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49  For effective land-use planning and policy, it is crucial to have a solid understanding of the importance, extent, and geographic patterns of freight activity. This will help to identify the key issues, to recognize the strength and weakness of the local economy, and to identify any opportuni- ties or threats. The resulting knowledge will assist policymakers in identifying suitable policies to ensure that freight activities take place in the most sustainable manner. This chapter describes a number of analyses that could help practitioners understand the local conditions at the study area, with a focus on (1) the geographical distribution of economic activi- ties, particularly in relation to the location of FIS and SIS; (2) the physical separation between the stages of supply chains; and (3) the amount of FTG. To facilitate the use of these techniques, they were designed to be used with publicly available data (e.g., zip code business data or data from data aggregators). 6.1 Understanding the Economic Geography Gaining insight into the local economy is extremely important because doing so helps land-use planners understand freight activity in their jurisdictions. The analyses in this section focus on identifying which are the most important economic sectors, where they are located, and how compact the underlying supply chains are. Generally speaking, the analyses in this section can be based on data at the county, zip code, or establishment level. Data about the number of establish- ments and employment at the county and zip code level are publicly available, see (U.S. Census Bureau 2018a). However, these data are typically released 2 or 3 years after they are collected. This could be acceptable if the economic conditions have not changed drastically during that period. In other cases, more recent establishment-level data can be purchased from data aggregators such as Dun & Bradstreet and InfoUSA. 6.1.1 Overall Composition of the Local Economy In the broadest sense, industry sectors can be separated into FIS and SIS. As defined in Chap- ter 5, industry sectors classified under FIS are sectors for which the production and consumption of freight are key components of their economic activity, while SIS are industry sectors in which the provision of services is their primary economic activity. The FIS are the key contributor of freight activity in metropolitan areas; more than 85% of the B2B urban freight traffic is generated by FIS. High concentrations of FIS will generate large amounts of freight activity. As such, understanding the spatial distribution of economic activities, particularly in terms of the FIS, is essential for land-use and transportation planning. The analyses suggested here are intended to determine the composition of the local economy, in terms of FIS and SIS, and identify any key industry sectors that dominate the economy. C H A P T E R 6 Understanding Existing Local Conditions
50 Planning Freight-Efcient Land Uses: Methodology, Strategies, and Tools Exhibit 2 outlines the suggested analyses to understand the composition of the local economy. is analysis can be conducted at dierent geographic levels, such as at county, zip code, or estab- lishment level if the data are available. 6.1.2 Geographic Distribution of Economic Activity It is very important to investigate the spatial patterns of the economy to gain insight into how the various industry sectors (and households) are distributed across the area. Such an under- standing provides a good idea about the location of suppliers and the consumers of these supplies in a given study area. Industry sectors such as Mining, Quarrying, and Oil and Gas Extraction and Manufacturing are related to the production segment (supply) of the economy, while industry sectors such as Retail Trade and Accommodation and Food Services are related to the consump- tion (demand) segment of the economy. ese analyses are important because, to foster FELUs, one must consider how ecient the supply chainsâfrom points of origin to the points of saleâ are, and what issues and potential for improvement exist. At this stage of the analyses, it is important to use Geographic Information System (GIS) to get a solid idea about the locations of various FIS in relation to each other, and in relation to the location of warehouses and distribution centers. e analysis process is summarized in Exhibit 3. 6.1.3 Geographic Concentration of the Local Economy Metropolitan areas can be classied in terms of their economies as monocentric, polycentric, or disperse (Anas et al. 1998). A monocentric form has a high-density central core with the Composition of Local Economy Software: ⢠Statistical Software (e.g., Excel, Google Sheets) Data: ⢠Establishment data at zip code level, or establishment-level data General Guidance: ⢠Calculate the percentage of establishment or employment for FIS and SIS, and by industry sectors. ⢠Visualize results. ⢠Identify the most important sectors by identifying areas with high employment and a large number of establishments. Example: Albany-Schenectady-Troy, New York MSA. Data Source: 2016 County Business Patterns Analysis: The analyses reveal that Albany MSA is a service-inclined economy, with 54% of the employ- ment in the service-inclined sectors. This is not surprising; Albany is the capital of New York State. However, the difference between the percentages of SIS and FIS employment, which is small (8%), is surprising. This could be because Public Administration (NAICS 92) is not included in the public CBP data, causing an underestimation of SIS employment. It is noteworthy that consumer-oriented sectors, such as Retail Trade (NAICS 44-45) and Accommodation and Food Services (NAICS 72), make up more than 50% of the FIS employment. Breakdown of Employment in Albany MSA Breakdown of FIS Employment in Albany MSA Note: Other industry sectors are not shown in the exhibit. FIS (46%)SIS (54%) Construction (12.1%) Manufacturing (13.5%) Wholesale Trade (9.5%)Retail Trade (34.2%) Transportation and Warehousing (7.0%) Accommodation and Food Services (23.3%) Exhibit 2. Guidance for understanding the composition of the local economy.
Understanding Existing Local Conditions 51  intensity of activities decreasing with increasing distance from the core. The polycentric form exhibits multiple high-density cores, while the disperse form does not have any distinctive core. These forms reflect that metropolitan areas have varying levels of centrality and dispersion. Centrality relates to the amount of activity concentrated at the economic pole(s), while disper- sion relates to the spread of those activities throughout the area of interest. Exhibit 4 shows these basic cases. To assess the level of geographic concentration in the local economy, it is important to first identify the location of the economic pole, which is the part of the metropolitan area that underpins the local economy. To identify the location of the economic pole(s), the authors developed and tested multiple methods, including the use of total employment, number of establishments, employment density, establishment density, and the interaction index. The interaction index, which uses a simplified gravity model to estimate the level of interaction between two locations, was found to provide the most consistent results. See Appendix B. However, this method requires computations that may not be suitable for everyone. Fortunately, the comparisons made with the other methods revealed that the technique based on the use of employment density provided results similar to the ones provided by the interaction index, with simpler calculations. The technique suggested in this Guide is the one based on employment density. The employment density technique assumes that the economic pole(s) are the areas with highest employment density in the study area that, collectively, represent about 10% of the total employment. The first step in the technique is to compute the employment densities for the Visualization of Geographic Distribution of Local Economy using GIS Software: ⢠GIS Data: ⢠Establishment data at zip code level, or establish- ment-level data General Guidance: ⢠Load the boundary shape file into GIS. ⢠Map the establishment or employment data. ⢠Calculate the density of establishment or employment. ⢠Color code the polygons based on the metric used. ⢠Boundary polygon shape file (e.g., zip code bound- ary, county boundary) ⢠Identify the areas with high levels of employment and establishments. Example: Albany-Schenectady-Troy, New York MSA. Data Sources: 2015 County Business Patterns; 2010 TIGER/Line® Zip Code Tabulation Areas Analysis: The images below show the spatial distribution of employment and employment density at the level of zip codes for the Albany MSA. As shown, the results are different. The reason is that the larger zip codes tend to naturally have more employment than smaller zip codes. However, if the analysis uses employment density, the distorting effect of size is removed, and the areas with higher intensities of economic activity are easier to identify. The decision of which metric to use depends on the intended use of the results. If the objective is to identify, for instance, which zip code produces more freight, using employment is the best choice because it is a key explanatory variable of freight production. If the ob- jective is to identify which are the areas where the freight traffic is most concentrated, using employment density is better because it takes into account the physical space. Employment at Albany MSA Employment Density at Albany MSA Exhibit 3. Guidance for visualizing the geographic distribution of the local economy.
52 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools various zip codes. The second step is to sort the zip codes in descending order of employment density. The final step is to add the employment numbers from the top down until the total number represents about 10% of the total employment in the MSA. The zip codes identified in this manner define the economic pole(s) in the MSA. If all the zip codes are contiguous, or very close to each other, it is safe to assume that they collectively represent the (only) eco- nomic pole. In other cases, the zip codes selected are relatively far away from each other, which suggests the existence of multiple economic poles. Exhibit 5 describes the analysis process to identify the economic pole(s) in the study area. The second part of the analysis seeks to examine the spread of economic activity as a func- tion of the distance from the primary economic pole. Theoretically, the economic pole will be the densest, and the density will decrease with increasing distance. To examine this hypoth- esis, the density of economic activity is plotted against distance to show the spread of economic activity with respect to the main pole. Exhibit 6 shows the suggested approach to analyze the geographical spread of economic activities. 6.2 Physical Separation between Key Stages of the Supply Chain For purposes of land-use planning, it is important to explicitly consider four different supply chain agents: freight gateways, manufacturers, distributors, and final receivers of the supplies. Freight gatewaysâports and rail stations and interstate highwaysâreceive large volumes of freight that are redistributed within their outlying areas. Manufacturers process input supplies to produce products that are then sent to the other manufacturers as input supplies, or to final receivers for consumption. Distributors and warehouses are intermediaries that store, process, and distribute supplies from the manufacturers to other businesses. Final receivers, such as the Retail Trade and Food Service sectors, primarily serve end consumers. The use of the term receiver deserves discussion. The vast majority of commercial establishments both receive and ship supplies to others because they are both shippers and receivers. However, for purposes of this Guide, the word receiver is used to refer to the final receivers. Gaining insight into the physical separation between these agents provides important insight into how efficient, or ineffi- cient, the land-use patterns are in terms of supply chain activity. Since each establishment could potentially have a different supply chain, quantifying the physical separations between the actual Exhibit 4. Description of urban regions based on centrality and dispersion.
Understanding Existing Local Conditions 53  Identification of Economic Pole(s) and Comparison of the Level of Centrality Software: ⢠GIS Data: ⢠Establishment data at zip code level, or establishment-level data ⢠Zip code boundary polygon shapefile ⢠Road network shapefile General Guidance: ⢠Load the zip code boundary and the road network into GIS. ⢠Calculate the travel distance between the zip codes and the main eco- nomic pole. ⢠Map the employment data to the zip code. ⢠Calculate the density of employment by dividing the employment data by their corresponding zip code area. ⢠Sort the zip codes from the densest to least dense. ⢠Select the densest zip codes that make up about 10% of total employ- ment. Example: Comparison of New York-Newark-Jersey City, NY-NJ-PA, Los Angeles-Long Beach-Anaheim, CA, and Albany-Schenectady-Troy, NY, MSA. Data Sources: 2015 County Business Patterns; 2010 TIGER/Line® Zip Code Tabulation Areas Analysis: The images illustrate the locations of the economic poles of three different MSAs. The economic pole in NYC MSA is located around Midtown Manhattan, where there is a large concentration of retail and office buildings, such as Rockefeller Center and Times Square. Washington, DCâs, economic pole is concentrated near the downtown area. In the Albany MSA, there are two economic poles, one at Albany downtown and a smaller one at Schenectady, where the newest casino in the region is located. Los Angeles MSA is a polycentric structure. It appears that the main economic poles are spread along the Santa Monica Freeway corridor between downtown Los Angeles and Santa Monica. The figures also show the average distances between the zip codes identified as part of the economic poles(s). As shown, in NYC and Washington, DC, the zip codes are relatively close; while in Albany and Los Angeles they are farther away. In other words, the NYC and Washington, DC, MSAs are monocentric, while the Albany and Los Angeles MSAs are polycentric. New York, New York MSA Los Angeles, CA MSA Number of Zip Codes Selected: 6 Average Distance: 1.3 miles Number of Zip Codes Selected: 20 Average Distance: 15.2 miles Washington, DC, MSA Albany, New York MSA Number of Zip Codes Selected: 7 Average Distance: 1.7 miles Number of Zip Codes Selected: 5 Average Distance: 8.0 miles Exhibit 5. Guidance to identify economic pole(s).
54 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools Spread of Economic Activities Software: ⢠GIS ⢠Statistical Software (e.g., Excel, Google Sheets) Data: ⢠Establishment data at zip code level, or establishment-level data ⢠Zip code boundary polygon shapefile ⢠Road network shapefile General Guidance: ⢠Load the zip code boundary shapefile and the road network shape- file into GIS. ⢠Calculate the travel distance between each zip codes and the main economic pole. ⢠Extract the area of each zip code, and the travel distance to the main economic pole. ⢠Calculate the density of employment by dividing the data with their corresponding zip code area. ⢠Create a histogram using employment or establishment data with travel distance away from economic pole. ⢠Review the rate of decline and identify peaks that do not fit the pattern, and assess the validity of the results. Example: Establishment Density in Albany-Schenectady-Troy, NY, MSA. Data Sources: 2015 County Business Patterns; 2010 TIGER/Line® Zip Code Tabulation Areas Analysis: The histogram illustrates the spread of employment density from the main economic pole at zip code 12205 in the Albany MSA. The establishments within the economic pole include a large mall, large and small retailers, consulting companies, insurance companies, and numerous support facilities for Albany International Airport. As expected, the density of establishments declines as the distance from the economic pole increases. The gradual decline is disrupted at 10 miles, 26 miles, and 30 miles. These disruptions are the result of local economic poles. The secondary economic pole (the casino in Schenec- tady) is approximately 10 miles from the main pole. A large manufacturing plant of 3,000 employees is located approximately 26 miles away. Saratoga Springs, a major tourist destination, is approximately 30 miles away. 0 25 50 75 100 125 150 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Es ta bl ish m en t D en sit y (E st ab lis hm en ts /m i2 ) Distance from ZIP Code 12205 (miles) FIS SIS Exhibit 6. Guidance to analyze the spread of economic activities. stages for all businesses in a metropolitan area would be a tremendous undertaking. Fortunately, important insight can be gained from analyses at a more aggregated level. The objective of this aggregated analysis is to assess the physical separation between the following: 1. Freight gateways and clusters of large establishments (e.g., manufacturing sites, distribution centers, warehouses, and large retailers): using the location of the gateways (NAICS 48), and the employment data for large establishments in Manufacturing (NAICS 31-33), Wholesale Trade (NAICS 42), and Retail Trade (NAICS 44-45)/Accommodation and Food Services (NAICS 72) as proxies; 2. Clusters of manufacturers and clusters of distributors (e.g., distribution centers, warehouses): using the employment data for Manufacturing (NAICS 31-33) to Wholesale Trade (NAICS 42) as proxies; 3. Clusters of manufacturers and clusters of receivers: using the employment data for Manu- facturing (NAICS 31-33) to Retail Trade (NAICS 44-45)/Accommodation and Food Services (NAICS 72) as proxies; and
Understanding Existing Local Conditions 55  4. Clusters of distributors and clusters of receivers: using the employment data for Whole- sale Trade (NAICS 42) to Retail Trade (NAICS 44-45)/Accommodation and Food Services (NAICS 72) as proxies. Focusing on these segments is appropriate because they represent the key interactions in supply chains. A small restaurant, for instance, is not likely to interact with freight gateways or large manufacturers. In all likelihood, they get their supplies from local food distributors. The emphasis on the interactions of these clusters reduces the complexity of the challenge because, at this level of analyses, data are available. The analyses of physical separation suggested in the Guide use distance because this is the most appropriate metric for land-use planning and policy. It is important to note that the average distances obtained in this manner do not directly mea- sure the efficiency of supply chains, as a large MSA would naturally have longer distances than a smaller MSA. Thus, to compare results among MSAs, it is important to normalize the average distances so that they are not affected by the size of the MSA. After considering different options, the team decided to normalize the distances using the square root of the MSA area. These analyses could be done both qualitatively and quantitatively. Both approaches are discussed. 6.2.1 Qualitative Analysis The intent here is to estimate the physical separation among these representative industrial sectors using basic techniques, without the use of any computational procedure. In this con- text, the GIS maps displaying the locations of freight gateways, manufacturers, distributors, and receivers are analyzed to get an idea about their physical separations and, possibly, to identify ways to improve the situation. Consultations and in-depth-interviews with private-sector rep- resentatives are bound to add further insight to understand the approximate distance traveled by the cargo that they receive and send. Exhibit 7 shows the suggested guidance to estimate the physical separation of key supply chain stages qualitatively. 6.2.2 Quantitative Analysis The main intent of this quantitative procedure is to quantify the average physical separa- tion between the industry sectors used as proxies, accounting for their size and their ability to produce and consume supplies, and the actual distances that separate them. However, for these computations to be meaningful, they must account for the amount of freight flows between the various industries sectors, albeit in a simplified manner. The reason is that simply computing the average distances between the locations of the various industry sectors would not take into account that large establishments handle larger volumes of cargo. To account for these effects, the team developed a mathematical procedure that ⢠Assumes that the amount of freight produced and attracted is proportional to the number of establishments, which is consistent with the freight generation models from (HolguÃn-Veras et al. 2017b); ⢠Assumes that the freight flows between origin locations and destination locations are pro- portional to the amounts produced and attracted at these locations, by industry sectors; and ⢠The appropriate metric of physical separation is the weighted distance for the resulting freight origin-destination matrix (weighed by the flows). A diagram illustrating the main assumptions is shown in Figure 18. The guidance for quan- titative estimation of the physical separation of key supply chain stages is provided in Exhibit 8. Case studies described in Section 10.3 present a discussion of the spatial distribution for the key supply chain stages in six selected cities using this procedure.
56 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools Understanding Physical Separation of Key Supply Chain Stages Software: ⢠GIS Data: ⢠Establishment data at zip code level, or establishment-level data ⢠Zip code boundary, county boundary ⢠Road network General Guidance: ⢠Identify supply chain stages of interest and relevant sectors. ⢠Use GIS to identify the main clusters of establishments in the rel- evant sectors (in this case Manufacturing, Transportation and Warehousing, and Retail Trade are selected). ⢠Load the zip code and the road network into GIS. ⢠Map the locations of freight gateways, manufacturers, distribu- tors, and receivers. Example: Albany-Schenectady-Troy, NY, MSA. Data Sources: 2015 County Business Patterns by industry sectors for zip codes; 2010 TIGER/Line® Zip Code Tabulation Areas Analysis: The images show the distribution of economic activities for the selected industry sectors. The manufac- turing activities in Albany MSA are primarily located around four areas: (1) along the opposite bank of Port of Albany; (2) around the cities adjacent to the City of Albany; (3) City of Amsterdam, northwest of City of Albany, where the city has a long history of manufacturing activities; and (4) Saratoga Springs, another major city in the Albany MSA. The key locations for Transportation and Warehousing include areas near to the Port of Albany, Albany International Airport, and Rotterdam, where a CSX rail terminal is located. The majority of the manufac- turing zones are within a 15-mile radius of the nearest key Transportation and Warehousing zone, with the excep- tion of manufacturing areas around Saratoga Springs. The geographic patterns for Retail Trade, and Accommoda- tion and Food Services tend to peak at, or nearby, the downtown areas of Albany and Saratoga Springs. This is expected as both industry sectors target consumers, and tend to locate where the number of potential customers is the highest. A point of concern is the increasing use of the Amsterdam area for the relocation of distribution centers for last-mile deliveries, as it is considerably farther away than Rotterdam or Colonie, resulting in longer journeys to reach the receivers. As this trend continues, delivering to establishments in Retail Trade and Accommodation and Food Servicesâprimarily located at the core of the MSAâwould be increasingly difficult, and more disruptive to local communities. NAICS 31-33: Manufacturing NAICS 48-49: Transportation and Warehousing NAICS 44-45: Retail Trade NAICS 72: Accommodation and Food Services Exhibit 7. Qualitative guidance to estimate physical separation of key supply chain stages.
Understanding Existing Local Conditions 57  Figure 18. Illustration of assumptions used for quantitative estimation. Physical Separation of Key Supply Chain Stages Software: ⢠Statistical Software (e.g., Excel, Google Sheets) Data: ⢠Establishment data at zip code level, or establishment-level data ⢠Zip code boundary polygon shapefile ⢠Travel distance between zip codes General Guidance: ⢠Identify supply chain stage of interest. ⢠Identify industry sectors involved. ⢠Estimate the volume of economic activities that will take place at any stage using a trip distribution model. ⢠Compute the average of distance for all zip codes based on the estimated flows between origins and destinations. Example: Comparison of physical separation of key supply chain stages in selected U.S. MSAs. Data Sources: 2015 County Business Patterns; 2010 TIGER/Line® Zip Code Tabulation Areas Analysis: These analyses estimate the physical separation between manufacturers and distributors, and between distributors and retail. The results show that across the six cities, the distances between these stages range from 21 to 34 miles. Albany MSA has the shortest travel distances, which may be due to being the smallest in terms of size and population. The Washington, DC, MSA has the longest distance, which is likely to be the result of its land-use patterns, with high Manufacturing and Transportation and Warehousing activities that tend to be located outside the urban core, requiring longer journeys to reach the urban areas. In the case of Houston, the average distances between these stages is approximately 29 miles, which is relatively high compared with other MSAs. However, the ratio of average distance over the square root area of the MSA is 30%, which is the smallest. This does not mean that the Houston MSA has the most compact supply chains, because the ratio is also influenced by Houstonâs total area. The issue is that, because of the lack of land-use planning, there are no restrictions to urban development. Thus, Houstonâs urban footprint is significantly larger than other comparable cities. Since the ratio uses the square root of the MSA area as a normalization value, the ratio for Houston is lower than it would otherwise be. Washing- ton, DC, has the largest average distance of approximately 34 miles, and has the most sprawled supply chains, with the ratio of average FIS distance over the square root area of the MSA being 41%. Exhibit 8. Quantitative guidance to estimate physical separation of key supply chain stages.
58 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools 6.3 Understanding Freight Activity Gaining solid insight into how freight activityâparticularly FTGâis distributed across the study area is essential. Among other reasons, quantifying the FTG at the zip code level or at a finer level if desired by the analyst, allows land-use planners to identify the need for FELU initia- tives. This section explains how to conduct FTG analyses. The discussion focuses on (1) B2B and (2) B2C. The approach to estimate B2B FTG will be discussed first, then B2C FTG. 6.3.1 Business-to-Business Freight Trip Generation There are a number of ways that B2B FTG can be estimated. To obtain order-of-magnitude estimates, the FTG indicators shown in Chapter 5âwhich express FTG as a function of popula- tion, number of establishments, or employmentâcould be used. The indicators are provided for a variety of U.S. cities and MSAs. It is useful to note that the accuracy of the estimates is expected to improve when the indicators for an area with similar characteristics are selected. For more accurate results, the FG and FTG models developed as part of NCFRP Report 37 should be used. The process outlined in Exhibit 9 is based on the use of the FASTGS (HolguÃn-Veras et al. 2017c). Estimation of Freight Trips or Freight Production using FASTGS Software: ⢠FASTG Data: Establishment data at zip code level, or establishment-level data General Guidance: ⢠Upload a custom dataset or filter the national dataset by state, city or zip code into FASTGS. ⢠Select the metrics to estimate (e.g., freight trip production). ⢠Select model type to use on the FG/FTG models. ⢠Select the size of the region (small, intermediate or large). ⢠Save and click on ârun the fileâ button to generate the models. Example: Washington, DC. Data Sources: 2016 County Business Patterns by industry sectors for zip codes; 2010 TIGER/Line® Zip Code Tabulation Areas Analysis: The image shows the volume of daily FTG around the Washington, DC, urban core. The main urban core includes the areas surrounding downtown, Dupont Circle, and Logan Circle (zip codes: 20036, 20005, 20004, 20002, and 20001). The majority of the establishments belong to SIS (78%), Ac- commodation and Food Services sector (12%), and Retail Trade sector (6%). Notwithstanding their small share of the number of establishments, the Accommodation and Food Services and Retail Trade sectors represent about 70% of the total FTG. The density of establishments decreases with increasing distance from the urban core. This pattern translates into peak FTG at the urban core and declining FTG with distance from the urban core. Other than the urban core, the area around Georgetown University (zip code: 20007) has a high volume of FTG. Similar to the urban core, the majority of the establishments in this area belong to the SIS (67%), Accommodation and Food Services sector (12%), and Retail Trade sector (16%), most likely serving the needs of the student population or local residents. Interestingly, the results reveal FTG around the Na- tional Mall, located south of the urban core. This reflects the needs of cafeterias and restaurants that require frequent deliveries. ⢠Exhibit 9. Guidance to estimate freight activities using the FASTGS.
Understanding Existing Local Conditions 59  6.3.2 Business-to-Consumer Freight Activity As with the B2B models, the team estimated models for different time periods using the NHTS (Wang and Zhou 2015). These models express the number of internet purchases as a function of the socioeconomic characteristics of the household, which enables the use of the models to estimate B2C deliveries for the wide range of socioeconomic conditions in the United States. There are multiple ways that B2C FTG can be estimated. Although models are available, they are not for everyone or for every occasion. For quick estimates, practitioners can refer to the indicators in Chapter 5 for ballpark estimations based on population. 6.4 Local-Level Data Sources As stated in Section 2.2.4, the heterogeneous nature of land-use patterns and economic condi- tions in metropolitan areas and cities presents a challenge for FELU programs. This heterogeneity extends as well to their institutional and decision-making environments. For example, each juris- diction is responsible for land-use controls within their boundaries, including zoning regulations, comprehensive plans, and managing these regulations within their own political environments. In addition, each state has its own unique land-use enabling legislation and case law. Governance structures within a metropolitan area can be very diverse as well. At the local level, economic activities performed at a given parcel of land produce and attract both passenger and freight trips (e.g., businesses attracting customers and truck deliveries), while trans- portation improvements are responsible for inducing changes in land uses (e.g., vacant proper- ties being developed into distribution centers near a new freeway off-ramp, increasing congestion and truck trips). Land uses change relatively slowly, in accordance with the legal rules, regulations, and control mechanisms enforced by individual jurisdictions. In addition, land uses will most likely change when economic conditions are conducive to development. Once a property converts from a vacant state to an active use (e.g., built structures and infrastructure for access), even if the original activity ceases, the structures remain (sometimes modified to accommodate another use), or are demolished. As a result, existing land-use patterns are difficult to change, creating a challenge for MPOs and state DOTs attempting to move rapidly toward new solutions. At the same time, simply expanding road capacity to accommodate additional trucks without an under- standing of existing land-use activities and their ramifications can actually worsen local conditions. The heterogeneity of regional land-use patterns, the fragmentation of jurisdictional regulations, and the lack of standards for understanding land uses, all hinder attempts to increase efficiencies for freight. This creates further complications for MPOs and state DOTs in their attempts to develop a better understanding of land uses. A key to the success of the implementation of FELU programs will be the development of strong partnerships with local governments to establish broader under- standing and harmonizing strategies for metrowide land-use decision-making to foster FELU. Several recent data processing advancements offer opportunities for harmonization strategies using new data sources. Harmonization enables a shared dialogue among stakeholders, includ- ing freight community members, and land-use and transportation planners at both local and regional levels. These local-level sources include building footprint data; parcel shapefiles that contain tax assessor attributes; proprietary, geo-coded, firm-level data (e.g., InfoGroup), speed probe data at local-level geographies [e.g., Traffic Messaging Channel (TMC)], with various per- formance measures [e.g., Truck Travel Time Reliability (TTTR)]; and truck counts [e.g., Aver- age Annual Daily Truck Traffic (AADTT)]. These data are currently available in the National Performance Management Research Data Set (NPMRDS) across the nation. 6.4.1 Building Footprint Data The backbone for local-level information is building footprint data that provide the exact location and configuration of a structure. New techniques [e.g., machine-learning (ML)] can
60 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools efficiently generate spatial representations of structures. For example, Microsoft developed a two-stage process that generates building footprints for the entire nation. The first stage uses semantic segmentation, a technique that recognizes building pixels from aerial photographs, using ResNet34, a Deep Neural Network (DNN) algorithm. The second stage, referred to as polygonization, creates polygons from the building pixel âblobs.â Figures 19 and 20 illustrate the stages of the data processing used for this technique, from the original aerial photo to the creation of the building footprint data. The final output is produced as a GeoJSON file, a format for encoding geographic structures. Using this two-stage process, Microsoft generated, and then posted, 125,192,184 building footprint geometries for all fifty states in GeoJSON format. These data are available in a GitHub repository (GitHub 2020). To access the building footprint data for a specific jurisdiction or region, users need to locate the appropriate state link on the GitHub site and download the compressed zip file containing GeoJSON files. After opening the zip file and extracting the GeoJSON files, the data can be viewed in QGIS, an open-source spatial data analytics tool (QGIS Development Team 2020). QGIS allows for the conversion of GeoJSON files to shapefile. It is also possible to access shapefile versions from the ESRI link posted on the GitHub site that provides the building footprint data accessed as post- processed data in the form of vector tiles (ESRI 2018). Another source of building footprint data is available in Open Street Maps (OSM), which is a crowdsource site (OpenStreetMap 2020). It is worth noting that the OSM data are most accurate in dense, urbanized areas, while the Microsoft building footprint data are superior for suburban and rural structures. The building footprint data, while providing location and structure configuration, lacks attri- butes about the structures, the land-use activities occurring on the site where the structure is Source: (Microsoft 2019) Figure 19. Application of the DNN ResNet34 to create building pixels. Figure 20. Illustration of the polygonization process used to form building footprint data. Source: (Microsoft 2019)
Understanding Existing Local Conditions 61  located, or any localized freight-related traffic impacts from the on-site land-use activities. To address the lack of these essential elements, users need to add other spatial datasets. For example, jurisdictions can spatially join their building footprint shapefile to a shapefile of their tax parcel polygons, with tax assessor data in the associated attribute tables. These tables contain detailed information for each property (e.g., identification number, property ownership, dimensions of structures, land-use designations utilized for tax purposes, etc.). Through this process, stake- holders can realistically identify target properties for potential freight system solutions. Another enhancement for decision-makers and stakeholders links firm-level details to the building footprint data. For example, MPOs can acquire geo-coded (latitude and longitude) proprietary firm-level data, with detailed information including NAICS codes, number of employees, and value of output. After linking these data, MPOs can aggregate the informa- tion into various geographies used by local jurisdictions (e.g., areas designated in comprehen- sive plans). Importantly, having the NAICS code for each firm, by structure, provides land-use and transportation planners with the ability to predict the number of truck trips generated and attracted to a site (or aggregated geography), using various NAICS code-specific equations, see (HolguÃn-Veras et al. 2012a) and (HolguÃn-Veras et al. 2017b). In addition, all MPOs and state DOTs have access to shapefiles with probe speed data (e.g., the NPMRDS). The data processing technique aggregates raw probe data from vehicles traveling on the National Highway System (NHS) and creates road segments referred to as TMC geographies. Specifically, the process uses the time to traverse the length of TMC. These data, provided by the FHWA, are used to meet new reporting requirements for mandated performance metrics for MPOs and state DOTs. The TTTR measure provides a metric calculated as the 95th percentile divided by the 50th percentile travel time for five time periods (e.g., AM peak, PM peak, midday, overnight, and weekends). The average annual daily traffic (AADT) is included for each TMC, in addition to the AADTT, the average speed, and direction, of traffic flows. MPOs and state DOTs have access to these data for the per- formance measures program every month for the previous month. Spatially joining NPMRDS performance measures for TMCs to the building footprint data, the parcel data, and the firm-level business points, creates a rich data resource for land-use planners to use to participate in local and regional FELU discussions, with the ability to develop strategies and the capacity to monitor outcomes going forward. For example, planners will have sufficient data to conduct before and after analyses of land-use policies and freight-related impacts, to guide decision-makers. Using building footprint data as the backbone for FELU at the local level enables land-use planners, in every jurisdiction, to participate in the FELU decision-making process, with the support of MPOs and state DOTs. Enhancing the building footprints allows stakeholders to visualize clusters of freight-related activities as part of the freight system, including the type of activity generating or attracting freight activity, truck traffic behavior on major roadways (addi- tional truck traffic coverage is available from INRIX), with the opportunity to link to other spatial datasets. Linking both detailed land-use information, and the TMC truck traffic data, would provide an effective foundation for evaluating local conditions. Issues include conges- tion issues (by time of day); first- and last-mile concerns (including the volume of truck traffic); proximity to other land-use activities (residential and truck conflicts); and site configuration problems (inability to maneuver a tractor-trailer on a site or through an alley). Accurate diag- nosis of the actual local conditions affecting freight efficiency is the key to starting a productive dialogue among freight community members, local land-use officials and planners, MPO and state DOT staff, and moving together toward solutions.