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Wednesday, October 10, 2012

The Influence of Design Decisions

Twentieth century architecture pointed to our symbiotic imperative with two famous phrases: “form follows function” and “organic architecture”. They both meant that a flower grows from its roots in the land and blooms when in harmony with a universe of forces beyond its comprehension, in my opinion. Architecture has roots in the land, but only symbiotic human decisions can make it bloom in harmony with the planet's sovereign power.

Five decisions determine the development capacity of land to shelter activity when surface parking is the preferred storage solution. Architects often take these decisions for granted because they learn to evaluate options and make decisions intuitively; but this impedes the accumulation of knowledge since it depends on talent that cannot be taught. It can only be improved.

These decisions involve five primary variables that affect gross building area GBA potential on a given buildable land area BLA when surface parking around, but not under, the building is contemplated (G1 design category).

I’ll explain these topics by beginning with an equation derived in “Replacing Density”. It stated that:

GBA = f*(CORE) / (1+ (fs/a))
f = Number of building floors
CORE = BLA – (S+M)
BLA = Buildable land area in sq. ft.
S= Project open space as a % of BLA
M = Misc. pavement as % of BLA that includes loading area & pavement beyond parking lot area
s = Average area per parking space in sq. ft., including landscaping, within the parking lot perimeter
a = GBA sq. ft. permitted per parking space provided
This can be reduced to a universal equation with five variables when the equation is unwrapped.
Given: GBA = f*(CORE) / (1+ (fs/a))
GBA*(1+ (fs/a)) = f*(CORE)
Since CORE = BLA-(S+M)
GBA*(1+ (fs/a)) = f*(BLA-(S+M))
GBA = (f*(BLA-(S+M))) / (1+ (fs/a))
When BLA=1,
Equation (1):  GBA = (f*(1-(S+M)) / (1+ (fs/a))   
NOTE: GBA is expressed as a fraction of BLA

The five variables in Equation (1) are (f), S, M, (s), and (a) and the values assigned represent shelter design decisions that set the stage for all decisions that follow. The values (f) and (a) are generally specified in a zoning ordinance, but the values S, M, and (s) are often overlooked. They are critical to successful leadership, however; and their omission is the easiest way to explain why zoning has been able to separate incompatible land uses but unable to avoid over-development and sprawl. Any regulation that omits one or more of these five elements for the G1 design category simply encourages arbitrary debate over isolated detail. Equation (1) shows that all five are needed in the equation and that results are produced by their interaction. The next five tables are examples of this interaction.

Table 1 illustrates the GBA options produced by Equation (1) when project open space S is a variable along the x-axis; building height is a variable along the y-axis; and the values (a), (s), and M are held constant. GBA options are expressed as percentages of BLA and the option range is stated as a percentage of BLA in the upper right hand corner of the table. In this case, the range is 30.1% and begins with GBA=1.4% BLA when (f) =1 and S=90%. Some of these options are undesirable, but research is still not available to support intuition with knowledge.

Tables 2-4 repeat the exercise with different variables along the x-axis. Table 2 illustrates GBA options when the average parking lot area provided per parking space (s) varies. The GBA range noted is 14.2% and begins with GBA=1.15% when (f) =1 and (s) =900 sq. ft. of total parking lot area per parking space.

Table 3 illustrates GBA options when the parking space requirement per thousand sq. ft. of GBA (alt-a) varies along the x-axis. The GBA range noted is 192.4% of BLA and begins with GBA=7.6% when (f) =1 and (alt-a) =20 parking spaces required per thousand sq. ft. of GBA.

Table 4 illustrates GBA options when the miscellaneous pavement percentage M varies along the x-axis. The GBA range noted is 12.3% of BLA and begins with GBA=12.9% when (f) =1 and M=25% BLA.

Table 5 is the primary battlefield of zoning. It presents GBA options when project open space S varies along the x-axis and parking requirements (a) vary along the y-axis. The number of building floors is constant at f=5 for this example. The greatest development capacity potential GBA can be found in the S.1, or 10% open space, column. This GBA capacity can be increased further by requesting a variance to the parking requirement (a) that applies. For instance, a variance from 5 to 4 parking spaces per thousand sq. ft. of GBA would produce a 9% increase in GBA potential in the S.1 column.

The ten percent open space in Table 5 is not desirable, nor is 375 sq. ft. of parking lot area per space, and a parking requirement of 4 spaces per thousand is not enough to support some land use activities. I make this point because I’m not trying to advocate individual design specification values. I’m trying to explain how they interact. When one or more is omitted it is impossible to accurately predict development capacity with Equation (1); and I have already pointed out that three are often overlooked in zoning ordinances and the other two are considered independently. In other words, it is impossible to plan and lead shelter for growing populations within sustainable geographic limits when these equations are not understood. This in turn makes it impossible to protect the Natural Domain from sprawl and the Built Domain from excessive intensity because special interest arguments often trump public uncertainty.

At the present time my guess is that most zoning ordinances do not regulate project open space S, miscellaneous pavement M, and/or minimum parking lot area per space (s). Even if they did, their requirements in isolation can be contradictory when not correlated.

In addition, zoning requirements are rarely based on a land use plan with self-imposed geographic limits; or a massing plan for the urban form of shelter that is correlated with its physical, social, psychological, environmental, and economic implications. In other words, city planning has separated incompatible land use activities, but it has done it with sprawl that threatens our source of life.

Architects take the five variables in Equation (1) for granted. They use intuition to correlate these elements with design sketches. He or she cannot complete a project without considering these five variables; but the graphic methods of solution have limited the options that could be evaluated, and their mathematical foundation has been overlooked by an emphasis on “talent”. This has limited the accumulation of knowledge.

Table 6 summarizes the results in Tables 1 -5, but these results only illustrate how Equation (1) works. They do not represent recommendations. All results are expressed as a multiple of the buildable land area BLA available.
It should be fairly obvious from the results that building height (f) influences development capacity GBA, but that the surface parking requirement (a) is the most influential. A closer look at Table 3 will explain this more fully. If you look at the (f-1) row, the development capacity GBA varies from 7.6% to 40% of BLA depending on the parking requirement (a). This is a range of 32.4%. The two-story range is 64.7%. The five-story range is 161.8%. All of these ranges are a function of a variable parking requirement (a) within a constant building height (f) row, and they all exceed the development capacity ranges available in the other tables. In other words, modifying the value (a) has the greatest potential to increase development capacity GBA for the G1 design category. It’s not hard to understand the number of variance requests from parking requirements (a) when looking at the potential GBA range in Table 6, but keep all of the tables in mind. It is not only (a) that influences development capacity, and design leadership must have all five reins under control to prevent a run-away.

Design begins with the correlation of relationships in Equation (1). This defines the context format for architecture and city design when the G1 design category is considered. Buildings emerge from a context format and symbolize our progress toward shelter for growing populations within symbiotic limits that do not threaten our source and quality of life.

Intensity and over-development become a problem because project open space S, miscellaneous pavement M, and minimum parking lot area per parking space (s) are rarely specified or correlated in zoning ordinances. The omission inadvertently emphasizes building mass and pavement. The public is compressed in a right-of-way of increasing traffic and pollution. Storm sewer capacity is threatened by excessive impervious cover, and the list goes on. The battle is often fought within Tables 1-5 when surface parking is involved, and many real estate investors / building owners / speculators would prefer choices in the left-hand column of each, but this is a recipe for excessive intensity that will not protect our quality of life.

Architects will have a lot to offer when they decide to quantify intuition, demand context, and accumulate knowledge to defend design decisions that benefit the public interest.


In the end it’s all about gross building area GBA potential on a given buildable land area BLA because GBA can be used to shelter any activity. For instance, if you divide the gross area of an existing apartment building by the number of dwelling units present the result will be an average gross dwelling unit area ADU statistic. If you divide the GBA forecast for another buildable land area by the same ADU, its dwelling unit capacity can be predicted, assuming the same dwelling unit mix and areas. The critical piece of information is the development capacity GBA of land, and it can be forecast.

Single family homes are no different. Gross building area shelters a specific activity on a given land area. The GBA capacity of a residential lot is a function of the five variables in Equation (1). Gross building area is the universal currency. It is simply tailored to meet the needs of a specific activity. I’ll have more to say about this in the future.

Wednesday, October 3, 2012

Quantifying Intuition

Intuition is telling many that we can’t continue to consume, pollute, and disrupt the land and resources of the Natural Domain without consequences. When the built environment includes agriculture, I’ve referred to the combination as a Built Domain that must not consume our source of life - the Natural Domain. When population grows within a limited Built Domain however, shelter intensity must increase based on the development capacity of land because sprawl is not an option. This means we must be able to efficiently and comprehensively predict shelter capacity options and evaluate the impact of shelter intensity on our quality of life.  In other words, intuition is telling me that we must protect our source of life from sprawl and our quality of life from excessive intensity within sustainable geographic limits.

I have focused on the prediction of shelter intensity options with templates related to generic building design categories to quantify the evaluation of options within sustainable geographic limits. These building categories are part of a classification system for the Shelter Division of the Built Domain. Choices within the classification system lead to a specific forecast model. The values entered for each template topic in a forecast model are used by the model’s embedded equations to predict gross building area GBA options in its planning forecast panel. These GBA alternatives represent potential levels of intensity for a given buildable land area. Table 2 illustrates the specification template and forecast panel format of a typical model.

Forecast models can be used to evaluate the development capacity of land areas in a city, but first let me explain the term. Development capacity is the gross building area GBA that can be constructed under the conditions specified in a design specification template. Changing one or more values in a template changes the GBA forecast. The GBA produced by a set of template values has also been referred to as a level of intensity. This means that design specification templates can be used to correlate land use allocation with intensity. This knowledge can be used to predict anything that is a function of gross building area intensity such as, but not limited to, population, traffic, construction cost, and return on investment; not to mention municipal revenue and expense.

Urban economic stability is a function of land use allocation, shelter intensity, building condition, and prosperous activity. This combination also affects a city’s physical, social, psychological, and environmental quality of life. Even if you don’t agree that our source of life can be consumed by the sprawl we build, this may persuade you to more seriously consider the impact of intensity on a quality of life that begins with the economic stability present.

In other words, shelter intensity is the relationship of building mass and pavement to open space. It is a condition that can be measured, predicted, and has lifestyle implications. In my opinion, it is one key to protecting our quality of life within sustainable geographic limits; but many are required. For instance, acceptable levels of shelter intensity must be supported by organic functions before we can consider them part of a symbiotic survival solution.

The term “organic” began with the paraphrase “form follows function” from the poetry of Louis Sullivan. “Organic” was a Frank Lloyd Wright translation that referred to building style, space, appearance, and landscape integration; but this was not Sullivan’s intent in my opinion. Sullivan meant that a flower blooms from organic function that is programmed by design from a power beyond comprehension, and that building design must attempt to emulate this example. Building appearance has yet to bloom from organic function, and this is the design challenge architecture, city planning, engineering, and science have been given. The fact that this must occur within sustainable geographic limits introduces the issue of development capacity and shelter intensity.

I was able to forecast the development capacity of land (gross building area potential per acre) long before I was able to calibrate the intensity options represented with a standard measurement system. In fact, I’ve made a number of attempts that were too complicated to explain or too simple to consistently lead many efforts toward common objectives. This is my best effort to date, but it only addresses shelter intensity. Organic architecture is still a dream that began with fine art when domination began to threaten global survival and coexistence became a common concern for many. It continues to remind us of the goal that must be won.

Intensity design represents the context format for organic architectural functions. In other words, the urban form of shelter is produced by intensity design that must eventually be served by organic systems.  Shelter intensity represents my effort to begin quantifying context intuition, and it begins with the following assumptions. The result is an intensity equation and a method of intensity measurement that can help us index research and build knowledge for succeeding generations. In the end organic functions will improve our chances of survival and intensity context will make life worth living.


1)   Increasing building height (f) increases intensity INT on the same land area.

2)   Increasing gross building area GBA increases intensity on the same land area.

3)   Increasing parking, loading, and miscellaneous pavement PVT increases intensity on the same land area.

4)   Increasing a project open space percentage S decreases intensity on a given land area.

Based on these assumptions, intensity increases when the number of building floors (f) increase and project open space S remains constant on the same land area. Intensity declines when project open space increases and building height remains constant. In other words, f/S represents a partial index of intensity. This explains the relation of building height and project open space to intensity but does not explain the relationship of gross building area and pavement.

Gross building area and pavement combine to produce total development area TDA. Intensity increases when total development area increases and the buildable land area BLA remains constant. Intensity declines when the buildable land area increases and the total development area remains constant. In other words, TDA/BLA also represents a partial index of intensity.

To think of intensity as simply a function of building height overlooks the effect of building mass, pavement, and project open space. Intensity is a function of all four. Any equation that predicts intensity INT therefore must show that intensity on a given land area increases with building height (f) and total development area TDA. It must also show that intensity INT declines when project open space S and/or buildable land area BLA increase.


Multiplying (f/S) by (TDA/BLA) is a simple way of expressing these intensity INT relationships.

INT = (f/S) * (TDA/BLA)

The equation states that intensity INT increases with increasing f and TDA values. It declines with increasing S and BLA values. In other words, increasing building height (f) and/or total development area TDA increases the intensity INT on a given buildable land area BLA and project open space provision.

This equation illustrates the complexity of intensity when you realize that total development area potential TDA is equal to gross building area plus pavement; that both are a function of many values entered in the design specification template of a forecast model; that one or more values in a template can be changed to produce a new TDA forecast; and that many templates are needed to define the range of generic building design categories available. (See Figures 1.1, 1.2, and 1.3 in “Planning with Architectural Intensity” for decision trees that lead to a specific forecast model. Each model includes a customized design specification template.)

To avoid confusion, I have referred to gross building area GBA options as “development capacity options” and to gross building area plus pavement options as “total development area options” TDA. In other words, TDA=GBA+PVT. Pavement area PVT is equal to the sum of parking, loading, and miscellaneous pavement areas.

Table 1 presents several generic intensity calculations to illustrate a range of intensity results. Table 2 illustrates how intensity is predicted in the forecast model CG1L when a full set of design specification values is entered.


Shelter intensity is similar to blood pressure, which is an analogy I’ve used in the past. Blood pressure is a benchmark that indicates the health of a complex set of anatomical relationships. Samuel von Basch is credited with the discovery of systolic blood pressure in 1881. Scipione Riva-Ricci introduced a more easily used version in 1896. Harvey Cushing modernized and popularized systolic measurement after visiting Ricci around 1900. Nikolai Korotkov added diastolic measurement in 1905.

Medical history is not the point, however, even though I’ve always found it amazing that medical progress of significant general benefit only began in the 20th century. My point has been that von Basch developed a method of measurement  for intensity based on his intuitive belief that there was a relationship to illness. Diagnosis was correlated with measurement and knowledge accumulated over time to improve the credibility of prediction.

I am suggesting that shelter intensity is a similar topic related to the anatomy of our Built Domain. It can be measured and correlated with the evaluation of health, safety, and welfare that ensues. The knowledge gained will add to the credibility of planning and prediction based on measurement with leadership potential, and I hope it will help us learn to live within sustainable limits that do not threaten our source of life with sprawl and our quality of life with excessive intensity.