geography (a.k.a. spatial autocorrelation):

[…] everything is related to everything else, but near things are more related than distant things."

Waldo Tobler 1970 (link to paper)

Put more explicitely: measurements of ecological phenomena are more interconnected at close distances versus distant ones

Further: Ignoring spatial autocorrelation leads to…

Biased parameter estimates
Inflated significance (increased chance of Type-I error)
Reduced model precision
Inaccurate model selection
Violated statistical assumptions (iid errors)
Poor predictive accuracy
Non-demonic intrusion 😈

How to account for spatial autocorrelation in my model?

First, what type of spatial data do you have?

Each data type has specialized methods, assumptions, and computational considerations.

Data types:

Point-Referenced Data (geostatistical data)
Areal/Lattice Data (regional/grid data)
Point Pattern Data (locations are the response)
Movement/Trajectory Data (tracks and telemetry)

Note: Many ecological studies combine multiple data types! We’ll breifly discuss integration approaches.

1. Point-Referenced Data

Measurements at specific geographic coordinates

What is Point-Referenced Data?

Definition: Observations collected at specific locations, representing a continuous spatial process.

Ecological Examples:

Species abundance at survey points
Vegetation cover at plot locations
Soil samples across a landscape
Individual tree measurements in a forest

Key characteristic: Locations are fixed by design, not random.

A visual example

Typical scenario:

50-5000 sampling locations
Environmental covariates at each point
Response variable (abundance, presence, etc.)
Want to predict across unsampled locations

Common Questions

How does my response vary across space?
What drives spatial patterns?
Can I predict to new locations?
Is there residual spatial autocorrelation?

Point-Referenced data: The Core Challenge

After accounting for environmental covariates, you often have residual spatial structure:

$$y(\mathbf{s}) = \mathbf{X}(\mathbf{s})\boldsymbol{\beta} + w(\mathbf{s}) + \epsilon(\mathbf{s})$$

Where:

$y(\mathbf{s})$ = response at location $\mathbf{s}$
$\mathbf{X}(\mathbf{s})\boldsymbol{\beta}$ = fixed effects (environmental covariates)
$w(\mathbf{s})$ = spatial random effect (what we need to model!)
$\epsilon(\mathbf{s})$ = non-spatial noise

Point-Referenced data: Five Approaches

We’ll cover five main approaches, organized by their relationship to Gaussian Processes:

Full Gaussian Processes (GPs) - theoretical gold standard
SPDE Approximation - Exact GPs using basis functions
Low-Rank Gaussian Processes - Approximate GPs for efficiency
Nearest Neighbor GPs - Local GP approximations
Spatial Splines - Alternative smoothing framework (non-GP)

Each represents different trade-offs between accuracy, computation, and flexibility.

Understanding the GP Family Tree

Conceptual Relationships

Full GP ($O(n^3)$)

Exact, but computationally prohibitive for large $n$

Reduced-Rank GP Approximations:

SPDE - Exact Matérn GP via finite elements (sparse matrices)
Low-Rank GP - Approximate using subset of basis functions
Nearest Neighbor GP - Local conditioning on nearby points

Alternative Framework:

Splines - Smoothness penalty instead of covariance structure

Key insight: Methods 2-4 are all ways to make GPs computationally feasible while maintaining the probabilistic framework.

Method 1: Full Gaussian Processes

The gold standard for point-referenced data

$$w(\mathbf{s}) \sim \mathcal{GP}(0, k(\mathbf{s}, \mathbf{s}’))$$

Covariance function defines spatial correlation:

$$k(\mathbf{s}, \mathbf{s}’) = \sigma^2 \exp\left(-\frac{||\mathbf{s} - \mathbf{s}’||^2}{2\ell^2}\right)$$

Parameters:

$\sigma^2$ = spatial variance (how much spatial variation?)
$\ell$ = length-scale (how far does correlation extend?)

Full GP: Covariance Functions

Matérn family (most common in ecology):

$$k(d) = \sigma^2 \frac{2^{1-\nu}}{\Gamma(\nu)}\left(\sqrt{2\nu}\frac{d}{\ell}\right)^\nu K_\nu\left(\sqrt{2\nu}\frac{d}{\ell}\right)$$

Special cases:

$\nu = 0.5$ → Exponential (rough surfaces)
$\nu = 1.5$ → Once differentiable (good default)
$\nu = 2.5$ → Twice differentiable
$\nu \to \infty$ → Squared exponential (infinitely smooth)

Practical Advice

Start with $\nu = 1.5$ (good default) or estimate it if you have enough data.

Full GP: The Computational Problem

Standard GP inference requires:

Inverting an $n \times n$ covariance matrix: $O(n^3)$ operations
Storing the matrix: $O(n^2)$ memory

Practical limits:

~1,000 locations: comfortable
~5,000 locations: challenging
10,000+ locations: infeasible

This is why we need approximations!

Reduced rank GPs make modeling feasible for realistic ecological datasets.

Full GP: R Implementation

library(geoR)
library(fields)

# Using geoR for ML estimation
geo_data <- as.geodata(data, coords.col = 1:2, data.col = 3)

# Fit variogram
vario <- variog(geo_data, max.dist = 100)
plot(vario)

# Fit Matérn model
fit_ml <- likfit(
  geo_data,
  ini.cov.pars = c(1, 10),  # Initial sigma^2, range
  cov.model = "matern",
  kappa = 1.5,              # nu parameter
  fix.kappa = TRUE
)

summary(fit_ml)

# Kriging predictions
pred_grid <- expand.grid(
  x = seq(min(data$x), max(data$x), length = 50),
  y = seq(min(data$y), max(data$y), length = 50)
)

kc <- krige.control(
  type.krige = "OK",
  obj.model = fit_ml
)

predictions <- krige.conv(
  geo_data,
  locations = pred_grid,
  krige = kc
)

# Plot predictions
image.plot(predictions$predict)

Method 2: SPDE Approximation

The SPDE approach (Lindgren et al. 2011) makes GPs computationally tractable!

Key insight: A Matérn GP is the solution to a stochastic partial differential equation:

$$(\kappa^2 - \Delta)^{\alpha/2}(\tau w(\mathbf{s})) = \mathcal{W}(\mathbf{s})$$

Critical distinction: SPDE provides an exact representation of the Matérn GP, not an approximation.

Practical result:

Represent continuous GP using finite element basis functions
Sparse precision matrices enable fast computation
Reduces $O(n^3)$ to approximately $O(n^{3/2})$ or better
Makes analysis of 10,000+ locations feasible

SPDE: How It Works

Create triangular mesh over study area
GP represented exactly at mesh vertices
Values between vertices interpolated

Mesh construction is critical:

Finer mesh = better approximation
Coarser mesh = faster computation
Balance accuracy and speed
Extend mesh beyond study area

Rule of Thumb: Mesh resolution should be finer than the spatial range ($\ell$), ideally <1k

{ width=100% }

SPDE: R Implementation with `sdmTMB`

library(sdmTMB)

# Create mesh object that contains matrices to apply the SPDE approach
mesh <- make_mesh(pcod, xy_cols = c("X", "Y"), cutoff = 10)

# Fit a spatial model with a smoother for depth:
fit <- sdmTMB(
  density ~ s(depth),
  data = pcod,
  mesh = mesh,
  family = tweedie(link = "log"),
  spatial = "on"
)

# Run some basic sanity checks on our model:
sanity(fit)

# Use the ggeffects package to plot the smoother effect:
ggeffects::ggpredict(fit, "depth [50:400, by=2]") %>%
   plot()

# Obtain spatial predictions: 
p <- predict(fit, newdata = qcs_grid)

# Map predictions 
ggplot(p, aes(X, Y, fill = exp(est))) + geom_raster() +
  scale_fill_viridis_c(trans = "sqrt")

SPDE: R Implementation with `INLA`

library(INLA)
library(inlabru)

# Create mesh over study region
mesh <- inla.mesh.2d(
  loc = cbind(data$x, data$y),  # Data locations
  max.edge = c(5, 20),           # Inner and outer max edge
  cutoff = 1,                    # Min distance between mesh nodes
  offset = c(10, 30)             # Inner and outer extension
)

# Visualize mesh
plot(mesh)
points(data$x, data$y, col = "red", pch = 16)

# Define SPDE model (Matérn with alpha=2 gives nu=1)
spde <- inla.spde2.matern(
  mesh = mesh, 
  alpha = 2  # alpha = nu + d/2, where d=2 (spatial dimensions)
)

# Create projection matrix (links data locations to mesh)
A <- inla.spde.make.A(
  mesh = mesh,
  loc = cbind(data$x, data$y)
)

# Stack data for INLA
stack <- inla.stack(
  data = list(y = data$abundance),
  A = list(A, 1),
  effects = list(
    spatial = 1:spde$n.spde,
    data.frame(
      Intercept = 1,
      elevation = data$elevation,
      forest_cover = data$forest_cover
    )
  )
)

# Fit model
formula <- y ~ -1 + Intercept + elevation + forest_cover +
  f(spatial, model = spde)

result <- inla(
  formula,
  family = "poisson",
  data = inla.stack.data(stack),
  control.predictor = list(
    A = inla.stack.A(stack),
    compute = TRUE
  ),
  control.compute = list(
    dic = TRUE,
    waic = TRUE,
    cpo = TRUE
  )
)

summary(result)

# Extract spatial field estimates
spde_result <- inla.spde2.result(result, "spatial", spde)

SPDE: Making Predictions

# Create prediction grid
pred_coords <- expand.grid(
  x = seq(min(data$x), max(data$x), length = 100),
  y = seq(min(data$y), max(data$y), length = 100)
)

# Projection matrix for predictions
A_pred <- inla.spde.make.A(
  mesh = mesh,
  loc = as.matrix(pred_coords)
)

# Create prediction stack
stack_pred <- inla.stack(
  data = list(y = NA),  # NA for prediction
  A = list(A_pred, 1),
  effects = list(
    spatial = 1:spde$n.spde,
    data.frame(
      Intercept = 1,
      elevation = mean(data$elevation),
      forest_cover = mean(data$forest_cover)
    )
  )
)

# Combine with original data
stack_full <- inla.stack(stack, stack_pred)

# Refit model with prediction stack
result_pred <- inla(
  formula,
  family = "poisson",
  data = inla.stack.data(stack_full),
  control.predictor = list(
    A = inla.stack.A(stack_full),
    compute = TRUE,
    link = 1
  )
)

# Extract predictions
idx_pred <- inla.stack.index(stack_full, "stack_pred")$data
pred_coords$mean <- result_pred$summary.fitted.values$mean[idx_pred]
pred_coords$sd <- result_pred$summary.fitted.values$sd[idx_pred]

# Map predictions
library(ggplot2)
ggplot(pred_coords, aes(x, y, fill = mean)) +
  geom_raster() +
  scale_fill_viridis_c() +
  coord_equal() +
  theme_minimal()

Method 3: Low-Rank Gaussian Processes

Reduced-rank GP approximations for computational efficiency

Key idea: Approximate the spatial field using a smaller set of basis functions:

$$w(\mathbf{s}) \approx \sum_{j=1}^{m} \beta_j \phi_j(\mathbf{s})$$

Where $m « n$ (number of basis functions much less than data points).

Common approaches:

Predictive Processes - GPs at knot locations
Subset of Regressors - GP conditioning on subset
Fixed Rank Kriging - Low-rank spatial covariance

Low-Rank GP: Predictive Processes

Concept:

Choose $m$ knot locations (e.g., 50-200 knots)
Define full GP at knots
Predict data locations as function of knot GPs

$$w(\mathbf{s}) \approx \mathbf{C}(\mathbf{s}, \mathbf{s}^) \mathbf{C}(\mathbf{s}^, \mathbf{s}^)^{-1} w(\mathbf{s}^)$$

Where $\mathbf{s}^*$ are knot locations.

Computational cost: $O(nm^2 + m^3)$ - dominated by $m^3$ for reasonable $n$

Choosing m More knots = better approximation but slower. Start with $m = \sqrt{n}$ or 50-100 knots.

Low-Rank GP: R Implementation

library(spBayes)
library(MBA)  # For knot placement

# Define knots (can be data locations or regular grid)
n_knots <- 100

# Create regular grid of knots
knot_coords <- expand.grid(
  x = seq(min(data$x), max(data$x), length = sqrt(n_knots)),
  y = seq(min(data$y), max(data$y), length = sqrt(n_knots))
)

# Alternatively, use k-means clustering for adaptive knots
# kmeans_result <- kmeans(cbind(data$x, data$y), centers = n_knots)
# knot_coords <- kmeans_result$centers

# Prepare data for spBayes
coords <- cbind(data$x, data$y)
X <- model.matrix(~ elevation + forest_cover, data = data)
y <- data$abundance

# Set up predictive process model
n_samples <- 5000
starting <- list(
  "phi" = 3/max(dist(coords)),  # 3/max_distance rule of thumb
  "sigma.sq" = 1,
  "tau.sq" = 0.1
)

tuning <- list(
  "phi" = 0.1,
  "sigma.sq" = 0.1,
  "tau.sq" = 0.1
)

priors <- list(
  "beta.Flat",
  "phi.Unif" = c(3/max(dist(coords)), 3/min(dist(coords))),
  "sigma.sq.IG" = c(2, 1),
  "tau.sq.IG" = c(2, 0.1)
)

# Fit predictive process model
pp_model <- spLM(
  y ~ X - 1,
  coords = coords,
  knots = as.matrix(knot_coords),
  starting = starting,
  tuning = tuning,
  priors = priors,
  cov.model = "exponential",
  n.samples = n_samples,
  verbose = TRUE,
  n.report = 500
)

# Burn-in and thin
burn_in <- 0.5 * n_samples
pp_samples <- spRecover(
  pp_model,
  start = burn_in,
  thin = 2
)

summary(pp_samples)

Low-Rank GP: Alternative with `FRK`

library(FRK)  # Fixed Rank Kriging

# Create basis functions (2 resolutions)
basis <- auto_basis(
  data = data.frame(x = data$x, y = data$y),
  nres = 2,           # Number of resolutions
  type = "bisquare"   # Basis function type
)

# Prepare data
sp_data <- SpatialPointsDataFrame(
  coords = cbind(data$x, data$y),
  data = data.frame(
    z = data$abundance,
    elevation = data$elevation,
    forest_cover = data$forest_cover
  )
)

# Fit FRK model
frk_model <- FRK(
  f = z ~ elevation + forest_cover,
  data = list(sp_data),
  basis = basis,
  response = "poisson"  # Or "gaussian" for continuous
)

# Predictions
pred_grid <- SpatialPoints(
  coords = cbind(pred_coords$x, pred_coords$y)
)

predictions <- predict(frk_model, newdata = pred_grid)

Method 4: Nearest Neighbor Gaussian Processes

For very large datasets (>10,000 points)

Key idea: Each location conditions only on its $m$ nearest neighbors (typically $m = 10-30$)

$$w(\mathbf{s}_i) | {w(\mathbf{s}_j): j \in \mathcal{N}_m(i)} \sim \text{Normal}(\boldsymbol{\mu}_i, \sigma^2_i)$$

Advantages:

Scales to massive datasets (100,000+ locations)
Maintains proper GP framework
Computationally efficient: $O(nm^3)$
Can be highly accurate

Key development: Datta et al. (2016) - Nearest Neighbor Gaussian Process

Nearest Neighbor GP: How It Works

Response model: Define ordered observations $\mathbf{y} = (y_1, \ldots, y_n)$

Neighbor set: For each $i$, define $\mathcal{N}_m(i)$ = indices of $m$ nearest neighbors (prior to $i$)

Conditional specification:

$$p(w_i | w_1, \ldots, w_{i-1}) = p(w_i | w_{\mathcal{N}_m(i)})$$

Result: Sparse precision matrix, fast computation

The ordering of your data affects results!

Common factors to order by:

by coordinate
space-filling curves
random

Nearest Neighbor GP: R package `spNNGP`

library(spNNGP)

# Prepare data
coords <- cbind(data$x, data$y)
X <- model.matrix(~ elevation + forest_cover, data = data)
y <- data$abundance

# Set up NNGP model
n_neighbors <- 15  # Typical range: 10-30

# Starting values
starting <- list(
  "phi" = 3/max(dist(coords)),
  "sigma.sq" = 1,
  "tau.sq" = 0.1,
  "beta" = rep(0, ncol(X))
)

# Tuning parameters
tuning <- list(
  "phi" = 0.5,
  "sigma.sq" = 0.1,
  "tau.sq" = 0.1,
  "beta" = 0.1
)

# Prior distributions
priors <- list(
  "beta.Flat",
  "phi.Unif" = c(3/max(dist(coords)), 3/0.01),
  "sigma.sq.IG" = c(2, 1),
  "tau.sq.IG" = c(2, 0.1)
)

# Fit NNGP model
nngp_model <- spNNGP(
  formula = y ~ X - 1,
  coords = coords,
  starting = starting,
  method = "response",
  n.neighbors = n_neighbors,
  tuning = tuning,
  priors = priors,
  cov.model = "exponential",
  n.samples = 5000,
  n.omp.threads = 4,  # Parallel threads
  verbose = TRUE
)

# Posterior summaries
summary(nngp_model$p.beta.samples)
summary(nngp_model$p.theta.samples)

# Predictions at new locations
pred_coords_mat <- as.matrix(pred_coords[, c("x", "y")])
X_pred <- cbind(
  1,
  mean(data$elevation),
  mean(data$forest_cover)
)

predictions <- predict(
  nngp_model,
  X.0 = X_pred,
  coords.0 = pred_coords_mat,
  n.omp.threads = 4
)

# Extract mean predictions
pred_mean <- apply(predictions$p.y.0, 1, mean)
pred_sd <- apply(predictions$p.y.0, 1, sd)

Nearest Neighbor GP: R package `spAbundance`

library(spAbundance)

# Fit Bayesian heirarchical distance sampling model
out <- spDS(
  # State process formula
  abund.formula = ~ forest + grass ,

  # Observation process formula
  det.formula = ~ day + I(day^2) + wind,

  # Model controls
  data = dat.MODO, n.batch = 800, batch.length = 25,
  accept.rate = 0.43, cov.model = "exponential",
  transect = 'point', det.func = 'halfnormal',
  NNGP = TRUE, n.neighbors = 15, n.burn = 10000,
  n.thin = 5, n.chains = 3, verbose = FALSE)

# Posterior predictive check
ppcAbund(out, fit.stat = 'freeman-tukey', group = 1) %>%
  summary()

# Model selection
waicAbund(out)

# Predictions

out.pred <- predict(out, predictors, coordinates, verbose = FALSE)

Method 5: Spatial Splines

Alternative framework: Not based on Gaussian Processes

Key idea: Approximate spatial surface with smooth basis functions

$$w(\mathbf{s}) = \sum_{k=1}^{K} \beta_k \phi_k(\mathbf{s})$$

Distinguishing features:

Minimize roughness penalty (not maximize likelihood of covariance)
Deterministic smoothing approach
Can have Bayesian interpretation (splines as random effects)
Very fast computation
Different uncertainty quantification

Spatial Splines: Types

Thin plate splines - Optimal smoothness properties

Minimize: $$\sum_{i=1}^n (y_i - f(\mathbf{s}_i))^2 + \lambda \int \int \left[\left(\frac{\partial^2 f}{\partial x^2}\right)^2 + 2\left(\frac{\partial^2 f}{\partial x \partial y}\right)^2 + \left(\frac{\partial^2 f}{\partial y^2}\right)^2\right] dx,dy$$

Tensor product splines - Product of 1D basis functions

Radial basis functions - Distance-based kernels

When to Use Splines

Splines are excellent for quick exploratory analysis and when you dont need explicit covariance modeling.

Spatial Splines: R Implementation with mgcv

library(mgcv)

# Basic spatial smooth
model_basic <- gam(
  abundance ~ s(x, y, bs = "tp", k = 50),
  family = poisson,
  data = data,
  method = "REML"
)

# With covariates
model_full <- gam(
  abundance ~ 
    s(elevation, k = 10) +
    s(forest_cover, k = 10) +
    s(x, y, bs = "tp", k = 100),
  family = poisson,
  data = data,
  method = "REML"
)

# Check model
summary(model_full)
gam.check(model_full)  # Check basis dimensions and residuals

# Plot effects
plot(model_full, pages = 1, scheme = 2)

# Spatial smooth only
plot(model_full, select = 3, scheme = 2, asp = 1)

# Predictions
pred_data <- expand.grid(
  x = seq(min(data$x), max(data$x), length = 100),
  y = seq(min(data$y), max(data$y), length = 100),
  elevation = mean(data$elevation),
  forest_cover = mean(data$forest_cover)
)

pred_data$fit <- predict(model_full, pred_data, type = "response")
pred_data$se <- predict(model_full, pred_data, type = "response", se.fit = TRUE)$se.fit

# Map predictions
library(ggplot2)
ggplot(pred_data, aes(x, y, fill = fit)) +
  geom_raster() +
  scale_fill_viridis_c() +
  coord_equal() +
  labs(fill = "Predicted\nAbundance") +
  theme_minimal()

Spatial Splines: Tensor Products

# Tensor product of marginal smooths (useful for anisotropy)
model_tensor <- gam(
  abundance ~ 
    s(elevation) +
    te(x, y, k = c(10, 10)),  # Tensor product
  family = poisson,
  data = data,
  method = "REML"
)

# Adaptive smooth (varying complexity)
model_adaptive <- gam(
  abundance ~ 
    s(elevation) +
    s(x, y, bs = "ad", k = 100),  # Adaptive smooth
  family = poisson,
  data = data,
  method = "REML"
)

# Soap film smooth (with boundary)
# Useful when study area has complex shape
library(sf)
boundary <- st_read("study_boundary.shp")

# Convert boundary to appropriate format
# (See mgcv::smooth.construct.so.smooth.spec for details)

Point-Referenced: Comprehensive Comparison

Method	Framework	Complexity	Speed	n Limit	Uncertainty	Best Use Case
Full GP	GP	$O(n^3)$	Slow	~1000	Exact	Small datasets, methodological work
SPDE	GP (exact)	$O(n^{3/2})$	Fast	~50,000	Exact	Medium-large datasets, Bayesian inference
Low-Rank GP	GP (approx)	$O(nm^2 + m^3)$	Medium	~10,000	Bayesian	Moderate datasets, flexible
NN-GP	GP (approx)	$O(nm^3)$	Fast	100,000+	Bayesian	Very large datasets
Splines	Non-GP	$O(n)$	Very Fast	50,000+	Frequentist	Quick analysis, exploration

Key R packages

Full GP: gstat (classical geostats) + fields (spatial random fields)

SPDE:

INLA + inlabru (highly customizable)
sdmTMB (user friendly, single species) or tinyVAST (multi-species)

Low-Rank GP: spBayes or nimble (highly customizable Bayesian modeling)

Nearest Neighbor GP:

spNNGP - Nearest Neighbor Gaussian Processes
spAbundance & spOccupancy (Hierarchical Bayesian models)

Splines: mgcv (GAMs) or mvgams (Hierarchical Bayesian GAMs)

Common Pitfalls and Solutions

Watch Out For:

Over-smoothing:

Splines: Check gam.check() and increase k if needed
GPs: Ensure spatial range parameter is estimable

Under-smoothing:

Creates overly complex surfaces that don’t generalize
Use cross-validation to check predictive performance

Computational Issues:

Start with small datasets to test workflow
Use reduced-rank methods before attempting full GP
Consider parallelization for MCMC-based methods

Mesh/Knot Placement:

SPDE: Mesh should extend beyond data
Low-Rank: Try different knot configurations

Validation and Model Checking

Essential diagnostics for all methods:

# 1. Residual spatial autocorrelation
library(spdep)
coords <- cbind(data$x, data$y)
neighbors <- dnearneigh(coords, 0, max_distance)
weights <- nb2listw(neighbors)
moran.test(residuals(model), weights)

# 2. Variogram of residuals
library(gstat)
resid_variogram <- variogram(
  residuals ~ 1, 
  locations = ~x+y, 
  data = data
)
plot(resid_variogram)  # Should be flat if spatial structure captured

# 3. Spatial cross-validation
library(blockCV)
spatial_blocks <- cv_spatial(
  x = data_sf,
  column = "response",
  k = 5,
  size = 10000
)

# 4. Predictive performance
# Compare RMSE, MAE, coverage of prediction intervals

Integration with Covariates

All methods can include environmental covariates:

Additive structure: $$y(\mathbf{s}) = \beta_0 + \sum_{j=1}^p f_j(x_j(\mathbf{s})) + w(\mathbf{s}) + \epsilon(\mathbf{s})$$

Where $f_j$ can be:

Linear effects: $\beta_j x_j$
Smooth effects: Spline or polynomial
Interactions: $x_j \times x_k$ or tensor products

Interpretation Challenge

Spatial random effects can “absorb” covariate effects if covariates are spatially structured. Consider restricted spatial regression or spatial confounding adjustments.

Advanced Topics

Extensions of point-referenced methods:

Non-stationary GPs - Spatial parameters vary across space
Anisotropic models - Correlation depends on direction
Space-time models - Add temporal dimension
Multivariate spatial models - Multiple responses
Preferential sampling - Locations not random
Measurement error - Covariate uncertainty

These typically require specialized implementations of the core methods.

Summary: Point-Referenced Data

Key Takeaways

Gaussian Processes provide the theoretical foundation
SPDE offers exact GP representation with computational efficiency
Low-Rank and NN-GP are approximations that scale to large datasets
Splines provide a fast alternative framework
Choice depends on: dataset size, computational resources, and inference goals
Start simple (splines) and add complexity as needed
Always validate using spatial cross-validation
Check residuals for remaining spatial structure

Resources and Further Reading

Key Papers:

Lindgren et al. (2011) “An explicit link between Gaussian fields and GMRFs” JRSS-B
Datta et al. (2016) “Hierarchical Nearest-Neighbor Gaussian Process Models” JASA
Banerjee et al. (2008) “Gaussian predictive process models” JRSS-B
Wood (2017) Generalized Additive Models: An Introduction with R

Online Resources:

Books:

Banerjee et al. (2014) Hierarchical Modeling and Analysis for Spatial Data
Cressie & Wikle (2011) Statistics for Spatio-Temporal Data

2. Areal / Lattice Data

Aggregated data in discrete spatial units

What is Areal Data?

Definition: Data aggregated to discrete spatial units with defined boundaries.

Ecological Examples:

Species counts by county or grid cell
Disease prevalence by administrative unit
Land cover by hexagonal bins
Population density by watershed
Camera trap detections by site

Key characteristic: Space is partitioned into units; data are aggregated within units.

Areal Data: Visual Example

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Typical scenario:

20-5000 spatial units (polygons or grid cells)
Clear neighborhood structure
Count or aggregated data per unit
Want to account for spatial dependence

Common Questions

Are neighboring units more similar?
How do I borrow strength across space?
Can I smooth noisy estimates?
Should I worry about edge effects?

Areal Data: Neighborhood Structure

Critical concept: Who are my neighbors?

Common definitions:

Rook adjacency - shared border
Queen adjacency - shared border or corner
Distance-based - within threshold distance
k-nearest neighbors - k closest centroids

Adjacency Matrix Neighborhood structure encoded in matrix $\mathbf{W}$: $$w_{ij} = \begin{cases} 1 & \text{if } i \text{ and } j \text{ are neighbors} \ 0 & \text{otherwise} \end{cases}$$

Areal Data: Method 1 - CAR Models

Conditional Autoregressive (CAR) Models

Each area’s random effect depends on its neighbors:

$$w_i | \mathbf{w}{-i}, \tau^2 \sim \text{Normal}\left(\sum{j \sim i} b_{ij}w_j, \tau_i^2\right)$$

Most common: Intrinsic CAR (ICAR)

$$w_i | \mathbf{w}{-i}, \tau^2 \sim \text{Normal}\left(\frac{1}{n_i}\sum{j \sim i} w_j, \frac{\tau^2}{n_i}\right)$$

Where $n_i$ = number of neighbors of area $i$.

ICAR Model: Properties

Interpretation: Each area’s value is the average of its neighbors, plus noise.

Key properties:

Induces strong spatial smoothing
Prior is improper (infinite variance)
Requires sum-to-zero constraint: $\sum_i w_i = 0$
Variance proportional to $1/n_i$ (islands have high variance!)

Island Problem Areas with few neighbors have high uncertainty. Consider removing islands or using proper CAR.

Areal Data: Method 2 - Proper CAR

Adds spatial decay parameter $\rho$ to control smoothing strength:

$$w_i | \mathbf{w}{-i}, \tau^2, \rho \sim \text{Normal}\left(\frac{\rho}{n_i}\sum{j \sim i} w_j, \frac{\tau^2}{n_i}\right)$$

Properties:

$\rho = 0$ → independent random effects (no spatial structure)
$\rho \to 1$ → approaches ICAR (maximum smoothing)
Proper prior (finite variance)
Can estimate $\rho$ from data

When to use: When you want data to determine smoothing strength.

Areal Data: Method 3 - BYM Model

Besag-York-Mollié (BYM) Model - convolution of spatial and non-spatial effects:

$$w_i = u_i + v_i$$

Where:

$u_i \sim \text{ICAR}(\tau_u^2)$ → spatially structured
$v_i \sim \text{Normal}(0, \tau_v^2)$ → unstructured

Advantage: Separates spatial and non-spatial variation

BYM2 Parameterization

Modern version uses a mixing parameter $\phi \in [0,1]$: $$w_i = \frac{1}{\sqrt{\tau}}(\sqrt{\phi} \cdot u_i + \sqrt{1-\phi} \cdot v_i)$$ Easier to specify priors!

Areal Data: Method 4 - SAR Models

Simultaneous Autoregressive (SAR) Models

Different from CAR! Response depends on neighbors’ responses:

$$\mathbf{y} = \rho \mathbf{W}\mathbf{y} + \mathbf{X}\boldsymbol{\beta} + \boldsymbol{\epsilon}$$

Key difference:

CAR: Spatial dependence in random effects
SAR: Spatial dependence in response variable itself
SAR has “spillover” effects (feedback loops)

Implementation: spdep package in R

Areal Data: CAR vs SAR

Feature	CAR	SAR
What’s spatial?	Random effects	Response variable
Interpretation	Smoothing	Spillover/feedback
Computation	Easy (INLA)	More complex
Bayesian	Yes	Possible but harder
Ecology use	Very common	Less common

In Practice CAR models are much more common in ecology. SAR models used more in econometrics.

Areal Data: Method 5 - Spatial Eigenvectors

Moran’s Eigenvector Maps (MEM) / PCNM

Idea: Decompose spatial relationships into orthogonal eigenvectors

Create distance/connectivity matrix
Eigendecompose to get spatial filters
Use eigenvectors as covariates in regression

$$y_i = \beta_0 + \sum_{k=1}^K \beta_k \text{EV}_{ik} + \epsilon_i$$

Advantages:

Works with any spatial configuration
Frequentist framework (familiar to many ecologists)
Can select eigenvectors (forward selection)

Spatial Eigenvectors: Example

library(adespatial)
library(spdep)

# Create neighborhood structure
coords <- st_coordinates(st_centroid(polygons))
nb <- poly2nb(polygons)

# Generate spatial eigenvectors
mem <- scores.listw(nb2listw(nb), 
                    wt = coords,
                    MEM.autocor = "positive")

# Use in regression
model <- lm(abundance ~ covariate1 + covariate2 + 
            MEM1 + MEM2 + MEM3, data = mydata)

# Or select eigenvectors via forward selection
mem.sel <- forward.sel(y = mydata$abundance,
                       x = mem$vectors,
                       alpha = 0.05)

Areal Data: Comparison

Method	Framework	Smoothing	Flexibility	Best For
ICAR	Bayesian	Strong	Low	Small n, strong spatial signal
Proper CAR	Bayesian	Data-driven	Medium	Estimate smoothing strength
BYM/BYM2	Bayesian	Flexible	High	Separate spatial/non-spatial
SAR	Frequentist	Feedback	Medium	Spillover processes
MEM	Frequentist	Data-driven	High	Flexible, frequentist

Areal Data: R Example with INLA

library(INLA)
library(spdep)
library(sf)

# Load spatial data
regions <- st_read("regions.shp")

# Create neighborhood structure
nb <- poly2nb(regions)
nb2INLA("region.adj", nb)  # Save adjacency for INLA

# Prepare data
data <- st_drop_geometry(regions)
data$region_id <- 1:nrow(data)

# BYM2 model
formula <- cases ~ 
  covariate1 + 
  covariate2 +
  f(region_id, 
    model = "bym2",
    graph = "region.adj",
    hyper = list(
      prec = list(prior = "pc.prec", param = c(1, 0.01)),
      phi = list(prior = "pc", param = c(0.5, 0.5))
    ))

result <- inla(
  formula,
  family = "poisson",
  data = data,
  E = data$expected,  # For disease mapping
  control.predictor = list(compute = TRUE),
  control.compute = list(dic = TRUE, waic = TRUE)
)

# Extract spatial random effects
spatial_effects <- result$summary.random$region_id

# Map results
regions$fitted <- result$summary.fitted.values$mean
plot(regions["fitted"])

3. Point Pattern Data

When locations themselves are the response

What is Point Pattern Data?

Definition: The spatial locations of events/individuals are what we’re modeling.

Ecological Examples:

Tree locations in a forest plot
Animal sightings/occurrences
Nest locations of colonial birds
Burrow entrances
Lightning strike locations

Key characteristic: The pattern of locations is the phenomenon of interest, not a measurement at those locations.

Point Pattern: Visual Example

Key questions:

Random, clustered, or regular?
Does intensity vary across space?
What drives the spatial pattern?
Interaction between points?

Three Main Patterns

Random (CSR - Complete Spatial Randomness)
Clustered (aggregated, contagious)
Regular (dispersed, uniform, inhibition)

Point Pattern: Core Concept - Intensity

Intensity function $\lambda(\mathbf{s})$ = expected number of points per unit area at location $\mathbf{s}$

$$\mathbb{E}[N(A)] = \int_A \lambda(\mathbf{s}) d\mathbf{s}$$

Types:

Homogeneous: $\lambda(\mathbf{s}) = \lambda$ (constant)
Inhomogeneous: $\lambda(\mathbf{s})$ varies with location
Can depend on covariates: $\lambda(\mathbf{s}) = \exp(\mathbf{X}(\mathbf{s})\boldsymbol{\beta})$

Point Pattern: Method 1 - Inhomogeneous Poisson Process

Simplest model: Points are independent given intensity function

$$\lambda(\mathbf{s}) = \exp(\beta_0 + \beta_1 x_1(\mathbf{s}) + … + \beta_p x_p(\mathbf{s}))$$

Fitting:

Divide study area into grid/quadrats
Count points per cell
Fit Poisson GLM or use spatstat for continuous intensity

Assumption Points are independent - no interaction or clustering beyond what covariates explain.

Point Pattern: Method 2 - Cox Processes

Adds spatial random effects to intensity:

$$\lambda(\mathbf{s}) = \exp(\mathbf{X}(\mathbf{s})\boldsymbol{\beta} + w(\mathbf{s}))$$

Where $w(\mathbf{s})$ is a spatial Gaussian process.

Types:

Log-Gaussian Cox Process (LGCP) - GP on log-intensity
Shot-noise Cox Process - clusters around random parent points

Result: Accounts for residual spatial clustering not explained by covariates.

LGCP: Implementation Approaches

Approach 1: Discretize space

Divide area into fine grid
Model counts per cell with spatial random effects
Use INLA with SPDE

Approach 2: Integrated nested Laplace approximation

Direct likelihood for point pattern
Continuous intensity surface
Package: inlabru in R

Point Pattern: Method 3 - Interaction Models

For processes with point-to-point interaction:

$$\lambda(\mathbf{s} | \text{other points}) = \lambda(\mathbf{s}) \times g(\text{distances to other points})$$

Types:

Hard-core: Minimum distance between points
Strauss process: Inhibition within distance $r$
Geyer saturation: Limited inhibition strength

Use when: Points repel or attract each other (territoriality, facilitation).

Package: spatstat in R for interaction models

Point Pattern: Comparison

Model Type	Complexity	Interaction	Spatial Trend	Best For
Poisson	Low	None	Yes (via covariates)	Independent points
LGCP	Medium	Apparent (via GP)	Yes	Clustering from environment
Hard-core	Medium	Strong repulsion	Yes	Territorial species
Strauss	High	Tunable	Yes	Flexible interaction

Point Pattern: R Example with spatstat

library(spatstat)

# Create point pattern object
pp <- ppp(x = trees$x, y = trees$y,
          window = owin(c(0, 100), c(0, 100)))

# Test for complete spatial randomness
plot(envelope(pp, Kest))  # K-function envelope test

# Fit inhomogeneous Poisson with covariates
# Create spatial covariates
elevation_im <- as.im(elevation_raster)

# Fit model
fit_pois <- ppm(pp ~ elevation + I(elevation^2),
                covariates = list(elevation = elevation_im))

# Fit log-Gaussian Cox process
fit_lgcp <- kppm(pp ~ elevation,
                 clusters = "LGCP",
                 covariates = list(elevation = elevation_im))

# Compare models
AIC(fit_pois)
AIC(fit_lgcp)

# Predict intensity
pred_intensity <- predict(fit_lgcp)
plot(pred_intensity)

Point Pattern: R Example with inlabru

library(inlabru)
library(INLA)
library(sp)

# Prepare data
points <- SpatialPoints(cbind(trees$x, trees$y))
boundary <- SpatialPolygons(list(Polygons(list(Polygon(
  matrix(c(0,0, 100,0, 100,100, 0,100, 0,0), ncol=2, byrow=TRUE)
)), "boundary")))

# Create mesh
mesh <- inla.mesh.2d(
  boundary = boundary,
  max.edge = c(5, 10),
  cutoff = 2
)

# Define LGCP model with covariates
cmp <- coordinates ~ 
  Intercept(1) + 
  elevation(elevation_data, model = "linear") +
  spatial(coordinates, 
          model = inla.spde2.matern(mesh))

# Fit model
fit <- lgcp(
  cmp,
  data = points,
  domain = list(coordinates = mesh),
  options = list(control.inla = list(int.strategy = "eb"))
)

# Predict intensity surface
pred_coords <- expand.grid(
  x = seq(0, 100, length = 100),
  y = seq(0, 100, length = 100)
)

predictions <- predict(
  fit,
  pred_coords,
  ~ exp(Intercept + elevation + spatial)
)

3. Movement / Trajectory Data

GPS tracks, telemetry, and animal paths

What is Movement Data?

Definition: Locations of individuals tracked through time, creating paths or trajectories.

Ecological Examples:

GPS collars on mammals
Bird migration tracks
Fish acoustic telemetry
Insect flight paths
Marine turtle tracking

Key characteristic: Data have temporal and spatial structure; autocorrelation in both dimensions.

Movement Data: Visual Example

Typical data structure:

Individual ID
Timestamp
Longitude, Latitude
Environmental covariates
Behavioral state (sometimes)

Key Questions

What drives movement decisions?
How do animals select habitat?
Can we identify behavioral states?
What’s the home range?
How do individuals differ?

Movement Data: Unique Challenges

Multiple sources of autocorrelation:

Temporal: Location at $t$ depends on $t-1$
Spatial: Nearby environmental conditions are similar
Individual: Each animal has consistent behavior

Observation models:

GPS error (location uncertainty)
Irregular sampling intervals
Missing data

Standard spatial models don’t account for movement process!

Movement Data: Method 1 - Step Selection Functions

Idea: Model animal’s choice among available steps at each time point

$$w(\mathbf{s}t | \mathbf{s}{t-1}) \propto \exp(\beta_1 x_1(\mathbf{s}_t) + … + \beta_p x_p(\mathbf{s}_t))$$

Steps:

For each observed step, generate “available” alternative steps
Compare chosen vs available using conditional logistic regression
Covariates describe step characteristics (length, angle, habitat)

Interpretation: Habitat selection during movement

Step Selection Functions: Details

Used step: What the animal actually did $$\text{step length}, \quad \text{turn angle}, \quad \text{end habitat}$$

Available steps: What it could have done

Sample from movement kernel (e.g., gamma distribution for length)
Usually 10-100 available steps per used step

Model:

$$\text{Pr(step chosen)} = \frac{\exp(\mathbf{X}\boldsymbol{\beta})}{\sum_{j=1}^n \exp(\mathbf{X}_j\boldsymbol{\beta})}$$

SSF: R Example

library(amt)
library(survival)

# Prepare tracking data
track <- make_track(gps_data, 
                    .x = x, .y = y, .t = timestamp,
                    id = animal_id, crs = 32612)

# Create steps
steps <- track %>% 
  steps_by_burst()

# Generate random available steps (20 per used)
rand_steps <- steps %>%
  random_steps(n_control = 20)

# Extract habitat at end of each step
rand_steps <- rand_steps %>%
  extract_covariates(habitat_raster) %>%
  extract_covariates(elevation_raster)

# Fit conditional logistic regression (step selection function)
ssf_model <- rand_steps %>%
  fit_clogit(case_ ~ 
               forest + 
               elevation +
               I(elevation^2) +
               sl_ + log_sl_ +  # Step length terms
               cos_ta_ +         # Turn angle
               strata(step_id_))

summary(ssf_model)

Movement Data: Method 2 - State-Space Models

Two-level model:

Movement process (latent true locations): $$\mathbf{s}t = \mathbf{s}{t-1} + \mathbf{v}_t$$
Observation process (GPS measurements): $$\mathbf{y}_t = \mathbf{s}_t + \boldsymbol{\epsilon}_t$$

Advantages:

Accounts for GPS error
Handles irregular sampling
Can incorporate behavior
Smooths erratic paths

State-Space Models: Behavioral States

Hidden Markov Models (HMM): Switch between behavioral states

Example states:

Encamped (resting, feeding)
Exploratory (traveling, migrating)

Each state has different movement parameters:

State 1: Short steps, high turning
State 2: Long steps, low turning

$$\begin{bmatrix} p_{11} & p_{12} \ p_{21} & p_{22} \end{bmatrix} \leftarrow \text{Transition matrix}$$

State-Space Model: R Example

library(momentuHMM)

# Prepare data
data <- prepData(gps_data,
                 type = "UTM",
                 coordNames = c("x", "y"))

# Define 2-state model parameters
# State 1: encamped, State 2: traveling

# Step length distributions (Gamma)
stepPar0 <- c(
  mean_state1 = 100,   # Short steps when encamped
  mean_state2 = 1000,  # Long steps when traveling
  sd_state1 = 50,
  sd_state2 = 500
)

# Turning angle distributions (von Mises)
anglePar0 <- c(
  mean_state1 = 0,     # Random turning
  mean_state2 = 0,     # Directional
  concentration_state1 = 0.1,  # Low concentration
  concentration_state2 = 0.8   # High concentration
)

# Fit HMM
hmm_fit <- fitHMM(
  data = data,
  nbStates = 2,
  dist = list(step = "gamma", angle = "vm"),
  Par0 = list(step = stepPar0, angle = anglePar0)
)

# Decode states (Viterbi algorithm)
states <- viterbi(hmm_fit)

# Plot results
plot(hmm_fit)
plotStates(hmm_fit)

Movement Data: Method 3 - Continuous-Time Models

Problem: Discrete-time models assume regular intervals

Solution: Continuous-time movement models handle irregular sampling

Common approach: Ornstein-Uhlenbeck process

$$d\mathbf{s}_t = -\mathbf{B}(\mathbf{s}_t - \boldsymbol{\mu})dt + \boldsymbol{\Sigma}^{1/2}d\mathbf{W}_t$$

Interpretation:

$\boldsymbol{\mu}$ = “home range center”
$\mathbf{B}$ = rate of return to center
$\boldsymbol{\Sigma}$ = movement variability

Continuous-Time: ctmm Package

library(ctmm)

# Prepare telemetry object
telemetry_obj <- as.telemetry(gps_data)

# Fit variogram (explore temporal autocorrelation)
vario <- variogram(telemetry_obj)
plot(vario)

# Guess movement model parameters
guess <- ctmm.guess(telemetry_obj, 
                    interactive = FALSE)

# Fit Ornstein-Uhlenbeck model
fit_ou <- ctmm.fit(telemetry_obj, guess)

# Calculate home range
home_range <- akde(telemetry_obj, fit_ou)
plot(home_range)

# Summary
summary(fit_ou)
summary(home_range)

Movement Data: Method 4 - Integrated Models

Combine step selection with state-space models

Advantages:

SSF for habitat selection
State-space for behavioral states and GPS error
Best of both worlds!

Implementation:

bsam package (Bayesian state-space models)
Custom JAGS/Stan models
crawl package for state-space + habitat

Movement Data: Comparison

Method	Temporal Structure	Habitat Selection	Behavioral States	GPS Error
SSF	Discrete steps	Yes	No	No
HMM	Discrete time	Possible	Yes	No
State-Space	Continuous	Limited	Yes	Yes
Integrated	Both	Yes	Yes	Yes

Start Simple Begin with SSF for habitat selection, add state-space if GPS error or behavioral switching is important.

Integrating Across Data Types

Real ecological studies often involve multiple spatial data types

Multi-Scale / Multi-Type Integration

Common scenarios:

Point + Areal: Bird surveys (points) + land cover (areal)
Point + Movement: Home range (movement) + resource selection (points)
Point Pattern + Covariates: Tree locations + soil maps
Movement + Remote Sensing: Tracks + NDVI time series

The Challenge Different data types have different spatial support, resolution, and uncertainty.

Integration Approach 1: Change of Support

Problem: Data at different spatial scales

Example:

Response: Point-level species occurrence
Covariates: Areal land cover by county

Solutions:

Aggregate points to areal units (lose resolution)
Downscale areal to points (assumes homogeneity)
Model explicitly with geostatistical fusion (best but complex)

Integration Approach 2: Joint Likelihood Models

Idea: Model all data types simultaneously with shared parameters

Example: Integrated Species Distribution Model (ISDM)

Data source 1: Structured surveys (presence-absence)
Data source 2: Opportunistic sightings (presence-only)
Data source 3: Telemetry tracks (habitat use)

Shared parameter: True intensity/abundance surface $\lambda(\mathbf{s})$

Implementation: PointedSDMs package in R

Integration Approach 3: Spatial Misalignment

When spatial supports don’t match:

$$\begin{aligned} \text{Point data:} \quad & Y_p(\mathbf{s}) = f(\lambda(\mathbf{s})) \ \text{Areal data:} \quad & Y_A(R) = g\left(\int_R \lambda(\mathbf{s})d\mathbf{s}\right) \end{aligned}$$

Model latent process $\lambda(\mathbf{s})$ and link to both:

Point process observation model
Aggregation to areal support

Package: areal in R for interpolation

Practical Integration Example

Scenario: Modeling lynx habitat selection

Data sources:

GPS telemetry (movement data)
Camera trap detections (point pattern)
Forest cover by grid cell (areal)
Prey abundance point surveys (point-referenced)

Approach:

Use SSF for fine-scale habitat selection from GPS
Use LGCP for broad-scale intensity from cameras
Extract covariates from areal forest data
Create prey abundance surface from surveys (kriging/SPDE)
Link all through shared spatial random effect

Model Selection and Validation

How do we choose and evaluate spatial models?

Validation for Spatial Data

Critical Issue

Standard cross-validation is wrong for spatial data!

Nearby points are autocorrelated, so “test” data are not independent of “training” data.

Solution: Spatial cross-validation

Spatial blocking: Leave out spatial blocks
Leave-location-out: Remove all data near test points
Clustered CV: Remove spatial clusters

Spatial Cross-Validation Methods

1. Spatial blocking:

library(blockCV)

# Create spatial blocks
blocks <- cv_spatial(
  x = species_data,
  column = "presence",
  k = 5,              # 5-fold CV
  size = 50000,       # 50km blocks
  selection = "random"
)

# Use blocks for CV
cv_results <- cv_spatial_fold(
  model, blocks
)

2. Leave-region-out:

Hold out entire regions (e.g., one study site)
Tests spatial transferability

Model Comparison for Spatial Models

Information criteria:

WAIC (Widely Applicable IC) - for Bayesian models
DIC - Bayesian, but can be problematic for spatial models
Spatial AIC - adjusts for effective sample size

Predictive performance:

Mean squared prediction error (MSPE)
Continuous ranked probability score (CRPS)
Log-likelihood on held-out data

Best Practice Use spatial CV + predictive metrics, not just information criteria.

Diagnosing Spatial Model Fit

Check residual autocorrelation:

Moran’s I test

library(spdep)
moran.test(residuals(model), nb2listw(neighbors))

Variogram of residuals

library(gstat)
v <- variogram(residuals ~ 1, locations = ~x+y, data = data)
plot(v)  # Should be flat if spatial structure captured

Spatial correlograms

Residuals should show no spatial autocorrelation if model is adequate!

Computational Considerations

Data Type	Typical n	Fast Methods	Slow but Flexible
Point-referenced	100-10,000	Splines (mgcv), SPDE	Full GP
Areal	50-5,000	CAR (INLA), MEM	Complex interaction models
Point pattern	100-100,000	Discretized LGCP	True LGCP
Movement	1,000-100,000	SSF, HMM	State-space, continuous-time

Modern Trend Approximate methods (INLA, SPDE, low-rank GPs) make complex spatial models feasible for large ecological datasets.

Software Summary

R Packages by data type:

Point-referenced: mgcv, INLA, inlabru, gstat, spBayes

Areal: INLA, CARBayes, spdep, adespatial

Point pattern: spatstat, inlabru, ppmlasso

Movement: amt, momentuHMM, ctmm, crawl, adehabitatLT

Multi-purpose: sf, terra, sp (spatial data structures)

Recommended Workflow

Visualize data - maps, variograms, spatial correlograms
Start simple - non-spatial model, then add space
Choose method based on data type and question
Fit multiple models - compare approaches
Validate spatially - spatial CV or block CV
Diagnose residuals - check for remaining spatial structure
Interpret carefully - spatial effects ≠ causal mechanisms

Don’t Overfit! More complex ≠ better. Use simplest model that captures spatial structure.

Common Pitfalls

Watch Out For:

Confounding: Spatial effects absorbing true covariate effects
Edge effects: Boundary areas have high uncertainty
Non-stationarity: Spatial structure varies across landscape
Anisotropy: Spatial correlation depends on direction
Computational limits: Be realistic about data size
Over-smoothing: Too much spatial structure removes real variation
Ignoring uncertainty: Report it! Especially for predictions

When to Use Each Data Type Approach

Use point-referenced methods when:

You have lat/lon coordinates from surveys
Want to predict continuous surface
Sample size: 50-10,000 locations

Use areal methods when:

Data aggregated to regions/cells
Clear neighborhood structure
Often: count data, rare events

Use point pattern methods when:

Locations are the outcome of interest
Want to model spatial intensity
Concern: point-to-point interactions

Use movement methods when:

Tracking individuals over time
Interested in habitat selection during movement
Need to account for temporal autocorrelation

Future Directions

Emerging methods:

Deep learning for spatial prediction
Spatial conformal inference (uncertainty quantification)
Multi-resolution models
Non-stationary spatial processes
Real-time spatial models (citizen science, camera traps)
Massive-scale spatial models (>1 million locations)

Software development:

Better integration across data types
User-friendly interfaces for complex models
Improved computational efficiency

Key Takeaways

Remember:

Data structure determines appropriate methods - identify your data type first
Start simple, add complexity as needed - parsimony matters
Validate spatially - standard CV fails with spatial data
Computational feasibility - consider dataset size and resources
Interpretation - spatial structure helps prediction but may obscure causation
Residual diagnostics - always check for remaining spatial autocorrelation

Resources

Books:

Banerjee et al. (2014) Hierarchical Modeling and Analysis for Spatial Data
Cressie & Wikle (2011) Statistics for Spatio-Temporal Data
Illian et al. (2008) Statistical Analysis and Modelling of Spatial Point Patterns

Papers:

Lindgren et al. (2011) “An explicit link between Gaussian fields and Gaussian Markov random fields” JRSS-B
Hooten et al. (2017) “Animal Movement Models for Spatial Ecology” Wiley

Online:

Questions?

Contact & Discussion

Email: jbaecher@gmail.com
Website: www.alexbaecher.com
GitHub: @slamander

What spatial data challenges are you facing?

Let’s discuss specific applications to your research!

Thank you!

Spatstat | Alex Baecher

Spatial Structures in Ecological Models

What this document will cover:

Why include spatial structures in your model?

Tobler’s First law Geography (a.k.a. spatial autocorrelation):

Further: Ignoring spatial autocorrelation leads to…

How to account for spatial autocorrelation in my model?

First, what type of spatial data do you have?

Data types:

1. Point-Referenced Data

What is Point-Referenced Data?

A visual example

Point-Referenced data: The Core Challenge

Point-Referenced data: Five Approaches

Understanding the GP Family Tree

Conceptual Relationships

Method 1: Full Gaussian Processes

Full GP: Covariance Functions

Practical Advice

Full GP: The Computational Problem

This is why we need approximations!

Full GP: R Implementation

Method 2: SPDE Approximation

SPDE: How It Works

SPDE: R Implementation with sdmTMB

SPDE: R Implementation with INLA

SPDE: Making Predictions

Method 3: Low-Rank Gaussian Processes

Low-Rank GP: Predictive Processes

Low-Rank GP: R Implementation

Low-Rank GP: Alternative with FRK

Method 4: Nearest Neighbor Gaussian Processes

Nearest Neighbor GP: How It Works

The ordering of your data affects results!

Nearest Neighbor GP: R package spNNGP

Nearest Neighbor GP: R package spAbundance

Method 5: Spatial Splines

Spatial Splines: Types

When to Use Splines

Spatial Splines: R Implementation with mgcv

Spatial Splines: Tensor Products

Point-Referenced: Comprehensive Comparison

Key R packages

Common Pitfalls and Solutions

Watch Out For:

Validation and Model Checking

Integration with Covariates

Interpretation Challenge

Advanced Topics

Summary: Point-Referenced Data

Resources and Further Reading

2. Areal / Lattice Data

What is Areal Data?

Areal Data: Visual Example

Common Questions

Areal Data: Neighborhood Structure

Areal Data: Method 1 - CAR Models

ICAR Model: Properties

Areal Data: Method 2 - Proper CAR

Areal Data: Method 3 - BYM Model

BYM2 Parameterization

Areal Data: Method 4 - SAR Models

Areal Data: CAR vs SAR

Areal Data: Method 5 - Spatial Eigenvectors

Spatial Eigenvectors: Example

Areal Data: Comparison

Areal Data: R Example with INLA

3. Point Pattern Data

What is Point Pattern Data?

Point Pattern: Visual Example

Point Pattern: Core Concept - Intensity

Point Pattern: Method 1 - Inhomogeneous Poisson Process

Point Pattern: Method 2 - Cox Processes

LGCP: Implementation Approaches

Point Pattern: Method 3 - Interaction Models

Point Pattern: Comparison

Point Pattern: R Example with spatstat

Point Pattern: R Example with inlabru

3. Movement / Trajectory Data

What is Movement Data?

SPDE: R Implementation with `sdmTMB`

SPDE: R Implementation with `INLA`

Low-Rank GP: Alternative with `FRK`

Nearest Neighbor GP: R package `spNNGP`

Nearest Neighbor GP: R package `spAbundance`