382
represent an agglomeration of processes under a single ownership and management,
rather than an irreducible process. Nevertheless, the establishment spatial scale is
often the closest we can get in practice to the “micro” scale for analysis and research
methods purposes (Chap. 9).
Scales that refer to a networked entity than a spatial unit are the Firm, which is
a single economic entity that operates at multiple establishments, and the Enterprise,
which is a broader arrangement that may include multiple Firms and Enterprises. A
common confounding factor for establishment data is that firms tend to aggregate
data at the level of the Firm rather than collecting it for individual Establishments.
Firm-level data may be incorrectly coded as establishment-level data or vice versa.
Examples and terms for the establishment scale include address, individual, business, facility, point, process, warehouse, treatment plant, dock, terminal, grain elevator, or reservoir. Establishment scale data is of private origin and is collected
utilizing surveys, transactions, permits, tax records, and increasingly digital surveillance and tracking—usually by the government or large corporations. A good example of establishment scale data is the private data on US businesses that are
provided via carefully restricted access by the U.S. Census Bureau’s Federal
Statistical Research Data Centers.
Establishment scale data is highly variable in availability and quality, depending
on who is collecting the data, the purpose of the data collection, and whether there
are government requirements and standards for data collection. One type of data
will be highly accurate, and 100% accessible, as in the USA for property tax records,
and another type of data will be practically nonexistent, as for most individuals’ and
companies’ FEW consumptions and purchasing data. This data is not originally or
primarily collected for FEW research purposes, so some information that is critical
for FEW systems analysis can be completely deficient, and other data is abundant at
the establishment level.
Establishment scale data is highly actionable and valuable for research because
the FEW system’s function is an aggregation of Billions of microeconomic and
microenvironmental decisions that are often made at the scale of a household or
business facility. Individual data can change individual decisions and empower
direct and immediate solutions by providing actionable information about FEW
system quality, reliability, sourcing, etc. Establishment scale data is “big data” and
poses daunting challenges for data science and computational analysis.
The Process scale involves the creation, transformation, or transportation of a
specific product, good, or service, and implicates the inputs and outputs of the
process. The process scale most nearly corresponds to the “micro” scale that is
irreducible (Chap. 9). Examples and terms for the process scale are a product, service, machine, generating unit, farm field, stage, step, module, and value-add.
Process scale data may be associated with a firm or enterprise rather than an establishment, or, alternatively may be associated with the establishment where the process is carried out. Examples of processes include no-till corn growing, power
generation with a natural gas turbine, a dishwasher, lawn watering, bathroom operation, paper milling, and so on. This data is usually private and may be considered
Trade Secrets but is generally not categorized by law as PCII or PII. This data is
B. L. Ruddell
represent an agglomeration of processes under a single ownership and management,
rather than an irreducible process. Nevertheless, the establishment spatial scale is
often the closest we can get in practice to the “micro” scale for analysis and research
methods purposes (Chap. 9).
Scales that refer to a networked entity than a spatial unit are the Firm, which is
a single economic entity that operates at multiple establishments, and the Enterprise,
which is a broader arrangement that may include multiple Firms and Enterprises. A
common confounding factor for establishment data is that firms tend to aggregate
data at the level of the Firm rather than collecting it for individual Establishments.
Firm-level data may be incorrectly coded as establishment-level data or vice versa.
Examples and terms for the establishment scale include address, individual, business, facility, point, process, warehouse, treatment plant, dock, terminal, grain elevator, or reservoir. Establishment scale data is of private origin and is collected
utilizing surveys, transactions, permits, tax records, and increasingly digital surveillance and tracking—usually by the government or large corporations. A good example of establishment scale data is the private data on US businesses that are
provided via carefully restricted access by the U.S. Census Bureau’s Federal
Statistical Research Data Centers.
Establishment scale data is highly variable in availability and quality, depending
on who is collecting the data, the purpose of the data collection, and whether there
are government requirements and standards for data collection. One type of data
will be highly accurate, and 100% accessible, as in the USA for property tax records,
and another type of data will be practically nonexistent, as for most individuals’ and
companies’ FEW consumptions and purchasing data. This data is not originally or
primarily collected for FEW research purposes, so some information that is critical
for FEW systems analysis can be completely deficient, and other data is abundant at
the establishment level.
Establishment scale data is highly actionable and valuable for research because
the FEW system’s function is an aggregation of Billions of microeconomic and
microenvironmental decisions that are often made at the scale of a household or
business facility. Individual data can change individual decisions and empower
direct and immediate solutions by providing actionable information about FEW
system quality, reliability, sourcing, etc. Establishment scale data is “big data” and
poses daunting challenges for data science and computational analysis.
The Process scale involves the creation, transformation, or transportation of a
specific product, good, or service, and implicates the inputs and outputs of the
process. The process scale most nearly corresponds to the “micro” scale that is
irreducible (Chap. 9). Examples and terms for the process scale are a product, service, machine, generating unit, farm field, stage, step, module, and value-add.
Process scale data may be associated with a firm or enterprise rather than an establishment, or, alternatively may be associated with the establishment where the process is carried out. Examples of processes include no-till corn growing, power
generation with a natural gas turbine, a dishwasher, lawn watering, bathroom operation, paper milling, and so on. This data is usually private and may be considered
Trade Secrets but is generally not categorized by law as PCII or PII. This data is
B. L. Ruddell