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-- City & Regional Demographic Economic Characteristics & Patterns Making and maintaining great cities is a continuing work in progress. It requires top-down leadership at the city and regional levels. It requires a bottom-up group of neighborhoods working together. It is important that leaders and stakeholders know "where we are", how things are changing where and by how much, and how things might change in the future. A continuous monitoring, forming a data-driven outlook ... determining what is feasible ... setting goals in that framework. America's Communities Program The ProximityOne America's Communities Program (ACP) provides organized, multi-sourced data and tools to facilitate planning and community development by individual communities. ACP users gain data-driven insights into the where and how of change. What is our city relatively good at ... or not so good? What might be our net advantages? Are we basing decisions on what we think or what the data are telling us? How might good trends be augmented? What might have changed over the past several years that could work to our advantage or disadvantage? Do we really know patterns of low and moderate income households by neighborhood? What areas might be most likely to improve based on better knowledge of patterns? Are we able to use GIS and data resources that might improve our chances at grant awards and funding? What data and information might we provide to prospective businesses locating here? Are we conveying those data in the right ways? How might population, employment and income change between now and 2030, patterns proceeding as they have? What tools and resources do we have that could change this? City/Place Demographic-Economic Interactive Tables Use the national scope demographic-economic interactive tables to view, rank, compare selected or all cities/places (approximately 29,500 places) using an extended set of data as used in the community profiles. Cities/Places Main Section • Cities/Places American Community Survey 2015 5-year estimates and organized into four subject matter groups: • General Demographics • Social Characteristics • Economic Characteristics • Housing Characteristics Model-based Estimates • School District Demographic-Economic Trend Profiles Appalachia Focus • Appalachia Cities, Counties, Region Visually Examining Cities Develop your own map(s) that show city(s) of interest. City boundaries are very precise and shown in context with roads and other geography. Flexibly add labels. Create pattern views. Add your own data. City maps can be saved as a graphic and used in any manner. See more about making city maps. Illustrative Profiles -- scroll section -- register for updates ... insights into the current situation and trends. ... click a link to view a profile; the link in brackets is the city/place geocode. ... the 2016 total population is shown in parentheses. City Demographic-Economic Trend Profiles go top Click a city link in the scroll sections below to view a demographic-economic trend profile for that city. • Group 1 - uses ACS 1-year estimates (areas 65,000 population and over) • Group 2 - uses ACS 5-year estimates (all areas) One master list of cities has been used. All cities in Group 1 appear in Group 1 and Group 2 (the ACS sourced values differ). The scope of ACS subject matter and layout are the same for Group 1 and Group 2 profiles. See about using ACS 5-year and 1-year data. Note that the Group 1 and Group 2 profiles differ only with respect to the ACS data; Data from other sources are the same in the Group 1 and Group 2 profiles. See related School District profiles. Group 1 -- cities 65,000 population or more
Alabama
• Birmingham, AL [0107000] (212,157) • Montgomery, AL [0151000] (200,022) Alaska • Anchorage, AK [0203000] (298,192) Arizona • Phoenix, AZ [0455000] (1,615,017) • Scottsdale, AZ [0465000] (246,645) • Yuma, AZ [0485540] (94,906) Arkansas • Fayetteville, AR [0523290] (83,826) • Little Rock, AR [0541000] (198,541) California • Los Angeles, CA [0644000] (3,976,322) • Pasadena, CA [0656000] (142,059) • Sacramento, CA [0664000] (495,234) • Salinas, CA [0664224] (157,218) • San Diego, CA [0666000] (1,406,630) • San Jose, CA [0668000] (1,025,350) Colorado • Boulder, CO [0807850] (108,090) • Denver, CO [0820000] (693,060) • Lakewood, CO [0843000] (154,393) Connecticut • Hartford, CT [0937000] (123,243) • Stamford, CT [0973000] (129,113) Delaware • Wilmington, DE [1077580] (71,442) District of Columbia • Washington, DC [1150000] (681,170) Florida • Boca Raton, FL [1207300] (96,114) • Deerfield Beach, FL [1216725] (79,764) • Fort Lauderdale, FL [1224000] (178,752) • Lakeland, FL [1238250] (106,420) • St. Petersburg, FL [1263000] (260,999) • Tallahassee, FL [1270600] (190,894) • Tampa, FL [1271000] (377,165) Georgia • Atlanta, GA [1304000] (472,522) • Savannah, GA [1369000] (146,763) Hawaii • Honolulu, HI [1571550] (351,792) Idaho • Boise City, ID [1608830] (223,154) • Meridian, ID [1652120] (95,623) Illinois • Chicago, IL [1714000] (2,704,958) • Evanston, IL [1724582] (74,895) • Naperville, IL [1751622] (147,122) • Springfield, IL [1772000] (115,715) Indiana • Indianapolis, IN [1836003] (855,164) Iowa • Des Moines, IA [1921000] (215,472) Kansas • Topeka, KS [2071000] (126,808) • Wichita, KS [2079000] (389,902) Kentucky • Lexington-Fayette, KY [2146027] (318,449) Louisiana • Baton Rouge, LA [2205000] (227,715) Maine • Portland, ME [2360545] (66,937) Maryland • Baltimore, MD [2404000] (614,664) • Rockville, MD [2467675] (66,940) Massachusetts • Boston, MA [2507000] (673,184) • Cambridge, MA [2511000] (110,651) Michigan • Lansing, MI [2646000] (116,020) Minnesota • Minneapolis, MN [2743000] (413,651) • St. Paul, MN [2758000] (302,398) Mississippi • Jackson, MS [2836000] (169,148) Missouri • Columbia, MO [2915670] (120,612) • Kansas City, MO [2938000] (481,420) Montana • Billings, MT [3006550] (110,323) • Missoula, MT [3050200] (72,364) Nebraska • Lincoln, NE [3128000] (280,364) Nevada • Las Vegas, NV [3240000] (632,912) New Hampshire • Manchester, NH [3345140] (110,506) New Jersey • Trenton, NJ [3474000] (84,056) New Mexico • Albuquerque, NM [3502000] (559,277) • Santa Fe, NM [3570500] (83,875) New York • Albany, NY [3601000] (98,111) • New York, NY [3651000] (8,537,673) North Carolina • Greensboro, NC [3728000] (287,027) • Raleigh, NC [3755000] (458,880) North Dakota • Fargo, ND [3825700] (120,762) • Bismarck, ND [3807200] (72,417) Ohio • Mason, OH [3916000] (298,800) • Cleveland, OH [3916000] (385,809) • Columbus, OH [3918000] (860,090) Oklahoma • Oklahoma City, OK [4055000] (638,367) • Tulsa, OK [4075000] (403,090) Oregon • Medford, OR [4147000] (81,636) • Portland, OR [4159000] (639,863) • Salem, OR [4164900] (167,419) Pennsylvania • Philadelphia, PA [4260000] (1,567,872) • Pittsburgh, PA [4261000] (303,625) Rhode Island • Providence, RI [4459000] (179,219) South Carolina • Columbia, SC [4516000] (134,309) • Greenville, SC [4530850] (67,453) • North Charleston, SC [4550875] (109,298) • Rock Hill, SC [4561405] (72,937) South Dakota • Rapid City, SD [4652980] (74,048) • Sioux Falls, SD [4659020] (174,360) Tennessee • Knoxville, TN [4740000] (186,239) • Nashville, TN [4752006] (660,388) • Memphis, TN [4748000] (652,717) Texas • Austin, TX [4805000] (947,890) • College Station, TX [4815976] (112,141) • Dallas, TX [4819000] (1,317,929) • Frisco, TX [4827684] (163,656) • Houston, TX [4835000] (2,303,482) • Laredo, TX [4841464] (257,156) • League City, TX [4841980] (102,010) Utah • Salt Lake City, UT [4967000] (193,744) Virginia • Alexandria, VA [5101000] (155,810) • Lynchburg, VA [5147672] (80,212) • Richmond, VA [5167000] (223,170) Wisconsin • Madison, WI [5548000] (252,551) Wyoming Group 2 -- all cities in master list irrespective of population size
Alabama
• Auburn, AL [0103076] (63,118) • Birmingham, AL [0107000] (212,157) • Montgomery, AL [0151000] (200,022) Alaska • Anchorage, AK [0203000] (298,192) • Juneau, AK [0236400] (32,468) Arizona • Fountain Hills, AZ [0425300] (24,482) • Phoenix, AZ [0455000] (1,615,017) • Prescott, AZ [0457380] (42,513) • Prescott Valley, AZ [0457450] (43,132) • Scottsdale, AZ [0465000] (246,645) • Sedona, AZ [0465350] (10,397) • Yuma, AZ [0485540] (94,906) Arkansas • Bentonville, AR [0505320] (47,093) • Fayetteville, AR [0523290] (83,826) • Little Rock, AR [0541000] (198,541) California • Eureka, CA [0623042] (27,226) • Los Angeles, CA [0644000] (3,976,322) • Malibu, CA [0645246] (12,879) • Pasadena, CA [0656000] (142,059) • Sacramento, CA [0664000] (495,234) • Salinas, CA [0664224] (157,218) • San Diego, CA [0666000] (1,406,630) • San Jose, CA [0668000] (1,025,350) • Seaside, CA [0670742] (34,312) • Woodside, CA [0686440] (5,551) Colorado • Boulder, CO [0807850] (108,090) • Buena Vista, CO [0810105] (2,778) • Denver, CO [0820000] (693,060) • Gunnison, CO [0833640] (6,261) • Lakewood, CO [0843000] (154,393) Connecticut • Hartford, CT [0937000] (123,243) • Norwich, CT [0956200] (39,556) • Stamford, CT [0973000] (129,113) Delaware • Dover, DE [1021200] (37,786) • Wilmington, DE [1077580] (71,442) District of Columbia • Washington, DC [1150000] (681,170) Florida • Boca Raton, FL [1207300] (96,114) • Deerfield Beach, FL [1216725] (79,764) • Fort Lauderdale, FL [1224000] (178,752) • Lakeland, FL [1238250] (106,420) • Palm Beach Gardens, FL [1254075] (53,778) • St. Petersburg, FL [1263000] (260,999) • Tallahassee, FL [1270600] (190,894) • Tampa, FL [1271000] (377,165) Georgia • Atlanta, GA [1304000] (472,522) • Carrollton, GA [1313492] (26,562) • Cumming, GA [1320932] (6,225) • Savannah, GA [1369000] (146,763) Hawaii • Honolulu, HI [1571550] (351,792) Idaho • Boise City, ID [1608830] (223,154) • Idaho Falls, ID [1639700] (60,211) • Meridian, ID [1652120] (95,623) • Rexburg, ID [1667420] (28,222) Illinois • Chicago, IL [1714000] (2,704,958) • Evanston, IL [1724582] (74,895) • Naperville, IL [1751622] (147,122) • Springfield, IL [1772000] (115,715) Indiana • Indianapolis, IN [1836003] (855,164) Iowa • Des Moines, IA [1921000] (215,472) • Indianola, IA [1938280] (15,785) • Oskaloosa, IA [1959925] (11,523) Kansas • Iola, KS [2034300] (5,454) • Topeka, KS [2071000] (126,808) • Wichita, KS [2079000] (389,902) Kentucky • Frankfort, KY [2128900] (27,885) • Lexington-Fayette, KY [2146027] (318,449) Louisiana • Baton Rouge, LA [2205000] (227,715) Maine • Augusta, ME [2302100] (18,494) • Portland, ME [2360545] (66,937) Maryland • Baltimore, MD [2404000] (614,664) • Annapolis, MD [2401600] (39,418) • Cumberland, MD [2421325] (19,978) • Frostburg, MD [2430900] (8,676) • Rockville, MD [2467675] (66,940) Massachusetts • Boston, MA [2507000] (673,184) • Cambridge, MA [2511000] (110,651) Michigan • East Lansing, MI [2624120] (48,870) • Lansing, MI [2646000] (116,020) Minnesota • Minneapolis, MN [2743000] (413,651) • St. Paul, MN [2758000] (302,398) Mississippi • Jackson, MS [2836000] (169,148) Missouri • Columbia, MO [2915670] (120,612) • Jefferson City, MO [2937000] (43,013) • Kansas City, MO [2938000] (481,420) Montana • Billings, MT [3006550] (110,323) • Helena, MT [3035600] (31,169) • Missoula, MT [3050200] (72,364) Nebraska • Lincoln, NE [3128000] (280,364) Nevada • Carson City, NV [3209700] (54,742) • Las Vegas, NV [3240000] (632,912) New Hampshire • Concord, NH [3314200] (42,904) • Manchester, NH [3345140] (110,506) New Jersey • Trenton, NJ [3474000] (84,056) New Mexico • Albuquerque, NM [3502000] (559,277) • Santa Fe, NM [3570500] (83,875) New York • Albany, NY [3601000] (98,111) • New York, NY [3651000] (8,537,673) • Rome, NY [3663418] (32,415) North Carolina • Greensboro, NC [3728000] (287,027) • Raleigh, NC [3755000] (458,880) North Dakota • Fargo, ND [3825700] (120,762) • Bismarck, ND [3807200] (72,417) Ohio • Cincinnati, OH [3916000] (298,800) • Cleveland, OH [3916000] (385,809) • Columbus, OH [3918000] (860,090) • Mason, OH [3916000] (33,037 ) • Middletown, OH [3949840] (48,813) • Monroe, OH [3916000] (13,473) Oklahoma • Oklahoma City, OK [4055000] (638,367) • Tulsa, OK [4075000] (403,090) Oregon • Medford, OR [4147000] (81,636) • Portland, OR [4159000] (639,863) • Salem, OR [4164900] (167,419) Pennsylvania • Harrisburg, PA [4232800] (48,904) • Philadelphia, PA [4260000] (1,567,872) • Pittsburgh, PA [4261000] (303,625) Rhode Island • Providence, RI [4459000] (179,219) South Carolina • Aiken, SC [4500550] (30,937) • Columbia, SC [4516000] (134,309) • Florence, SC [4525810] (38,317) • Georgetown, SC [4528870] (9,024) • Greenville, SC [4530850] (67,453) • Greenwood, SC [4530895] (23,320) • North Charleston, SC [4550875] (109,298) • Rock Hill, SC [4561405] (72,937) • Sumter, SC [4570405] (40,723) • Yemassee, SC [4579450] (972) South Dakota • Pierre, SD [4649600] (14,008) • Rapid City, SD [4652980] (74,048) • Sioux Falls, SD [4659020] (174,360) Tennessee • Arlington, TN [4701740] (11,566) • Bartlett, TN [4703440] (58,622) • Collierville, TN [4716420] (49,177) • Germantown, TN [4728960] (39,056) • Knoxville, TN [4740000] (186,239) • Lakeland, TN [4740350] (12,494) • Millington, TN [4749060] (10,974) • Nashville, TN [4752006] (660,388) • Memphis, TN [4748000] (652,717) Texas • Austin, TX [4805000] (947,890) • Atascocita CDP, TX [4804462] (310) • College Station, TX [4815976] (112,141) • Dallas, TX [4819000] (1,317,929) • Dayton, TX [4819432] (7,734) • Frisco, TX [4827684] (163,656) • Houston, TX [4835000] (2,303,482) • Laredo, TX [4841464] (257,156) • League City, TX [4841980] (102,010) • Mont Belvieu, TX [4849068] (5,584) Utah • Salt Lake City, UT [4967000] (193,744) Vermont • Montpelier, VT [5046000] (7,535) Virginia • Alexandria, VA [5101000] (155,810) • Lynchburg, VA [5147672] (80,212) • Richmond, VA [5167000] (223,170) Washington • Olympia, WA [5351300] (51,202) West Virginia • Charleston, WV [5414600] (49,138) Wisconsin • Madison, WI [5548000] (252,551) Wyoming • Cheyenne, WY [5613900] (64,019) ProximityOne User Group Join the ProximityOne User Group to keep up-to-date with new developments relating to metros and component geography decision-making information resources. Receive updates and access to tools and resources available only to members. Use this form to join the User Group. Support Using these Resources Learn more about accessing and using demographic-economic data and related analytical tools. Join us in a Data Analytics Lab session. There is no fee for these one-hour Web sessions. Each informal session is focused on a specific topic. The open structure also provides for Q&A and discussion of application issues of interest to participants. Additional Information ProximityOne develops geodemographic-economic data and analytical tools and helps organizations knit together and use diverse data in a decision-making and analytical framework. We develop custom demographic/economic estimates and projections, develop geographic and geocoded address files, and assist with impact and geospatial analyses. Wide-ranging organizations use our tools (software, data, methodologies) to analyze their own data integrated with other data. Follow ProximityOne on Twitter at www.twitter.com/proximityone. Contact us (888-364-7656) with questions about data covered in this section or to discuss custom estimates, projections or analyses for your areas of interest. |
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