Feature attributes refer to the properties of features (“things”) that do not describe the feature’s geometry. Feature attributes can be derived from geometry (such as length of a LINESTRING or area of a POLYGON), but they can also refer to non-derived properties, such as:
In some cases, time properties can be seen as attributes of features, for instance the date of birth of a person or the construction year of a road. When an attribute such as for instance air quality is a function of both space and time, time is best handled on equal footing with geometry (often in a data cube, see Chapter 6).
Spatial data science software implementing simple features typically organises data in tables that contain both geometries and attributes for features; this is true for geopandas in Python, PostGIS tables in PostgreSQL, and sf objects in R. The geometric operations described in Section 3.2 operate on geometries only, and may occasionally yield new attributes (predicates, measures, or transformations), but they do not operate on attributes present.
When, while manipulating geometries, attribute values are retained unmodified, support problems may arise. If we look into a simple case of replacing a county polygon with the centroid of that polygon on a dataset that has attributes, we see that R package sf issues a warning:
library(sf) |> suppressPackageStartupMessages()
library(dplyr) |> suppressPackageStartupMessages()
system.file("gpkg/nc.gpkg", package="sf") |>
read_sf() |>
st_transform(32119) |>
select(BIR74, SID74, NAME) |>
st_centroid() -> x# Warning: st_centroid assumes attributes are constant over
# geometriesThe reason for this is that the dataset contains variables with values that are associated with entire polygons – in this case population counts – meaning they are not associated with a POINT geometry replacing the polygon.
In Section 1.6 we already described that for non-point geometries (lines, polygons), feature attribute values either have point support, meaning that the value applies to every point, or they have block support, meaning that the value summarises all points in the geometry. (Other options, non-point support smaller than the geometry, or support larger than the associated geometry, may also occur.) This chapter will describe different ways in which an attribute may relate to the geometry, its consequences on analysing such data, and ways to derive attribute data for different geometries (up- and downscaling).
Changing the feature geometry without changing the feature attributes does change the feature, since the feature is characterised by the combination of geometry and attributes. Can we, ahead of time, predict whether the resulting feature will still meaningfully relate to the attribute value when we replace all geometries for instance with their convex hull or centroid? It depends.
Take the example of a road, represented by a LINESTRING, which has an attribute property road width equal to 10 m. What can we say about the road width of an arbitrary sub-section of this road? That depends on whether the attribute road length describes, for instance the road width everywhere, meaning that road width is constant along the road, or whether it describes an aggregate property, such as minimum or average road width. In case of the minimum, for an arbitrary subsection of the road one could still argue that the minimum road width must be at least as large as the minimum road width for the whole segment, but it may no longer be the minimum for that subsection. This gives us two “types” for the attribute-geometry relationship (AGR):
For polygon data, typical examples of constant AGR (point support) variables are:
A typical property of such variables is that they have geometries that are not man-made and also not associated with a sensor device (such as remote sensing image pixel boundaries). Instead, the geometry follows from mapping the variable observed.
Examples for the aggregate AGR (block support) variables are:
A typical property of such variables is that associated geometries come for instance from legislation, observation devices or analysis choices, but not intrinsically from the observed variable.
A third type of AGR arises when an attribute identifies a feature geometry; we call an attribute an identity variable when the associated geometry uniquely identifies the variable’s value (there are no other geometries with the same value). An example is county name: the name identifies the county, and is still the county for any sub-area (point support), but for arbitrary sub-areas, the attributes loses the identity property to become a constant attribute. An example is:
The challenge here is that spatial information (ignoring time for simplicity) belongs to different phenomena types (Scheider et al. 2016), including:
but that different spatial geometry types (points, lines, polygons, raster cells) have no simple mapping to these phenomena types:
Properly specifying attribute-geometry relationships, and warning against their absence or cases when change in geometry (change of support) implies a change of information can help to avoid a large class of common spatial data analysis mistakes (Stasch et al. 2014) associated with the support of spatial data.
Aggregating records in a table (or data.frame) involves two steps:
In SQL, this looks for instance like
SELECT GroupID, SUM(population) FROM table GROUP BY GroupID;indicating the aggregation function (SUM) and the grouping predicate (GroupID).
R package dplyr for instance uses two steps to accomplish this: function group_by specifies the group membership of records, summarise computes data summaries (such as sum or mean) for each of the groups. The (base) R method aggregate combines both in a single function call that takes the table, the grouping predicate(s), and the aggregation function(s) as arguments.
An example for the North Carolina counties is shown in Figure 5.1. Here, we grouped counties by their position (according to the quadrant in which the county centroid is with respect to ellipsoidal coordinate POINT(-79, 35.5)) and summed the number of disease cases per group. The result shows that the geometries of the resulting groups have been unioned (Section 3.2.6): this is necessary because the MULTIPOLYGON formed by just putting all the county geometries together would have many duplicate boundaries, and hence would not be valid (Section 3.1.2).
nc <- read_sf(system.file("gpkg/nc.gpkg", package = "sf"))
# encode quadrant by two logicals:
nc$lng <- st_coordinates(st_centroid(st_geometry(nc)))[,1] > -79
nc$lat <- st_coordinates(st_centroid(st_geometry(nc)))[,2] > 35.5
nc.grp <- aggregate(nc["SID74"], list(nc$lng, nc$lat), sum)
nc.grp["SID74"] |> st_transform('EPSG:32119') |>
plot(graticule = TRUE, axes = TRUE)
Plotting collated county polygons is technically not a problem, but for this case would raise the wrong suggestion that the group sums relate to individual counties, rather than to the grouped counties.
One particular property of aggregation in this way is that each record is assigned to a single group; this has the advantage that the sum of the group-wise sums equals the sum of the un-grouped data: for variables that reflect amount, nothing gets lost and nothing is added. The newly formed geometry is the result of unioning the geometries of the contributing records.
nc <- st_transform(nc, 2264)
gr <- st_sf(
label = apply(expand.grid(1:10, LETTERS[10:1])[,2:1], 1, paste0, collapse = " "),
geom = st_make_grid(nc))
plot(st_geometry(nc), reset = FALSE, border = 'grey')
plot(st_geometry(gr), add = TRUE)
When we need an aggregate for a new area that is not a union of the geometries for a group of records, and we use a spatial predicate, then single records may be matched to multiple groups. When taking the rectangles of Figure 5.2 as the target areas, and summing for each rectangle the disease cases of the counties that intersect with the rectangles of Figure 5.2, the sum of these will be much larger:
# sid74_sum_counties sid74_sum_rectangles
# 667 2621Choosing another predicate, such as contains or covers, would on the contrary result in much smaller values, because many counties are not contained by any of the target geometries. However, there are a few cases where this approach might be good or satisfactory:
POINT geometries by a set of polygons, and all points are contained by a single polygon. If points fall on a shared boundary than they are assigned to both polygons (this is the case for DE-9IM-based GEOS library; the s2geometry library has the option to define polygons as “semi-open”, which implies that points are assigned to maximally one polygon when polygons do not overlap)A more comprehensive approach to aggregating spatial data associated with areas to larger, arbitrary shaped areas is by using area-weighted interpolation.
When we want to combine geometries and attributes of two datasets such that we get attribute values of a source dataset summarised for the geometries of a target, where source and target geometries are unrelated, area-weighted interpolation may be a simple approach. In effect, it considers the area of overlap of the source and target geometries, and uses that to weigh the source attribute values into the target value (Goodchild and Lam 1980; Do, Thomas-Agnan, and Vanhems 2015a, 2015b; Do, Laurent, and Vanhems 2021). This method is also known as conservative region aggregation or regridding (Jones 1999). Here, we follow the notation of Do, Thomas-Agnan, and Vanhems (2015b).
Area-weighted interpolation computes, for each of \(q\) spatial target areas \(T_j\), a weighted average from the values \(Y_i\) corresponding to the \(p\) spatial source areas \(S_i\),
\[ \hat{Y}_j(T_j) = \sum_{i=1}^p w_{ij} Y_i(S_i) \tag{5.1}\]
where the \(w_{ij}\) depend on the amount of overlap of \(T_j\) and \(S_i\), and the amount of overlap is \(A_{ij} = T_j \cap S_i\). How \(w_{ij}\) depends on \(A_{ij}\) is discussed below.
Different options exist for choosing weights, including methods using external variables (including dasymetric mapping, Mennis 2003). Two simple approaches for computing weights that do not use external variables arise, depending on whether the variable \(Y\) is intensive or extensive.
Extensive variables correspond to amounts, associated with a physical size (length, area, volume, counts of items). An example of a extensive variable is population count. It is associated with an area, and if that area is cut into smaller areas, the population count needs to be split too. Because population is rarely uniform over space, this does not need to be done proportionally to the smaller areas but the sum of the population count for the smaller areas needs to equal that of the total. Intensive variables are variables that do not have values proportional to support: if the area is split, values may vary but on average remain the same. An example of a related variable that is intensive is population density. If an area is split into smaller areas, population density is not split similarly: the sum of population densities for the smaller areas is a meaningless measure, as opposed to the average of the population densities which will be similar to the density of the total area.
When we assume that the extensive variable \(Y\) is uniformly distributed over space, the value \(Y_{ij}\), derived from \(Y_i\) for a sub-area of \(S_i\), \(A_{ij} = T_j \cap S_i\) of \(S_i\) is
\[\hat{Y}_{ij}(A_{ij}) = \frac{|A_{ij}|}{|S_i|} Y_i(S_i)\]
where \(|\cdot|\) denotes the spatial area. For estimating \(Y_j(T_j)\) we sum all the elements over area \(T_j\):
\[ \hat{Y}_j(T_j) = \sum_{i=1}^p \frac{|A_{ij}|}{|S_i|} Y_i(S_i) \tag{5.2}\]
For an intensive variable, under the assumption that the variable has a constant value over each area \(S_i\), the estimate for a sub-area equals that of the total area,
\[\hat{Y}_{ij} = Y_i(S_i)\]
and we can estimate the value of \(Y\) for a new spatial unit \(T_j\) by an area-weighted average of the source values:
\[ \hat{Y}_j(T_j) = \sum_{i=1}^p \frac{|A_{ij}|}{|T_j|} Y_i(S_i) \tag{5.3}\]
Dasymetric mapping distributes variables, such as population, known at a coarse spatial aggregation level over finer spatial units by using other variables that are associated with population distribution, such as land use, building density, or road density. The simplest approach to dasymetric mapping is obtained for extensive variables, where the ratio \(|A_{ij}| / |S_i|\) in Equation 5.2 is replaced by the ratio of another extensive variable \(X_{ij}(S_{ij})/X_i(S_i)\), which has to be known for both the intersecting regions \(S_{ij}\) and the source regions \(S_i\). Do, Thomas-Agnan, and Vanhems (2015b) discuss several alternatives for intensive \(Y\) and/or \(X\), and cases where \(X\) is known for other areas.
GDAL’s vector API supports reading and writing so-called field domains, which can have a “split policy” and a “merge policy” indicating what should be done with attribute variables when geometries are split or merged. The values of these can be “duplicate” for split and “geometry weighted” for merge, in case of spatially intensive variables, or they can be “geometry ratio” for split and “sum” for merge, in case of spatially extensive variables. At the time of writing this, the file formats supporting this are GeoPackage and FileGDB.
Up- and downscaling refers in general to obtaining high-resolution information from low-resolution data (downscaling) or obtaining low-resolution information from high-resolution data (upscaling). Both activities involve the attributes’ relation to geometries and both change support. They are synonymous with aggregation (upscaling) and disaggregation (downscaling). The simplest form of downscaling is sampling (or extracting) polygon, line or grid cell values at point locations. This works well for variables with point-support (“constant” AGR), but is at best approximate when the values are aggregates. Challenging applications for downscaling include high-resolution prediction of variables obtained by low-resolution weather prediction models or climate change models, and the high-resolution prediction of satellite image derived variables based on the fusion of sensors with different spatial and temporal resolutions.
The application of areal interpolation using (Equation 5.1) with its realisations for extensive (Equation 5.2) and intensive (Equation 5.3) variables allows moving information from any source area \(S_i\) to any target area \(T_j\) as long as the two areas have some overlap. This means that one can go arbitrarily to much larger units (aggregation) or to much smaller units (disaggregation). Of course this makes only sense to the extent that the assumptions hold: over the source regions extensive variables need to be uniformly distributed and intensive variables need to have a constant value.
The ultimate disaggregation involves retrieving (extracting) point values from line or area data. For this, we cannot work with equations -Equation 5.2 or -Equation 5.3 because \(|A_{ij}| = 0\) for points, but under the assumption of having a constant value over the geometry, for intensive variables the value \(Y_i(S_i)\) can be assigned to points as long as all points can be uniquely assigned to a single source area \(S_i\). For polygon data, this implies that \(Y\) needs to be a coverage variable (Section 3.4). For extensive variables, extracting a value at a point is rather meaningless, as it should always give a zero value.
In cases where values associated with areas are aggregate values over the area, the assumptions made by area-weighted interpolation or dasymetric mapping – uniformity or constant values over the source areas – are highly unrealistic. In such cases, these simple approaches could still be reasonable approximations, for instance when:
In other cases, results obtained using these methods are merely consequences of unjustified assumptions. Statistical aggregation methods that can estimate quantities for larger regions from points or smaller regions include:
Alternative disaggregation methods include:
Where relevant, try to make the following exercises with R.
nc dataset by nc$State = "North Carolina" (i.e., all counties get assigned the same state name). Which value would you attach to this variable for the attribute-geometry relationship (agr)?sf object from the geometry obtained by st_union(nc), and assign "North Carolina" to the variable State. Which agr can you now assign to this attribute variable?st_area to add a variable with name area to nc. Compare the area and AREA variables in the nc dataset. What are the units of AREA? Are the two linearly related? If there are discrepancies, what could be the cause?area variable intensive or extensive? Is its agr equal to constant, identity or aggregate?