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Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools (2022)

Chapter: Appendix C - Clusters of Metropolitan Statistical Areas

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Suggested Citation:"Appendix C - Clusters of Metropolitan Statistical Areas." National Academies of Sciences, Engineering, and Medicine. 2022. Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools. Washington, DC: The National Academies Press. doi: 10.17226/26737.
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Suggested Citation:"Appendix C - Clusters of Metropolitan Statistical Areas." National Academies of Sciences, Engineering, and Medicine. 2022. Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools. Washington, DC: The National Academies Press. doi: 10.17226/26737.
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Suggested Citation:"Appendix C - Clusters of Metropolitan Statistical Areas." National Academies of Sciences, Engineering, and Medicine. 2022. Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools. Washington, DC: The National Academies Press. doi: 10.17226/26737.
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Suggested Citation:"Appendix C - Clusters of Metropolitan Statistical Areas." National Academies of Sciences, Engineering, and Medicine. 2022. Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools. Washington, DC: The National Academies Press. doi: 10.17226/26737.
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Suggested Citation:"Appendix C - Clusters of Metropolitan Statistical Areas." National Academies of Sciences, Engineering, and Medicine. 2022. Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools. Washington, DC: The National Academies Press. doi: 10.17226/26737.
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Suggested Citation:"Appendix C - Clusters of Metropolitan Statistical Areas." National Academies of Sciences, Engineering, and Medicine. 2022. Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools. Washington, DC: The National Academies Press. doi: 10.17226/26737.
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C-1   The purpose of clustering the metropolitan statistical areas is to enable planners to identify peer MSAs, which exhibit similar economic characteristics despite coming from different geographical regions of the United States. Among other benefits, lessons learned in one MSA are more likely to apply to other cities in the cluster. The team applied a statistical technique, called cluster analysis, to identify natural groupings of MSAs based on characteristics relevant to freight activity. The characteristics used include socioeconomic criteria, such as population and employment, the distribution of freight activities, and average commute time. Five variables were found to be significant: population, population density, average commute time, FTG within the MSA, and the interaction index described in Appendix B. The interaction index was found to be the most important variable. The clustering algorithm separated all of the country’s MSAs into six clusters. These six clusters were further split into freight­inclined and service­inclined economies. This appendix provides the list of MSAs in each cluster. A P P E N D I X C Clusters of Metropolitan Statistical Areas Cluster 1 Economy Population Population Density Interaction Index Average Commute Travel Time FTG Service-inclined 20,215,694 people 2,234 people/mi2 558,800,000 35.6 mins 1,816,290 daily freight trips • New York-Newark-Jersey City, NY-NJ-PA Cluster 2 Economy Population Population Density Interaction Index Average Commute Travel Time FTG Service-inclined 6,066,589 to 13,283,824 people 951 to 2,711 people/mi2 176,300,000 to 223,900,000 29.0 to 34.3 mins 401,963 to 1.217,099 daily freight trip • Chicago-Naperville-Elgin, IL-IN-WI • Philadelphia-Camden-Wilmington, PA-NJ- DE-MD • Los Angeles-Long Beach-Anaheim, CA • Washington-Arlington-Alexandria, DC-VA- MD-WV Cluster 3 Economy Population Population Density Interaction Index Freight-inclined 2,795,036 to 7,101,031 people 645 to 1,298 people/mi2 90,137,510 to 125,900,000 Average Commute Travel Time FTG 26.6 to 30.7 mins 224,344 to 640,948 daily freight trips

C-2 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools • Houston-The Woodlands-Sugar Land, TX Cluster 4 Economy Population Population Density Interaction Index Average Commute Travel Time FTG Service-inclined 2,795,036 to 7,101,031 people 645 to 1,298 people/mi2 90,137,510 to 125,900,000 26.6 to 30.7 mins 224,344 to 640,948 daily freight trips • Atlanta-Sandy Springs-Roswell, GA • Baltimore-Columbia-Towson, MD • Boston-Cambridge-Newton, MA-NH • Dallas-Fort Worth-Arlington, TX • Detroit-Warren-Dearborn, MI • Miami-Fort Lauderdale-West Palm Beach, FL Cluster 5 Economy Population Population Density Interaction Index Average Commute Travel Time FTG Freight-inclined 948,116 to 4,657.985 people 163 to 1,820 people/mi2 35,729,069 to 73,312,465 23.2 to 31.4 mins 98,447 to 384,937 daily freight trips • Riverside-San Bernardino-Ontario, CA Cluster 6 Economy Population Population Density Interaction Index Average Commute Travel Time FTG Service-inclined 948,116 to 4,657.985 people 163 to 1,820 people/mi2 35,729,069 to 73,312,465 23.2 to 31.4 mins 98,447 to 384,937 daily freight trips • Austin-Round Rock, TX • Bridgeport-Stamford-Norwalk, CT • Charlotte-Concord-Gastonia, NC-SC • Cincinnati, OH-KY-IN • Cleveland-Elyria, OH • Columbus, OH • Denver-Aurora-Lakewood, CO • Hartford-West Hartford-East Hartford, CT • Indianapolis-Carmel-Anderson, IN • Nashville-Davidson; Murfreesboro; Franklin, TN • Orlando-Kissimmee-Sanford, FL • Phoenix-Mesa-Scottsdale, AZ • Pittsburgh, PA • Providence-Warwick, RI-MA • Richmond, VA • San Diego-Carlsbad, CA • San Francisco-Oakland-Hayward, CA • Kansas City, MO-KS • Milwaukee-Waukesha-West Allis, WI • Minneapolis-St. Paul-Bloomington, MN-WI • Seattle-Tacoma-Bellevue, WA • St. Louis, MO-IL • Tampa-St. Petersburg-Clearwater, FL • Virginia Beach-Norfolk-Newport News, VA- NC Cluster 7 Economy Population Population Density Interaction Index Average Commute Travel Time FTG Freight-inclined 373,104 to 2,382,181 people 97 to 1,631 people/mi2 10,554,125 to 33,097,797 19.0 to 28.1 mins 34,219 to 225,283 daily freight trips

Clusters of Metropolitan Statistical Areas C-3   • Akron, OH • Allentown-Bethlehem-Easton, PA-NJ • Asheville, NC • Baton Rouge, LA • Canton-Massillon, OH • Cape Coral-Fort Myers, FL • Charleston-North Charleston, SC • Chattanooga, TN-GA • Deltona-Daytona Beach-Ormond Beach, FL • Fort Wayne, IN • Fresno, CA • Grand Rapids-Wyoming, MI • Greensboro-High Point, NC • Greenville-Anderson-Mauldin, SC • Lakeland-Winter Haven, FL • Lancaster, PA • Las Vegas-Henderson-Paradise, NV • Lexington-Fayette, KY • Louisville/Jefferson County, KY-IN • Manchester-Nashua, NH • Memphis, TN-MS-AR • New Orleans-Metairie, LA • North Port-Sarasota-Bradenton, FL • Oxnard-Thousand Oaks-Ventura, CA • Palm Bay-Melbourne-Titusville, FL • Reading, PA • Salisbury, MD-DE • Scranton-Wilkes-Barre-Hazleton, PA • Tulsa, OK • Wichita, KS • York-Hanover, PA • Youngstown-Warren-Boardman, OH-PA Cluster 8 Economy Population Population Density Interaction Index Average Commute Travel Time FTG Service-inclined 373,104 to 2,382,181 people 97 to 1,631 people/mi2 10,554,125 to 33,097,797 19.0 to 28.1 mins 34,219 to 225,283 daily freight trips • Albany-Schenectady-Troy, NY • Albuquerque, NM • Augusta-Richmond County, GA-SC • Birmingham-Hoover, AL • Buffalo-Cheektowaga-Niagara Falls, NY • Colorado Springs, CO • Columbia, SC • Oklahoma City, OK • Omaha-Council Bluffs, NE-IA • Portland-South Portland, ME • Portland-Vancouver-Hillsboro, OR-WA • Raleigh, NC • Rochester, NY • Sacramento-Roseville-Arden-Arcade, CA • Dayton, OH • Des Moines-West Des Moines, IA • Durham-Chapel Hill, NC • Harrisburg-Carlisle, PA • Jackson, MS • Jacksonville, FL • Knoxville, TN • Lansing-East Lansing, MI • Little Rock-North Little Rock-Conway, AR • Madison, WI • New Haven-Milford, CT • Salt Lake City, UT • San Antonio-New Braunfels, TX • San Jose-Sunnyvale-Santa Clara, CA • Springfield, MA • Syracuse, NY • Toledo, OH • Trenton, NJ • Tucson, AZ • Winston-Salem, NC • Worcester, MA-CT Cluster 9 Economy Population Population Density Interaction Index Average Commute Travel Time FTG Freight-inclined 129,40 to 993,716 people 2 to 1,616 people/mi2 0 to 10,194,509 11.3 to 38.7 mins 535 to 77,921 daily freight trips • Abilene, TX • Albany, GA • Albany, OR • Altoona, PA • Amarillo, TX • Ames, IA • Anniston-Oxford-Jacksonville, AL • Appleton, WI • Athens-Clarke County, GA • Atlantic City-Hammonton, NJ • Kalamazoo-Portage, MI • Kankakee, IL • Kennewick-Richland, WA • Kingston, NY • Kokomo, IN • Lafayette, LA • Lafayette-West Lafayette, IN • Lake Charles, LA • Lake Havasu City-Kingman, AZ • Laredo, TX

C-4 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools • Auburn-Opelika, AL • Bakersfield, CA • Bangor, ME • Barnstable Town, MA • Battle Creek, MI • Bay City, MI • Beaumont-Port Arthur, TX • Beckley, WV • Bellingham, WA • Bend-Redmond, OR • Billings, MT • Binghamton, NY • Bismarck, ND • Blacksburg-Christiansburg-Radford, VA • Bloomington, IN • Boise City, ID • Lawrence, KS • Lawton, OK • Lebanon, PA • Lima, OH • Longview, TX • Longview, WA • Lubbock, TX • Lynchburg, VA • Madera, CA • Manhattan, KS • Mankato-North Mankato, MN • Mansfield, OH • Medford, OR • Merced, CA • Michigan City-La Porte, IN • Midland, MI • Bowling Green, KY • Brunswick, GA • Burlington, NC • Burlington-South Burlington, VT • Carbondale-Marion, IL • Carson City, NV • Casper, WY • Cedar Rapids, IA • Chambersburg-Waynesboro, PA • Champaign-Urbana, IL • Cheyenne, WY • Chico, CA • Cleveland, TN • Coeur d'Alene, ID • College Station-Bryan, TX • Columbia, MO • Columbus, IN • Corpus Christi, TX • Crestview-Fort Walton Beach-Destin, FL • Dalton, GA • Danville, IL • Daphne-Fairhope-Foley, AL • Decatur, AL • Decatur, IL • Dothan, AL • Dover, DE • Dubuque, IA • East Stroudsburg, PA • Eau Claire, WI • El Centro, CA • El Paso, TX • Elizabethtown-Fort Knox, KY • Elkhart-Goshen, IN • Elmira, NY • Erie, PA • Eugene, OR • Fairbanks, AK • Farmington, NM • Fayetteville, NC • Flagstaff, AZ • Florence, SC • Florence-Muscle Shoals, AL • Fond du Lac, WI • Fort Collins, CO • Gadsden, AL • Gainesville, GA • Gettysburg, PA • Glens Falls, NY • Midland, TX • Mobile, AL • Modesto, CA • Monroe, MI • Montgomery, AL • Morristown, TN • Mount Vernon-Anacortes, WA • Muskegon, MI • Napa, CA • Naples-Immokalee-Marco Island, FL • New Bern, NC • Niles-Benton Harbor, MI • Norwich-New London, CT • Ocala, FL • Ocean City, NJ • Odessa, TX • Ogden-Clearfield, UT • Oshkosh-Neenah, WI • Owensboro, KY • Panama City, FL • Pine Bluff, AR • Pocatello, ID • Port St. Lucie, FL • Prescott, AZ • Punta Gorda, FL • Racine, WI • Rapid City, SD • Reno, NV • Rochester, MN • Rockford, IL • Rocky Mount, NC • Rome, GA • Saginaw, MI • Salem, OR • Salinas, CA • San Angelo, TX • San Luis Obispo-Paso Robles-Arroyo Grande, CA • Santa Cruz-Watsonville, CA • Santa Fe, NM • Santa Maria-Santa Barbara, CA • Santa Rosa, CA • Savannah, GA • Sheboygan, WI • Sherman-Denison, TX • Shreveport-Bossier City, LA • Spartanburg, SC • Springfield, OH

Clusters of Metropolitan Statistical Areas C-5   • Goldsboro, NC • Grand Island, NE • Grand Junction, CO • Grants Pass, OR • Great Falls, MT • Greeley, CO • Green Bay, WI • Gulfport-Biloxi-Pascagoula, MS • Hammond, LA • Hanford-Corcoran, CA • Harrisonburg, VA • Hattiesburg, MS • Hickory-Lenoir-Morganton, NC • Hilton Head Island-Bluffton-Beaufort, SC • Hinesville, GA • Hot Springs, AR • Houma-Thibodaux, LA • Idaho Falls, ID • Ithaca, NY • Jackson, MI • Jackson, TN • Jacksonville, NC • Janesville-Beloit, WI • Jefferson City, MO • Jonesboro, AR • Joplin, MO • Kahului-Wailuku-Lahaina, HI • St. Cloud, MN • St. George, UT • State College, PA • Staunton-Waynesboro, VA • Stockton-Lodi, CA • Sumter, SC • Terre Haute, IN • The Villages, FL • Tuscaloosa, AL • Urban Honolulu, HI • Valdosta, GA • Vallejo-Fairfield, CA • Victoria, TX • Vineland-Bridgeton, NJ • Visalia-Porterville, CA • Warner Robins, GA • Waterloo-Cedar Falls, IA • Watertown-Fort Drum, NY • Wausau, WI • Wenatchee, WA • Wichita Falls, TX • Williamsport, PA • Wilmington, NC • Yakima, WA • Yuba City, CA • Yuma, AZ Cluster 10 Economy Population Population Density Interaction Index Average Commute Travel Time FTG Service-inclined 129,40 to 993,716 people 2 to 1,616 people/mi2 0 to 10,194,509 11.3 to 38.7 mins 535 to 77,921 daily freight trips • Alexandria, LA • Anchorage, AK • Ann Arbor, MI • Bloomington, IL • Bloomsburg-Berwick, PA • Boulder, CO • Bremerton-Silverdale, WA • Brownsville-Harlingen, TX • California-Lexington Park, MD • Charleston, WV • Missoula, MT • Monroe, LA • Morgantown, WV • Muncie, IN • Olympia-Tumwater, WA • Parkersburg-Vienna, WV • Pensacola-Ferry Pass-Brent, FL • Peoria, IL • Pittsfield, MA • Provo-Orem, UT • Charlottesville, VA • Corvallis, OR • Flint, MI • Gainesville, FL • Greenville, NC • Homosassa Springs, FL • Huntsville, AL • Iowa City, IA • Johnson City, TN • Johnstown, PA • Killeen-Temple, TX • Las Cruces, NM • Lewiston-Auburn, ME • Lincoln, NE • Macon, GA • McAllen-Edinburg-Mission, TX • Pueblo, CO • Redding, CA • Roanoke, VA • Sebastian-Vero Beach, FL • Sebring, FL • Sierra Vista-Douglas, AZ • Sioux Falls, SD • Spokane-Spokane Valley, WA • Springfield, IL • Springfield, MO • Tallahassee, FL • Topeka, KS • Tyler, TX • Utica-Rome, NY • Waco, TX • Walla Walla, WA

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Land-use planning is essential to fostering quality of life and harmony among the myriad social and economic activities that take place and compete for space in urban and metropolitan areas. Land-use planning also profoundly affects the commercial supply chains that deliver the goods and services that constitute urban and regional economies, and contribute to the quality of life.

The TRB National Cooperative Highway Research Program's NCHRP Research Report 998: Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools is designed to prepare practitioners to make land-use decisions that minimize the private and external costs associated with the production, transportation, and consumption of goods by providing them with the tools needed to analyse the freight efficiency of current and future land uses in their jurisdictions, and identify and select land-use and transportation initiatives.

Supplemental to the report are a tool for assessment of the overall impacts of freight land uses, a document about the research effort, and a presentation.

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