Most cities do not know the revenue they receive from every acre
of activity and every square foot of shelter capacity per activity within their
boundaries. (Shelter capacity is gross building area per acre of project area.)
This means:
(1)
A city cannot confidently plan to adjust land
use activity, shelter capacity, and intensity to meet its increasing expense
per acre for operations, maintenance, improvement, and debt service; and,
(2)
A city’s fight to maintain a desirable quality
of life produces annexation gambles that expand to consume land that is its
source of life.
These gambles seek to achieve financial stability with an
expanding pattern of land consumption based on hope. Hope is based on limited
knowledge and forecasting ability that eventually fails as capital improvement,
maintenance, and debt service expense increases. The problem is exacerbated for
cities that have no more land to annex; but they may be the first to learn how
to live within geographic limits that economically sustain a desirable quality
of life. When land is available, budget shortfalls drive additional annexation
gambles. This produces a pattern we call sprawl that is a metastasizing disease
with amorphous form and a cellular pattern of lots.
Sprawl can only be treated with accumulated and correlated
information that leads to improved knowledge. Much of the information needed for
treatment already resides in separate, jealously guarded databases. This
isolation prevents correlation that is the key to a city’s stable future. The
least a city should know to begin correlation is itemized in Table 1 under the database
categories: (1) Location Data; (2) Lot Data; (3) Gross Building Area Data; (4)
Gross Habitable Building Area Data; (5) Pavement Data; (6) Zoning Data; (7)
Engineering Data; (8) Geographic Data; (9) Real Estate Tax Data; (10) Income Tax
Data; (11) Other Tax Data; (12) Other Revenue; and (13) Expense Data.
The list of items in each category is separated by topic in
the memo field columns that begin in cells B3 and D3. The lists will inevitably
be amended over time if a policy to begin accumulation and correlation is
adopted with privacy safeguards. I’ll get to Table 1 after setting the stage
with the following comments.
A city is a farm and an urban crop is a group of similar
activities. A crop is planted in a field called a zone. The yield from the crop
is a function of the gross building area constructed and the activity sheltered
o every lot in a zone. The amount of gross building area provided per acre is
called shelter capacity, and the amount produces a measureable condition called
intensity. The shelter capacity and intensity constructed to serve activity on a
lot combines to determine the yield (revenue) from a zone. What grows in all
zones combines to determine municipal income and economic stability.
A planning strategy involves the allocation of fields
(zones) for crops (shelter capacity, activity, and intensity) that combine to
produce annual yield (revenue) that must equal or exceed a city’s annual cost.
A farmer can increase yield on an annual basis by changing his or her planting
strategy. A city does not have this luxury, and it has far less ability to
evaluate strategic planning options than a farmer because it lacks compiled and
accessible data. In fact, a city does not understand its current average yield
from activity per productive acre, the equivalent yield per total acre, and the
expense per total acre required to sustain a quality of life desired by a
majority of its residents. Several questions are implied by this sentence.
(1)
How do we determine the current yield from every
sq. ft. of activity located within shelter capacity on every productive lot in
a city?
(2)
How do we translate yield per sq. ft. of
activity into yield per acre of land devoted to the activity, since land
allocation for shelter capacity, activity, and intensity determines the economic
productivity of a city’s master plan?
(3)
How do we increase yield per acre from activity
to improve a city’s total yield per productive acre within its geographic
limits?
(4)
What is the intensity of current shelter
capacity per lot and its aggregating impact on our quality of life?
(5)
How do we determine acceptable intensity levels
for diverse activities?
(6)
How do we determine the services that a city
considers essential to its “quality of life”?
The answers to questions 1 and 2 depend on the data
accumulated in Table 1. This data is the foundation for the queries in Tables 2
and 3 that establish benchmarks for a city’s current economic performance. Question
3 will be answered with the use of a forecast model that predicts gross
building area options, shelter capacity implications, and intensity results for
any given land area and building design category. This is the information
needed to improve and adjust the yield from existing lots and land areas within
a city’s master plan. Questions 4 and 5 can only be answered with the
measurement and evaluation of shelter capacity and intensity at existing
locations. Question 6 is currently answered by decisions from elected political
representatives. A list of desirable city services is never put to the vote,
and this leaves these services vulnerable to the claim that a city is spending
too much on their presence and delivery. It is a criticism that can never be
completely answered because essential services and cost for quality are matters
of opinion. It might help to publish a list of the services delivered, but this
will not quell the complaint that too much is being spent on services that
benefit too few. A general vote would put these services at risk. They
represent decisions of conscience that inevitably add blame to the shoulders of
elected politicians, and there may be no better solution in a democratic
society.
TABLE 1 – DATABASE INFORMATION
A database is a digital folder containing pages of data entered
in recurring cells of requested information per page. The military calls such
information “intelligence”, and an army that moves with inadequate intelligence
carries with it an increased risk of defeat. The following is a brief
explanation of the topics chosen for each database category in Table 1. Each paragraph
begins with a database title, but each database contains one or more line items
called “fields” in database terminology. I’ll limit this essay to an
explanation of the databases and let interested readers consider the line item
memo fields.
Location Database. A location is a lot in a city
anatomy. It is essential to begin by identifying each of these lots because all
further evaluation will be based on their characteristics and combined
potential.
Lot Database. A lot is a cell at a specific location
that may contain one or more parcel numbers. Its location is identified in
cells A18 and 19 of Table 1. Its characteristics are recorded in cells A20-A31.
Most of these items should be familiar. The activity data recorded in cell A20
is not the name of the zone, but a specific activity that may be conforming or
non-conforming within the zone. The block number requested in cell A19 must be
a unique, consecutive number assigned to every block in a city. The buildable
area within setback lines requested in cell A23 may not be familiar, but can be
useful when evaluating requests for expansion. The buildable land area
requested in cell A24 is the total buildable land area on a lot that may
include unbuildable soil, ravines, wetlands, ponds, and so on. The definition
of non-conforming activity on a lot in cell A25 can be useful when mapping and
evaluating conflict and opportunity. The definition of vacant land in cell A31
can be useful when mapping future development opportunities throughout a city.
Gross Building Area. Building data abounds in
building and code compliance departments, but the fundamental characteristics
that determine shelter capacity and intensity per acre for any activity are
often omitted or missing. They are rarely compiled in a searchable database
that can be correlated with others to support city design mapping, research,
evaluation, and decision.
Building cover is often referred to as a building
“footprint”. It combines with floor quantity to create building mass. The
combination of building mass, pavement, and unpaved open space determines the
physical intensity constructed on a lot. The combination of pavement and
unpaved open space with habitable building mass determines the shelter capacity
of a lot. When lots accumulate, the result is a pattern of shelter capacity and
intensity that is woven together with movement, life support and public open
space. Shelter capacity is expressed as the sq. ft. of shelter provided per
acre. A shelter capacity calculation will be greater than the sq. ft. of
shelter provided when a lot is less than one acre. In other words, a smaller
lot can be more efficient at providing shelter capacity, but the result can be
excessive intensity when knowledge is limited.
Gross Building Area - Habitable. I doubt that anyone
will argue with the statement that shelter is required to survive, but many may
not have considered that this statement applies to both residential and
non-residential activity. In other words, gross building area can shelter any activity,
assuming zoning and building code compliance. The quantity of gross building
area provided lot has revenue implications that affect a city’s financial
stability. It also has intensity implications that affect a city’s physical,
social, psychological, environmental, and economic quality of life. Therefore,
the correlation of shelter capacity, activity, and intensity decisions has
revenue implications that determine a city’s financial stability and intensity
implications that affect its quality of life. There are other revenue sources,
but none with the same productivity potential as shelter capacity, activity,
and intensity.
Impervious Cover Database. Impervious cover includes
anything that increases storm water runoff from that produced by land in its
natural state. This includes, but is not limited to, pavement and building
cover.
This data is significant because storm sewers have a limited
capacity to accept runoff. This capacity is expressed as an impervious
percentage limit. This percentage represents a land owner’s share of storm sewer
capacity; and 1 minus this percentage represents the amount of unpaved open
space that should be present on any given lot, unless storm detention or
retention is introduced using on a civil engineer’s calculations.
The problem is that impervious cover percentages are rarely
recorded. This can lead boards of zoning adjustment to grant variances that
unknowingly exceed these percentages. In the worst case, these variances
accumulate along storm sewer lines to prompt flooding and demands for relief
sewers that increase a city’s debt burden.
If a lot is served by a storm sewer or combined sewer, it
has an impervious cover limit that is expressed as a percentage. This
percentage is requested in cell C14 under Engineering Data and must often be
determined by reverse engineering when unrecorded. It is, however, an important
lot-based statistics, and should be influencing variance recommendations and
city design decisions. If the engineering design information remains unknown,
city design decisions will continue to be based on assumption, opinion,
influence, and unnecessary risk.
Zoning Database. A zoning area is a collection of
city blocks containing individual lots and compatible activity. Lot and block
characteristics may differ from one zone area to another, even though they
contain the same zoning district designation. The consecutive zone number
requested in cell C39 for each independent zone area has been introduced for
this reason.
Engineering Database.
There are many fields of information that could be recorded in this
database, but the impervious cover limit percentage per lot is the most
suitable for this essay. It establishes a building cover and pavement
percentage limit. Subtracting one from the percentage produces the unpaved open
space percentage required to protect storm sewer capacity, unless storm
detention is introduced by civil engineering calculation.
Geographic Database. There are many fields of
information that could be recorded in this database as well, but only one is
needed for this essay. A city must know the total acres within its boundaries
before it can evaluate the implications of correlated information from related
databases.
Real Estate Tax Database – Tax. The most important
thing to recognize is that the city real estate tax millage in cell D26 is a
fraction of the total real estate tax collected. The total often leaves the
impression that real estate taxes are high when a city’s percentage can
actually decline with every school tax levy approved. This does not mean that
the total tax is not high. It means that a city can struggle to maintain
essential services when the city share is low.
Real Estate Tax Database – Value. Real estate tax is
a percentage of appraised real estate value. Recording the percentage can be
useful when considering future revenue from potential development. Mapping
existing lot data from cells D33-D35 indicates the existing revenue potential
of land per productive acre within a city’s boundaries. These acres can be
identified by filtering the data in cell B30 in Table 1 for “yes” and mapping
the results.
Real Estate Tax Database – Correlation. Real estate
tax is collected by the county in my State and percentages are transferred to
designated recipients. The data is considered public information, but data linking
to a local jurisdiction database is a current impediment to informed city
design for economic stability. Real-time information will be a dream until county
databases are protected and shared to become part of a relational city database
system like that outlined in Table 1.
Income Tax Database. This is a major source of
potential city revenue that is often compromised by a city’s lack of commercial
employment centers. Its importance is increasing as the city share of total
real estate revenue declines in response to tax levies for other operations
that increase total real estate tax revenue.
Income tax information is already compiled by street number and
occupant but is protected. If it can be
aggregated by block number, zone number, or activity, the data will help a city
understand its revenue per acre of activity allocated in its land use plan. If
this can be calculated, it can also be divided by the sq. ft. of activity
sheltered per acre to understand the revenue implications of shelter capacity,
intensity, and activity. This information can help City Design plan a
combination of shelter capacity, intensity, and activity allocation that will increase
the productive potential of a city’s total land area.
Other Tax Database – Inventory and Equipment. Inventory
and equipment tax information is already compiled by street number but is
protected. It can be aggregated by block number, zone number, or activity and
divided by the acres and gross building area involved to find a city’s revenue
per acre of alnd sq. ft. of building activity per acre in its land use plan.
This information can help City Design plan a combination of shelter capacity,
intensity, and activity that increases the productive potential of a city’s
total land area.
Other Revenue Database – Not Listed per Lot. This is
a catch-all topic for the many sources of city revenue that cannot be
classified by street number. Individual totals related to a specific land use
activity could be averaged per lot associated with the revenue, or the entire
amount could be averaged over all productive acres in the city.
Expense Database. There are many fields of
information that could be recorded in this database as well, but only one is
needed to explain the correlation of city expense per acre with its revenue per
acre, and the adjustments that can be made to improve revenue its productivity.
TABLE 2 - PHYSICAL CHARACTERISTIC
QUERIES
Column B in Table 2 lists basic information that can be
compiled by linking data items from two or more of the databases in Table 1.
The linking formulas are referred to as database queries, and can be found in Col.
C of Table 2. These queries produce statistical information that is not obvious
without data correlation.. The notes in Col. A identify a few line item queries
that can provide new leadership insights and strategic planning alternatives when
mapped, since columns of statistical information often conceal their geographic
implications.
The ratio of productive to unproductive land is easily found
on lines 9 and 11 of Table 2, and can be mapped with geographic information software.
Productive acres can be further identified by activity, shelter capacity, and
intensity. This is the intelligence needed to begin understanding a city’s current
economic profile, and the strategic adjustments that can improve its economic
stability. These options can be as simple as increasing taxes, but this is a regressive
solution to an unstable problem. A city’s income deficiency can also be improved
by adjusting the shelter capacity, activity, and intensity ratios on lots at
the cellular level of its anatomy, if it can benchmark the current productivity
of these lots as a starting point.
The answer begins with the information assembled in the
databases of Table 1 and the correlation formulas in Col. D of Table 2. The
most unfamiliar data is included under the titles, “Shelter Capacity” and
“Shelter Intensity”. It may be unfamiliar, but is essential when attempting to
understand a city at the cellular level of an anatomy that determines its current
economic status. This is the level where treatment begins. Comprehensive
diagnosis is impossible with annual accounting summaries that simply take the
annual temperature of the patient. A budget can be a symptom of a much more
fundamental disease that involves inadequate land use ratios of shelter
activity, capacity, and intensity within a city’s incorporated area. These
ratios, or land use allocation percentages, can be thought of as fields on a
farm with crops of unequal yield that combine to produce overall revenue per
acre. In both cases, inadequate yield produces budget cuts that often impair
future performance.
I’ve referred to shelter capacity and intensity occupied by
activity as physical characteristics. I’ve attempted to avoid the term “urban
form” because it refers to a visual impression produced by all four divisions
of the Built Domain. The Shelter Division is building mass, pavement, and open
space introduced at the project level and woven into urban fabric with its
Movement, Open Space, and Life Support Divisions. I’m exaggerating to some
degree because public open space is rarely woven throughout a city. It would be
more accurate to say that projects containing building mass, pavement, and open
space are woven into urban fabric with its Movement and Life Support systems. The
first description is desirable. The second is often reality that produces
excessive intensity.
TABLE 3 - ECONOMIC CHARACTERISTIC
QUERIES
Table 3 is a statement of city economic characteristics that
could not be produced without correlating the data from Tables 1 and 2. The correlations
in Table 3 produce line item information in enough detail to understand and
evaluate the economic performance of a city’s current land allocation for
shelter capacity, activity, and intensity. Allocation for land use
compatibility is simply not adequate to ensure a city’s economic stability.
Total revenue per productive acre in cell A49 is equal to
the sum of the subtotals in cells A13, A23, A34, and A43. Total revenue per
productive acre in cell A49 will equal total expense per productive acre in
cell A58 because it is mandated by law, but the significance is represented by
the average value found In cells A49 and A58. The value is a benchmark that
gains significance when a city considers the quality of life it has been able
to deliver with the funds available from this average yield per productive
acre. The significance increases when the average is compared to average yields
per block number, zoning area, zoning district, and activity group acre in
cells A50-54. If these yields are less than the average required to balance the
budget, they are being subsidized by others and the challenge is to increase
their subsidy with shelter capacity, activity, and intensity at other locations
that have greater potential yield per productive acre.
If a city can predict the gross building area options that
can be constructed on any given land area, it can predict the real estate and
income tax revenue that will be generated by these options when occupied by
various activity groups. This can be useful when considering development and
redevelopment that will improve a city’s average revenue per productive acre. A
building category forecast model and design specification template can be used
to make these predictions.
Building category choices and their related forecast models
are listed in Table 4. The forecast model CG1L listed in Table 4 and presented
in Table 5 will be used for this example. I’ve written about this model, its
design specification template, master equation, and forecast panel many times
and will try not to repeat myself. The gross building area predictions in cells
B42 to B51 are a function of the design specification values entered in the
boxes of the template and the floor quantity options entered in cells A42 to A51.
These predictions are generated by the master equation in cell A37.
Shelter capacity and intensity implications are predicted in
columns F and G of the Planning Forecast Panel. A change to one or more values
entered in the boxes of the design specification template will change the gross
building area options forecast. If the average revenue per sq. ft. of potential
occupant activity is known, it can be multiplied by the shelter capacity
options in Col. F to determine their revenue per acre implications, and their
relationship to the benchmark revenue per acre found cell C49 of Table 3. If the
prediction is less than the benchmark, adjustments to the values entered in the
design specification of Table 5. A different activity could also be considered
with greater revenue per sq. ft. potential; a different building category could
be chosen from Table 4; parking specifications in cells F33 and 34 of Table 5 could
be reduced or eliminated; unpaved open space could be reduced in cell F11; and
so on.
Table 6 illustrates the increased gross building area,
shelter capacity, and intensity that results when just two changes are made to
the design specification in Table 5. The open space percentage in cell F11 and
the parking requirement in cell F34 have been modified in Table 6. When a floor
quantity of 4 is compared in the two tables, gross building area potential has
increased from 51,813 sq. ft. to 80,747 sq. ft. based on the adjustments. Shelter
capacity has increased to 19,226 sq. ft. per buildable acre and intensity has
increased from 0.845 to 1.442. You can find these intensity statistics within
the Table of Relative Intensity presented as Table 7, but there is no research
to tell you if this intensity level is excessive.
Most cities will not know the average yield per sq. ft. of
shelter for an activity group, but this data can be distilled when the database
information in Table 1 is assembled and used by the query formulas in Table 2,
and the query formulas on lines 14, 24, 35, 44, and 50 of Table 3. For
instance, the query formula in cell C14 of Table 3 draws information from the
databases in Tables 1, 2, and 3 to produce rel estte tax per sq. ft. of
activity. It is critical information that a smart city should know to plan the
future use of its land, and the building mass that grows from each acre to
shelter three dimensional activities referred to as “uses”. In my opinion, the
difference between two-dimensional plans and three-dimensional shelter for
activity has been confused by the term “use”. Two and three-dimensional
correlation has been further complicated by an inability to accurately forecast
shelter capacity and intensity on a given land area, or the land area required
to construct a given gross building area. This essay has attempted to explain
how the two can be correlated with relational databases and forecast models to
search for strategic decisions that lead to economic stability and a symbiotic
future.
PRIVACY
The control of data in government is a serious issue that
involves privacy and accountability. For instance, a county real estate tax
database is visible to the public but secure to prevent tampering. An income
tax database does not permit public access. In both cases, however, a city
cannot accurately evaluate its economic stability and improvement options
without access to this information on a continuing basis. The need implies the
design and introduction of acceptable real estate and income tax interfaces for
local government use in city planning and design evaluation.
This essay has attempted to show that relevant database
information is needed before a city can benchmark its current economic condition
and define a leadership strategy for improvement in the detail needed. This
strategy will involve a shelter capacity, activity and intensity strategy that
begins with a single lot to form the ratios a city needs to generate average revenue
per acre that meets or exceeds its average expense per acre from the land
within its boundaries.
CONCLUSION
I hope the databases in Table 1, the queries in Table 2, and
the benchmark summaries in Table 3 serve, at the very least, to place citizen
participation in perspective. Observing symptoms of disease does not qualify
the observer to offer treatment at the cellular level of its formation. The
attempt is like a general staff planning to invade Normandy with no military
education or intelligence, but plenty of opinion from dominating personalities.
City planning and design is not a social problem that can be solved with
judgment rendered by opinion. It is a physical problem with social,
psychological, environmental, and economic consequences that can only be
resolved with a scientific approach.
I have mentioned that a cell in urban terms is a lot that
can be as big as a farm. Assembling lots for compatible activity is called planning
and zoning, but compatibility has economic consequences. They result from the
ratios of shelter capacity, activity, and intensity that do not provide the
average revenue per acre that a city needs to meet its average expense per
acre. Budget cuts to provide a city’s essential services ensue. This prompts a
debate over the definition of “essential”, and a search for additional revenue
with sprawl; but sprawl lacks an understanding of the shelter capacity,
activity, and intensity ratios required to meet expense on the land within a
city’s boundaries. This lack of knowledge is a threat to our quality of life
within a Built Domain that is currently sprawling to consume our source of life
– The Natural Domain.
Citizen forums are not provided with the information needed
to reach informed decisions because benchmarks are not available. Even if they
were, these forums would not have the forecasting models needed to explore
alternative shelter capacity, intensity, and activity quantities at a cellular
level that has the potential to achieve economic stability when aggregated into
ratios.
Tables 1, 2, and 3 represent my attempt to outline the
relational database information needed. Table 4 is a list of the building
design categories and forecast models that can be used to predict shelter
capacity and intensity options for any occupant activity on any lot in a city.
Table 5 illustrates one of these forecast models. Its Planning Forecast Panel
presents gross building area, shelter capacity, and intensity options for a
given land area. Table 6 illustrates the changes that occur when the
specification values in cells F11 and F34 of Table 5 are modified. Table 7
presents a table of relative intensity that is like a table of blood pressure
levels without the research required to identify healthy spectrums for building
category choices. Taken together, these tables and forecast models represent
the least a smart city should know, in my opinion. A city forum that debates
city planning issues without this city design knowledge will struggle to lead
cellular decisions toward the formation of a stable urban anatomy that is
limited to protect its source of life.