380
14.2.3.1 Spatial Scale and Resolution
The macro-scale involves data aggregation at spatial scales ranging from regions,
states, large river basins, or nations, up to the planetary boundary. No greater scale
currently exists while humanity is confined on “spaceship earth.” At the macro
scale, there are few coherent bottom-up data sources, but top-down data sources are
both widely available and usually adequate to approximately describe the system.
The annual or decadal timescale often corresponds with Macro scale data. The
macro scale is also the most common size for the “Macro” scale where averages are
taken across extremely broad categories of space, time, and category (Chap. 9).
Macro resolution data tends to be minimally useful for FEW systems research, as it
tends to capture only the broadest gradients in economic development level or climate type between nations.
Validity and Provenance tend to be good for truly global data (e.g., remote sensing) because data sources and methods are transparent and standardized, but precision tends to be very poor. It is not usually possible to relate global, national, or
regional numbers to specific agents on the ground that can make decisions or shape
policy based on the information. There are not currently global decision-makers in
the FEW space, although there are national agents that operate to a limited extent in
the FEW spaces concerning geopolitics and national security. Macro data is actionable for answering questions about regional stresses, regional interdependencies,
geopolitical hotspots, and planetary footprints and sustainability boundaries. Macro
data is inadequate for most FEW systems questions because the FEW system operates primarily at much finer “human” scales. There are no major challenges for data
privacy, security, or availability for Macro data. Macro scale data is easy to work
with conceptually and computationally and makes nice maps for communication.
When working with macro data be aware of nonstandard and extremely incomplete
bottom-up data that is “pretending” to be standardized and seamless top-down data;
some United Nations information sources, along with for instance the USGS Water
Census, fit this type.
The major discontinuity in the top-to-bottom continuum of FEW systems data is
between the top-down macro-scale sources and the bottom-up meso-scale sources.
It is easy to associate the two by downscaling macro- to meso-scales, but they cannot be expected to show similar results because they come from dramatically different source data. Macro-data is rarely validated by bottom-up comparisons or
aggregated from bottom-up data.
The meso-scale is where top-down and bottom-up methods usually meet and is
the finest scale at which aggregated census-style data is aggregated up from
establishment- level data to avoid the need to invoke the use of private, PII, or PCII
data. Meso is one of the two primary spatial scales at which FEW systems function
(community scale and process scale; Lant et al. 2019); this scale involves data
aggregation at spatial scales of neighborhoods, blocks, municipalities, watersheds,
irrigation districts, or counties; seasonal or monthly timescales often correspond
with meso-scale data. Meso data captures sub-national gradients in the economy
B. L. Ruddell
14.2.3.1 Spatial Scale and Resolution
The macro-scale involves data aggregation at spatial scales ranging from regions,
states, large river basins, or nations, up to the planetary boundary. No greater scale
currently exists while humanity is confined on “spaceship earth.” At the macro
scale, there are few coherent bottom-up data sources, but top-down data sources are
both widely available and usually adequate to approximately describe the system.
The annual or decadal timescale often corresponds with Macro scale data. The
macro scale is also the most common size for the “Macro” scale where averages are
taken across extremely broad categories of space, time, and category (Chap. 9).
Macro resolution data tends to be minimally useful for FEW systems research, as it
tends to capture only the broadest gradients in economic development level or climate type between nations.
Validity and Provenance tend to be good for truly global data (e.g., remote sensing) because data sources and methods are transparent and standardized, but precision tends to be very poor. It is not usually possible to relate global, national, or
regional numbers to specific agents on the ground that can make decisions or shape
policy based on the information. There are not currently global decision-makers in
the FEW space, although there are national agents that operate to a limited extent in
the FEW spaces concerning geopolitics and national security. Macro data is actionable for answering questions about regional stresses, regional interdependencies,
geopolitical hotspots, and planetary footprints and sustainability boundaries. Macro
data is inadequate for most FEW systems questions because the FEW system operates primarily at much finer “human” scales. There are no major challenges for data
privacy, security, or availability for Macro data. Macro scale data is easy to work
with conceptually and computationally and makes nice maps for communication.
When working with macro data be aware of nonstandard and extremely incomplete
bottom-up data that is “pretending” to be standardized and seamless top-down data;
some United Nations information sources, along with for instance the USGS Water
Census, fit this type.
The major discontinuity in the top-to-bottom continuum of FEW systems data is
between the top-down macro-scale sources and the bottom-up meso-scale sources.
It is easy to associate the two by downscaling macro- to meso-scales, but they cannot be expected to show similar results because they come from dramatically different source data. Macro-data is rarely validated by bottom-up comparisons or
aggregated from bottom-up data.
The meso-scale is where top-down and bottom-up methods usually meet and is
the finest scale at which aggregated census-style data is aggregated up from
establishment- level data to avoid the need to invoke the use of private, PII, or PCII
data. Meso is one of the two primary spatial scales at which FEW systems function
(community scale and process scale; Lant et al. 2019); this scale involves data
aggregation at spatial scales of neighborhoods, blocks, municipalities, watersheds,
irrigation districts, or counties; seasonal or monthly timescales often correspond
with meso-scale data. Meso data captures sub-national gradients in the economy
B. L. Ruddell