CONTRIBUTING KNOWLEDGE

 

I have recently self-published The Equations of Urban Design: Leading the Evolution of Shelter Capacity, Context and Intensity within Cities, 2020. It has taken me three books to arrive at the final building design classification system, architectural algorithms, and master equations that make the prediction of shelter capacity for any given land area mathematically predictable and scientifically consistent in my fourth. (Shelter capacity is gross building area in sq. ft. divided by the acres of buildable land occupied.) This is significant for two reasons: (1) Every acre we consume to expand the shelter, movement, open space, and life support divisions of the Built Domain is an acre we remove from our source of life; and (2) The scope of activity on every acre we consume must produce revenue that combines to produce an average yield per acre equal to a city’s average expense per acre to provide a desired quality of life.

The percentage of each activity sheltered within a city determines its economic potential to support a desired quality of life, but excessive building mass, pavement and movement can compromise the pedestrian spaces remaining with oppressive intensity. Economically stable proportions of shelter for activity can now be measured and predicted at the cellular level of the urban anatomy. This means we can write our own DNA for the sustainable urban aggregations we must form with the equations of urban design.

I’ve added a second book entitled, Symbiotic Architecture: Collected Essays on Architecture, Urban Design and Development Capacity Evaluation, to contain essays that have been stepping stones on my path to the completion of “Equations”. It contains 63 of the 178 essays that have appeared on my blog. Some have also appeared at various other receptive sites such as Linked-In and Facebook. The essays in this book have been left along the path I have traveled. I hope they stimulate the work we need to undertake. Both books are available from Amazon.com.

ZONING DESIGN SPECIFICATIONS: Expanding the Language of Urban Design

Note: Table 1 is located at the end of this text. The complete book can be found on Amazon.com.

Zoning ordinances attempt to lead the increment-

al growth of urban areas with a vocabulary that has not consistently produced success and avoided failure. It addresses the Shelter Division of a Built Domain that is served by its Movement, Open Space, and Life Support Divisions, but its success to date can be summarized with the terms “sprawl” and “over-development”. Random success has received awards that hope to encourage similar results, but these awards struggle with inadequate measurement, evaluation, and direction toward the success pictured but not adequately defined. This arbitrary pattern of success and failure can be improved with an amended vocabulary of zoning specification and the design leadership it enables.

The gray boxes in Table 1 combine to define the characteristics of a building served by grade parking around, but not under, the building. The shelter design alternative is referred to as G1. In this example, gross land area is given and its capacity to accommodate gross building area is to be found. Answers depend on the values and floor quantity options entered in the gray design specification boxes of Table 1. The values entered are processed by the algorithm noted in the Planning Forecast Panel of Table 1. Optional line item answers are related to the floor quantity alternatives entered in cells A44-A53. The line item implications of each gross building area option are calculated in the adjoining Implications Module.

Shelter capacity, or gross building area per buildable acre, is calculated in Col. F of the Planning Forecast Panel and may be occupied by any activity. It is a critical piece of planning information that will determine our ability to shelter the activities of growing populations, without excessive intensity, within geographic limits that do not expand to consume our source of life.

The G1 design values entered in the gray specification boxes of Table 1 define the relationship of building mass and service pavement to the amount of offsetting unpaved open space provided. The master equation in cell B39 is used to predict the gross building area options presented in Col. B of the Planning Forecast Panel. When the forecasts are divided by the buildable acres occupied, the shelter capacity options in Col. F are produced. The intensity of these options is measured in Col. G to compare increasing shelter capacity with its intensity implications.

Gross building area alternatives produce measurable levels of shelter capacity, intensity, intrusion, and dominance within the neighborhoods, districts, cities, and regions they combine to create. These implications are calculated at the cellular level in the Implications Module of Table 1. A change to one or more of the values entered in the gray specification boxes of a design specification template will produce a new set of planning forecasts and implication measurements.

A chosen shelter capacity and intensity alternative can be defined by the specification values that are correlated to create the option. Thousands of technical form, function, and appearance decisions ensue to define a final product, but the foundation is established with these initial urban design decisions. The G1.L1 forecast model presented in Table 1 enables measurement, evaluation, prediction, and definition. The topics and values involved create an urban design vocabulary that can be used for knowledge formation and leadership improvement within a Built Domain that must be limited to coexist with its source of life – the Natural Domain.

When values are entered in the gray boxes of Table 1, they define the contents of a G1 cell in the urban anatomy. An algorithm correlates these choices to calculate leadership information. The correlation produces a prediction of options in a Planning Forecast Panel and a prediction of implications in an Implications Module. These predictions will change whenever one or more of the values in the specification are modified. The process offers the opportunity to measure existing conditions and predict future capacity and intensity options with a consistent set of criteria that permit comparison and evaluation of the implications calculated. As a result, success can be measured, failure can be avoided, and knowledge can be accumulated on a track parallel to that of traditional aesthetic criticism.

A zoning ordinance attempts to consistently produce the results intended by the master plan it supplements based on a concept of minimum standards that are written to protect the public’s health, safety, and welfare. The problem has been that these standards have not been mathematically correlated. The resulting contradictions have been one source of “hardship” variance requests and inconsistent judgments by appointed residents from the community. Table 1 resolves this issue for the G1 Building Design Category by mathematically correlating the design topics and items that interact to produce gross building area results for any given land area; and it calculates the capacity, intensity, intrusion, and dominance implications produced by a set of value entries. The fact that these values can be modified to produce alternative results presents the opportunity to evaluate and define correlated sets of minimum standards with the confidence that they will produce the implications forecast.

Table 1 illustrates the use of one fail-safe measure that deserves special mention. The total unpaved open space percentage of a buildable land area must be specified in cell F11. This can be either a planned, present, or required percentage; but the entire topic is often overlooked, ignored, or marginalized in an effort to maximize the gross building area and parking potential of a given land or lot area. When it is ignored, the intensity added to the neighborhood is unknown and the runoff produced by excessive impervious cover is rarely correlated with the storm sewer capacity present or planned. Cell F11 in Table 1 ensures that unpaved open space is included as a conscious decision and correlated within a complete design specification.

The aggregation of unpaved cellular open space can lead to open space arteries that let urban anatomies breathe. Its absence adds to the suffocation of body and soul; but if this argument does not resonate, the absence of unpaved open space has also led to serious flooding implications. A conscious consideration of unpaved open space as a portion of cell content on every lot in a city will be one step toward the arteries of open space needed to breathe life into the urban forest of building mass and pavement we travel seeking the green places we left.

DENSITY and the CORONA VIRUS

NOTE: Tables 1 and 2 are located at the end of this text

I'm writing this during the Covid-19 plague because discussion has begun over the role of density and social distance in its propagation and prevention. We made progress during the 20^th century in addressing density’s relationship to health, safety, and welfare; but our ability to lead density toward a desired quality of life has been severely hampered by our inability to comprehensively define and correlate the components of its definition. I hope to add a few suggestions with this brief essay.


The term “density” has many meanings. In this case it refers to both population and dwelling unit quantity per acre. Excessive amounts have produced terms like “overdevelopment”, “excessive intensity”, and “congestion”. Low density has produced “suburban sprawl”. None of these terms indicate desirable results. They imply threats to either our quality or source of life.

Density is a product of correlated design specification decisions. It does not lead them and it cannot consistently produce desired results when the components of its definition are randomly and incompletely addressed.


Building Design Categories


Shelter density is produced by choices that begin with the selection of one building design category from a universe of six. The six are classified by the method of parking they employ and are: (1) G1 buildings served by grade parking around but not under a building; (2) G2 buildings served by grade parking around and under a building; (3) S1 buildings served by structure parking adjacent to a building on the same premise; (4) S2 buildings served with structure parking underground on any percentage of the buildable land area; (5) S3 buildings served with structure parking beneath a building footprint that may be above, below, or at grade; (6) NP buildings with no parking required. A building design category for parking that is not intended for human habitation is designated PG.


Shelter Capacity Specifications


A building design category choice leads to a specification template in a forecast model related to the choice. Values assigned to items and topics in the specification template are correlated by an architectural algorithm. Summations are used by a building category master equation to predict either: (1) Gross building area options for a given land area; or (2) Buildable land area options for a given gross building area objective.


G1 Building Design Category


As an example, Table 1 applies to the G1 Building Design Category when gross land area is given and gross building area options ae to be forecast. The values entered in its gray boxes define the land area and design concept under consideration. Any value or combination of values in the gray boxes may be modified to test alternate design decisions, but they cannot be isolated from their combined influence.


The values entered in the gray boxes of Table 1 define pavement, unpaved open space, parking, and floor quantity options that are correlated by an architectural algorithm to serve the master equation in cell B39. The equation predicts gross building area options in cells B44-B53 based on the floor quantity options entered in cells A44-A53. Companion building footprint and parking lot area options are predicted in the remaining columns of the Planning Forecast Panel. The secondary equations at the top of each column have been used for these predictions. A change to one or more of the values entered in the 27 gray boxes of Table 1 would produce a new table of gross building area predictions in its Planning Forecast Panel.


Implications


The results predicted in the Planning Forecast Panel have the shelter capacity, intensity, intrusion, and dominance implications forecast in cells F44-J53 of the table’s Implications Module. These implications vary with the floor quantity options in cells A44-A53 and are the measurable characteristics of density produced by correlating the 27 specification values entered in the Design Specification Template of Table 1. 


The first thing to notice in Table 1 is that there is no mention of dwelling unit quantity in the Implications Module. The calculation is not included because a G1 building is a “shell building”. It may be occupied by any permitted activity. In this discussion, the term “density” applies to gross building area per acre, or shelter capacity, and represents measurable quantities of intensity, intrusion, and dominance.


The values entered in the gray boxes of Table 1 represent the building design decisions associated with G1 density; and they must be correlated to lead the relationship of buildings, parking, pavement, and unpaved open space toward a desired objective. These are the site planning decisions that set the stage for all ensuing building form, function, occupancy, and appearance decisions. The extent of topics involved explains the broad spectrum of design possibilities that can be created, since one or more value changes will produce a new forecast of options - and not all are desirable.


Intensity


Physical intensity is created by the extent of building mass, pavement area, and floor quantity introduced per acre. It is offset by the amount of unpaved open space provided. Social density is produced by population quantity per acre. In other words, shelter capacity, intensity, intrusion, and dominance are created by building mass and its surrounding site plan support. Implication topics are the measurable characteristics of shelter density that can be led by a master equation that is served by design specification value decisions. Occupancy may vary over time, but the physical impact of building mass, pavement, open space, and floor quantity remains constant until physically modified, and I repeat that not all options are desirable.


Population density is a separate issue that is enabled by shelter capacity. Excessive population density and shelter intensity eventually produced the planning, zoning, and building regulations of the twentieth century; but the partial, uncorrelated regulations written to address over-development and blight have been unable to arrest the flight from excessive density and oppression. Flight from congestion and intensity continues to create sprawl that threatens our source of life. It has been relatively easy to ignore these conditions for the sake of population growth and economic development in the past because the planet was considered a “world without end”, but the corona virus is forcing us to consider the issue of physical and social distance more carefully. This will require an improved leadership language capable of correlating the design decisions that combine to determine the physical capacity, intensity, intrusion, and dominance of shelter that is served by movement, open space, and life support within cities.


We use the term “over-development” to describe physical excess when we see it, but have not been able to define the condition with leadership precision. Table 1 has just illustrated the 27 design specification items and topics associated with the definition when the G1 Building Design Category is involved. It illustrates the design specification quantities, architectural algorithm, and master equation that combine to calculate the three dimensional implications of correlated G1 shelter capacity design decisions. The issue of social distance and density is inextricably associated with these intensity decisions. When correlated, they represent a leadership recipe for the shelter capacity, intensity, intrusion, and dominance that emerges. These initial massing decisions are then shaped by thousands of additional form, function, and appearance choices. There is no “world without end”, and we must adjust our definitions of growth and intensity to protect a source of life that we currently threaten with our limited awareness.


Population density is enabled by the physical intensity of building mass, pavement, and unpaved open space that serves the population. Building design categories, design specification quantities, and master equations produce shelter capacity, intensity, intrusion, and dominance options for these populations. These are the physical components of intensity that can be measured without reference to the occupant activity involved. In other words, it is a universal measurement system for the impact of shelter capacity within cities. This means that capacity can be led to produce the intensity and distance objectives we must define to achieve our public health, safety, and quality of life objectives. 


Our leadership language must improve before we can begin to guide shelter capacity in our Built Domain toward a relationship with the Natural Domain that protects our quality and source of life.


Apartments – the G1.R3 Activity Group


Table 2 introduces an apartment module on lines 34-48 to illustrate what happens when a G1 Building Design Category or “shell building”, is occupied by an R3 Apartment Use Group. The apartment occupancy proposal is added to Table 1 in cells A34-J47 of Table 2. I won’t go into great detail concerning this table because I have a limited objective.


My first point is to illustrate that the traditional residential density calculations in Col. N of the Implications Module result from the 51 design specification values entered in the gray boxes of Table 2. A density calculation does not lead the 51 decisions. It is a product of them. Random results will always occur when there are too many specification options without leadership direction. This is the case when a density limit is used without further correlated specification. 


My second point is that gross building area options predicted in cells B44-B53 of Table 1 have increased in cells B56-B65 of Table 2 because residential occupancy specifications have been added to the shell building specifications entered in Table 1. The intensity implications in cells J56-N65 of Table 2 have increased in response because reduced residential parking has permitted building mass to increase on an increased building footprint area. If you compare the “a” value entered in Cell A36 of Table 1 to the calculated apartment value “a” in cell J47 of Table 2, the reason for the increase becomes apparent. The value “a” defines the building square feet per G1 grade parking space planned, permitted, or required. A higher value permits more gross building area per parking space, and a reduced number of parking spaces permits greater land area for the building footprint. The value “a” in Table 2 has changed because the apartment occupancy defined in Table 2 has replaced the general occupancy statistics in Table 1. It is the only shell specification value that has changed, but the parking revision enabled by apartment occupancy has increased gross building area potential and had a significant impact on potential shelter capacity, intensity, intrusion, and dominance.


Table 2 shows that there are 51 interrelated design decisions that affect gross building area potential when a G1 Building Design Category is occupied by R3 apartment activity. A density range from 28 to 68 dwelling units per shelter acre is possible, as shown in cells N56-N65, given the design specification values entered in Table 2 and the floor quantity options in cells A56-A65. The shelter capacity, intensity, intrusion, and dominance implications vary as noted in cells J56-M65. None of the shelter intensity values calculated in cells K56-K65 may contribute to a desired quality of life on the gross land area specified in cell K3 however, since the social distance implied may contribute to a condition we have nebulously referred to as “over-development” and “congestion”. The point is that we don’t know without further measurement and research.


Summary


The Covid-19 plague has brought the issue of shelter intensity, over-development and social distance to our attention once again and exposed our continuing lack of knowledge. We do know that lower density produces sprawl that expands with population growth to consume increasing quantities of agriculture and the Natural Domain. We also know that excessive density produces intolerable congestion, but our efforts to define acceptable levels have failed to correlate the many building design categories and specification topics that combine to form a definition. We can’t manage what we can’t measure, and this leaves us with a concept of social distance and shelter density that is poorly formed with an inadequate definition. This, in turn, leaves us waiting for a vaccine that will allow us to revert to our old definitions of growth and economic success on a world without end. 


Density has measureable shelter capacity, intensity, intrusion, and dominance implications that are produced by the quantities of building mass, parking, pavement, and unpaved open space introduced. Occupant activity may add social congestion, but a physical pattern is established by design decisions that begin at the site planning stage of shelter creation.


Shelter is served by divisions of movement, open space, and life support within the urban and rural phyla of our Built Domain. Shelter is capable of protecting any social activity and is governed by design specification values that can threaten our physical, social, psychological, environmental, and economic quality of life when uncorrelated and unrestrained.

Covid-19 has given us a glimpse of the threat posed by inadequate social distance and excessive physical intensity occupied by social congestion, but sprawl is not a solution. It is a threat to a Natural Domain that is our source of life. The dilemma is forcing us to face public policy issues of growth, density, intensity, and geographic limits on a planet that is no longer a world without end.

A Farmer Knows More Than a City

A farmer's field is like any city zoning district. The yield per acre from both must be subsidized when less than the cost of support per acre. (To visualize municipal yield, divide the total revenue received per lot, block, zone, or tract by the taxable acres within these boundaries. Compare this revenue per acre to a city’s total annual expense divided by its taxable acres.) The municipal revenue imbalance found in many cases will make it apparent that “big data” is required to provide the information needed to manage the city as a farm, since each must become productive within geographic limits that protect our source of life.

The fact that acreage is a divisor in this yield equation conveys a serious message. If a taxable acre is providing $1,000 to local government, its yield is $1,000 per acre. If the same total tax is provided by 0.1 acre, its yield is $10,000 per acre. This simple arithmetic explains the importance of land use, since a city has a limited number of acres and the activity located on each determines a city’s ability to support its lifestyle. Annexing land to increase these acres can be self-defeating when the activity planned provides new money that proves inadequate to meet a city’s average expense per acre over time. New revenue can be deceptive since it isn't reduced by public maintenance expense that increases with age.  It can be a mirage that declines for later governments and is one source of the disease we call "sprawl". 

I doubt that a city knows the total revenue per acre produced by its individual lots and parcels, census tracts, census districts, or zoning district areas. In this context, a farmer knows more about the productivity of his land and crops, and a city cannot easily change the crops it has planted. A city cannot manage what it has not measured. “Big data” is needed to visualize the city as a farm that must become a productive part of a symbiotic future.


It doesn’t take much to visualize the city as a farm.

·        Every lot, block, zone, and tract produces revenue per acre that is a function of its shelter capacity, intensity, and activity.

·        Total yield divided by taxable acres produces average municipal revenue per taxable acre.

·        Total expense divided by taxable acres produces average municipal expense per taxable acre.

·        Some taxable municipal acres produce less revenue than the minimum required to equal expense and must be subsidized by others.

·        The objective is to improve the average yield from all municipal acres to support a desirable quality of life.

·        The misallocation of land use areas, activity, capacity, and intensity can easily disrupt the fragile physical, social, psychological, environmental, and economic balance a city must maintain to ensure a reasonable quality of life that exceeds a minimum standard of survival.

A few related thoughts have come to mind while writing this.


Property Value. Property value is determined by a city’s ability to deliver basic public services. Value is compromised by crumbling curbs and sidewalks, potholed streets, flooding basements, sewer backups, deficient water quality, failing bridges, traffic congestion, maintenance deferral, government conflict, budget reductions, inadequate social services, and so on. 


The rate of property value appreciation is a function of a city’s school system. A school system has very limited ability to offset the physical decline fought by government, but consumes the greatest share of local tax revenue. Sacrificing basic government services to meet the increasing cost of public education leads to a market-timing exodus as residents become aware that they are investors in a depreciating asset with deficient physical, social, and economic equilibrium.


Minimum Standards. The concept of minimum standards began with Hammurabi for some and with the Ten Commandments for others. The definition of “minimum” has been a battlefield ever since. Protection of public health, safety, and welfare with minimum standards became a grudgingly accepted objective in the 20^th century, but is still seen as an infringement on individual freedom to achieve at the expense of others by those who object to the definition of “minimum”. 


Services defined as “minimums” by some are considered excessive by others; but I doubt that any resident can recite the full list of his or her city departments, let alone the services provided by each. Under these circumstances, it is no wonder that residents often consider the cost of government excessive for the benefit received since many apply to limited segments of the population. A simple list with related costs might help to create a more informed discussion.


Quality of Life. The term “quality of life” has become a frequent substitute for the term “welfare” in an attempt to refine the intent of the term, but in either case I believe the intent has always been to protect the physical, social, psychological, environmental, and economic interests of entire populations from domination by a few under the banner of “freedom”.


Quality of life is compromised by municipal deficits per acre that must be offset with annual budget reductions. We will continue to assume that budget reductions are improvements without the assistance of “big data” evaluation; and will continue to flee decline with metastasizing sprawl in the absence of more informed diagnoses and treatment.

Summary. The expansion of internal urban decline and fringe area sprawl over the face of the planet are visible symptoms of our inability to manage the city as a farm within geographic limits that protect its source of life – The Natural Domain. The Agricultural Phylum of the Built Domain and the entire Natural Domain will remain at risk until cellular content classification, “big data” collection, and leadership language formation improve to support knowledge assembly, diagnostic success, and leadership direction. Science has already taught us that an ignorant parasite will consume its source of life and a symbiotic parasite will survive. City design of the future will reveal if we have learned to live this lesson.