Correlating the Decisions that Create Density

Density in terms of dwelling units per acre is a definition containing two ambiguities and a lack of correlation that has compromised its leadership ability. There is a better way to define an objective that has global significance.

The first ambiguity is an unspecified percentage of the acre that is available for shelter construction. This immediately makes physical intensity an unmeasurable quantity. As an example, portions of the acre may be required for public right-of-way. In this case a “gross acre” is reduced to an available “net area”. Portions of the net area may be consumed by ravines, protected wetlands, streams, and so on. The remainder then becomes a “buildable land area”. Portions of the buildable area may be devoted to common open space shared by a number of dwelling units. In this case the remainder becomes a “shelter area”. A density calculation or regulation that does not accurately define the area on which it is based introduces uncertain intensity, ambiguity, and argument.

The second ambiguity is an unspecified maximum dwelling unit area for the quantity permitted per acre. This problem is easiest to visualize by considering the same dwelling unit on decreasing land areas. The result is increasing amounts of physical intensity, intrusion, and dominance, but we have been unable to calculate or measure these implications. We have simply formed opinions based on individual perception.

The following discussion will explain the measurement topics that are required to adequately define density and predict future implications in the leadership detail required. The template used will address one building design category occupied by one activity group. The format is similar, but not identical, for all forecast models related to six building design categories that are occupied by a wide variety of activity groups around the globe. The alternative is simply a continuation of the methods that have brought us annexation, sprawl, and continuing consumption of both agriculture and the land of our natural domain. I have frequently explained this category classification in the past and will avoid duplication here for the sake of brevity.

Density does not lead shelter design decisions. It is a product of them. The broad design discretion permitted by the concept, and the lack of correlation among its variable determinants, has produced random results fashioned with opinion. Annexation and sprawl symbolize the continuing leadership confusion with excessive land consumption. This isn’t the first time I’ve said this, but I keep trying to simplify the explanation.

It takes a correlated set of design value decisions to determine density, and these measurable decisions have shelter capacity, intensity, intrusion, and dominance implications that can be measured. I intend to explain one set in this brief essay. Many optional design specification values are not desirable, but this remains to be proven with evaluation of accurate, measureable analysis. The objective is simple and probably controversial. We must learn to live within a geographically limited Built Domain that shelters the many activities of growing populations while protecting their quality of life and their source of life – the Natural Domain. We cannot do this with a random approach that depends on opinion without an adequate foundation of acquired knowledge.

I’m going to explain density by leading you through the 51 measureable design specification decisions that lead to this conclusion in the example I have chosen. It will also show that these decisions produce measureable capacity, intensity, intrusion, and dominance implications that we either enjoy or endure within the Shelter Division of our cities. Correlation of these decisions is the key. Quality of life and economic potential are outcomes encouraged by the right decisions.

THE FORECAST MODEL

The following example begins with a given gross land area, a given building design category, and apartment activity as the given occupant group. If you have read my earlier work you will recognize the G1.R3.L1 Forecast Model included as Table 1. The notation “G1” refers to all buildings served by surface parking around but not under the building on the same premise. The notation “R3” refers to gross building areas subdivided to contain three or more single floor apartment “flats” for residential activity. The notation “L1” indicates that gross land area must be given.

Currently, activities are grouped under the term “land use category” rather than “activity group”. This has confused the distinction between a building design category and an occupant activity. This essay is based on a shelter classification system that contains six building design categories occupied by most, if not all, activity groups around the planet. A building design category may be occupied by an activity. It is the correlation of shelter capacity with activity on land that determines the economic potential of the land as well as the physical intensity introduced. We have had to rely on opinion and rules of thumb to assess these opportunities. Opinion is enough to debate the merits of fine art; but only enough to start the measurement and evaluation needed to proceed with an effort to build transferrable knowledge regarding shelter capacity, intensity, and economic stability.

Land Module

This explanation begins with the Land Module in Table 1. Gross land area given in shaded cell F3. An unbuildable land area percentage is requested in cell F4. The area percentages to be set aside for existing conditions to remain and future expansion are requested in cells F5 and F6. Subtraction produces the net land area percentage remaining in cell F7. The percentage of net land area estimated for public right-of-way is requested in cell F8. Subtraction of this area from net area produces the buildable land area remaining in cell F10. The percentage of buildable land area to be set aside for unpaved open space is requested in cell F11. The impervious cover remaining for building cover and all other pavement is found in cell F12 by subtracting the value entered in cell F11 from 100%. Portions of the project area unpaved open space and impervious cover remaining in the buildable land area may be devoted to common area serving more than one building in a larger project area. If so, the percentages of buildable land area contemplated are requested in cells F13 and F14. Common space allocation, when introduced, reduces the impervious cover and unpaved open space percentages of buildable land area remaining. These reductions, if present, are shown in cells F15 and F16. The sum of the impervious cover and unpaved open space percentages remaining in cells F16 and F17 produce the remaining project shelter area in cell F17. Finding this value is the objective of the Land Module.

Core Module

The objective of the Core Module is to subtract all additional estimated miscellaneous impervious cover percentages that reduce total impervious cover percentage in the shelter area. Subtraction finds the core area remaining for building cover and surface parking in cell F32. Defining this core area makes the derivation of a master equation capable of predicting gross building area options for the core area feasible.

The master equation is located in cell B51, but the values (a), (f), and (s) in the equation remain to be defined. The value (f) represents floor quantity. The values under consideration are entered as options in cells A56-A65. The value (a) represents the gross building area square feet that may be constructed per parking space provided. The value (s) represents the average parking lot square feet per parking space planned or provided. Both are found using the R3 Apartment Module.

R3 Apartment Module

I mentioned at the beginning of this essay that density expression does not specify the average dwelling unit area (ADU) used to produce the density involved. It also omits the values (s) and (a) from its definition. These omissions can produce a wide range of shelter capacity, intensity, intrusion, and dominance results.  The R3 Apartment Module has been used to find these values and make the prediction of gross building area (GBA) options feasible with the use of the master equation in cell B51.

The dwelling unit types planned are entered in cells A41-A45. The percentage allocation for each type is entered in cells B41-B45. The habitable area planned for each is entered in cells C41-C45. These habitable areas are increased by the efficiency factor entered in cell F37 to determine the estimated gross building area required for each habitable area in cells D41-D45. The average dwelling unit area involved (ADU) is calculated from the data entered and calculated in cell D47.

The number of garage spaces planned for each dwelling unit type is entered in cells E41-E45. The number of parking spaces planned for each dwelling unit type is entered in cells F41-F45. The average parking area per parking space for all dwelling units planned (a) is calculated in cell J47.

The value (s) is a design variable decision entered in cell F36. The value chosen has a great deal to do with the landscape quality anticipated for the parking lot.

I’ve already mentioned that master equation value (f) is represented by a series of floor quantity options entered in cells A56-A65.

The master equation in cell B51 is the product of an extensive derivation that is not included for the sake of brevity.

At this point I hope it is clear that design value decisions must be entered in the 51 shaded cells of Table 1 before their density implication can be accurately calculated by the master equation in cell B51. Omitting this information introduces too much ambiguity for adequate leadership within a limited Built Domain that must be constrained from consuming its agriculture and source of life.

Planning Forecast Panel

The gross building area (GBA) options calculated in cells B56-B65 of Table 1 have been the objective of this forecast model, and they are a function of the values entered in the shaded cells of Table 1. A change to one or more of these values would produce different forecast results since they are all correlated. This should indicate the sensitivity of the correlation that has been managed with instinct, intuition, and talent for centuries. When these gross building area options are divided by the buildable acres involved, the options that result are presented as shelter capacity (SFAC) options in cells J56-J65.

The calculation of gross building area options for a given land area makes any number of related predictions feasible. A few are calculated with simple arithmetic in Columns C-H of the Planning Forecast Panel as examples of this potential. I won’t spend time with them since they are relatively self-explanatory.

Implications Module

I’ve previously mentioned that an inability to accurately calculate gross building area and shelter capacity per buildable acre can result in estimations that either waste land or produce excessive intensity on inadequate land area. The Implications Module introduces a method of measuring shelter capacity, intensity, intrusion, and dominance that will be produced by a given or measured set of design specifications.

An example set of specifications has been entered in the 51 shaded cells of Table 1. The implications calculated in Columns J-M of the Implications Module are like the first blood pressure measurements. We will not understand their meaning until we measure and evaluate a test group of project areas to understand the unwritten knowledge they convey.

I’ve included density per shelter acre (dSHAC) and density per gross acre (dGAC) in Columns M and N to show how density implications are a product of the leadership decisions entered in the 51 shaded cells of Table 1. As an isolated requirement without the specifications provided, it is simply too imprecise to lead these decisions toward a desired objective.

Dwelling Unit Forecast Panel

The forecast panel beginning on Row 67 of both Tables 1 and 2 is provided to help assess the design implications of the design specification values entered and the floor quantity entered in cell B70. The rounded dwelling unit quantities calculated on Row 71 are intended as a guide, not as a floor plan solution. 

Postscript

I’m going to ask you to imagine for a moment. If you, your client, or an applicant filled in the values requested in the shaded boxes of Table 1 on paper, you could enter them in the digital template illustrated to determine their implications. If prior research and evaluation had defined capacity, intensity, intrusion, and dominance targets, the specification could either be approved or modified with a few keystrokes to meet them. This doesn’t mean that all differences of opinion regarding the values entered can be resolved without recourse to a Zoning Board of Adjustment. It simply provides a more objective, efficient, and comprehensive common platform for evaluation by all parties involved.

               Unpaved Open Space – Stormwater

This is a topic that has been largely ignored in the quest to increase the permitted number of dwelling units on a given land area with the least expense. If I reduced the unpaved open space percentage specified in shaded cell F11 of Table 1 to that shown in Table 2, the dwelling unit quantities in Column D of the Table 2 Forecast Panel increase along with the densities calculated in Columns M and N of the Implications Module. The impervious cover percentage in cell F11 also increases.

Density approval of the increase requested can often overlook the storm sewer capacity required by the increased impervious cover. Later variance approvals for more impervious cover on the same lots can easily increase the problem. The conflict between public benefit and private expense should be obvious. I won’t expand on the issue since I’ve discussed it in previous essays. I note it here because it is one reason for the unpaved open space percentage requested for evaluation in cell F11 of Table 1, and the resulting maximum impervious cover percentage that is calculated in cell F12. These values would apply to all future variance requests as well. If a city defines these percentages and insists on recording these decisions to protect its infrastructure, it will need to depend on accurate site plan review of the percentages requested in the future.

Cell Aggregation

Table 1 is a design specification for a single project area, or cell, in the Shelter Division of our Built Domain. Cells aggregate to form an urban anatomy we call cities in a Built Domain we have failed to recognize as a parasite. The challenge is to measure and design every existing and proposed cell in this domain using forecast models to conserve land and encourage economic stability with the same intensity that we devote to weather evaluation. The anatomy of the Built Domain has not been born in symbiotic harmony with the planet. It has grown under mistaken assumptions. City design with authority is the only remedy available.

Walter M. Hosack: June, 2023

Making the Argument for Shelter Capacity Evaluation

 I have pursued the proposition that we must learn to shelter the many activities of growing populations within limited geographic areas, without excessive intensity; since it seems self-evident that we cannot continue to consume the land for expanding shelter and cities indefinitely -- without consuming our source of life. This effort has produced a new building design category classification system and related series of forecast models for gross building area prediction, measurement, evaluation, knowledge formation, and leadership improvement. The emphasis has been on the gross building area potential of land, the shelter capacity options per buildable acre available, and the intensity implies, since shelter capacity determines the scope of activity we can shelter and the revenue potential implied long before final building plan, form and appearance are established.

Annexation and sprawl currently symbolize our approach to land conversion for shelter construction. Regulating land use relationships with zoning has proven to be a partial answer since this is not simply a matter of compatibility. This concept has stimulated sprawl and unnecessary consumption of an irreplaceable resource. Its limited success has encouraged annexation for more shelter and revenue since the effort gives the illusion of fiscal responsibility over the term of an elected official. This often proves inadequate over time as the annexed area ages and its “new” revenue does not keep pace with the city’s increase in public service cost per acre. Further annexation continues the illusion with more sprawl that often seeks future fiscal rescue with more consumption of a limited resource.

The problem continues because the physical relationship between shelter capacity, activity, intensity, intrusion, and dominance is not understood, cannot be defined, and cannot be mathematically correlated with economic potential. The result has been the promiscuous consumption of land in a blind search for an affordable and desirable quality of life. The tools available have simply been too limited to: (1) evaluate the entire set of options available; (2) build knowledge; and (3) produce leadership direction without unlimited consumption of our source of life.

The forecast models I’ve mentioned are based on a new classification system for building design based on the parking system employed. The result is six comprehensive categories that can produce increasing quantities of gross building area to shelter the many activity groups we have created. This is significant because the amount of gross building area in a city combines with permitted occupant activity to determine the majority of revenue received per buildable acre. The total received must equal a city’s total average annual cost per acre to operate. When it doesn’t a city begins to discuss the alternatives available to balance its budget.

Two templates, or forecast models, have been created for each building design category to predict either:

1)      The gross building area capacity of a given land area based on a given building design category and the values entered in the shaded cells of its design specification panel, or 

2)      The buildable land area required for a given gross building area objective based on a given building design category and the values entered in the shaded cells of its design specification panel.

A forecast model has been created for each of the objectives mentioned above for each building design category. An algorithm in each model processes the values entered for use by its master equation. The equation has been derived to answer one of the two questions above for a given building design category. Results are predicted for a series of floor quantity options entered in the first column of the model’s forecast panel. The gross building area or buildable land area results vary with floor quantity option considered. These options are translated into shelter capacity per buildable acre, intensity, intrusion, and dominance implications with the help of the equations noted in the implications module of the forecast model. The correlation of building category, design specification, forecast panel, and implications module permits the measurement and evaluation needed to build knowledge regarding the capacity of land, quality of life, and intensity implied by the specification values under consideration.

When gross building area can be accurately predicted or measured per acre, anything that is a function of the building square feet involved, such as cost, revenue, location, condition, population, traffic volume, and so on can be measured, estimated, or correlated to achieve a given objective within a city’s incorporated limits. It is the correlation of capacity, activity, intensity, location, and condition per acre that determines a city’s ability to meet a revenue objective and afford a desired quality of life.

To my knowledge, this is the first time that strategic architectural design decisions and implications have been separated, measured, and predicted in mathematical terms that have the potential to lead. Strategic decisions precede such tactical decisions as: building appearance; building form; building setbacks; floor plan subdivision; parking arrangement; site plan appearance; engineering sytems; and so on. Strategic decisions determine the shelter capacity, intensity, intrusion, and dominance present or proposed by the relationship of building mass, pavement, and unpaved open space specified for a given buildable land area. These decisions produce gross building area results and shelter capacity per buildable acre that have not been correlated with permitted activity to achieve an economic objective. They are, however, the foundation for tactical decisions that determine what we see. In other words, strategic and tactical decisions combine to produce and symbolize the quality of life we create.

I’ve called the forecast model package Shelter Capacity Evaluation for want of a better title, and written about earlier versions in my first book with McGraw-Hill in 2001. It relates in my opinion to: city planning, urban design, landscape architecture, architecture, civil engineering, government, law, zoning; real estate development, environmental assessment, demography, appraisal, banking, and investment. These are at least some of the professions that consume or evaluate land to provide shelter for all human activity.

I’ve pursued this effort because I believe we must do a much better job of predicting the capacity of land to shelter the many activities of growing populations within a geographically limited Built Domain. Annexation and sprawl currently symbolize the threat this lack of leadership ability represents.

I am attaching a copy of a sample spreadsheet that predicts the gross building area potential of a given land area based on the variables entered in the shaded cells of its template. It pertains to the G1 Building Design Category (G1 buildings have surface parking around, but not under, the building on the same premise.) When the gross land area is given the forecast model is labeled G1.L1. When a gross building area objective is given and the objective is to determine the buildable land area needed given various floor quantity options, the forecast model is labeled G1.B1. 

PS: I would like to introduce the forecast models I’ve created as a subscription software application and am inquiring if you would be willing to undertake this effort in the hope of multiplying your investment as a percentage of its profit. I am not interested in pursuing the sales and marketing associated with the business aspects of this effort, but am willing to serve as a consultant. I will only respond to investors I consider serious with a proven business track record and a willingness to explore new opportunities.