Monday, February 27, 2017

Updating a Comprehensive Plan


A comprehensive plan update will suffer from later inattention until it is linked to a city’s economic welfare. In other words, a city is a farm and every acre within its boundaries combines to produce an average yield per acre. This yield must meet or exceed a city’s average expense per acre as operating, maintenance, improvement, and debt service expense increase with age. Annexation of acres for new revenue often repeats past mistakes by assuming that the new income will not be reduced by the increasing expense of aging. As expense increases, a city with fixed income is often accused of profligacy when seeking to increase revenue in response. Budget cuts ensue and decline takes one more step toward blight.

As the journey continues, decline becomes visually obvious and flight from fear begins as annexation attempts to surround disease. The disease expands and the city fights to protect annexation corridors and avoid encirclement by surrounding suburbs. This occurs time and again across the nation because a city does not recognize that it is a farm and must understand the yield from each of its acres over time. It becomes more severe when a city is surrounded by suburbs and must meet the increasing expense of aging with inadequate development capacity and activity allocation. At this point redevelopment and increasing taxation become unwelcome visitors met with skepticism, cynicism, assumption, opinion, prejudice, disrespect, and obdurate behavior resistant to change. A comprehensive plan cannot meet these objections with credible solutions until it can explain and correlate land use allocation, shelter capacity, occupant activity, and economic productivity. A new mathematical language and science of city design is required to credibly defend comprehensive plan recommendations.

Arguing for a Science of City Design


These are slides from a presentation that argues for new tools to empower our approach to the growth of cities and the shelter they serve.

THE PROBLEM

Growing populations are currently building shelter for a wide range of activities in a pathogenic pattern of sprawl that is slowly consuming agriculture and our source of life – the land of The Natural Domain.  This continues because we have not been able to accurately predict the development capacity of land given the current disorganized nature of shelter design decisions and regulation.

THE OBJECTIVE

The challenge is to predict our capacity to shelter human activity within limited geographic areas defined to protect our quality and source of life – The Natural Domain.

THE APPROACH

(1)    Comprehensively list the shelter design categories available and their related specification topics;

(2)    Mathematically correlate specification topic relationships; and

(3)    Predict the gross building area potential of land based on the design category chosen and the values assigned to its specification topics.

THE DESIGN CATEGORIES

Six primary design categories may be occupied by any activity group. A category forecast model includes two specification modules and a forecast panel. An activity module is added to the category specification when it is tailored to a specific activity group.

THE FORECAST MODELS

A forecast model is written to predict answers to a question based on the specification values entered. A change to one or more values produces optional answers for comparison, evaluation, and direction.

THE QUESTIONS

1)      How much gross building area can be constructed on a given land area?

2)      How much buildable land area is needed to accommodate a given gross building area?

THE RESOURCES
Table 1 is a current list of forecast models. The Design Category Group can be occupied by any activity. The Activity Group addresses residential activity that occupies the G1 Category of buildings.




Table 2 is a sample of a completed G1 Forecast Model. It contains a Land Specification Module, a G1 Specification Module, and a Planning Forecast Panel. The values entered in the boxes of the specification modules represent design decisions that can be modified for comparison and evaluation. The implications of a mathematically correlated set of design specification decisions are presented in the Planning Forecast Panel.

Column A in the panel is a specification column of optional building heights. Columns B-E forecast the design implications of a full set of optional specification decisions. Column F forecasts shelter capacity per acre of land consumed. It is a key measurement of the land use efficiency being proposed by the design specification. When a city has a limited geographic area, the efficient allocation of shelter capacity and occupant activity determines the total revenue available to support operations, maintenance, improvement, debt service, and quality of life.

Columns G-J calculate the intensity, intrusion, and dominance implied by the shelter specification. They are quality of life measurements that will build knowledge with continued use.



THE IMPLICATIONS

1)      Gross building area predictions are relevant to real estate evaluation and private enterprise economics.

2)      The allocation of shelter capacity and activity per acre determines public revenue potential and municipal economic stability.

3)      Evaluation of the intensity, intrusion, and dominance produced by a design category specification will produce quality of life knowledge and improvement.

CONCLUSION

The solution to a problem begins with a language that can express concepts, define options, measure implications, evaluate results, and build knowledge. The argument for a science of city design[1] is an argument for a language that can begin the search for shelter solutions to the problem of symbiotic survival. Shelter solutions, however, are only one of the many essential answers required.



[1] Hosack, Walter M., The Science of City Design, CreateSpace, 2016. (Available in paperback and e-book versions from Amazon.com. Available in paperback from CreateSpace)

Monday, February 13, 2017

Surface Parking Limits on the Shelter Capacity of Land


A surface parking lot must expand to increase parking space quantity. An increased quantity increases permitted gross building area (GBA), but decreases the land remaining for building footprint (BCA). The conflict is resolved by either increasing the number of building floors associated with the reduced building footprint, or adjusting other areas of the site plan. This rather simple relationship has caused endless debate because surface parking requirements affect the shelter capacity of land, and their credibility is often challenged by the history of a specific activity at a specific location. This essay will ignore activity and drill down to understand the specification topics, values, and relationships that determine parking lot area and reduce shelter capacity for any activity on any land area.
Basics
A parking requirement specifies the square feet of gross building area (GBA) permitted per parking space provided. For instance, a parking requirement of 250 means that 250 sq. ft. of gross building area may be constructed for every parking space introduced.
Figure 1 plots the increase in gross building area (GBA) potential for a 5 story building as the parking requirement declines. (Keep in mind that a parking requirement of 600 is less restrictive than a requirement of 250 because more GBA is permitted per parking space provided.) The x-axis increments represent this decline in GBA restriction. The increase from 5,912 sq. ft. to 41,382 sq. ft. of GBA shows the dramatic impact of parking requirements.
Figure 2 plots the increase in GBA potential per floor of building height as increased amounts of GBA per parking space are permitted along the x-axis. The increase from 4,966 sq. ft. to 41,382 sq. ft. shows the combined impact of parking requirements and building height options.

The increases plotted in Figures 1 and 2 can be found in Table 1. They serve to explain why parking requirements and building height options are hotly debated across the table in every planning office of the nation. Frustration prevails because the accurate prediction options and implications is not a common practice.
Line (cd) in Figure 2 shows that GBA increases decline with each floor of increased building height; and that height increases above 2 stories produce declining increases in GBA. This make these height options increasingly less cost effective when all other design specification values remain constant.
Line (ab) shows that 15,000 sq. ft. of GBA can be achieved with a 1 story building when 1 parking space is required per 600 sq. ft. of GBA, and with a 5 story building when 1 parking space is required for every 275 sq. ft. of GBA. If you look at 20,000 sq. ft. of GBA on the y-axis in Figure 2, you’ll see that it cannot be achieved with a 1 story building; given the design specification values involved. Design specifications will be explained in the section to follow.
The point is that parking requirements and building height options play a significant role in determining the shelter capacity of core land area; that shelter capacity can be occupied by any activity; and that one design category equation makes it possible to calculate its GBA potential on any core land area. (The G1 Design Category is being used for this example and applies when surface parking around, but not under the building, is chosen as a parking design solution.)
Design Specifications and Core Area
The design specification decisions in Table 2 are represented by the values entered in the boxes of Column F. Calculating core area in cell F32 is the objective, since this is the land remaining for building footprint and surface parking after percentage estimates for all other site plan areas have been subtracted.
Cell F34 in Table 2 specifies the parking requirement (a) under study, and cell F33 specifies the total surface parking lot area per parking space (s) estimated. The master equation in Table 2 uses the (a) and (s) values entered; the core area calculated (CORE); and the building height options entered in cells A42-A51 to calculate the GBA options in cells B42-B51. The GBA options from cell B7 to L11 in Table 1 were found by modifying the (a) value entered in cell F34 of Table 2. These (a) value alternatives were noted in cells B6-L6 of Table 1.
The value (a) is only one of 25 specification boxes in Table 2, and a change to one or more of the values entered will produce a different set of GBA options in Col. B. All values were held constant as the (a) value was modified to produce the GBA options from cell B7 to L11 in Table 1.

The Specification Value (s)
The value (a) entered in cell F34 of Table 2 is a straight-forward parking requirement that restricts shelter capacity. The accuracy of all parking requirements is a matter of continuing debate. It results from their effect on GBA potential and the lack of research available to justify the values involved. The only certainty at this point in time is that parking is needed for the transition to sheltered activity, and can be provided in either remote or adjacent locations.
The value (s) is equal to total parking area, excluding loading area, divided by the number of parking spaces provided. The value is easy to find when a site plan can be measured, but difficult to predict. The 18 specification decisions that combine to create the value (s) are shown in the boxes of Table 3. Each of these decisions is a variable that can be modified to alter the results calculated in cells C15-K20. The objective is to find the values in column K. The calculated result in cell K15 shows that the (s) value entered in cell F33 of Table 2 is based on a 90 degree parking layout decision.

The design specification values (l), (a), and (e) in Cells J3-J5 of Table 3 are shown in Figure 3 as dimensions of a parking lot bay. (A “bay” includes a circulation aisle and parking spaces on both sides of the parking aisle.) A parking lot may also contain parking stalls, service stalls, and circulation aisles in addition to those found in parking bays. It may also contain landscape islands to relieve the sea of asphalt. The design specification values (C%) and (L%) in cells J7-J8 of Table 3 are introduced to estimate their presence in this example. The values chosen show that a utilitarian parking lot is planned with little amenity. Parking space angle and circulation aisle options are entered in cells A15-B20 of Table 3, and each value affects the gross and net parking lot areas predicted in cells H15-J20. The number of parking spaces in one row of a bay must be estimated in Cell J6 of Table 3 to forecast parking lot area. The 10 spaces entered in Table 3 produce an average of 406 sq. ft. per space in cell K15. Thirty spaces produce an average of 389 sq. ft. and 5 spaces produce an average of 432 sq. ft. when all other specification values remain constant. Understanding this range of areas can be helpful when choosing the value to enter in cell F33 of Table 2.

The objective is to forecast average parking lot area per space in cells K15-K20 of the Planning Forecast Panel in Table 3. The value in cell K15 has been chosen for entry in cell F33 of Table 2 because it is related to a 90 degree parking angle. The area options in column K are calculated from the values entered in Columns A and B of the Planning Forecast Panel and the values entered in Cells J3-J8. A change to one or more of these values will produce a new forecast.
The point of this exercise has been to explain the scope of design specification decisions that must be correlated to produce the value entered in cell F33 of Table 2. The decisions entered in Table 3 could use refinement, but the underlying purpose is to explain the many related decisions that stand behind the production of a single parking estimate (s), and the major role this value plays in the forecast of GBA options in Table 2.
Surface Parking Coefficients
I’ve created Table 4 to illustrate the impact of (s)-value options on gross building area potential (GBA). The results are based on the surface parking equation GBA=(af/(a+fs))*CORE. When CORE is equal to 1, gross building area potential is represented by the coefficient in this equation. It becomes a function of optional (a), (f), and (s) value decisions, and expresses GBA potential as a multiple of the core area available.
Table 4 places optional (s)-values in boxes above six matrices labeled 350-800. Each matrix locates (f) values on the y-axis and (a) values on the x-axis. A larger (s)-value indicates that more parking lot area is provided per parking space. This increased average area may result from increased landscaping; more generous parking space and circulations areas, or both.

Cell L18 is part of matrix 400 in Table 4. It shows that a 5 story building with a parking requirement (a) equal to 1,000 sq. ft. of GBA per parking space can yield a total GBA equal to 1.667 times the core area available. This is possible when the parking lot contains an average of 400 sq. ft. per space (s). (The maximum building footprint area would be equal to 1.667 / 5 times the core area.) In contrast, cell B14 shows that a one story building and a parking requirement of 100 sq. ft. of GBA per parking space will yield total GBA equal to 0.200 times the core area available when (s) is equal to 400.
In matrix 800, if 100 sq. ft. of GBA (a) is permitted per average parking space (s) equal to 800 sq. ft.; the parking lot will grow much more rapidly that gross building area. This ratio limits GBA potential to 0.111 times the core area as shown in cell B50 of Table 4. If 1,000 sq. ft. of GBA is permitted per average parking space of 800 sq. ft., cell L54 shows that the ratio produces GBA equal to 1.000 times the core area for a 5 story building. If you compare the results in matrix 800 to those in matrix 400, the differences in GBA potential illustrate the impact of the (s) and (a) decisions represented. The significance of the values entered in cell F33 and F34 of Table 2 may now be more apparent.
Line 50 in Table 4 illustrates a typical trade-off decision related to the G1 surface parking design category that is not immediately apparent. In the G1 category, gross building area potential declines when average parking lot area per space (s) increases and all other design specification decisions remain constant. In many cases, more parking lot area per space indicates greater landscape provisions. These provisions soften the impact of asphalt pavement, but fewer parking spaces reduce GBA potential in the G1 Design Category. This places landscape provisions at a distinct disadvantage. It has been exacerbated because comparative options and implications have been limited by the time required to manually prepare alternate site plans. Table 2 was introduced to explain how GBA options can be forecast, compared, and evaluated by changing one or more values in its design specification template. The (s) and (a) parking values represented two of these design decisions. Tables 3 and 4 have explained the implications of choice among these values. The mathematical format involved will not eliminate argument and debate over GBA implications when landscape provisions are considered, but it can eliminate confusion and distrust around a table that has had to depend on intuition, assumption, and opinion for evaluation and conclusion. In fact, confusion surrounds every specification value entered in Table 2 at this time. This has severely compromised the leadership potential of all public and private sectors responsible for the provision of shelter for the activities of growing populations within a geographically limited Built Domain that protects their quality and source of life – The Natural Domain.

Friday, February 10, 2017

Strategic Architecture


Architecture collects and correlates isolated information to reconcile complexity and confusion with logic and talent. The form, function, and appearance of a building symbolize this complicated process. The increasing focus on fine art emphasizes appearance, but does not transfer knowledge for improvement by future generations.

This is not an attempt to abandon fine art. It is an attempt to place it in the context of a creative process that can be taught. Talent will always die with the owner. Knowledge can be inherited, and improvement will enhance the contribution of exceptional talent and its many peers.

An architect is not an engineer, although it could be argued that he/she engages in systems engineering. This is a fancy term for creation of a logical strategy that correlates the contributions from many related technical disciplines. The result is project completion, but this is no longer the goal. Project completion represents the cellular formation of an artificial environment across the face of a planet that is no longer a world without end.

Architecture has been occupied with schematic design for shelter at the cellular level of The Built Domain, and this has contributed to an unlimited, pathogenic anatomy we call sprawl. If you believe that we must learn to shelter the activities of growing populations within a limited Built Domain that protects their quality and source of life – The Natural Domain, then you may agree that architecture has an expanded role to play. It involves strategic planning for schematic design within a limited Built Domain, and this has the potential to protect the public interest. Strategic architecture involves the science of city design,[1] and it can be taught, improved, and inherited. The form, function, and appearance of schematic design that rests on its foundation will begin to symbolize a new Symbiotic Period of human awareness.



[1] Hosack, Walter M., The Science of City Design, CreateSpace, 2016. (Available in paperback and e-book versions from Amazon.com)