Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.
3 There has long been a desire by transportation professionals to understand the relationship between the physical conditions at the locations where trips begin and end (the âland useâ), and the trip-making process. Early efforts to gather information on these locations established the need for a systematic meth- odology for the collection and classification of data elements to facilitate analysis. Land use analyses and classification have been used for many years for planning. Bartholomew (1955), for example, published his âLand Use in American Cities,â containing land use data for 97 municipalities over 20 years. Land use classifications were used in many early Metropolitan Transportation Planning studies, including the Detroit Metro- politan Area Traffic Study (DATS), the Mass Transportation Study National Capital Region (MTS), the Chicago Area Trans- portation Study (CATS), the Pittsburgh Area Transportation Study (PATS), and the Penn-Jersey Transportation Study (P-J), all begun prior to 1960 (Zettle and Caril 1962). According to Zettle and Caril (1962), the primary method used for assigning land uses required aerial mapping of the entire study area, with field investigators recording land uses, parcel-by-parcel. This information was used to establish âland useâ areas, by analysis zones, based on class of activity (e.g., acres of residential use) and intensity of activity (e.g., number of housing units, retail sales). The problem with these data collection practices is that they make it difficult to analyze changes over time because they focus exclusively upon the present purposes of a study in the application of land use classifications (Sparks 1958). At the same time, Chapin (1957) stated that land use classification systems must anticipate the âexact needsâ of each application. This chapter reviews classification strategies for categorizing commercial and non-commercial land uses and their related characteristics. In a seminal monograph published in 1965 by the American Society of Planning Officials (Guttenberg 1993), the notion that there should be a land use classification system to adequately understand the major elements of land use was introduced. This included the observable (caused) factors, and the underlying causative factors. The observable (caused) factors include: adapted spaces (sites); physical framework (facilities); and activity type and activity effect (sight, sound, smell). The underlying causative factors include economic processes (functions) and legal relationships (e.g., ownership). This suggests that it is necessary to consider the attributes of âland useâ as a concept that goes far beyond a set of numerical codes. Berke et al. (2006) identified a set of the attributes that should be considered, including: (1) land as functional space devoted to various uses (e.g., urban, rural, residential, commer- cial, industrial, public); (2) land as a setting for activities (e.g., working, studying, recreating, commuting); (3) land as part of an environmental system (e.g., floodplain, wetland, forest, wildlife habitat); (4) land as real estate exchange commodity to be bought, developed, and sold (e.g., ownership, assessed value, price, development feasibility); (5) land as publicly planned, ser- vices, and regulated space (e.g., future land use, density, zoning, infrastructure); and (6) land as a visual feature for orientation and social symbolism (e.g., corridor, node, neighborhood). In transportation, the authoritative source for assigning categories of commercial and non-commercial âland usesâ has been the ITE Trip Generation Manual, 8th edition (Insti- tute of Transportation Engineers 2008). The ITE Manual provides trip generation rates for both passenger and freight trips using a single designated coding system to classify âland uses.â The underlying assumptionâthat passenger and freight trips share the same behavioral mechanismsâis a potential concern for associating freight activities with land uses. For more details on the ITE Manual, see Appendix A. In addition to the ITE Manual, other land use classification strategies with the potential to include freight trips genera- tion include: â¢ Local real property assessorsâ tax classifications; â¢ Local land use planning classification systems: â Local land use inventories, zoning maps, and related land use planning processes and products; C h a p t e r 2 Land Use Characteristics, Classes, and Contexts
â The Standard Land Use Coding Manual (SLUCM); â The pioneering LBCS developed by the Federal Highway Administration (FHWA), American Planning Associa- tion (APA), and other federal agencies. â¢ Employment categories: â SIC codes; â NAICS. â¢ Remote sensing; and â¢ Activity systems approach using geospatial dynamics and a multi-dimensional programming algorithm. Tax Assessorâs Classification Codes for Real Property The development of land use classification codes for tax assessment purposes is a function of state government. See, for example, the New York State Constitution, Article XVI, section 2 (1938): âThe legislature shall provide for the super- vision, review and equalization of assessments for purposes of taxation.â See also Wallis (2001, 123â145), for the history of real property taxation in the United States, and a rationale for its evolution as a tax used primarily at the local level, rather than the federal or state level. Tax assessors are empowered to determine the value of real property for local taxing purposes (Wallis 2001, 123â145, 144â145). To facilitate this process, they use real property classification codes. The Tax Assessorâs classification code, which is cross-referenced to a parcel of land on a map, is a numerical code that classifies the use of the parcel for real property taxing purposes. In some states a uniform real property use classification code is applied. For example, the New York State Office of Real Prop- erty Services has developed a âsimple and uniformâ classifica- tion system, see, New York State Assessorâs Manual (New York State Office of Real Property Services 2006, vii). Pursuant to the New York State Constitution, Article XVI section 2 (1938), â. . . the legislature shall provide for the supervision, review and equalization of assessments for purposes of taxation.â Massa- chusetts also has a uniform real property use classification code (Bureau of Local Assessment 2009). The real property tax asses- sorsâ land use classification codes used in New York are shown in Table 1. However, uniformity is not universal. According to Fisher (1996) 21 states have more than one class of real estate, with the number of classes varying from 2 to 34. For example, the situation in Minnesota is âso complex that it is difficult to specify the number of classesâ (Fisher 1996, 190). In California, designation of classification schema for tax assessment takes place at the county level, in each of 58 counties. Land Use Planning Classification Systems âLand Use Planningâ is a multi-dimensional discipline derived from the complex inter-relationship of physical plan- ning (space); ecology (existing systems on the land); and human systems of land use (demographics, economic devel- opment, industrial, commercial, residential and societal needs, political systems, particularly at sub-Federal level, availabil- ity of funding, and law). Land use planning is implemented through the legal system (Blaesser and Weinstein 1989). For a century, this discipline has been associated with assigning use categories to land areas. Land use inventories are site-specific to the parcel, but land use classifications in zoning maps and ordinances are aggregations, superimposed on land parcels, and assigned to a specific land area on a map. The practice of land use planning is supported by theoreti- cally based but practically oriented concepts such as âEuclid- ian Zoningâ (separation of uses of land, particularly sepa- rating residential from commercial/industrial uses) (Nolon et al. 2008, 156â167); âNew Urbanismâ (mixed residential/ commercial uses, in walkable neighborhoods); âSmart Growthâ (increased urban density, infill development on âbrownfieldsâ and the protection of exurban âgreenfieldsâ for agriculture and open space) (Mandelker et al. 2008, 852â886; Freilich 1999); and âSustainable Developmentâ (attempt to protect and pre- serve local employment opportunities, quality of life, natural resources, and the environment) (Duerksen 2008). Each of these land use planning concepts has been derived from community needs in a historical context, for example, âEuclidian Zoning,â which is named for Village of Euclid v. Ambler Realty Co. (272 U.S. 365 (1926), the U.S. Supreme Court case that upheld the constitutional validity of zon- ing, developed in response to late 19th century public health issues (e.g., urban overcrowding and industrial pollution). âNew Urbanismâ is one response to the downside of subur- banization, including social anomie and sprawl; and âSmart Growthâ and âSustainable Developmentâ attempt to address scarce natural resources and the impact of climate change [e.g., Mandelker et al. (2008, 218â435), Freilich (1999) and Duerksen (2008)]. Although more recent concepts such as New Urbanism have developed in partial reaction to single- use zoning, Euclidian zoning, adopted in a preponderance of states in the course of the 20th century, may provide a frame- work for understanding economic activities at the local level due to the separation of activities. From time to time, model land use codes may be pub- lished, but there is no uniformity of land use codes or their implementation among the states. Decision-making in land use planning, which is complex, takes place primarily at the local level by elected officials and appointed boards and commissions. It also tends to be driven by political con- siderations. Since some of these zoning codes have been in existence for about a century (albeit with amendments), the language that regulates land use is so specific that it can be examined for indications of allowable industries, building sizes and heights, and density (e.g., ratio of floor area to site size, number of shipping docks per square foot of built space) (Mandelker et al. 2008, 281â296). 4
Local Land Use Inventories, Zoning Inventories, and Zoning Maps Making a land use inventory is an initial step for land use planners, using actual observation in a site-specific parcel- by-parcel on-the-ground survey (Berke et al. 2006, 287â473). This inventory can be graphically expressed in a land classi- fication plan (Metropolitan Planning Commission of Nash- ville and Davidson County 2006). In the practice of land use planning, the land use zoning map can vary from the local land use inventory map. For example, the land area on a land use zoning map designated for commercial use can be much greater than the actual land area of commercial uses found on the existing land use inven- tory map. Thus, the land use type âcommercial,â can be applied in a different manner depending on whether it is a land use classification on a zoning map, or an observed land use on an inventory map. 5 Type 111 Poultry and Poultry Products: eggs, 112 Dairy Products: milk, butter and cheese 113 Cattle, Calves, Hogs 114 Sheep and Wool 115 Honey and Beeswax 116 Other Livestock: donkeys, goats 117 Horse Farms 120 Field Crops 130 Truck Crops - Mucklands 140 Truck Crops - Not 151 Apples, Pears, Peaches, Cherries, etc. 152 Vineyards 421 Restaurants 422 Diners and Luncheonettes 423 Snack Bars, Drive-Ins, Ice Cream Bars 424 Night Clubs 425 Bar 426 Fast Food Franchises 431 Auto Dealers - Sales and Service 433 Auto Body, Tire Shops, Other Related Auto Sales 441 Fuel Storage and Distribution Facilities 443 Grain and Feed Elevators, Mixers, Sales Outlets 444 Lumber Yards, Sawmills 447 Trucking Terminals 448 Piers, Wharves, Docks and Related Facilities 449 Other Storage, Warehouse and Distribution 451 Regional Shopping Centers 452 Area or Neighborhood Shopping Centers 453 Large Retail Outlets 454 Large Retail Food Stores 455 Dealerships - Sales and Service (other than auto with large sales operation) 460 Banks and Office Buildings 464 Office Building 712 High Tech. Manufacturing and Processing 714 Light Industrial Manufacturing and Processing 715 Heavy Manufacturing and Processing 720 Mining and Quarrying 800 PUBLIC SERVICES 840 Transportation 450 Retail Services Category Sub-Category 100 AGRICULTURAL 110 Livestock and Products 150 Orchard Crops 700 INDUSTRIAL 710 Manufacturing and Processing 400 COMMERCIAL 420 Dining Establishments 430 Motor Vehicle Services 440 Storage, Warehouse and Distribution Facilities Table 1. New Yorkâs Tax Assessorâs classification codes for freight-related land uses.
6Each local jurisdiction has the ability to prescribe what activities are allowed under each zoning designation. In addition, existing uses are often âgrandfatheredâ as non- conforming use while still a functioning business, e.g., a gas station in an area designated as a residential area. The Standard Land Use Coding Manual (SLUCM) In the early 1960s, the Urban Renewal Administration, the Bureau of Public Roads, and Barton-Aschman Associ- ates, Inc., were tasked with determining the feasibility of developing a uniform and universally applicable land use classification and coding system (Urban Renewal Adminis- tration Housing and Home Finance Agency and Bureau of Public Roads, 1965). Such a system was needed to collect the data to support the federal Urban Planning Assistance (701) Program, and to reimburse state and local planning agencies for conducting comprehensive planning efforts (Jeer 1997). The expectation was that the system would be useful for both small communities (under 50,000) and large communities. The most important finding from this work was that dif- ferent characteristics, or dimensions, used to describe land should not be combined into a single classification systemâ thus, a single code system was not desirable (Urban Renewal Administration Housing and Home Finance Agency and Bureau of Public Roads 1965). Instead, the authors envi- sioned a set of variables, to be organized under three head- ings: Parcel (to include data on location, area of parcel, slope, soil type, etc.); Structure (to include type of structure, total floor area, ground floor area, etc.); and Space Use (to include activity, ownership, floor area, nuisance characteristics, num- ber of employees, etc.). From this breakdown, they chose to focus on âactivityâ within the category of Space Use, as the primary area of need for uniformity in definition. The purpose of the SLUCM classification of activities was to establish an extensive system suitable for automated data processing. Previously, records of land uses were prepared manually and used locally. Through the use of data process- ing equipment, large volumes of processing could be accom- plished using a uniform coding system. Although the goal was to consider a standard system of identification for one specific characteristic of land use, i.e., the âactivity,â they found that no rigid system for classifying land use activity was applicable across all communities. Most prominent from their work was the realization that a primary activity in one community was a miscellaneous activity in another, based on local economic activities and other factors. Even in the face of this finding, the research team was determined to meet its goal of developing a land use activity classification system that would allow for standard- ization in coding of the data, while remaining flexible in the use of the data (Urban Renewal Administration Housing and Home Finance Agency and Bureau of Public Roads 1965). The Space Use system that was developed focused on activity, based on nine one-digit categories (2 categories for manufacturing), 67 2-digit, 294 3-digit, and 772 4-digit categories. This hierarchical system allowed for the great- est detail to be associated with activities at the four-digit level, with the ability to become ever more general at the three-, two- and one-digit levels. The SLUCM structure provided flexibility, with the ability for different agencies to establish the level of aggregation appropriate for their needs (Urban Renewal Administration Housing and Home Finance Agency and Bureau of Public Roads 1965). Although there were concerns among agencies about the additional expense associated with collecting four-digit data, as com- pared to existing local data systems, the additional costs would be justified with the increased flexibility and acces- sibility for multiple users. Table 2 shows the freight-related land use classes. As shown in the table, in the developed SLUCM code, freight activi- ties could be associated with: Codes 2 and 3 (manufactur- ing); Code 4 (transportation, communication, and utilities); Code 5 (trade [retail]), and Code 8 (resource production and extraction). There were a series of auxiliary codes within the SLUCM structure to add more detail, when necessary. One area of concern, however, was the ability to properly identify these auxiliary land use activities that appear to be located sepa- rately from a primary activity, but are functionally linked. For example, it was difficult to differentiate, without an aux- iliary code, a warehouse used exclusively by one retailer; or a parking lot used exclusively by a warehousing operation for its employees, rather than the public. In both cases, the activ- ity associated with the warehouse or the parking lot could be misinterpreted without additional information about the retailer or the warehousing operation. With respect to the number of SLUCM categories, manu- facturing produced the largest number, due to the diverse nature of this activity. A much smaller number of manufac- turing activities are found in any one community. To make the dataset sufficiently robust for national use, all of the possible categories needed to be included. Warehousing and storage were coded depending on the set of relationships, with a basic code, an auxiliary code, and a combined code. Although by the late 1960s, the federal classification effort provided indi- vidual municipalities with SLUCM, and the SLUCM Manual was reprinted in 1972, local use of the system was voluntary (American Planning Association 1994). By the late 1970s, as the focus of land use planning changed from data-intensive long-range planning, to comply with federal programs, to short-term, small-scale projects, primarily oriented towards local activities, there was less reliance on the SLUCM.
7 â¢ Facilitate data sharing; â¢ Develop a coding system with accompanying metadata (e.g., the source of the data); â¢ Facilitate the updating process; â¢ Enable regional agencies to work effectively and efficiently with land use/land-cover data; and â¢ Incorporate the geographic information systems (GIS) capabilities for spatial data. As part of the scope of work for the APA Study, 21 case studies were conducted to illustrate successful coding systems available at the time. From these case studies, findings were made on the classification of coding schemes used by scale, source of data, and classification method (see www.planning. org/lbcs/). An additional part of the APA Study was the col- lection of 104 examples of land use coding classifications. Using the findings from the APA Study, in 1996 the APA and six participating federal agencies (including FHWA) ini- tiated the LBCS project, under the leadership of Sanjay Jeer. The first problem that the team faced was trying to define âland use.â Historically, in the planning profession, the term âland useâ in classification codes included not only land uses Land-Based Classification Standards (LBCS) In the early 1990s, there was a concern that the SLUCM needed to be updated to reflect changes that had already been recognized through changes in the SIC coding system (a discussion of the SIC system is presented in a latter sec- tion of this chapter). The FHWA partnered with the APA to conduct a feasibility study to determine the level of inter- est in updating the SLUCM, which was at the time the only national-level standardized land use coding manual for local, regional, and state land use planning applications (American Planning Association 1994). The Research Department of the APA (American Planning Association 1994) found that there was a need to revise the SLUCM to: â¢ Develop an up-to-date and comprehensive list of land uses and a flexible approach to categorizing new land uses in urban, suburban, and rural areas; â¢ Provide a system of coding land uses to support the needs of the Clean Air Act Amendments of 1990, the Environ- mental Justice Order, and the Intermodal Surface Trans- portation Efficiency Act; Category Sub-Codes 21 Food and kindred products â manufacturing 22 Textile mill products manufacturing 23 Apparel and other finished products made from fabrics, leather, and similar materials â manufacturing 24 Lumber and wood products (except furniture) â manufacturing 25 Furniture and fixtures â manufacturing 26 Paper and allied products - manufacturing 27 Printing, publishing, and allied industries 28 Chemicals and allied products - manufacturing 29 Petroleum refining and related industries 31 Rubber and miscellaneous plastic products â manufacturing 32 Stone, clay and glass products â manufacturing 33 Primary metal industries 34 Fabricated metal products â manufacturing 35 Professional, scientific, and controlling instruments; photographic and optical 41 Railroad, rapid rail transit, and street railway transportation 42 Motor vehicle transportation 43 Aircraft transportation 44 Marine craft transportation 51 Wholesale 52 Retail trade - building materials, hardware, and farm equipment 53 Retail trade - general merchandise 54 Retail trade - food 55 Retail trade - automotive, marine craft, aircraft and accessories 56 Retail trade - apparel and accessories 57 Retail trade - furniture, home furnishings, and equipment 81 Agriculture 82 Agricultural related activities 83 Forestry activities and related services 84 Fishing activities and related services 85 Mining activities and related services 2 Manufacturing 3 Manufacturing (continued) 4 Transportation, communication, and utilities 5 Trade 8 Resource production and extraction Table 2. SLUCM categories for freight.
8to a common classâ (Jeer 1997, 15). The team decided that a hierarchical coding scheme should be used, rather than text descriptions, in order to facilitate the collection, organiza- tion, and extraction of the data. In a hierarchical classifica- tion system, digits are used to build a database that is easy to use for aggregating statistics within the database environ- ment (Jeer 1997, 17). According to the APA, the LBCS called for the classification of land uses in the following dimensions: â¢ Activity refers to the actual use of land based on its observ- able characteristics. It describes what actually takes place in physical or observable terms (e.g., farming, shopping, manufacturing). â¢ Function refers to the economic function or type of enter- prise using the land. Every land use can be characterized by the type of enterprise it serves. Land use terms, such as agricultural, commercial, and industrial, relate to enter- prises. The type of economic function served by the land gets classified in this dimension; it is independent of actual activity on the land [emphasis supplied]. â¢ Structural character refers to the type of structure or building on the land. Land use terms embody a structural or building characteristic, which suggests the utility of the space (in a building) or land (when there is no building). Land use terms, such as single-family house, office build- ing, warehouse, hospital building, or highway, also describe structural characteristics. Although many activities and functions are closely associated with certain structures, it is not always so. Many buildings are often adapted for uses other than their original use. This is a potential issue for freight transportation as there are many users of freight, e.g., those in retail and services, who occupy structures not directly associated with an obvious freight-related use. â¢ Site development character refers to the overall physical development character of the land. It describes âwhat is on the landâ in general physical terms. For most land uses, it is simply expressed in terms of whether the site is devel- oped or not. But not all sites without observable develop- ment can be treated as undeveloped. Land uses, such as parks and open spaces, which often have a complex mix of activities, functions, and structures, need categories inde- pendent of other dimensions. â¢ Ownership refers to the relationship between use and its land rights. Since the function of most land uses is either public or private and not both, distinguishing ownership characteristics seems obvious. However, relying solely on the functional character may obscure such uses as private parks, public theaters, private stadiums, private prisons, and mixed public and private ownership. Moreover, ease- ments and similar legal devices also limit or constrain land use activities and functions. (undefined) but also, by implication, land cover (e.g., trees, bushy plants) and land rights (e.g., ownership). In fact, when some agencies used the term âland usesâ in policy discus- sions (e.g., environmental concerns) they tended to extend its meaning to include more than physical or functional char- acteristics. For example, operations conducted for control purposes (e.g., clear cutting forests, draining swamp lands) could be considered new ways of thinking about land use (Amari et al. n.d.). Given the nature of the problemâtrying to define the term âland useââthe LBCS project team made the decision to use the term âland-basedâ to refer to all the concepts encountered (Jeer 1997). This made it possible to broaden the scope to include all types of land uses and land use activities, land cover, and land rights. The first task for the team was to update the database of land uses with new uses and activities, as described in the 1987 revised SIC categories, all of which had been added since the original 1965 SLUCM. At this time, another major change was underwayâthe replacement of the SIC program with the NAICS. (Although the complete transition from SIC to NAICS was not scheduled until 2009, many of the changes occurred in 1999.) The LBCS was designed to include concepts beyond the strict coding used in SIC or NAICS, including such land use planning applications as comprehensive plans, zoning ordi- nances, statutes, court case definitions, and other planning- related materials. The overarching guiding principle for the development of the classification systems was to provide a classification scheme for land-based data that could be shared across jurisdictions, both horizontally and vertically. The sys- tem was to be user-oriented: easy to understand and use. It was a challenge to build such a classification system, consisting of defined and ordered categories, with established relationships between the categories. â[A] land-based classifi- cation should contain: categories about land-based informa- tion; enough categories to differentiate various characteristics of land-based information; and the identification of relation- ships between those categories.â (Jeer 1997, 14). In addition, the LBCS project team would need to decide whether the variables used in the classification system were considered to be nominal, ordinal, or ratio scales. For example, nominal data describes the use of terms such as residential, commer- cial, or industrial, which only serve the purpose of identifying a class. Variables such as âsingle-familyâ and âduplexâ are ordinal because they serve to describe an order for the dif- ferent values. If categories are determined by some numeric description (e.g., number of units per acre), it is ratio scale, as they could be used in mathematical operations. Another consideration for developing the LBCS classification system was what data model to use: levels of abstraction, followed by generalization, association, and aggregation. Within the data model, classification is the âmapping of several objects
9 assigned the appropriate code for each of the five fields. The site also provides the color coding standards for application in mapping software (e.g., GIS). Using the LBCS dimensions (American Planning Asso- ciation 1994), it is possible to identify categories for each dimension relevant for freight. Tables 3 through 7, specifi- cally address the freight-related activity codes within each of the respective dimensions. Employment Categories Some transportation studies have used employment codes as a proxy for âland use.â The classification systems are national in scope and are applied to establishments to iden- tify industry sectors. The need for these codes developed over a number of years, going back to the 1930s. There is a long history of continued review and improvement associated with these employment classification codes. The underlying principle of the LBCS model was its flexibil- ity, which was provided by making it easier to adapt to a variety of planning applications, data collection methods, data-sharing and data-integrating methods, and color coding and mapping. It also made it possible to assign new categories for new land uses, to accommodate new methods and technologies for anal- ysis, and to customize the model for local needs without losing the ability to share data. Each of these aspects of LBCS called for applying a variety of standards or conventions to maintain consistency in land use classifications. The available resources can be accessed at www.planning.org/LBCS/, including work- ing papers, case study papers, a standard field testing report, an annotated bibliography, and various online resources, includ- ing two Access database âseedâ files. To implement the LBCS, users need to apply a prescribed top-level classification scheme, using the following five data collection fields: LBCSActivity, LBCSFunction, LBCSStruc- ture, LBCSSite, and LBCSOwnership. Each parcel of land is LBCS Activity - Category Sub-Codes 2100 Shopping 2110 Goods-oriented shopping 2200 Restaurant-type activity 2300 Office activities 3110 Primarily plant or factory-type activities 3120 Primarily goods storage or handling activities 8100 Farming, tilling, plowing, harvesting, or related activities 8400 Logging 8500 Quarrying or stone cutting 8600 Mining including surface and subsurface strip mining 3000 Plant, factory, or heavy goods storage or handling activities 2000 Shopping, business, or trade activities 8000 Natural resources-related activities Table 3. Land-based classification standards activity codes for freight. LBCS Function - Category Sub-Codes 2100 Retail sales or services 2110 Automobile sales or service establishment 2120 Heavy consumer goods sales or services 2130 Durable consumer goods sales and service 2140 Consumer goods, other 2150 Grocery, food, beverage, dairy, etc. 3100 Food, textiles, and related products 3200 Wood, paper, and printing products 3300 Chemicals, and metals, machinery and electronics 3400 Miscellaneous manufacturing 3500 Wholesale trade establishment 3600 Warehouse and storage services 4100 Transportation services 4110 Air Transportation 4120 Rail Transportation 4140 Truck and freight transportation services 4150 Marine and water transportation 8000 Mining and extraction establishments 9000 Agriculture, forestry, fishing and hunting 2000 General sales or services 3000 Manufacturing and wholesale trade 4000 Transportation, communication, information, and utilities Table 4. Land-based classification standards function codes for freight.
10 sense of all economic activity. The first set of industries to be classified was manufacturing. By June 1938, the Interagency Committee accepted a list of manufacturing industries. To overcome coding issues for non-manufacturing industries, the Committee established a number of subcommittees of experts from various non-manufacturing fields (Pearce 1957). The first complete edition of the SIC manual was a series of vol- umes. According to Pearce, there was a set of guiding princi- ples for inclusion in the SIC manual. They were the following: â¢ The classification should conform to the existing structure of American industry; â¢ The reporting units are the establishments, instead of legal entities/companies; â¢ Each establishment was to be classified according to its major activity; and â¢ To be recognized as an industry, each group of establish- ments must have significance from the standpoint of the number of establishments, number of wage earners, vol- ume of business, employment and payroll fluctuations, and other important economic features. After the 1939 SIC system had been in use for a âreason- ableâ period of time, there was a review of the coding system, Standard Industrial Classification (SIC) Codes In 1934, in the depths of the Great Depression, there was a need to develop a standardized approach to collecting statistics on industries. According to Pearce (1957), the Standardization of the United States Government Industrial Classification pro- gram was originally proposed at an Interdepartmental Confer- ence on Industrial Classification held in 1934. A recommenda- tion to develop continuing committee processes to tackle the problem of establishing an industrial classification of statisti- cal data was then transmitted to the Central Statistical Board. In 1937, the Central Board established an Interdepartmental Committee on Industrial Classification, which first met on June 22, 1937. At this first meeting, the Interagency Commit- tee established a Technical Committee to prepare a proposed standard classification of industries. The importance of devel- oping a uniform set of classifications, which could be used by a variety of agencies, was apparent. Otherwise, one agency might classify an establishment in one industry, and another agency, using its own set of classification codes, might classify the same establishment in a different industry (Pearce 1957). Under these circumstances, it would be impossible to collect and use statistics on industries. The standardization project was designed to help clarify the term âindustryâ in its broadest LBCS Structure - Category Sub-Codes 2100 Office or bank building 2200 Store or shop building 2500 Mall, shopping centers, or collection of shops 2600 Industrial buildings and structures 2700 Warehouse or storage facility 5100 Linear or network feature 5500 Water transportation or marine related 5600 Air and space transportation facility 5700 Railroad facility 8000 Sheds, farm buildings, or agricultural facilities 2000 Commercial buildings and other specialized structures 5000 Transportation-related facilities Table 5. Land-based classification standards structure codes for freight. LBCS Site - Category Sub-Codes 3000 Developed site - crops, grazing, forestry, etc. 6000 Developed site - with buildings Table 6. Land-based classification standards site codes for freight. LBCS Ownership - Category Sub-Codes 1000 No constraints - private ownership 5200 Port authorities Table 7. Land-based classification standards ownership codes for freight.
11 ity being undertaken by a firm. Table 9 lists NAICS codes associated with freight. Remote Sensing for Diagnostic Land Use Applications Remote sensing uses sensors to measure the amount of electromagnetic energy leaving an object or geographic area from a distance. This emitted energy is used as a surrogate for the actual properties under investigation. The technique extracts valuable information from the data transmitted, using mathematically and statistically based algorithms. The electromagnetic energy measurements are converted using visual and digital image processing techniques. Remote sens- ing integrates other geographic information sciences, includ- ing GIS, cartography and surveying (Jensen 2007, 4). If the sensors are passively recording electromagnetic energy, they are considered unobtrusive (Jensen 2007, 7). Remote sensing devices are programmed to systematically collect data (e.g., a single 9 Ã 9 inch frame of vertical aerial photography or a matrix, as a raster, of Landsat 5 Thematic Mapper data). In most cases, the data itself is collected by various parties, rather than by the researcher who works on the conversion or interprets the results. While remote sens- ing can bring a new source of data to researchers, it is also at risk of being oversold because so many things can go wrong with it. For example, the devices used to create the data can become uncalibrated, and as a result, the output will be incorrect, and errors imbedded in the data can propagate, resulting in incorrect interpretations in the analysis. There is a risk that remote sensing techniques that are considered active systems (e.g., LIDAR, RADAR, SONAR) could influence the data being collected. These systems are considered obtrusive because they emit their own electromag- netic radiation (Jensen 2007, 8). The remote sensing process includes: creation of a statement of the problem; data collec- tion; data-to-information conversion; and information pre- sentation (Jensen 2007, 9). The United States Geological Survey (USGS) Land-Use/ Land-Cover Classification System (circa 1976) was designed for the detection of resource-oriented land-cover data rather than land use data (Jensen 2007, 451). The classification was initially developed to include land use data that was visually photo-interpreted, but has also been used for digital multi- spectral remote sensing classification studies (Jensen 2007, 451). According to Jensen (2007, 451), the USGS Land-Use/ Land-Cover Classification System, although not originally intended for urban applications, was used in urban land use studies by âembellishingâ the classification system with detailed Level III, IV, and V urban class definitions. The modification concept made it possible to include as many levels as desired, while remaining compatible with all the USGS Level I and UU land use and land-cover data compiled and appropriate revisions were made. The Central Statistical Board transferred the SIC program to the Bureau of Budget, which funded the revision process. Following this review, the first edition of the Manufacturing Industries was published in 1941, followed soon after by the publication in 1942, of the Non-manufacturing Industries (Pearce 1957). In 1945, the Manufacturing Industries manual was reviewed and revised to reflect technological advances and changes in industries. The Non-manufacturing Industries manual was reviewed and revised in 1949. Over time, additional review and revi- sion resulted in the publication of the SIC manual, combin- ing manufacturing and non-manufacturing industries into a single book, published in 1957. In 1967, another review and revision was undertaken. The last review and revision of the SIC was conducted by the Office of Management and Budget (OMB) in 1987 (U.S. Census Bureau 2010c). Early freight studies used the SIC system as a proxy for âland use.â Table 8 includes the SIC codes associated with freight. North American Industry Classification System (NAICS) In 1991, the International Conference on the Classifica- tion of Economic Activities expressing concern about the SIC system, particularly its poor coverage of the emerging service sector, decided to rethink the classification strategy for industries (NAICS Association n.d.). In 1992, partially in contemplation of the North American Free Trade Agreement (NAFTA, implemented in 1994) the OMB established the Economic Classification Policy Committee (ECPC), chaired by the Bureau of Economic Analysis, which included the U.S. Department of Commerce and the Bureau of Labor Statistics, U.S. Department of Labor. The ECPC and Statistics Canada conducted a review of the 4-digit SIC codes and the 1980 Canadian SIC for conformance to economic concepts (NAICS Association, n.d.). The NAICS procedures were finalized by the OMB, jointly with the U.S. ECPC, Statistics Canada, and Mexicoâs Instituto Nacional de EstadÃstica y GeografÃa. The goal was to provide a high level of comparability in business statistics across North America (U.S. Census Bureau 2010c). The new NAICS codes identified industries using a 6-digit coding system, as the longer code accommodated a larger number of sectors and more flexibility in designating subsec- tors. The sixth digit can be used for special designations asso- ciated with a country (e.g., United States, Canada, or Mexico). The NAICS currently in use was last updated in 2007 and is scheduled for updating in 2012. NAICS is the current stan- dard used by all federal statistical agencies to classify business establishments for the purposes of collecting, analyzing, and publishing statistical data (U.S. Census Bureau 2010c). It is applied to individual establishments. NAICS is not a âland useâ classification per se, but rather a description of the activ-
12 Code SIC Title Code SIC Title 01 Agricultural Production-Crops 02 Agricultural Production- Livestock 10 Metal Mining 201 Meat Products 202 Dairy Products 203 Preserved Fruits and Vegetables 204 Grain Mill Products 205 Bakery Products 206 Sugar and Confectionery Products 207 Fats and Oils 208 Beverages 209 Misc. Foods and Kindred Products 22 Textile Mill Products 23 Apparel & Other Textile Products 24 Lumber and Wood Products 25 Furniture and Fixtures 26 Paper and Allied Products 27 Printing and Publishing 28 Chemicals and Allied Products 29 Petroleum and Coal Products 30 Rubber & Misc. Plastics Products 31 Leather and Leather Products 32 Stone, Clay, and Glass Products 33 Primary Metal Industries 34 Fabricated Metal Products 35 Industrial Machinery & Equipment 36 Electronic and Other Electric Equipment 37 Transportation Equipment 38 Instruments & Related Products 39 Misc. Manufacturing Industries 42 Trucking and Warehousing 511 Paper and Paper Products 512 Drugs, Proprietaries and Sundries 513 Apparel, Piece Goods and Notions 514 Groceries and Related Products 515 Farm Product Raw Materials 516 Chemicals and Allied Products 517 Petroleum and Petroleum Products 518 Beer, Wine and Distilled Beverages 519 Misc. Nondurable Goods 52 Building Materials and Garden Supplies 53 General Mechandise Stores 20 Food and Kindred Products 51 Wholesale Trade-Nondurable Goods 541 Grocery Stores 542 Meat & Fish Markets 544 Candy, Nut and Confectionery Stores 545 Dairy Product Stores 546 Retail Bakeries 549 Misc. Food Stores 551 New and Used Car Dealers 552 Used Car Dealers 553 Auto and Home Supply Stores 56 Apparel and Accessory Stores 57 Furniture and Homefurnishings Stores 58 Eating and Drinking Places 59 Miscellaneous Retail 55 Automotive Dealers & Service Stations 54 Food Stores Table 8. SIC codes for freight-related sectors.
13 Code 2007 NAICS U.S. Title Code 2007 NAICS U.S. Title 111 Crop Production 112 Animal Production 113 Forestry and Logging 115 Support Activities for Agriculture and Forestry 211 Oil and Gas Extraction 212 Mining (except Oil and Gas) 213 Support Activities for Mining 311 Food Manufacturing 312 Beverage and Tobacco Product Manufacturing 313 Textile Mills 314 Textile Product Mills 315 Apparel Manufacturing 321 Wood Product Manufacturing 322 Paper Manufacturing 323 Printing and Related Support Activities 324 Petroleum and Coal Products Manufacturing 325 Chemical Manufacturing 326 Plastics and Rubber Products Manufacturing 327 Nonmetallic Mineral Product Manufacturing 331 Primary Metal Manufacturing 332 Fabricated Metal Product Manufacturing 333 Machinery Manufacturing 334 Computer and Electronic Product Manufacturing 335 Electrical Equipment, Appliance, and Component Manufacturing 336 Transportation Equipment Manufacturing 337 Furniture and Related Product Manufacturing 339 Miscellaneous Manufacturing 11 Agriculture, Forestry, Fishing and Hunting 21 Mining, Quarrying, and Oil and Gas Extraction 31-33 Manufacturing Source: http://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2007. 423 Merchant Wholesalers, Durable Goods 424 Merchant Wholesalers, Nondurable Goods 425 Wholesale Electronic Markets and Agents and Brokers 441 Motor Vehicle and Parts Dealers 442 Furniture and Home Furnishings Stores 443 Electronics and Appliance Stores 444 Building Material and Garden Equipment and Supplies Dealers 445 Food and Beverage Stores 446 Health and Personal Care Stores 447 Gasoline Stations 448 Clothing and Clothing Accessories Stores 451 Sporting Goods, Hobby, Book, and Music Stores 452 General Merchandise Stores 453 Miscellaneous Store Retailers 454 Nonstore Retailers 481 Air Transportation 482 Rail Transportation 483 Water Transportation 484 Truck Transportation 485 Transit and Ground Passenger Transportation 486 Pipeline Transportation 488 Support Activities for Transportation 491 Postal Service 492 Couriers and Messengers 493 Warehousing and Storage 48-49 Transportation and Warehousing 42 Wholesale Trade 44-45 Retail Trade Table 9. NAICS codes for freight-related sectors.
14 â¢ Processing industries that can be identified using remote sensing techniques include: mechanical processing indus- tries; chemical-processing industries; and heat-processing industries. Fabrication industries can be identified as either heavy or light fabrication. â¢ Transportation facilities, such as railroads, airports, and water facilities are all identifiable using remote sensing techniques. It should be mentioned, however, that remote sensing is of limited usefulness to identify land use classes such as those in urban areas, where it is difficult to remotely identify if a building is being used for habitation or for commercial use. These cases require the use of other more direct data gathering techniques. An example of remote sensing land use classes is shown in Table 10. in local jurisdictions (see Jensen (2007, 453) for classification levels). According to Jensen (2007), remote sensing is useful for land uses such as department stores (e.g., Walmart, K-Mart) which can be identified, along with their large parking lots; food and drug manufacturing establishments; warehousing; and shipping facilities. Jeer (1997) reported that satellite based remote sensing methods for land use were undergoing rapid changes, as improvements in imaging and scanning technolo- gies made them increasingly feasible. Identification of various industry-related components in an image include: extraction; processing; and fabrication (Jensen 2007, 479â480). They are defined as the following: â¢ Extraction industries that can be identified, based on diag- nostic recognition, include: open-pit sites; normal and over- sized equipment; site-based transportation infrastructure; piles of extracted materials; and ponds of waste. 12 1211 Building materials, hardware and farm equipment 1212 General merchandise/department store 1213 Food/groceries 1214 Automotive, marine craft, aircraft and accessories/dealers 1215 Furniture, home furnishings and equipment 1216 Eating and drinking (restaurants) 1221 Food/sundries/beverages 1222 Agricultural products/supplies 1223 Lumber/hardware/building supplies/paper 1224 Industrial product/chemical/petroleum 1225 Motor vehicles/parts/supplies 13 14 15 21 22 23 136 137 141 142 143 151 131 132 133 134 135 Confined Feeding Operations 121 122 Industrial park Office park Shopping center/mall Other industrial/commercial complexes Cropland and Pasture Orchards, Bust Fruits, Vineyards, and Ornamental Horticulture 152 153 159 Mining Transportation, Communication and Utilities Air transportation Rail transportation Water transportation Industrial and Commercial Complexes Land Use/ Land Cover Codes Commercial wholesale Industrial 02 AGRICULTURAL LAND Primary metal production Petrochemicals Primary wood processing and paper mills Stone, clay, glass Metal & non metal fabrication Food processing 01 URBAN AND BUILT-UP LAND Commercial, Services And Institutional Commercial Retail Table 10. Remote sensing land use classifications.
15 Boston, Massachusetts The City of Boston, Massachusetts, has adopted Article 42D (City of Boston [Massachusetts] Redevelopment Author- ity 1990 as amended through 2006), establishing a Water- front Manufacturing District. The zoning code language is designed to protect the activities necessary for a working waterfront, and to preserve particular areas for manufactur- ing uses and waterfront services. The code language explicitly lists the permitted activities by manufacturing use (e.g., cot- ton ginning, the manufacturing of food products, fur goods and leather products). By examining the Boston zoning map and code, it may be possible to understand the range of potential truck trips to be generated from the district as a whole. At the same time, while the zoning code provides dimensions of the possible activities that could produce truck trips, it may not be suffi- cient to establish activity levels, without additional informa- tion from available data (e.g., business licenses) or without requiring new data collection efforts (e.g., a survey of busi- ness activities on a periodic basis). Chicago, Illinois Planners in Chicago, Illinois, are in the process of prepar- ing a regional freight framework that explicitly recognizes land uses associated with freight through the designation of Planned Manufacturing Districts. Policy Recommendation #22 describes the 15 Planned Manufacturing Districts within the 24 industrial corridors in the Chicago area (Cambridge Systematics Inc., 2010b). The purpose of these districts is to preserve land for industrial development using special zon- ing designations. In addition, the document discusses find- ings relevant to the understanding of land use and FTG. The growth of freight volumes in the Chicago region is directly tied to the overall population and employment growth, changes in the national and global logistics patterns, and the evolution of the regionâs industry structure (Cam- bridge Systematics Inc., 2010b, 3-3). Thus, by looking at the economic factors of business type, growth, and location as well as population growth, income, and clustering, with the addition of forecasts, it is possible to understand the relation- ship between land use and current and future regional freight demand. The framework uses: economic structure, industry logistics patterns, freight infrastructure, commodity/vehicle traffic flows, and organization and public policy, as the key elements to understanding the connections between freight and the overall economic health of the region. Implicitly within this framework, land use activities are described (e.g., warehousing and distribution industry activities in suburban locations). The document illustrates how intermodal activities impact a community by inducing Multi-Dimensional Spatially Flexible Land Use Classification Strategy Trying to implement a standardized set of land use clas- sification codes has proved to be elusive, and will most likely be expensive and time-consuming. As has been echoed through the various attempts to accomplish a stan- dardized approach, the system needs to flexible, and readily adaptable to numerous users, all of whom have different sets of needs. After reviewing the various approaches to classification strategies for categorizing land uses, there appears to be no land use single code solution that would be appropriate for understanding FTG. The strategies range from a single code (e.g., ITE Manual, Tax Assessor property classification codes) to a multi-dimensional strategy (e.g., the LBCS concept). The geographic representations for land use range from single- point, firm-level codes (e.g., SIC/NAICS employment codes) to large area land use zoning designations (e.g., General Industrial). Since land use has a spatial component, regardless of which classification strategy is used, if the information has been digitized, it is possible to use GIS technologies to dis- play it. Thus, in addition to examining the relationships across the data sources in tabular format, it is also possible to assemble the land use data spatially, using GIS. Once the spatial layers are loaded, there are emerging technologies for querying and reassembling this spatial information. While spatial data layers can be used to produce composite maps, GIS is even more useful as a methodology for sorting, reclassifying, linking, joining, querying and understand- ing the dynamic nature of the data. Using GIS as a plat- form for reclassifying data provides the appropriate mix of standards and flexibility. Analysts and researchers can use GIS to develop approaches to freight trip generating opera- tions with the most effective and efficient combination of data sources, making all these steps possible through the use of automation. Using a set of algorithms and a set of advanced computer science techniques, an interface that would facilitate the use of existing spatial data could be developed as a web service, or a stand-alone personal com- puting process. Emerging Land Use Practice of Interest to Freight Transportation Planning In recent years, some jurisdictions have developed zoning classifications and/or new land use code applications that may contribute to the identification of freight trip generating areas within a region. Some of these examples are discussed in this section.
16 to provide local truck circulation and access. In this instance, the transportation facilities are well associated with the land use activities. The use of a freight district designation and the associated truck behaviors should be useful in the determina- tion of freight truck trips. Seattle, Washington In Seattle, Washington, the land use planning community and the freight community have a long history of cooperation. For example, in the last decade, efforts have led to the estab- lishment of a freight advisory council (see http://www.cityof seattle.net/Transportation/fmac.htm) and other corridor-level efforts to understand freight issues. The current work on the Puget Sound Regional Council 2040 Plan (Cambridge Systematics Inc., 2010a) explicitly recognizes the relation- ship between land use and FTG, stating that some types of land uses will rely on a steady stream of trucks to deliver raw materials and pick up manufactured products. This requires an understanding of the cluster patterns of industrial and warehouse land uses that produce a higher volume of truck traffic than any other land use types (Cambridge Systematics Inc. 2010a, 84). This would also be true for areas with high concentrations of retail activity. These clusters would be of value for understanding FTG (Cambridge Systematics Inc. 2010a). The Puget Sound Regional Council 2040 Plan also includes strategies for incorporating freight into the urban fabric, including using zoning to guide new industrial and manufacturing activities to locations within the region that already have adequate freight transportation routes, and to reduce the potential for conflict with communities. Recommendations in the Puget Sound Regional Council 2040 Plan for land use on a region-wide basis include mak- ing an effort to streamline industrial and manufacturing land development into eight designated Manufacturing and Indus- trial Centers (MICs). This strategy would allow manufactur- ing and warehousing industries to cluster in areas where the price of land and proximity to a broader supply chain would result in benefits, including less time and money required to transport goods (Cambridge Systematics Inc. 2010a, 137). Such a regional zoning strategy would concentrate freight activities into designated areas, where the clustering of industries should prevent spillover externalities into residential neighborhoods, and increase the potential for understanding freight truck trip generation. San Francisco, California In San Francisco, California, a major effort has been under- way to understand the economic impacts of freight activities. Studies have recognized the impact of local land use decisions on the goods movement system, particularly the real estate pass-through traffic, not conducive to economic benefits for local communities, which contributes to congestion and pavement deterioration. This suggests the need to establish a land use classification strategy to identify intermodal activi- ties explicitly. In the regional freight framework plan, the Chicago stake- holders have pointed out the lack of regional coordination with respect to land use and FTG, due to individual munici- palities managing their land uses in their own best interests. A more focused planning effort could minimize the cumula- tive impacts of developments associated with FTG, and pro- vide benefits to shippers and haulers, while at the same time improving the quality of life for local residents. Portland, Oregon Planners in Portland, Oregon, have established a new land use designation: Freight Districts (City of Portland [Oregon] Office of Transportation 2006). These districts are deter- mined by the presence of industrial sanctuary zones, includ- ing IG1, IG2, and IH. Industrial sanctuary zones are part of the Comprehensive Plan for the city of Portland. The zoning language for the three zones is incorporated in the Zoning Code for the City of Portland (City of Portland [Oregon] Bureau of Planning and Sustainability 2010). These zones provide areas where most industrial uses may locate. Other uses are restricted to prevent potential conflicts and to pre- serve land for industry. The purpose of the development standards for each zone is to allow new development to be similar in character to existing development, creating more viable and attractive industrial areas. According to the City of Portland Bureau of Planning and Sustainability (2010): â¢ General Industrial areas generally have smaller lots and a grid block pattern. The area is mostly developed, with sites having high building coverage, and buildings that are usu- ally close to the street. IG1 areas tend to be the cityâs older industrial areas. â¢ General Industrial areas generally have larger lots and an irregular or large block pattern. The area is less developed, with sites having medium and low building coverage and buildings that are usually set back from the street. â¢ Heavy Industrial areas allow all kinds of industries to locate in the zone, including those not desirable in other zones, due to their objectionable impacts or appearance. The development standards are the minimum necessary to assure safe, functional, efficient, and environmentally sound development. In addition to the guidance provided by the zoning code, the streets located within a freight district are to be designed
17 Most important, this information was used to classify the goods movement businesses and industries into tiers. Tier 1 businesses/industries included those where goods move- ment is very important to operations, e.g., in- and outbound freight trips. Nearly 70% of the corridor businesses/industries fell into this classification. The Tier 2 businesses/industries depend on goods movement but only in a secondary manner. Table 11 (Metropolitan Transportation Commission 2009, 11-8) lists the types of businesses/industries in each tier, pro- viding a new classification strategy for understanding freight truck trips. Sacramento, California The Sacramento, California, Council of Governments SACOG Regional Goods Movement Study Phase One Report (SACOG Report) (The Tioga Group et al. 2006) recognized that Smart Growth policies are intended to increase density but have no provisions for street widening to accommodate freight trips. The mixing of uses, including live/work devel- opments, tends to exacerbate the problem by allowing uses industry trend of a shrinking land supply for activities that generate freight truck trips (Hausrath Economics Group and Cambridge Systematics Inc. 2004; Metropolitan Transporta- tion Commission 2009). In addition, these studies conclud- ed that the current desire of urban planners to increase the intensity of development through the use of so-called âSmart Growthâ strategies is harmful to goods movement in the San Francisco area. The San Francisco studies involved the use of a series of mapping exercises to compare existing and planned land uses along a specific set of corridors. In particular, they looked at: locations reserved for seaports and airports as desig- nated in regional agency plans; locations where local plans approved continuing industrial uses; locations where local plans identified a mix of permitted business uses; locations where residential and commercial uses will replace existing industrial uses; and locations with major plans for higher- value uses (e.g., research parks) within or near existing industrial uses. In addition, Priority Development Areas (PDAs), which are locally identified infill development sites, were mapped. Description Tier 1: Goods Movement Dependent Groups Air Carriers Airpots Postal, Parcel, and Express Maritime Industries Seaports Rail Carriers Truck Carriers Household Goods (HHG) Carriers Warehousing Truck Rental and Leasing Local Manufacturing Local/Regional Manufacturing Regional Manufacturing Local Wholesale Local/Regional Wholesale Regional Wholesale Pipelines and Refineries Fuel Dealers Resource Extraction Waste Management Tier 2: Other Goods Movement Groups Construction Computer and Electronics Manufacturing Pharmaceutical and Biotech Manufacturing Transport Support Vehicle Towing Equipment Rental Utilities and Telecom Agriculture and Husbandry Other Industries (equipment rental, utilities) Transportation and Related Manufacturing (excluding high tech manufacturing) Wholesale Trade Other Industries (oil/gas, waste management) High-Tech Manufacturing Transport / Vehicle Support Table 11. San Francisco goods movement intensity designations (Tier 1 and Tier 2).
18 and logistics facilities may be rated negatively with respect to form, character, and building shape. The Sacramento Region Blueprint Project, as described in the SACOG Report (The Tioga Group et al. 2006, 205), revis- its Smart Growth in an effort to address the needs of a truly comprehensive plan that includes freight. The Sacramento Region Blueprint Project discusses the opportunity for an expansion of the scope of Smart Growth principles to âtake advantage of goods movement improvement opportunities in the process of rethinking development patterns; insure that proposed developments and development patterns meet functional as well as aesthetic requirements; and avoid being âblindsidedâ by goods movement issues late in the develop- ment cycle.â Contexts for Land Use Designations As mentioned previously, many transportation engineers and transportation planners rely on the âland useâ classifica- tion codes provided in the ITE Manual, which are primarily assigned to types of structures or sites. Employment codes (e.g., SIC or NAICS) have also been applied as proxies for âland use.â It is difficult to generalize about the contexts of âland useâ with respect to freight activities, since these appli- cations differ greatly from those used by land use planners. Table 12 is a matrix using three geographies relevant for land with different freight transportation needs in the same devel- opment, and even the same building (The Tioga Group et al. 2006). Stakeholders in the Sacramento area were concerned that Smart Growth concepts appear to favor âlivabilityâ over functionality, and make no explicit provision for efficient truck access. Thus, the Sacramento area is facing the same kinds of land pressures as San Francisco. As was identified in the San Francisco studies (Hausrath Economics Group and Cambridge Systematics, Inc. 2004; Metropolitan Transporta- tion Commission 2009), when distribution centers are locat- ed out of the central industrial areas, it results in the need for increased truck miles to bring the goods to the market cen- ters where population and employment centers are located. This adds increased fuel and operating costs, longer travel times, and increased emissions. It also poses employment challenges. The reason is that if transportation and goods movement industries decline in a region, there are job losses for unskilled or marginally educated workers. âEmployment generation (quality and quantity) by use type should be a factor in land use decisionsâ (The Tioga Group et al. 2006, 204). In addition, the SACOG Report raised the issue that many communities are implementing a form-based zoning approach, which emphasizes the form, character, and shape of buildings and their relationship to streets and public spaces. Unfortunately for freight, it is highly likely that the structural and visual characteristics of typical distribution Table 12. Analysis of rural, suburban and urban contexts. Facilities Functional Characteristics Classifications Agricultural Heavy trucks Interstate Principal arterials Light trucks State routes Minor arterial Extraction Autos County roads roads Collector roads [No transit] Bridges Local roads Rail Minimal local roads Residential Single Family Residential Low density residential areas Manufacturing Autos Light trucks Interstate State routes Principal arterials Retail Heavy trucks County Roads Minor arterial streets Industrial Light Industrial Heavy industrial Limited clusters of single-family residential units Service Some transit Some dense local roads Collector streets Local streets Commercial Highway Rail Commercial Residential Multi-family residential High density in mixed use areas Light manufacturing Mass transit Dense local road network Principal arterials Industrial Single-family residential Higher density/ multi-family Retail Auto Light trucks Minor arterial streets Commercial Heavy Industrial Light Industrial residential units Office Service Heavy trucks Rail Access to Interstate Collector streets Commercial Local streets Central Business District Rural Agricultural Agricultural Sparse Suburban Urban Geography Comprehensive Plan Zoning Designations Demographics Employment Modal Characteristics
19 code, traffic analysis zone, or land use planning zone. These codes can provide a direct understanding of what activity is being conducted within a particular structure or on a parcel, often with additional attributes (e.g., number of employees, value of output). These codes, however, do not indicate the variation of intensity of operations (e.g., whether an estab- lishment is producing at full capacity or has slack capacity), or the level of demand for the output of the establishment. One disadvantage of using employment codes from gov- ernment agencies is the explicit limitation placed on using these data, due to confidentiality concerns. The strong restrictions make it difficult to share the data, or even for public agencies to use the data at the parcel level. Although comparable data are available from private sector agencies (e.g., InfoUSA or Dun & Bradstreet), gaining access can be very expensive. Land Use Zoning Designations and Freight While many municipalities apply specific land use classi- fications to specific geographic areas (zones), these terms are often very broad, or idiosyncratic (e.g., âheavy industrialâ or âhighway commercialâ). Although there have been various attempts to establish a national standardized land use clas- sification system (e.g., SLUCM and LBCS), it is unclear the extent to which states and municipalities have adopted or maintained either SLUCM or LBCS. Recently, there has been an effort to bring freight activi- ties into mainstream planning processes with special districts (e.g., Freight Districts in Portland, Oregon; MICS in Seattle, Washington). These areas can have specific facility require- ments (e.g., types of street configurations) to accommodate trucks, and other strategies to reduce conflict with residential development. Where local land use planning (e.g., zoning) is used to encourage industrial development to locate in specific areas (e.g., in Chicago, Illinois, in âPlanned Manufacturing Dis- trictsâ), the goal is to promote a positive relationship between freight-related land use and economic development. These districts, or freight-oriented zones, are intended to help direct the flow of freight traffic in the most efficient and effective manner within urban areas. The most advanced use of such designations is the emerging Global Freight Village concept, where strict attention is paid to assembling mixes of indus- tries and freight facilities (Weisbrod et al. 2002). Remote Sensing Land Use Designations Agricultural activities, found in the areas predominately considered rural, generate freight truck trips from farm sites that can be identified using remote sensing techniques. As previously described, industrial activities in suburban areas use applications: rural, suburban, and urban. These three contexts are organized to illustrate broad classes of land uses applied in land use planning through comprehensive plans; zoning (e.g., agricultural, industrial, and commercial uses); and related factors (including demographics, employment, modal characteristics, facilities characteristics and functional classifications). Structure Type or Site Descriptor The ITE Manual generally uses a structure type or site descriptor as a definition of âland useâ (e.g., furniture store). This enables transportation engineers to observe the number of trucks entering and leaving a structure or site, and assign a calculated trip rate to like-kind structures. Unlike the Tax Assessorâs system previously described, the ITE classification strategy lacks a generally accessible administrative mecha- nism capable of assigning a specific land use code (e.g., 890, furniture store) to a specific property (e.g., specific street address). Although the calculations of trip rates based on the ITE code can be automated using a spreadsheet, the codes still must be manually assigned to specific addresses, which is time-consuming and expensive. In addition, the calculated trip rates may not reflect the activities occurring at the specific site, including current occupancy, size of structure, number of employees, and elas- ticity of demand for output. For example, a furniture store (land use code 890) may be vacant; may have few or many employees; may be a very large or a very small building; and may have a varied customer base. Therefore, there could be a large variation in the consequences of freight activity using only a structure/site descriptor approach. In comparison, Tax Assessorâs codes are produced for all structures and parcels of land and can be assembled in a digital format and joined with other attributes (e.g., percentage of total square footage assigned retail activities, size of structure, structure type, etc.). However, as previously discussed in the Tax Assessor codes section, the codes, which are local, can be very diverse, even within a single state. This would make com- parisons across studies on freight trips very difficult in those cases where the underlying processes producing the trips are not related to size, but rather to the operations themselves. Employment Codes There are some advantages associated with using employ- ment codes (e.g., SIC and NAICS) as proxies for âland useâ with respect to FTG. Employment codes are directly relevant for describing the activities occurring on a site or parcel. The data can be geo-coded, with latitude and longitude provided for electronic mapping of the exact street address of the estab- lishment. The data can be aggregated to the data level of ZIP
20 egorized into three groups: those using structure type or site descriptor (e.g., ITE Manual or Tax Assessorâs codes); those using industry sectors at the establishment level (e.g., SIC or NAICS); and those using land use planning designations (e.g., local zoning or LBCS). Recent Interest in Freight Planning Has Created New âLand Useâ Designations. A number of urban areas have recently begun to address the relationship between freight activities and land use. Special areas, or districts, are being designated to protect industrial activities and better meet the needs of freight community members. The designations include Freight Districts (Portland, Oregon); Planned Man- ufacturing Districts (Chicago, Illinois); and MICs (Seattle, Washington). Cross-Walks and Digital Assembly May Make It Possible to Integrate Land Use Classifications. There are a variety of techniques available for combining datasets, including the use of cross-walks and GIS. It may be possible to combine, reclassify, or even create ânewâ land use categories that are more appropriate for FTG rates or freight modeling than any one classification system currently in use. While No Classification System is âIdeal,â Several Have Been Used with Limited Success and Some Show Great Promise. Several classification systems can be adapted to meet FTG needs. These include using employment codes, such as the NAICS and SIC, or using the limited set published in the ITE Trip Generation Manual. Additional tests need to be made on the feasibility of using local land use codes. Even more importantly, tests need to be made on the use of the LBCS approach, as its extensive land use classification strat- egy offers several features needed for FTG: flexibility, adapt- ability, and applicability. can also be diagnosed with data processing techniques. Urban areas, especially in dense, complex environments, are more likely to be problematic for application of remote sensing techniques for freight-related land use. Cross-Walks To facilitate the use of more than one land use classifica- tion coding system, a âcross-walkâ or connection is required to link similar elements from one coding system to another. For example, a cross-walk from SIC to NAIC codes has been provided by the Census Bureau (see http://www.census.gov/ epcd/www/naicstab.htm). It is also possible to reconstitute a cross-walk from the LBCS Function codes to NAICS, using some of the original resources produced for the LBCS project. A series of cross-walks would make it possible to connect each of the various land use classification codes with any and all of the like-kind codes in the other land use classification schemes. It is also possible to create a cross-walk between establishment codes (e.g., SIC or NAICS) and codes used for commodities (e.g., the Standard Transportation Commod- ity Group), allowing data on commodities to be linked to all other land use classification codes. Summary This section summarizes the findings from the review of various land use classification coding strategies. There is No One Single âLand Useâ Classification Sys- tem Appropriate for Freight. A review of a series of defi- nitions for âland useâ found a variety of non-integrated applications and classification codes currently in use. These âland useâ applications and classification codes can be cat-