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Monday, March 26, 2018

Facing Reality on a Finite Planet


Photo Courtesy of NASA
I have written this in response to an article I read by Alan Berger, a landscape architect who co-directs the Norman B. Leventhal Center for Advanced Urbanism at MIT. The title was, “Cities Are Not as Big a Deal as You Think”.
He advocates an approach called “bioregionalism,” which treats cities and suburbs as a holistic system. The following are a few quotes from his article that have stimulated my response below.
1)      Seto says, “What’s very clear is that we need a language and a finer-grain differentiation of different types of urban life and urban ecosystems, …”

2)      “…for 50 years, people talked about suburbs as distinct from the city,” he says. But as cities and suburbs have changed and blurred together, that distinction no longer makes sense.
3)      Looking across the many different ways that countries define urban in the UN data, Seto says, “‘urban’ suggests a higher quality of life, higher standards of living, and more opportunity for the people who live there.” In developing countries particularly, urban denotes a place where people have access to jobs, running water, electricity, and other municipal services.
4)      That’s a much more basic definition of urbanity than a place with skyscrapers and subways. And when you think about “urban” in that context, it throws the spotlight on how many people are still not living with those basic amenities.
RESPONSE

Mr. Berger’s holistic system contains urban, suburban, and rural areas are that are phyla within a Built Domain that is expanding to accommodate growing populations. The expansion is forming a metastasizing pattern we refer to as sprawl across the face of a planet that is its source of life. Each phylum contains Shelter, Movement, Open Space, and Life Support Divisions in quantities that distinguish one from the other. The Shelter Division in all phyla is served by movement, open space, and life support; but the Movement Division is a servant that dominates its patrons.
Focusing on the distinction between urban, suburban, and rural areas overlooks the threat that is facing us. The Built Domain currently attempts to shelter growing populations in a sprawling, pathogenic pattern that is slowly consuming our source of life - The Natural Domain. In other words, there are two competing worlds on a single planet; and The Natural Domain does not compromise with ignorance. We have been given the responsibility to define symbiotic survival; and discussing the distinction among urban, suburban, and rural areas in the Built Domain overlooks the threat we can now see with satellite photography. I’m not arguing that the topics are irrelevant. In fact, our knowledge is extremely limited at the present time. I’m arguing that the results will produce tactical concepts without the adopted policy, strategy, and goals required to effectively battle an enemy that we refuse to acknowledge. Keep in mind that we were told to be fruitful and multiply at a time when it was a world without end, Amen.
I related to quote (1) because it seeks what I have produced for the Shelter Division of the Built Domain in three books over a lifetime of effort. These books have contributed the language and fine grain differentiation needed to guide the cellular contents of sprawl toward symbiotic solutions. My last book, The Science of City Design, needs to be reorganized and rewritten to simplify its message; but it contains everything needed to begin the search for intelligence that is the foundation for successful leadership direction. The reader could also benefit from a cloud-based collection of spreadsheet applications that would make the pages interactive and far more useful to research efforts.
Our traditional approach has focused on shelter, movement, open space, and life support projects. Our ability to survive and multiply is forcing us to focus on the aggregation of projects on a finite planet. Our leadership success to date is reflected by the sprawl and contamination we now contemplate from space. Our leadership ability will only improve when we recognize a threat and improve the language, policy, strategy, goals, objectives, and tactics adopted in response. It appears that our current strategy is to infect other host planets with parasitic policies that ignore symbiotic reality.

Sunday, March 11, 2018

Elected and Appointed City Planning and Design Decisions


WHAT DO PLAN COMMISSION AND LOCAL ELECTED OFFICIALS KNOW ABOUT DEVELOPMENT?
– Pete Pointer

The question above has stimulated my response. The concept of reasonable men/women governing with opinion will continue to apply when there is limited knowledge to guide leadership decisions. The concept has been borrowed from a judicial system that has always struggled to replace opinion with science. City planning and design are in the same boat. These decisions affect populations and the planet but are based on popular, political, and special interest opinion. It’s like planning to free Europe and the Pacific Rim from tyranny in a town hall with no policy declaration, general staff, goals, intelligence, strategy, objectives, military training, or successful tactics; but with plenty of conflicting opinion and unrestrained behavior. Initiatives such as affordable housing, land use compatibility, bedroom suburbs, urban renewal, and so on are internal urban issues that have distracted attention from a threat that has only become visible with the use of satellite photography.  

The result of our focus on internal urban and suburban objectives has produced metastasizing sprawl across the face of our planet. Sprawl is a disease. It is a pathogenic product of limited awareness; and of mistaken opinion based on the belief that annexation is a solution to population growth on a world without end. Amen. It is a threat that requires an appropriate response based on a new scientific language and decision-making organization that can mobilize and correlate the diverse art and science interests involved. 

In my opinion, the only policy with enough scope to address sprawl will involve city planning and design that is capable of correlating the diverse interests associated with the production of shelter, movement, open space, and life support for growing populations within the urban and rural phyla of a Built Domain that is geographically limited to protect its source of life - The Natural Domain. In other words, the policy must become a declaration of symbiotic survival based on a new language and science of city design.

I have written two essays on my blog and on LinkedIn entitled, “The Least a Smart City Should Know” and “Open Space Metrics” for those interested in pursuing this train of thought.

Friday, March 9, 2018

Open Space Metrics


“Can the lessons of European squares be translated into metrics?”

Pete Pointer



Metrics will evolve from the measurement, comparison, and evaluation of many existing conditions when a consistent method of calculation is adopted. In my opinion, measurement must take the two and three-dimensional characteristics of any study area into account. I suggest that we look beneath appearance to begin with, and measure a project area in acres that includes the buildings surrounding the space. The entire set of measurements would include:



Total project area in sq. ft. and acres: TPA

Total building cover in sq. ft. and acres: BCA

Gross building area in sq. ft. and acres: GBA

Social pavement area in sq. ft. and acres: SOPA

Service pavement area in sq. ft. and acres: SEPA

Unpaved social open space in sq. ft. and acres: UPA



NOTE: In order to find gross building area around the piazza, all buildings would first be converted to building mass. (Mass is an imaginary envelope that encompasses all architectural detail.) Mass would be divided into horizontal slices at 12 foot or 3.658 meter vertical intervals. The area in each of these slices would be added to find gross building area. This includes the campanile.



The following calculations would be based on the previous measurements (SF=sq. ft.; AC=acres):



Shelter capacity SFAC = GBASF / TPAC

Shelter capacity percentage SFAC% = GBAC / TPAC

Impervious cover percentage IMP% = (BCAC + SOPAC + SEPAC) / TPAC

Intensity% = (GBAC + SEPAC) / TPAC



The intensity percentage suggested above is a simplified version of the universal intensity calculation I have suggested in my book. The formula for this intensity index is:



INT = SFAC * IMP% / 10,000



The equation says that the shelter intensity present in a project area, and imposed on a surrounding area, is equal to the shelter capacity in the project area times the impervious pavement percentage planned or present divided by 10,000. The use of 10,000 is introduced to make the universe of intensity options manageable and presentable in a single table of relative intensity. This table is included as Table 1.



Walter M. Hosack, March, 2018


Sunday, February 25, 2018

The Least a Smart City Should Know


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.






Monday, January 15, 2018

City Planning with Architectural Intensity


NOTE: The three tables mentioned are located at the end of this text.

The industrial revolution sheltered urban activity with excessive intensity and lack of concern for the public health, safety, and welfare. Tenements symbolized the abuse and intensity remained an obvious presence with an inadequate definition. Centuries of abuse culminated in the 20th century with a battle over human rights at a time when individual freedom to dominate was unrestrained by the collective freedom to demand a better quality of life. The battle eventually produced zoning plans to separate incompatible land use activity, annexation plans for expansion into agricultural areas, density plans to limit population compression, setback plans to separate building mass within neighborhoods, and building codes to improve the safety and hygiene of shelter design and construction. Unfortunately, this did not solve the fundamental problem of excessive intensity. It could only be defined with emotion that inevitably gave the term negative connotations. The automobile stimulated flight from excessive intensity, experiments with minimum lot sizes began, and suburbs started to form a pattern now referred to as sprawl. It was first mapped as a symbol of growth and success. Aerial photography over time has caused instinct to anticipate a visible symptom of disease as the pattern of sprawl, with pockets of excess intensity, began to form sectors and rings around a core of deterioration that continues to metastasize across the face of our planet. Sprawl begins with an inadequate understanding of shelter intensity. Intensity can actually be a beneficial prescription for the shelter of growing activities within limited geographic areas when it is understood and carefully correlated with relational databases.

Architectural intensity is like blood pressure. It can be measured and predicted with the following equation

EQUATION (1): Intensity = Shelter Capacity * Impervious Cover / 10,000, or

                                                                        INT = SFAC * IMP% / 10,000

Shelter Intensity (INT) is an emotional-psychological response to the relationship of building mass, building height, parking, pavement, and unpaved open space on one or more project areas.

Shelter Capacity (SFAC) is the gross building area present or planned in sq. ft. per buildable acre.

Impervious Cover (IMP%) is the percentage of a buildable land area that increases storm water runoff from that produced by land in its natural state. (E.g. building cover and pavement)

Buildable Land Area (BLA) is the project area that remains in sq. ft. after existing or contemplated rights-of-way, paved easements, and unbuildable areas are subtracted. (This term should not be confused it the more common zoning expression that often means the land area located within the building setback lines on a given lot.)



It has not been possible to accurately predict the vast number of feasible shelter capacity options for a given buildable land area in a brief period of time. The alternative has involved time consuming site plan evaluation of very few options at the drawing board, and intensity has been determined by what will fit on the land available. It is possible, however, to define shelter capacity with a deceptively simple equation.

EQUATION (2): Shelter Capacity = Gross Building Area / Total Buildable Acres Occupied, or

                                                                                  SFAC = GBA / BAC

Gross building area predictions forecast total potential floor area within a simple abstract volume that will contain all ensuing architectural features.

Gross building area measurements include all existing floor area beginning at the exterior perimeter of a building.

Buildable acres (BAC) is equal to buildable land area in sq. ft. divided by 43,560 sq. ft.

EQUATION (3): When the equation for SFAC in Equation (2) is substituted for SFAC in Equation (1), Equation (3) becomes a consolidated expression for architectural intensity.

                                                                 INT = (GBA * IMP%) / (BAC * 10,000)

The fly in the ointment has been an inability to accurately forecast all gross building area options (GBA) for a given land area in less time than it would take to prepare a single site plan; a consistent definition of buildable land area; and a lack of research to define “excessive” architectural intensity. This lack of knowledge and forecasting ability has prevented the careful correlation of shelter activity, capacity, and intensity within limited geographic areas that protect our physical, social, psychological, environmental, and economic quality of life.

CONTEXT

To start at the beginning, there are now two worlds on a single planet. The Built Domain is currently sprawling to shelter the activities of growing populations, and this threatens our source of life with its land consumption. The Built Domain contains Urban and Rural Phyla, and both phyla contain a Shelter Division that is served by its Movement, Open Space, and Life Support Divisions.

We need credible intensity measurement, evaluation, prediction, and correlation with related social and economic databases to connect shelter capacity, intensity, and activity options with their many quality of life implications.

There are six building design categories that provide the overwhelming majority of shelter capacity throughout the world. Gross building area options related to each category are a function of the values assigned to the topics in their design specification templates. A category algorithm and master equation correlate these values with floor quantity options to predict gross building area alternatives, and accurate gross building area predictions make it possible to forecast intensity options using Equation (3). A definition of “excessive” can then be written with the help of research measurement and evaluation.

Table 1 is an example of a specification template and forecast panel for the G1 Building Design Category. Forecasts are based on the specification values entered in its Land Module, Pavement Module, and Building Module. The gross building area predictions in cells B42-B51 and the intensity predictions in cells G42-G51 are based on the specification values entered, and one or more of these values may be modified to test additional options.

Table 2 presents an abbreviated matrix of the intensity options that can be produced by a building design category and its template of design specification variables. At this point it is not possible to draw conclusions regarding “excess” within the table, but measurement of existing conditions can produce the knowledge required.

Shelter intensity at the project level is simply an objective within a city design strategy, however, because projects combine to form neighborhoods, districts, cities, and regions. City design is the correlation of project activity, shelter capacity, and intensity within limited geographic areas to protect a population’s health, safety, quality, and source of life.

POSTSCRIPT

I’ve attempted to keep this as brief as possible in an attempt to convey a concept without complication that runs the risk of confusion, but I’ve mentioned shelter alternatives called building design categories without explanation. I’m including a list for reference in Table 3. Each category is represented by two forecast models. Two are required because: (1) Land area may be given and gross building area options must be found, or (2) A gross building area objective may be given and land acquisition options must be found.

I wrote my third book in 2016 entitled, The Science of City Design, for those who wish to know more. It can be found in paperback and e-book versions on Amazon.com and is intended to introduce a quantitative language that can address the very practical and very emotional dimensions of shelter intensity within limited geographic areas that must be defined before we can hope to reach a state of symbiotic survival.

Copyright: Walter M. Hosack, 2018. All Rights Reserved



Wednesday, January 10, 2018

A City Planning Opportunity


Original zoning laws attempted to separate incompatible land use activity and ensure that adequate light, air, and ventilation reached the internal and external places we inhabit. It was a declaration of human rights at a time when excessive intensity was a function of unbridled speculative interest, limited mobility, and lack of concern for the public health, safety, and welfare. Individual freedom to dominate was challenged by the collective freedom to demand a better quality of life.

Land use is a deceptively simple term. It means the activity that takes place on any given land area. Incompatible land use activity is separated by zoning district plans. Annexation law permits activity districts to expand over natural and agricultural land. The fact that most activity requires shelter, movement, open space, and life support is taken for granted. The result has been a sprawling Built Domain that consumes land as needed. The problem has become increasingly apparent, but awareness does not solve problems. It simply raises questions among populations taught to believe that this is a world without end, to be fruitful, and to multiply.

Sprawl was first seen with aerial photography. Time has shown that sprawl is growing. This awareness has alerted human instinct to anticipate implications; but anticipation requires a language that can measure, evaluate, and treat the problem. Sprawl is land use activity sheltered by building capacity and intensity. It is extended and served by movement, open space, and life support systems. Shelter capacity is simply gross building area per buildable acre. Shelter capacity options are a function of the values assigned to a building category template. Intensity is a function of the shelter capacity chosen from the options available. Intensity measurement and prediction is the key to sheltering growing populations within a geographically limited Built Domain that protects their quality of life from excessive intensity and their source of life from excessive encroachment.

Keep in mind that a building can shelter any activity, assuming zoning and building code compliance. Activity can be moved to another building to achieve land use compatibility, but the physical context of shelter remains to affect our social, psychological, environmental, and economic quality of life. Credible context measurement, evaluation, prediction, and correlation with related databases are needed to connect shelter capacity, intensity, and activity with its many quality of life implications.

Friday, January 5, 2018

An Expanded Role for Architecture


Architectural design is creative leadership with a limited vision that is not applied at the proper level of authority. Its position has been eroded by project thinking and a pattern language vocabulary that prevents accurate communication with the public it wishes to influence. This has severely restricted its ability to reach the people, places, states, and nations that require shelter for increasing activity within geographic limits that do not expand to threaten their source of life. Sprawl contradicts the concept of geographic limits and is a threat currently being met with an inadequate language of isolated, uncorrelated, and contradictory zoning regulations. Architectural relevance will improve when its leadership language can offer a better alternative. Capturing this potential involves a new design language based on the accurate measurement, evaluation, and prediction of shelter capacity and intensity options. Understanding the impact of these options can lead us to reduce and eventually eliminate our random consumption of land for shelter. The first is our source of life. The second is our source of survival. The challenge is symbiotic correlation.



The components of a site plan aggregate to form projects, neighborhoods, districts, cities, and regions. The threat of sprawl cannot be addressed without a comprehensive understanding of these cellular components and their mathematical relationships. These relationships form shelter capacity and intensity options, but the options must be limited before shelter capacity can be provided for growing populations without excessive intensity.



Shelter capacity is the gross building area produced per buildable acre by a building design category. It is influenced by the building design category chosen and the values entered in its design specification template. These values can also be measured at existing locations for comparison, evaluation, and accumulation of knowledge. Shelter capacity options produced by design specification values and floor quantity alternatives are predicted by an architectural algorithm and master equation that are related to a building design category. The values entered in the category’s design specification template represent decisions that may be modified to test alternatives. The gross building area options predicted for a given buildable land area represent shelter capacity options per acre. These options can be occupied by any activity, assuming zoning and building code compliance. These shelter capacity options are translated into levels of intensity by a universal measurement equation. It is a critical measurement, since intensity affects our physical, social, psychological, environmental, ecologic, and economic quality of life within the Urban and Rural Phyla of the Built Domain. We have attempted to escape excessive intensity with sprawl, but are now realizing that sprawl is a disease and a threat to our source of life.



Our primary policy must become symbiotic survival. Shelter is an indispensable consideration. It is served by movement, open space, and life support within the Urban and Rural Phyla of The Built Domain. The goal is to provide shelter for growing human activity without excessive intensity on geographically limited land areas that protect our source of life – The Natural Domain. To achieve this goal, we must be able to address the problem at its cellular unit of growth.



Urban form shelters cellular aggregations of activity. These aggregations must be correlated with economic data to protect the physical, social, psychological, environmental, and economic quality of life created. The effort requires city design that correlates many related technical specialties. Architects are uniquely qualified to address the massing correlation required if they choose to accept the challenge with a new vocabulary and language that is equal to the level of authority and credibility required.



The language begins with a set of forecast models. Each model is related to a building design category within a universal list. Each category is represented by a design specification template, architectural algorithm, master equation, floor quantity template, and forecast panel. The panel predicts the gross building area, shelter capacity per acre, and intensity options implied by the values entered in its design specification and floor quantity templates. The values are correlated by an architectural algorithm and master equation to accurately forecast shelter capacity options for land. Unfortunately, we presently convert this land from its ecological and agricultural role at random to form metastasizing sprawl.



Forecast models can be placed in the cloud for global access, but the values entered in their specification templates will be based on empirical knowledge until a research institute focuses on the distinction between desirable and excessive values. Built Domain classification, building design categories, specification templates, and shelter design values represent an initial vocabulary for the language of city design. Together, they represent the initiation of a science that can address the anatomy of sprawl at its cellular level of formation.



I’ve previously published “Comparing Shelter Design Decisions” to briefly outline the classification system, building design categories, design specification templates, and master equations that form the vocabulary, language, and science of city design. If I’ve maintained your interest, you may wish to review the document.




Photograph by Steve Swayne - File:O Partenon de Atenas.jpg, originally posted to Flickr as The Parthenon Athens, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=17065839

Thursday, November 2, 2017

Comparing Shelter Design Decisions


Updated from Design Specifications & Shelter Intensity written in 2011
NOTE: All exhibits, tables and figures are included at the end of this text. 
I’ve rewritten this essay to correspond with the equations and forecast model format in my new book, The Science of City Design. The equation for intensity in cell G41 of Tables 1 and 2 has also been revised to reflect an improvement in logic and is the basis for a revision to the universal Table of Relative Intensity that is presented as Table 4. These changes first appeared in my essay, “The Single-Family Detached Home Dilemma”. I’m repeating this introduction with a few changes to suit this new title and for its broad applicability. 

INTRODUCTION 

We call the growth of cities “sprawl”, but know little about the cellular content of this pathogenic organism as it spreads across the face of the planet. We call these cells lots, parcels, acreage and so on. They are created by an army of investors and specialists led by incremental plans for land use, property acquisition, subdivision, engineering, architecture, and sales. The plans produce new cells that multiply to slowly consume land that is our source of life. There has been no realistic discussion of geographic limits for cities because there has been no mathematical ability to predict, evaluate and correlate the shelter capacity of cells within these limits. This has made shelter sprawl a default condition based on annexation that produces new cells and revenue to repeat old mistakes at its leading edge of growth with declining cells in its core.  

Shelter capacity is gross building area per acre that can be occupied by any activity, assuming zoning and building code compliance. Sprawl results from population growth that cannot survive without shelter for its many activities, and an inability to correlate shelter capacity and activity with average public revenue and expense per acre over limited municipal land areas. This has prevented us from achieving a desired quality of life within areas that are limited to protect their source of life.

My focus in this brief essay is to discuss and compare the shelter capacity of land for office activity based on two sets of design specification values and the G1 Building Design Category. The effort is based on my belief that we must begin to understand, predict and correlate shelter capacity with activity, revenue, and expense before we can lead the combination to form cities with a desirable quality of life within geographic areas that are limited to protect their source of life. 

In order to proceed, I need to explain building design categories and their fit within a Built Domain classification hierarchy that permits shelter measurement, evaluation, prediction and knowledge to be accumulated, organized and taught on a consistent, comparable basis. This has the potential to improve our leadership performance. 

Population growth has produced two worlds on a single planet: The Built Domain and The Natural Domain. 


The Built Domain is divided into Urban and Rural Phyla that contain Shelter, Movement, Open Space, and Life Support Divisions. Each phylum is distinguished by the significantly different areas associated with the same divisions. (See Exhibit A for a more complete classification outline.) 

The Shelter Division in both phyla is served by its supporting Movement, Open Space and Life Support Divisions. Each cell in the Shelter Division contains one of the eight building design categories listed on lines 1-31 of Exhibit B. These categories are classified by their method of parking supply, not their internal activity or external appearance. This makes it possible to reconcile an infinite number of unique buildings into a common, limited set of classification categories. 

FORECAST MODEL G1.L1 

Forecast model G1.L1 on line 2 of Exhibit B will be used to define and compare two distinctly different projects. 

Typical Office 

Table 1 illustrates the G1.L1 Forecast Model and pertains when gross land area is given. It is occupied by office activity in this example. The values entered in the forecast model are measurements that pertain to the one story office project illustrated by Figure 1. Fifteen variable specification topics are identified with boxes in two design specification modules. The Land Module begins on line 2. The Pavement Module begins on line 23. Each box in a module must receive a specification value to define the two-dimensional site planning characteristics of a proposal. Ten specification boxes in cells A42-A51 of the Planning Forecast Panel receive floor quantity options that represent three-dimensional alternatives. These values complete the information needed by the master equation in cell A37.

In other words, fifteen two-dimensional specifications in the Land and Pavement Modules of Table 1 are correlated with the ten floor quantity options in Col. A of the Planning Forecast Panel to produce the line item options in the forecast panel. A change to one or more of the design specification values entered will produce a new forecast of implications in the Planning Forecast Panel. 

Land Module 

The Land Module in Table 1 is based on a given gross land area of 4.2 acres in cell F3. The module subtracts a number of potential demands on the gross lot area given to arrive at the buildable lot area calculated in cell F10. An unpaved open space percentage is entered in cell F11 and the remaining impervious cover is automatically calculated in cell F12. The impervious cover percentage is used to calculate the amount of buildable land that can be devoted to building and pavement cover in cell M19. 

Pavement Module 

The seven specification values entered in this module are used to calculate the amount of social and service pavement planned or present around the building. The total pavement area is calculated in cell F31 and it too reduces the amount of impervious cover remaining for building and parking lot area.

Core Area

Core area is the amount of buildable land area remaining for building footprint and parking lot area. It is calculated in cell N32 and is one of the values needed by the master equation in cell A37.

Master Equation 

The master equation in cell A37 requires values for a, s, and f in addition to the core area found in cell N32. The value (a) is the gross building area planned or permitted per parking space provided and is entered in cell F34. The value (s) is the gross parking lot area permitted or planned per space provided and is entered in cell F33. The floor quantity (f) required by the master equation is represented by the ten floor quantity options (f) entered in cells A42-A51. 

Gross Building Area Forecast 

The gross building area options forecast in cells B42-B51 are based on the twenty-five design specification values entered in Table 1. A change to one or more of these specification values will produce a new forecast. All additional line item predictions in the Planning Forecast Panel are derived from these values using the secondary equations noted on line 41. The predictions in columns C-E are a small fraction of those possible when accurate gross building area predictions can be calculated. 

Shelter Capacity Options  

Shelter capacity (SFAC) is equal to gross building area divided by the buildable acres occupied. It is predicted in cells F42-F51 of Table 1 based on the equation in cell F41. It is a prerequisite required by the intensity equation in cell G41 and is defined by the following equation when buildable land area (BLA) is expressed in sq. ft.: 
SFAC = GBA / BLA / 43560. This can be reduced to:
SFAC = GBA * 43,560 / BLA 

Intensity Correlation 

Intensity (INT) is the relationship of building mass and pavement to unpaved open space on a given land area in the Shelter Division of both the Urban and Rural Phyla of the Built Domain. The intensity equation in cell G41 of Table 1 produces the column of intensity options from G42-G51. There is no attempt to pass judgement on the intensity statistics presented in Col G, but I will compare the results to the intensity options produced by the corporate office building specified in Table 2. 

Corporate Office 

The values entered in Table 2 are measurements that pertain to the four story corporate office project illustrated by Figure 2. The green open space in Figure 2 is significantly greater than that in Figure 1. Table 2 uses the same G1.L1 forecast model and specification format because both buildings fall into the same design classification category, but the values entered in Table 2 define a completely different set of context relationships. I won’t bother repeating the format explanation used for the typical office covered in Table 1 because the format remains constant. It is the values that change in Table 2. 

When appearance is ignored in Figures 1 and 2, the relationship of building mass and pavement to unpaved open space is called intensity. Intensity can be measured with the equation in cell G41 of Tables 1 and 2. The intensity options for Figure 2 are presented in cells G42-G51 of Table 2. The four-story option on line 45 identifies the characteristics produced by its primary design specification decisions. 

COMPARATIVE SHELTER CAPACITY and INTENSITY

The acres within a city are its raw material. The shelter capacity potential of each acre combines with occupant activity to determine the economic productivity of each acre. From a public perspective, the average yield per acre of municipal land area must equal or exceed its average expense to avoid budget reductions. However, the push to increase shelter capacity per acre to increase its yield per acre can produce excessive intensity that detracts from the quality of life a city is attempting to afford. This means we need an improved method of measuring, evaluating and correlating shelter capacity with occupant activity to improve our quality of life and protect our source of life. The decisions at this land allocation level produce intensity, revenue and context quantities that represent leadership decisions awaiting refinement by talent. 

Comparison

The design specification values in Table 1 define the project illustrated by Figure 1 and are repeated in Col. C on lines 3-34 of Table 3. The design specification values in Table 2 define the project illustrated by Figure 2 and are repeated in Col. D of Table 3 on lines 3-34.

The planning forecast table at the bottom of Table 3 displays the gross building area, shelter capacity and intensity options that were available in Tables 1 and 2. The highlighted lines in the Planning Forecast Panel indicate the massing options chosen. (“Massing” is the relationship of building mass, service pavement, social pavement and unpaved open space in a given project, neighborhood, district, city, or regional area.)

The design decision options that were available for Figures 1 and 2 are repeated in the Planning Forecast Panel of Table 3 beginning on line 41. The options chosen for Figures 1 and 2 are highlighted. A simple comparison documents the obvious and not so obvious. The gross building area illustrated by Figure 1 is much smaller than the provided by Figure 2 because the initial land areas available were quite different and the ensuing design specifications did not cancel the advantage of a larger land area. The shelter capacity (SFAC) for Figure 1 is greater that Figure 2. This means that more gross building area per buildable acre has been provided by Figure 1. It also means that Figure 1 has an intensity level that is 4.3 times the level of Figure 2.

The point is that shelter capacity and intensity can now be measured, evaluated and predicted using a consistent building design category classification system and design specification format, and that comparison can be used to build knowledge that will be needed to shelter growing populations within a geographically limited Built Domain that protects their quality and source of life – The Natural Domain. 

Table of Relative Shelter Intensity

Shelter intensity is like blood pressure. Both readings fall within a matrix of possibilities. Their location within the matrix indicates the level of patient health present. The difference is that shelter capacity and intensity have never been measured, evaluated and predicted; nor has a common matrix been established as a consistent frame of reference. Table 4 is a sample Table of Relative Shelter Intensity that can be extended to encompass the full range of possibilities. The highlighted values locate Figures 1 and 2 within this table and represent two of the relatively infinite number of G1 possibilities that can be created by modifying the values entered in a G1.L1 Design Specification Template. One of the great challenges facing the 21st century and its current definitions of growth and success will involve the definition of acceptable shelter capacity and intensity levels within land use allocation plans that organize capacity, intensity and activity areas to produce economically stable aggregations capable of affording a desirable quality of life within a geographically limited Built Domain. 

PARKING LOT COMPARISON 

The parking lot areas shown in Figure 2 are often expanded to increase shelter capacity at the expense of project open space because every additional parking space justifies an increase in gross building area. Unpaved open space may also be reduced to increase the number of spaces provided. This increases shelter capacity and intensity, but decreases the project’s contribution to a city’s quality of life.

When a low value for the gross parking lot area planned or permitted per space (s) is entered in a design specification template, the parking lot design must use the entire area for pavement. Higher values for (s) include internal area for landscape improvement to reduce parking intensity. The internal parking lot open space implied by the value (s) combines with building cover and all remaining service and social pavement to produce total project impervious cover. This is offset by the unpaved project open space remaining and determines the relationship of people to the project intensity created.  

The value (s) for Figure 2 is low. This indicates that only pavement can be provided for the parking lot. However, the unpaved open space percentage is high. These statistics result from the fact that every parking bay is separated by a project open space finger that is included with the total unpaved open space provided. These fingers reduce the collective intensity of parking and provide a separate pedestrian route to the building entry for every parking space. Another statistical way of accomplishing the same design objective would consider the open space fingers part of the parking lot area. This would increase the total parking lot area provided per space (s) and result in a reduced percentage for the remaining unpaved open space quantity that is not part of the parking lot design. In other words, the same project could be represented by either approach, but the latter approach would place more emphasis on the relationship between pavement and open space within a parking lot perimeter.

More open space means less shelter capacity given the same design specification, but we have not been able to predict the options available; and when you cannot predict you cannot anticipate nor plan for coexistence. Building design categories and design specification values are at the heart of the shelter issue. Zoning regulations represent a 20th century attempt, but they have been incomplete and uncorrelated. The algorithms and equations in The Science of City Design have been written to correlate this interaction, but context research and analysis is needed to define value limits that will offer lifestyle options while protecting our health, safety and quality of life.  

OBSERVATIONS 

I hope Tables 1 and 2 have made it clear that it takes at least fifteen correlated design specification values and one building height value to accurately predict a G1.L1 option with the master equation in cell A37, and that this knowledge opens up a vast number of square foot related predictions. Two of these options have been compared in Table 3.

CONCLUSION

This text has been modified, but first appeared in my essay “The Single-Family Detached Home Dilemma”. 

This essay has focused on site plan quantities defined by the values entered in a design specification template. This is a tactical project issue. The strategic question involves the cellular aggregations that combine to form neighborhoods and districts within a city. The land use allocation of shelter capacity, activity and intensity must produce average revenue per acre that meets or exceeds its average expense per acre for operations, maintenance, improvement, and debt service without excessive intensity. Strategic answers will require data accumulation, correlation, and land management that is beyond the scope of this brief essay. Physical, social and financial balance is feasible, however, when specification templates are linked to each parcel of land within a city and correlated with other data sources to form a complete picture of a city’s current physical, social, and financial condition. This will permit comprehensive evaluation and adjustment with the credibility required to convince others. Planning leadership for a sustainable future cannot proceed without a scientific bridge capable of relating population and activity to its shelter imperative on a planet with limited resources and symbiotic demands. 

We have been preoccupied with growth to counter risk and threat since the beginning of time. This is the origin of the admonition to be fruitful and multiply. We have been successful. It is time to recognize that success from growth is the enemy of balance, and we must adapt to a new level of symbiotic responsibility. The planet is programmed to seek its own balance. I think we intuitively understand that the same law applies to us, but it requires anticipation without proof by extinction. This is a requirement of faith by any name. In fact, faith-based names have distracted us from reality. God has given us the planet. This power has not assigned ownership. It has assigned responsibility. It is up to us to define what that means and adapt accordingly. 

Copyright: Walter M. Hosack, 2017. All Rights Reserved