Structuring data with SDMX

This post expands on some text from the README of iam-units, in order to illustrate some improved ways to structure data and metadata. These are especially helpful in systems research, where data from multiple disciplines/domains/contexts are often combined.

iam-units is a thin wrapper around the very useful pint, that I wrote with some colleagues to handle unit conversions for data from integrated assessment models (IAMs) of energy and climate, including global warming potential (GWP) conversions between greenhouse gas (GHG) species:

In [1]: from iam_units import registry, convert_gwp
[18:36:45] WARNING  Redefining 'kt' (<class                            log.py:89
                    'pint.delegates.txt_defparser.plain.UnitDefinition          
                    '>)                                                         
           WARNING  Redefining 'EUR_2005' (<class                      log.py:89
                    'pint.delegates.txt_defparser.plain.UnitDefinition          
                    '>)                                                         
           WARNING  Redefining 'EUR' (<class                           log.py:89
                    'pint.delegates.txt_defparser.plain.UnitDefinition          
                    '>)                                                         

# Use a pint.UnitRegistry to convert from one unit to another
In [2]: qty = registry('3500 t').to('Mt')

# The result has the expected magnitude: 3500 t = 3.5 kt = 0.035 Mt
In [3]: qty
Out[3]: 0.0034999999999999996 <Unit('megametric_ton')>

# Convert from mass of N₂O to GWP-equivalent mass of CO₂,
# using the metric from the IPCC Fifth Assessment Report (AR5)
In [4]: convert_gwp('AR5GWP100', qty, 'N2O', 'CO2')
Out[4]: 0.9274999999999999 <Unit('megametric_ton')>

Contents

Concept, observation, quantity, magnitude, unit
- Measurement and nominal properties
Common ways to handle GHGs and GWP-equivalents
Using SDMX to be explicit
Take-aways
- Further topics

Here's the text I'd like to expand on, with emphasis added:

convert_gwp() converts from from mass (or mass-related units) of one specific greenhouse gas (GHG) species to an equivalent quantity of second species, based on GWP metrics.

The function also accepts input with the commonly-used combination of mass (or related) units and the identity of a particular GHG species:

Strictly, the original species is not a unit but a “nominal property”; see the International Vocabulary of Metrology (VIM) [1] used in the SI. To avoid ambiguity, code handling GHG quantities should also track and output these nominal properties, including:

Original species.

Species in which GWP-equivalents are expressed (e.g. CO₂ or C)

GWP metric used to convert (1) to (2).

This is one of many cases where a little up-front effort to be precise—as opposed to loose and casual—about measurement can help us write code and produce data that is as interoperable as possible. The investment pays dividends by reducing repeated work down the road in handling imprecise data.

Concept, observation, quantity, magnitude, unit

Before returning to GHGs, here's a second example: in transport research we often want to discuss the following concept:

total distance traveled by a person (or group of people)

For instance, if I walk to the corner store, ride my bike to a restaurant, and then take the train to visit a friend, I might travel in total 0.5 + 4 + 10 = 14.5 kilometres. This is a quantity that expresses one observation of the above concept. The observation was, hypothetically, obtained by a process of measurement (more on that shortly). The quantity consists of the magnitude “14.5” and unit reference “kilometre”.

We might also say:

In [5]: registry("14.5 km").to("mile")
Out[5]: 9.009882287441343 <Unit('mile')>

This is the very same concept, observation, and quantity, but expressed in a different unit and thus having a different magnitude. [2]

Measurement and nominal properties

Something is still missing in order to be unambiguous about the observation “the travel distance is 14.5 kilometres.” We might ask:

Is this the actual distance traveled in a specific period of time?
- What period? A specific day? [3]
- Or, as in this case, a hypothetical, counterfactual, etc.?
Is it a computed statistic? For instance:
- The mean or average travel distance across 2+ periods, for just one person (me)?
- The sum/collective travel distance across 2+ people, but in the same period?
- Something else?

The VIM calls these nominal properties.

In systems research, it is common to combine data and knowledge from different streams of research that take place in different contexts, with different scope and resolution. Within any particular context, there can be certain natural choices for concepts, measurement process(es), and thus certain nominal properties of the data created/handled. These will be understood implicitly by both author and audience as the background consensus, and don't vary within that context. To list them explicitly doesn't achieve much, and might even be a distraction and barrier to clear communication; noise that buries the signal.

However: when combining data from different contexts, problems can arise when we fail to notice that the concepts, measurement processes, and nominal properties differ. A burden thus falls on the researcher to:

Make explicit the implicit features of measurement in the different domains supplying data that she wishes to combine,
Identify any differences,
Choose methods to adjust for (2), and
Correctly implement (3).

These tasks consume resources and create opportunities for errors that could undermine the internal validity of research. But they're also routine, so we can use some judicious automation to reduce the work involved while getting equally- or more-valid results.

Common ways to handle GHGs and GWP-equivalents

Suppose we have this data:

In [6]: import pandas as pd

In [7]: species = ["N2O", "CH4", "CO2"]

In [8]: data_a = pd.DataFrame(
   ...:     [
   ...:         ["Emissions, N2O", 1.1, "kt"],
   ...:         ["Emissions, CH4", 5.2, "kt"],
   ...:         ["Emissions, CO2", 100.3, "kt"],
   ...:     ],
   ...:     columns=["variable", "value", "unit"],
   ...: )
   ...: 

In [9]: data_b = pd.DataFrame(
   ...:     [
   ...:         ["Emissions, N2O", 291.5, "kt"],
   ...:         ["Emissions, CH4", 146.5, "kt"],
   ...:         ["Emissions, CO2", 100.3, "kt"],
   ...:     ],
   ...:     columns=["variable", "value", "unit"],
   ...: )
   ...:

Let's be very precise about what we have:

Each observation in data_a expresses a measured mass of emissions.
Each observation in data_b expresses a mass of CO₂ that, using the AR5 100-year metric, has the potential to contribute the same amount of global warming as the corresponding mass in data_a.
The ‘value’ column contains magnitudes; actual quantities are given by the ‘value’ and ‘unit’ columns together.
The ‘variable’ column mixes concepts: the thing measured is the [mass] emitted and is the same for all observations; the species emitted varies.

Clearly, it's wrong to do the following:

In [10]: data_a["value"] + data_b["value"]
Out[10]: 
0    292.6
1    151.7
2    200.6
Name: value, dtype: float64

These magnitudes, as bare numbers, can of course be added togther. But the results are scientifically meaningless, since the operands are conceptually incoherent: their units are the same, but they measure different things.

There are some common ways to store information that is intended to help avoid errors like this. Strategy A is to use a unit-like expression that combines the actual unit with a nominal property, i.e. the species:

In [11]: data_a.assign(unit=[f"kt {s}" for s in species])
Out[11]: 
         variable  value    unit
0  Emissions, N2O    1.1  kt N2O
1  Emissions, CH4    5.2  kt CH4
2  Emissions, CO2  100.3  kt CO2

In [12]: data_b.assign(unit="kt CO2-eq")
Out[12]: 
         variable  value       unit
0  Emissions, N2O  291.5  kt CO2-eq
1  Emissions, CH4  146.5  kt CO2-eq
2  Emissions, CO2  100.3  kt CO2-eq

These expressions are a common prose shorthand or abbreviation, but are not standard. For instance, ‘-e’/‘e’, ‘-eq’/‘eq’, ‘-equiv’, and other symbols are all in use in various contexts.

Strategy B is to mash even more concepts into a column named something like ‘variable’, adding (3) and (4) to (1) and (2) here:

The property that is measured, i.e. mass.
The species emitted.
The species in which a GWP-equivalent is expressed.
The GWP metric used.

In [13]: data_a
Out[13]: 
         variable  value unit
0  Emissions, N2O    1.1   kt
1  Emissions, CH4    5.2   kt
2  Emissions, CO2  100.3   kt

In [14]: data_b.assign(
   ....:     variable=data_b.variable + " (CO₂ equivalent, AR5GWP100)"
   ....: )
   ....: 
Out[14]: 
                                     variable  value unit
0  Emissions, N2O (CO₂ equivalent, AR5GWP100)  291.5   kt
1  Emissions, CH4 (CO₂ equivalent, AR5GWP100)  146.5   kt
2  Emissions, CO2 (CO₂ equivalent, AR5GWP100)  100.3   kt

Both of these strategies have the same basic flaw: they combine instead of distinguishing different aspects of measurement. Researchers might make choices that feel ‘natural’ in specific contexts, yet which differ from equally natural choices made by others. This creates a proliferation of idiosyncratic formats, and entails further work in parsing and harmonizing.

Using SDMX to be explicit

I maintain sdmx1, a Python package that implements the SDMX Information Model, or ISO 17369:2013. An “information model” (IM) is a model (a set of concepts and their relationships) for talking about information (data and metadata) and its representation. (The documentation for the package links to some learning resources for SDMX; the details aren't repeated here.)

Because it is carefully developed, the SDMX IM allows us to precisely capture the concepts and relationships in our example data, including our choices in measurement and of units.

The rest of this post gives a minimal demonstration.

Specify the concepts and data structure

We start by defining each of the distinct concepts that occur in our data:

In [15]: import sdmx

In [16]: import sdmx.model.v21 as model

In [17]: emission = model.Concept(
   ....:     id="EMISSION",
   ....:     name="Mass of greenhouse gas emitted",
   ....: )
   ....: 

In [18]: species_concept = model.Concept(
   ....:     id="SPECIES",
   ....:     name="Chemical species or substance",
   ....: )
   ....: 

In [19]: gwp_metric = model.Concept(
   ....:     id="GWP_METRIC",
   ....:     name="Set of GWPs used to convert species",
   ....: )
   ....: 

# “UNIT_MEASURE” is commonly used in SDMX applications
In [20]: unit_concept = model.Concept(
   ....:     id="UNIT_MEASURE",
   ....:     name="Unit of measurement",
   ....: )
   ....:

Next, we define the structure of our data. We start with the primary measure, the concept that is measured:

In [21]: dsd = model.DataStructureDefinition(id="GHG_DATA")

# - Refer to the concept
# - Use concept's ID for the primary measure as well
In [22]: pm = model.PrimaryMeasure(
   ....:     concept_identity=emission,
   ....:     id=emission.id,
   ....: )
   ....: 

# Store in the DSD
In [23]: dsd.measures.append(pm)

Next, we specify that values for the gwp_metric and unit concepts are stored as attributes. SDMX allows several options for where we attach these attributes. Here, we specify that the attributes are attached to entire data sets. This means that, in a particular data set, only one GWP metric can be used, and one unit: [4]

# 1. Define the data attribute
#    - Refer to the concept
#    - Use concept's ID for the attribute as well
#    - NoSpecifiedRelationship = attached to data set
# 2. Store in the DSD
In [24]: for concept in (gwp_metric, unit_concept):
   ....:     da = model.DataAttribute(
   ....:         concept_identity=concept,
   ....:         id=concept.id,
   ....:         related_to=model.NoSpecifiedRelationship,
   ....:     )
   ....:     dsd.attributes.append(da)
   ....:

Finally, we specify that our data has two dimensions. Every observation in a data set must have a unique Key with values for these dimensions.

Both dimensions refer to the same concept (species), but we give them different IDs and names to explain what these signify.

In [25]: for order, (id, name) in enumerate((
   ....:     ("SPECIES", "Original species emitted"),
   ....:     (
   ....:         "SPECIES_GWP",
   ....:         "Species in which GWP equivalent is expressed",
   ....:     ),
   ....: )):
   ....:     dim = model.Dimension(
   ....:         concept_identity=species_concept,
   ....:         id=id,
   ....:         order=order,
   ....:     )
   ....:     dsd.dimensions.append(dim)
   ....:

We now have a complete description of our data structure:

In [26]: dsd.measures
Out[26]: <MeasureDescriptor: <PrimaryMeasure EMISSION>>

In [27]: dsd.attributes
Out[27]: <AttributeDescriptor: DataAttribute(annotations=[], id='GWP_METRIC', uri=None, urn=None, concept_identity=<Concept GWP_METRIC: Set of GWPs used to convert species>, local_representation=None, related_to=<class 'sdmx.model.v21.NoSpecifiedRelationship'>, usage_status=None, concept_role=None); DataAttribute(annotations=[], id='UNIT_MEASURE', uri=None, urn=None, concept_identity=<Concept UNIT_MEASURE: Unit of measurement>, local_representation=None, related_to=<class 'sdmx.model.v21.NoSpecifiedRelationship'>, usage_status=None, concept_role=None)>

In [28]: dsd.dimensions
Out[28]: DimensionDescriptor(annotations=[], id='', uri=None, urn=None, components=[<Dimension SPECIES>, <Dimension SPECIES_GWP>])

Use the definitions to structure the data

Now we can create a data set that is structured by this definition:

In [29]: ds_a = model.DataSet(structured_by=dsd)

We store the values of attributes attached to the data set:

# Define a function to create attribute values and store them.
# The `value_for` property links each value to the definition
# of the attribute in the DSD, and thus to a concept.
In [30]: def store_attributes(ds, **values):
   ....:     for concept_id, value in values.items():
   ....:         av = model.AttributeValue(
   ....:             value=value,
   ....:             value_for=dsd.attributes.get(concept_id),
   ....:         )
   ....:         ds.attrib[concept_id] = av
   ....: 

In [31]: store_attributes(
   ....:     ds_a,
   ....:     GWP_METRIC="(None)",
   ....:     UNIT_MEASURE="kt",
   ....: )
   ....:

Finally we store the individual observations. In the case of data_a, the labels for the SPECIES and SPECIES_GWP dimensions are the same:

# 1. Discard redundant portion of the ‘variable’ column.
# 2. Create a key object from labels for each dimension.
# 3. Create the observation.
#    - The value is ‘for’ the primary measure, i.e. emissions.
# 4. Store in the data set.
In [32]: for _, row in data_a.iterrows():
   ....:     s = row["variable"].split("Emissions, ")[-1]
   ....:     dims = dict(SPECIES=s, SPECIES_GWP=s)
   ....:     key = dsd.make_key(model.Key, dims)
   ....:     obs = model.Observation(
   ....:         dimension=key, value=row["value"], value_for=pm
   ....:     )
   ....:     ds_a.obs.append(obs)
   ....:

We now have a complete data set. sdmx1 provides features for converting to other data structures and file formats:

In [33]: ds_a
Out[33]: DataSet(annotations=[], action=None, valid_from=None, described_by=None, structured_by=<DataStructureDefinition GHG_DATA>, obs=[Observation(attached_attribute={}, series_key=None, dimension=<Key: SPECIES=N2O, SPECIES_GWP=N2O>, value=1.1, group_keys=set(), value_for=<PrimaryMeasure EMISSION>), Observation(attached_attribute={}, series_key=None, dimension=<Key: SPECIES=CH4, SPECIES_GWP=CH4>, value=5.2, group_keys=set(), value_for=<PrimaryMeasure EMISSION>), Observation(attached_attribute={}, series_key=None, dimension=<Key: SPECIES=CO2, SPECIES_GWP=CO2>, value=100.3, group_keys=set(), value_for=<PrimaryMeasure EMISSION>)], series={}, group={}, attrib={'GWP_METRIC': <AttributeValue: GWP_METRIC=(None)>, 'UNIT_MEASURE': <AttributeValue: UNIT_MEASURE=kt>})

# pandas.DataFrame
In [34]: sdmx.to_pandas(ds_a, attributes="od")
Out[34]: 
                     value GWP_METRIC UNIT_MEASURE
SPECIES SPECIES_GWP                               
N2O     N2O            1.1     (None)           kt
CH4     CH4            5.2     (None)           kt
CO2     CO2          100.3     (None)           kt

# Standardized SDMX-ML format
In [35]: sdmx.to_xml(ds_a, pretty_print=True)
Out[35]: b'<mes:DataSet xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:com="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/common" xmlns:data="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/data/structurespecific" xmlns:str="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/structure" xmlns:mes="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message" xmlns:gen="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/data/generic" xmlns:footer="http://www.sdmx.org/resources/sdmxml/schemas/v2_1/message/footer" structureRef="GHG_DATA">\n  <gen:Attributes>\n    <gen:Value id="GWP_METRIC" value="(None)"/>\n    <gen:Value id="UNIT_MEASURE" value="kt"/>\n  </gen:Attributes>\n  <gen:Obs>\n    <gen:ObsKey>\n      <gen:Value id="SPECIES" value="N2O"/>\n      <gen:Value id="SPECIES_GWP" value="N2O"/>\n    </gen:ObsKey>\n    <gen:ObsValue value="1.1"/>\n  </gen:Obs>\n  <gen:Obs>\n    <gen:ObsKey>\n      <gen:Value id="SPECIES" value="CH4"/>\n      <gen:Value id="SPECIES_GWP" value="CH4"/>\n    </gen:ObsKey>\n    <gen:ObsValue value="5.2"/>\n  </gen:Obs>\n  <gen:Obs>\n    <gen:ObsKey>\n      <gen:Value id="SPECIES" value="CO2"/>\n      <gen:Value id="SPECIES_GWP" value="CO2"/>\n    </gen:ObsKey>\n    <gen:ObsValue value="100.3"/>\n  </gen:Obs>\n</mes:DataSet>\n'

Convert carefully

Finally, we can convert ds_a using iam-units while maintaining an explicit and unambiguous data structure.

We create a second data set using the same data structure definition:

In [36]: ds_b = model.DataSet(structured_by=dsd)

# Store attributes:
# GWP metric name to use in conversion
In [37]: metric = "AR5GWP100"

# Unit used in both data sets
In [38]: unit = ds_a.attrib["UNIT_MEASURE"].value

In [39]: store_attributes(
   ....:     ds_b, GWP_METRIC=metric, UNIT_MEASURE=unit
   ....: )
   ....:

For each observation, we convert its magnitude, and change only the “SPECIES_GWP” key value:

# Target species for conversion
In [40]: target_species = "CO2"

# 1. Store the original species.
# 2. Create a new key with SPECIES_GWP set to `target_species`.
# 3. Convert the magnitude using the metric and iam-units.
# 4. Create a new observation and store in `ds_b`.
In [41]: for obs in ds_a.obs:
   ....:     species = obs.dimension["SPECIES"].value
   ....:     key = dsd.make_key(
   ....:         model.Key,
   ....:         dict(SPECIES=species, SPECIES_GWP=target_species),
   ....:     )
   ....:     value = convert_gwp(
   ....:         metric,
   ....:         registry.Quantity(obs.value, unit),
   ....:         species,
   ....:         target_species,
   ....:     ).magnitude
   ....:     ds_b.obs.append(
   ....:         model.Observation(
   ....:             dimension=key, value=value, value_for=pm
   ....:         )
   ....:     )
   ....:

The resulting data set has the same format as ds_a:

In [42]: ds_b
Out[42]: DataSet(annotations=[], action=None, valid_from=None, described_by=None, structured_by=<DataStructureDefinition GHG_DATA>, obs=[Observation(attached_attribute={}, series_key=None, dimension=<Key: SPECIES=N2O, SPECIES_GWP=CO2>, value=291.5, group_keys=set(), value_for=<PrimaryMeasure EMISSION>), Observation(attached_attribute={}, series_key=None, dimension=<Key: SPECIES=CH4, SPECIES_GWP=CO2>, value=145.6, group_keys=set(), value_for=<PrimaryMeasure EMISSION>), Observation(attached_attribute={}, series_key=None, dimension=<Key: SPECIES=CO2, SPECIES_GWP=CO2>, value=100.3, group_keys=set(), value_for=<PrimaryMeasure EMISSION>)], series={}, group={}, attrib={'GWP_METRIC': <AttributeValue: GWP_METRIC=AR5GWP100>, 'UNIT_MEASURE': <AttributeValue: UNIT_MEASURE=kt>})

In [43]: sdmx.to_pandas(ds_b, attributes="do")
Out[43]: 
                     value GWP_METRIC UNIT_MEASURE
SPECIES SPECIES_GWP                               
N2O     CO2          291.5  AR5GWP100           kt
CH4     CO2          145.6  AR5GWP100           kt
CO2     CO2          100.3  AR5GWP100           kt

Take-aways

To review, by applying the SDMX Information Model, we were able to:

Define exactly the concepts relevant to our data.
Use these to create a clear and unambigous structure, in which concepts were used for the primary measure, as attributes, or dimensions of multi-dimensional data.
Unpack a ‘variable’ column into its constituent concepts, and avoid overloading it further with additional concepts.
Store simple or bare SI units, parseable by pint, for the “UNIT_MEASURE” attribute, and avoid overloading this with unrelated concepts.
Access structured metadata and use it in the process of converting data.

Further topics

The amount of code used to handle only 3 observations might seem excessive. The up-front investment, however, unlocks further improvements in handling data and metadata. As a sketch of these:

Applications can additionally specify a representation for any concept used for a measure, attribute, or dimension.

For instance, we could specify that the “SPECIES” dimension represents the concept of species using only codes from a certain list of species understood to be GHGs: CH4, CO2, N2O, and so on. The code list is a mechanism for communicating about the data:
- “This data contains codes that are invalid, not in the specified list” can flag errors in data preparation.
- A data provider can specify a subset of the codes that will appear in their published data sets or data flows. Users then understand not to expect values outside this subset.
The concepts and other structures can be shared and reused. For instance, as described in footnote [4], we could construct a different data structure definition using the same concepts, as required for a different application.

The re-use of the same concepts tells users about links between multiple data sets and flows.
Published structures allow multiple data providers to prepare data that is guaranteed to be interoperable.

Footnotes

[1]	The VIM is good reading and food for thought on these topics; more giant shoulders that we stand on, even if it seems as pedestrian as a dictionary. I encourage everyone to skim it!

[2]

It's very common to conflate concepts and units. Do not do this.

For instance, “passenger/person distance traveled” (PDT) is often called “PKT” (meaning “passenger kilometres travelled”) or “PMT” (“person-miles traveled”). This conflates units (kilometres, miles) with the quantity (distance) measured. Two data sets with observations labelled “PKT” and “PMT” might measure the same concept—that is, PDT—in the same way with the same nominal properties, but merely express the resulting quantities in different units.

Always label dimensions or observations with the concept measured, not the units of particular measurements.

[3]	…or, during this pandemic, month or year? 😭️

[4]

(1, 2)

This is simply for brevity, not a limitation of SDMX.

We could equally, for instance, specify that gwp_metric is attached to series keys or individual observations. This would allow a single data set to contain the same measurements with magnitudes expressed in multiple ways via multiple GWP metrics.