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.