1 Introduction to spatial data

Learning goals

learn the different data types considerd in spatial statistics, and get familiar with simple features, as well as with the difference between discrete and continuous variability in space and in attributes
learn what spatial dependence is about, and cases where it can be ignored
introduce spatial point patterns and processes

Reading materials

From Spatial Data Science: with applications in R:

Preface
Chapter 1: Getting started
Chapter 2: Coordinates
Chapter 10: Statistical modelling of spatial data

Exercises that can be prepared for the first day:

Section 10.6: Exercises

Summary

Introduction to spatial data, support, coordinate reference systems
Introduction to spatial statistical data types: point patterns, geostatistical data, lattice data; imagery, tracks/trajectories
Is spatial dependence a fact? And is it a curse, or a blessing?
Spatial sampling, design-based and model-based inference
Intro to point patterns and point processes, observation window, first and second order properties
Checklist for your spatial data

1.1 What is special about spatial data?

Coordinates. What are coordinates? Dimension(s)?
Location. Does location always involve coordinates?
Time. If not explicit, there is an implicit time reference. Dimension(s)?
Attributes. at specific locations we measure specific properties
Quite often, we want to know where things change (space-time interactions).
Reference systems for space, time, and attributes: what are they?
Support: if we have an attribute value associated with a line, polygon or grid cell:
- does the value summarise all values at points? (line/area/cell support), or
- is the value constant throughout the line/area/cell (point support)?
Continuity:
- is a variable spatially continuous? Yes for geostatitical data, no for point patterns
- is an attribute variable continuous? Stevens’s measurement scales: yes if Interval or Ratio.

Support: examples

Road properties
- road type: gravel, brick, asphalt (point support: everywhere on the whole road)
- mean width: block support (summary value)
- minimum width: block support (although the minimum width may be the value at a single (point) location, it summarizes all widths of the road–we no longer know the width at any specific point location)
Land use/land cover
- when we classify e.g. 30 m x 30 m Landsat pixels into a single class, this single class is not constant throughout this pixel
- road type is a land cover type, but a road never covers a 30 m x 30 m pixel
- a land cover type like “urban” is associated with a positive (non-point) support: we don’t say a point in a garden or park is urban, or a point on a roof, but these are part of a (block support) urban fabric
Elevation
- in principle, we can measure elevation at a point; in practice, every measuring device has a physical (non-point) size
Further reading: Chapter 5: Attributes and Support

1.2 Spatial vs. Geospatial

Spatial refers (physical) spaces, 2- or 3-dimensional ($R^2$ or $R^3$)
- Most often spatial statistics considers 2-dimensional problems
- 3-d: meteorology, climate science, geophysics, groundwater hydrology, aeronautics, …
“Geo” refers to the Earth
For Earth coordinates, we always need a datum, consisting of an ellipsoid (shape) and the way it is fixed to the Earth (origin)
- The Earth is modelled by an ellipsoid, which is nearly round
- If we consider Earth-bound areas as flat, for larger areas we get the distances wrong
- We can (and do) also work on $S^2$, the surface of a sphere, rather than $R^2$, to get distances right, but this creates a number of challenges (such as plotting on a 2D device)
Non-geospatial spaces could be:
- Associated with other bodies (moon, Mars)
- Astrophysics, places/directions in the universe
- Locations in a building (where we use “engineering coordinates”, relative to a building corner and orientation)
- Microscope images
- MRT scans (3-D), places in a human body
- locations on a genome?

Code

library(rnaturalearth)
library(sf)
# Linking to GEOS 3.12.1, GDAL 3.8.3, PROJ 9.3.1; sf_use_s2() is TRUE
par(mar = c(2,2,0,0) + .1)
ne_countries() |> st_geometry() |> plot(axes=TRUE)

world map, with longitude and latitude map linearly to x and y (Plate Caree)

What is Statistics

… or what are statistics?

statistic: a descriptive measure summarising some data
Statistics: a scientific disciplin aiming at modelling data, using probability theory
- where does randomness come from? Design-based vs. model-based
- are parameters random or fixed? Bayesian vs. frequentist
- inference, prediction, simulations
Typical approach: observation = signal + noise, noise modelled by random variables

Design-based statistics

In design-based statistics, randomness comes from random sampling. Consider an area $B$, from which we take samples \[z(s), s \in B,\] with $s$ a location for instance two-dimensional: $s_i = \{x_i,y_i\}$. If we select the samples randomly, we can consider $S \in B$ a random variable, and $z(S)$ a random sample. Note the randomness in $S$, not in $z$.

Two variables $z(S_1)$ and $z(S_2)$ are independent if $S_1$ and $S_2$ are sampled independently. For estimation we need to know the inclusion probabilities, which need to be non-negative for every location.

If inclusion probabilities are constant (simple random sampling; or complete spatial randomness: day 2, point patterns) then we can estimate the mean of $Z(B)$ by the sample mean \[\frac{1}{n}\sum_{j=1}^n z(s_j).\] This also predicts the value of a randomly chosen observation $z(S)$. It cannot be used to predict the value $z(s_0)$ for a non-randomly chosen location $s_0$; for this we need a model.

Model-based statistics

Model-based statistics assumes randomness in the measured responses; consider a regression model $y = X\beta + e$, where $e$ is a random variable and as a consequence $y$, the response variable is a random variable. In the spatial context we replace $y$ with $z$, and capitalize it to indicate it is a random variable, and write \[Z(s) = X(s)\beta + e(s)\] to stress that

$Z(s)$ is a random function (random variables $Z$ as a function of $s$)
$X(s)$ is the matrix with covariates, which depend on $s$
$\beta$ are (spatially) constant coefficients, not depening on $s$
$e(s)$ is a random function with mean zero and covariance matrix $\Sigma$

In the regression literature this is called a (linear) mixed model, because $e$ is not i.i.d. If $e(s)$ contains an iid component $\epsilon$ we can write this as

\[Z(s) = X(s)\beta + w(s) + \epsilon\]

with $w(s)$ the spatial signal, and a noise compenent e.g. due to measurement error.

Predicting $Z(s_0)$ will involve (GLS) estimation of $\beta$, but also prediction of $e(s_0)$ using correlated, nearby observations (day 3: geostatistics).

Design- or model-based?

design-based requires a random sample, if that is the case it needs no further assumptions
model-based requires stationarity assumptions to estimate $\Sigma$
model-based is typically more effective for interpolation problems
design-based can be most effective when estimation e.g. average mapping errors

Using coordinates as covariates?

(day 4)

1.3 Spatial statistics: data types

Point Patterns

Points (locations) + observation window
Example from here

Figure 1.1: Wind turbine parks in Germany

The locations contain the information
Points may have (discrete or continuous) marks (attributes)
The observation window is, apart from the points, empty

Geostatistical data

2 locations + measured values

Code

library(sf)
no2 <- read.csv(system.file("external/no2.csv",
    package = "gstat"))
crs <- st_crs("EPSG:32632")
st_as_sf(no2, crs = "OGC:CRS84", coords =
    c("station_longitude_deg", "station_latitude_deg")) |>
    st_transform(crs) -> no2.sf
library(ggplot2)
# plot(st_geometry(no2.sf))
"https://github.com/edzer/sdsr/raw/main/data/de_nuts1.gpkg" |>
  read_sf() |>
  st_transform(crs) -> de
ggplot() + geom_sf(data = de) +
    geom_sf(data = no2.sf, mapping = aes(col = NO2))

NO2 measurements at rural background stations (EEA)

The value of interest is measured at a set of sample locations
At other location, this value exists but is missing
The interest is in estimating (predicting) this missing value (interpolation)
The actual sample locations are not of (primary) interest, the signal is in the measured values

Areal data

polygons (or grid cells) + polygon summary values

Code

# https://en.wikipedia.org/wiki/List_of_NUTS_regions_in_the_European_Union_by_GDP
de$GDP_percap = c(45200, 46100, 37900, 27800, 49700, 64700, 45000, 26700, 36500, 38700, 35700, 35300, 29900, 27400, 32400, 28900)
ggplot() + geom_sf(data = de) +
    geom_sf(data = de, mapping = aes(fill = GDP_percap)) + 
    geom_sf(data = st_cast(de, "MULTILINESTRING"), col = 'white')

NO2 rural background, average values per NUTS1 region

The polygons contain polygon summary (polygon support) values, not values that are constant throughout the polygon (as in a soil, lithology or land cover map)
Neighbouring polygons are typically related: spatial correlation
neighbour-neighbour correlation: Moran’s I
regression models with correlated errors, spatial lag models, CAR models, GMRFs, …
see Ch 14-17 of SDSWR
mostly skipped in this course

2.1 Data types that received less attention in the spatial statistics literature

Image data

Code

library(stars)
# Loading required package: abind
plot(L7_ETMs, rgb = 1:3)

are these geostatistical data, or areal data?
If we identify objects from images, can we see them as point patterns?

Tracking data, trajectories

Code

# from: https://r-spatial.org/r/2017/08/28/nest.html
library(tidyverse)
# ── Attaching core tidyverse packages ──────────── tidyverse 2.0.0 ──
# ✔ dplyr     1.1.4     ✔ readr     2.1.5
# ✔ forcats   1.0.0     ✔ stringr   1.5.1
# ✔ lubridate 1.9.3     ✔ tibble    3.2.1
# ✔ purrr     1.0.2     ✔ tidyr     1.3.1
# ── Conflicts ────────────────────────────── tidyverse_conflicts() ──
# ✖ dplyr::filter() masks stats::filter()
# ✖ dplyr::lag()    masks stats::lag()
# ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
storms.sf <- storms %>%
    st_as_sf(coords = c("long", "lat"), crs = 4326)
storms.sf <- storms.sf %>% 
    mutate(time = as.POSIXct(paste(paste(year,month,day, sep = "-"), 
                                   paste(hour, ":00", sep = "")))) %>% 
    select(-month, -day, -hour)
storms.nest <- storms.sf %>% group_by(name, year) %>% nest
to_line <- function(tr) st_cast(st_combine(tr), "LINESTRING") %>% .[[1]] 
tracks <- storms.nest %>% pull(data) %>% map(to_line) %>% st_sfc(crs = 4326)
storms.tr <- storms.nest %>% select(-data) %>% st_sf(geometry = tracks)
storms.tr %>% ggplot(aes(color = year)) + geom_sf()

Storm/hurricane trajectories colored by year

A temporal snapshot (time slice) of a set of moving things forms a point pattern
We often analyse trajectories by
- estimating densities, for space-time blocks, per individual or together
- analyising interactions (alibi problem, mating animals, home range, UDF etc)

2.2 Checklist if you have spatial data

Do you have the spatial coordinates of your data?
Are the coordinates Earth-bound?
If yes, do you have the coordinate reference system of them?
What is the support (physical size) of your observations?
Were the data obtained by random sampling, and if yes, do you have sampling weights?
Do you know the extent ($B$) from which your data were sampled, or collected?

2.3 Exercises for Day 2

See day 2 slides.

2.4 Further reading

Ripley, B. 1981. Spatial Statistics. Wiley.
Cressie, N. 1993. Statistics for Spatial Data. Wiley.
Cochran, W.G. 1977. Sampling Techniques. Wiley.

# Introduction to spatial data ### Learning goals * learn the different data types considerd in spatial statistics, and get familiar with simple features, as well as with the difference between discrete and continuous variability in _space_ and in _attributes_ * learn what spatial dependence is about, and cases where it can be ignored * introduce spatial point patterns and processes ### Reading materials From [Spatial Data Science: with applications in R](https://r-spatial.org/book/): * Preface * Chapter 1: Getting started * Chapter 2: Coordinates * Chapter 10: Statistical modelling of spatial data ### Exercises that can be prepared for the first day: * Section 10.6: Exercises ::: {.callout-tip title="Summary"} * Introduction to spatial data, support, coordinate reference systems * Introduction to spatial statistical data types: point patterns, geostatistical data, lattice data; imagery, tracks/trajectories * Is spatial dependence a fact? And is it a curse, or a blessing? * Spatial sampling, design-based and model-based inference * Intro to point patterns and point processes, observation window, first and second order properties * Checklist for your spatial data ::: ## What is special about spatial data? * **Coordinates**. What are coordinates? Dimension(s)? * **Location**. Does location always involve coordinates? * **Time**. If not explicit, there is an implicit time reference. Dimension(s)? * **Attributes**. _at_ specific locations we measure specific properties * Quite often, we want to know _where_ things change (**space-time interactions**). * **Reference systems** for space, time, and attributes: what are they? * **Support**: if we have an attribute value associated with a line, polygon or grid cell: * does the value summarise all values at points? (line/area/cell support), or * is the value constant throughout the line/area/cell (point support)? * **Continuity**: * is a variable _spatially_ continuous? Yes for geostatitical data, no for point patterns * is an _attribute variable_ continuous? [Stevens's measurement scales](https://www.science.org/doi/pdf/10.1126/science.103.2684.677?casa_token=H8am2h3sIYUAAAAA:eUd2ZU6ZRyJNqx4jdRv0E9WG7k3OBXAVbqgZ2O-Bl7pHNJSI0L2h9TM6i3YXve2nY5rD_4RbI2aecQ): yes if _Interval_ or _Ratio_. ### Support: examples * Road properties * road type: gravel, brick, asphalt (point support: everywhere on the whole road) * mean width: block support (summary value) * minimum width: block support (although the minimum width may be the value at a single (point) location, it summarizes all widths of the road--we no longer know the width at any specific point location) * Land use/land cover * when we classify e.g. 30 m x 30 m Landsat pixels into a single class, this single class is not constant throughout this pixel * road type is a land cover type, but a road never covers a 30 m x 30 m pixel * a land cover type like "urban" is associated with a positive (non-point) support: we don't say a point in a garden or park is urban, or a point on a roof, but these are part of a (block support) urban fabric * Elevation * in principle, we can measure elevation at a point; in practice, every measuring device has a physical (non-point) size * Further reading: [Chapter 5: Attributes and Support](https://r-spatial.org/book/05-Attributes.html) ## Spatial vs. Geospatial * Spatial refers (physical) spaces, 2- or 3-dimensional ($R^2$ or $R^3$) * Most often spatial statistics considers 2-dimensional problems * 3-d: meteorology, climate science, geophysics, groundwater hydrology, aeronautics, ... * "Geo" refers to the Earth * For Earth coordinates, we always need a _datum_, consisting of an ellipsoid (shape) and the way it is fixed to the Earth (origin) * The Earth is modelled by an ellipsoid, which is nearly round * If we consider Earth-bound areas as flat, for larger areas we get the distances wrong * We can (and do) also work on $S^2$, the surface of a sphere, rather than $R^2$, to get distances right, but this creates a number of challenges (such as plotting on a 2D device) * Non-geospatial spaces could be: * Associated with other bodies (moon, Mars) * Astrophysics, places/directions in the universe * Locations in a building (where we use "engineering coordinates", relative to a building corner and orientation) * Microscope images * MRT scans (3-D), places in a human body * locations on a genome? ```{r} #| code-fold: true #| out.width: '100%' #| fig.cap: "world map, with longitude and latitude map linearly to x and y ([Plate Caree](https://en.wikipedia.org/wiki/Equirectangular_projection))" library(rnaturalearth) library(sf) par(mar = c(2,2,0,0) + .1) ne_countries() |> st_geometry() |> plot(axes=TRUE) ``` ::: {.callout-tip title="What is Statistics"} ... or what _are_ statistics? * statistic: a descriptive measure summarising some data * Statistics: a scientific disciplin aiming at modelling data, using probability theory * where does randomness come from? Design-based vs. model-based * are parameters random or fixed? Bayesian vs. frequentist * inference, prediction, simulations * Typical approach: observation = signal + noise, noise modelled by random variables ::: ### Design-based statistics In design-based statistics, randomness comes from random sampling. Consider an area $B$, from which we take samples $$z(s), s \in B,$$ with $s$ a location for instance two-dimensional: $s_i = \{x_i,y_i\}$. If we select the samples _randomly_, we can consider $S \in B$ a random variable, and $z(S)$ a random sample. Note the randomness in $S$, not in $z$. Two variables $z(S_1)$ and $z(S_2)$ are _independent_ if $S_1$ and $S_2$ are sampled independently. For estimation we need to know the inclusion probabilities, which need to be non-negative for every location. If inclusion probabilities are constant (simple random sampling; or complete spatial randomness: day 2, point patterns) then we can estimate the mean of $Z(B)$ by the sample mean $$\frac{1}{n}\sum_{j=1}^n z(s_j).$$ This also predicts the value of a _randomly_ chosen observation $z(S)$. It cannot be used to predict the value $z(s_0)$ for a non-randomly chosen location $s_0$; for this we need a model. ### Model-based statistics Model-based statistics assumes randomness in the measured responses; consider a regression model $y = X\beta + e$, where $e$ is a random variable and as a consequence $y$, the response variable is a random variable. In the spatial context we replace $y$ with $z$, and capitalize it to indicate it is a random variable, and write $$Z(s) = X(s)\beta + e(s)$$ to stress that * $Z(s)$ is a random function (random variables $Z$ as a function of $s$) * $X(s)$ is the matrix with covariates, which depend on $s$ * $\beta$ are (spatially) constant coefficients, not depening on $s$ * $e(s)$ is a random function with mean zero and covariance matrix $\Sigma$ In the regression literature this is called a (linear) mixed model, because $e$ is not i.i.d. If $e(s)$ contains an iid component $\epsilon$ we can write this as $$Z(s) = X(s)\beta + w(s) + \epsilon$$ with $w(s)$ the spatial signal, and \epsilon a noise compenent e.g. due to measurement error. Predicting $Z(s_0)$ will involve (GLS) estimation of $\beta$, but also prediction of $e(s_0)$ using correlated, nearby observations (day 3: geostatistics). ### Design- or model-based? * design-based requires a random sample, if that is the case it needs no further assumptions * model-based requires stationarity assumptions to estimate $\Sigma$ * model-based is typically more effective for interpolation problems * design-based can be most effective when estimation e.g. average mapping errors ### Using coordinates as covariates? * (day 4) ## Spatial statistics: data types ### Point Patterns * Points (locations) + observation window * Example from [here](https://opendata-esri-de.opendata.arcgis.com/datasets/dc6d012f47d94fde99deacc316721f30/explore?location=51.099061%2C10.453852%2C7.45) ```{r fig-gdal-fig-nodetails, echo = FALSE} #| code-fold: true #| out.width: '100%' #| fig.cap: "Wind turbine parks in Germany" knitr::include_graphics("turbines.png") ``` * The locations contain the information * Points may have (discrete or continuous) _marks_ (attributes) * The observation window is, apart from the points, _empty_ ### Geostatistical data # locations + measured values ```{r} #| code-fold: true #| out.width: '100%' #| fig.cap: "NO2 measurements at rural background stations (EEA)" library(sf) no2 <- read.csv(system.file("external/no2.csv", package = "gstat")) crs <- st_crs("EPSG:32632") st_as_sf(no2, crs = "OGC:CRS84", coords = c("station_longitude_deg", "station_latitude_deg")) |> st_transform(crs) -> no2.sf library(ggplot2) # plot(st_geometry(no2.sf)) "https://github.com/edzer/sdsr/raw/main/data/de_nuts1.gpkg" |> read_sf() |> st_transform(crs) -> de ggplot() + geom_sf(data = de) + geom_sf(data = no2.sf, mapping = aes(col = NO2)) ``` * The value of interest is measured at a set of sample locations * At other location, this value exists but is _missing_ * The interest is in estimating (predicting) this missing value (interpolation) * The actual sample locations are not of (primary) interest, the signal is in the measured values ### Areal data * polygons (or grid cells) + polygon summary values ```{r} #| code-fold: true #| out.width: '100%' #| fig.cap: "NO2 rural background, average values per NUTS1 region" # https://en.wikipedia.org/wiki/List_of_NUTS_regions_in_the_European_Union_by_GDP de$GDP_percap = c(45200, 46100, 37900, 27800, 49700, 64700, 45000, 26700, 36500, 38700, 35700, 35300, 29900, 27400, 32400, 28900) ggplot() + geom_sf(data = de) + geom_sf(data = de, mapping = aes(fill = GDP_percap)) + geom_sf(data = st_cast(de, "MULTILINESTRING"), col = 'white') ``` * The polygons contain polygon summary (polygon support) values, not values that are constant throughout the polygon (as in a soil, lithology or land cover map) * Neighbouring polygons are typically related: spatial correlation * neighbour-neighbour correlation: Moran's I * regression models with correlated errors, spatial lag models, CAR models, GMRFs, ... * see Ch 14-17 of [SDSWR](https://r-spatial.org/book/) * mostly skipped in this course ## Data types that received less attention in the spatial statistics literature ### Image data ```{r} #| code-fold: true #| out.width: '100%' #| fig.cap: "RGB image from a Landsat scene" library(stars) plot(L7_ETMs, rgb = 1:3) ``` * are these geostatistical data, or areal data? * If we identify objects from images, can we see them as point patterns? ### Tracking data, trajectories ```{r} #| code-fold: true #| out.width: '100%' #| fig.cap: "Storm/hurricane trajectories colored by year" # from: https://r-spatial.org/r/2017/08/28/nest.html library(tidyverse) storms.sf <- storms %>% st_as_sf(coords = c("long", "lat"), crs = 4326) storms.sf <- storms.sf %>% mutate(time = as.POSIXct(paste(paste(year,month,day, sep = "-"), paste(hour, ":00", sep = "")))) %>% select(-month, -day, -hour) storms.nest <- storms.sf %>% group_by(name, year) %>% nest to_line <- function(tr) st_cast(st_combine(tr), "LINESTRING") %>% .[[1]] tracks <- storms.nest %>% pull(data) %>% map(to_line) %>% st_sfc(crs = 4326) storms.tr <- storms.nest %>% select(-data) %>% st_sf(geometry = tracks) storms.tr %>% ggplot(aes(color = year)) + geom_sf() ``` * A temporal snapshot (time slice) of a set of moving things forms a point pattern * We often analyse trajectories by * estimating densities, for space-time blocks, per individual or together * analyising interactions (alibi problem, mating animals, home range, UDF etc) ## Checklist if you have spatial data * Do you have the spatial coordinates of your data? * Are the coordinates Earth-bound? * If yes, do you have the coordinate reference system of them? * What is the support (physical size) of your observations? * Were the data obtained by random sampling, and if yes, do you have sampling weights? * Do you know the _extent_ ($B$) from which your data were sampled, or collected? ## Exercises for Day 2 See day 2 slides. ## Further reading * Ripley, B. 1981. Spatial Statistics. Wiley. * Cressie, N. 1993. Statistics for Spatial Data. Wiley. * Cochran, W.G. 1977. Sampling Techniques. Wiley.