Category Archives: DC Washington

U.S. State Capital City Demographic-Economic Characteristics

.. tools and data to examine demographic-economic characteristics of each U.S. State capital city. The profiles are part of the America’s Communities Program. The profiles help stakeholders know “where we are”, how things are changing where and by how much, and how things might change in the future. See related web section for more detail.

State Capital Cities
The following graphic shows state capital city locations as markers. This view was developed using GIS tools enabling creation of similar views in context of other geography and subject matter. Orange markers are cities with less than 65,000 population; blue markers are cities with more than 65,000 population. based on percent population change. Click graphic for larger view. Larger view shows city names and urban areas. Expand browser window for best quality view.

.. view developed with ProximityOne CV XE GIS and related GIS project.
Hawaii
Alaska

State Capital City Demographic-Economic Profiles
State capital cities are listed below organized by state. Click the link within the brackets to view a demographic-economic profile for that city. The 2016 total population is shown in parentheses.

Alabama
• Montgomery, AL [0151000] (200,022)
Alaska
• Juneau, AK [0236400] (32,468)
Arizona
• Phoenix, AZ [0455000] (1,615,017)
Arkansas
• Little Rock, AR [0541000] (198,541)
California
• Sacramento, CA [0664000] (495,234)
Colorado
• Denver, CO [0820000] (693,060)
Connecticut
• Hartford, CT [0937000] (123,243)
Delaware
• Dover, DE [1021200] (37,786)
District of Columbia
• Washington, DC [1150000] (681,170)
Florida
• Tallahassee, FL [1270600] (190,894)
Georgia
• Atlanta, GA [1304000] (472,522)
Hawaii
• Honolulu, HI [1571550] (351,792)
Idaho
• Boise City, ID [1608830] (223,154)
Illinois
• Springfield, IL [1772000] (115,715)
Indiana
• Indianapolis, IN [1836003] (855,164)
Iowa
• Des Moines, IA [1921000] (215,472)
Kansas
• Topeka, KS [2071000] (126,808)
Kentucky
• Frankfort, KY [2128900] (27,885)
Louisiana
• Baton Rouge, LA [2205000] (227,715)
Maine
• Augusta, ME [2302100] (18,494)
Maryland
• Annapolis, MD [2401600] (39,418)
Massachusetts
• Boston, MA [2507000] (673,184)
Michigan
• Lansing, MI [2646000] (116,020)
Minnesota
• St. Paul, MN [2758000] (302,398)
Mississippi
• Jackson, MS [2836000] (169,148)
Missouri
• Jefferson City, MO [2937000] (43,013)
Montana
• Helena, MT [3035600] (31,169)
Nebraska
• Lincoln, NE [3128000] (280,364)
Nevada
• Carson City, NV [3209700] (54,742)
New Hampshire
• Concord, NH [3314200] (42,904)
New Jersey
• Trenton, NJ [3474000] (84,056)
New Mexico
• Santa Fe, NM [3570500] (83,875)
New York
• Albany, NY [3601000] (98,111)
North Carolina
• Raleigh, NC [3755000] (458,880)
North Dakota
• Bismarck, ND [3807200] (72,417)
Ohio
• Columbus, OH [3918000] (860,090)
Oklahoma
• Oklahoma City, OK [4055000] (638,367)
Oregon
• Salem, OR [4164900] (167,419)
Pennsylvania
• Harrisburg, PA [4232800] (48,904)
Rhode Island
• Providence, RI [4459000] (179,219)
South Carolina
• Columbia, SC [4516000] (134,309)
South Dakota
• Pierre, SD [4649600] (14,008)
Tennessee
• Nashville, TN [4752006] (660,388)
Texas
• Austin, TX [4805000] (947,890)
Utah
• Salt Lake City, UT [4967000] (193,744)
Vermont
• Montpelier, VT [5046000] (7,535)
Virginia
• 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)

Related 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. The data are based the American Community Survey 2015 5-year estimates and organized into four subject matter groups:
General Demographics
Social Characteristics
Economic Characteristics
Housing Characteristics

See the related city population trends 2010-2016 interactive table to view, query, rank compare each cities are changing over time.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

City Population Characteristics & Trends: 2010-2016

.. the change in U.S. city population from 2010 to 2016 ranged from growth of 345,647 in New York City to a decline of -38,293 in Detroit, MI. New York City is actually five counties; the next largest city growth was Houston, TX with a 197,857 population gain.  Examine how the population is changing in cities of interest using the interactive table and other tools described in this post.  Use the interactive table to view a selected city, all cities in a state, cities in a county, cities in a metro or cities in a peer group size class.  See related Web section for more details.

Use the U.S. by cities shapefile with your GIS projects. See details. Thematic pattern maps illustrating use of these resources are shown below.

The July 1, 2016 Census Bureau model-based estimates (see about these data) for the U.S. 19,510 incorporated cities show a total population of 203,314,546 compared to 192,174,578 as of Census 2010. These areas are incorporated cities as recognized by their corresponding state governments and granted certain governmental rights and responsibilities.

Patterns of City Percent Change in Population 2010-16
— Cities 10,000 Population & Over
Use the CV XE GIS software with cities GIS project to examine characteristics of city/place population, 2010-2016. The following view shows patterns of population percent change, 2010-16 for cities with 2016 population of 10,000 or more. Use the interactive table below to see that among cities with 2016 population of 10,000 and over that Buda, TX had the largest percent change (98.8%) while Avenal, CA experienced the largest percent decrease (-18.4).

– View developed using the CV XE GIS software.
– Click graphic for larger view.

Fastest Growing Cities in the Dallas, TX Metro
— Cities 10,000 Population & Over; create views like this for any metro/county
It is easy to see which cities are growing the fastest using the thematic pattern view below. It is also easy to see how the cities relate to each other geographically and in context of county boundaries. The following view shows patterns of population percent change, 2010-16 for cities with 2016 population of 10,000 or more in the Dallas metro area.

– View developed using the CV XE GIS software.

Drill-down — Fastest Growing Cities in the Dallas, TX Metro
— Cities 10,000 Population & Over
Zoom into the north Dallas metro area and label the cities with name. The following view shows patterns of population percent change, 2010-16 for cities with 2016 population of 10,000 or more in the Dallas metro area.

– View developed using the CV XE GIS software. Click graphic for larger view; expand browser window for best quality view.

City/Place Demographics in Context
State & Regional Demographic-Economic Characteristics & Patterns
.. individual state sections with analytical tools & data access to block level
Metropolitan Area Situation & Outlook
.. continuously updated characteristics, patterns & trends for each/all metros
Related City/Place Demographic-Economic Interactive Tables
ACS 2015 5-year estimates
.. General DemographicsSocialEconomicHousing Characteristics

Using the Interactive Table
Use the full interactive table to examine U.S. national scope cities by annual population and change 2010-2016. The following graphic illustrates use of the table to view the largest cities ranked on 2016 population. Use the tools/buttons below the table to create custom views.

Click graphic for larger view.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Relating ZIP Codes to City/Places

.. relating ZIP codes to cities .. 214 ZIP code areas intersect with New York city — what are these ZIP codes, their population and how many are completely within the city? What part of a ZIP code area of interest intersects with what city? Conversely, what ZIP code areas intersect with a city of interest? This section provides data and tools that can be used to answer these types of questions and gain insights into geospatial relationships. See more detailed information in the related full Web section.

The 2010 ZIP Code Tabulation Area (ZCTA) to City/Place relationship data provide a means to equivalence ZCTAs with Census 2010 cities/places. ZCTAs are geographic areas defined as sets of Census 2010 census blocks closely resembling USPS ZIP codes (lines, not areas). ZCTA boundaries are fixed for the intercensal period 2010 through 2020. Census 2010 vintage city/place areas are likewise defined as sets of Census 2010 census blocks. The ZCTA-City/place relationship data are developed through the use of the intersecting census block geography and associated Census 2010 Summary File 1 demographic data.

ZCTA-Place Relationships
The following graphic shows relationships between two selected ZCTAs (red boundaries) and related cities/places (blue fill pattern) in the Pima/Cochise County, AZ area. Relationships between these geographies are reviewed in examples shown below.

– View developed using CV XE GIS and related GIS project.

Using the ZCTA-Place Relationship Data
Two examples illustrating how to use the ZCTA-place relationship data are provided below. The examples are interconnected to the GIS project used to develop the map views, interactive table and data file described in this section. Example 1 describes how to use the data for a ZIP code area entirely located within one city/place. Example 2 describes how to use the data for a ZIP code area located in more than one city/place and area not located in any city/place.

ZCTA to Place Relationships: Example 1
In this example, ZCTA 85711, highlighted in red in the graphic shown below, falls wholly within place 77000, outlined in bold black below. As a result, there is only one corresponding record for ZCTA 85711 in the relationship file. The 2010 Census population for this relationship record is 41,251 (POPPT) which is equal to the 2010 Census population for ZCTA 85711 (ZPOP). See more details about this example.

ZCTA to Place Relationships: Example 2
In this example, ZCTA 85630, highlighted below in red in the graphic shown below, contains two places: all of place 62280 and part of place 05770, both are outlined in black below. As a result, there are two corresponding relationship records in the relationship file. For the first relationship record, the total 2010 Census population for ZCTA is 2,819 (ZPOP). See more details about this example.

Using the Interactive Table
Use the full interactive table to examine U.S. national scope ZCTA-city/place relationships. The following graphic illustrates how ZIP code can be displayed/examined for one city — Tucson, AZ. Each row summarizes characteristics of a ZIP code in Tucson. The last row in the graphic shows characteristics of ZIP code 85711 — the same ZIP code reviewed in Example 1 above.

Click graphic for larger view.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Making & Using Custom 115th Congressional District Maps

.. using GIS resources to create custom 115th Congressional District maps .. use the methods, data and tools described in this section to develop custom congressional district maps. View patterns of economic prosperity by neighborhood for one or all congressional districts. Flexibly associate a congressional district boundary with related geography and subject matter.  See related Web section for more details.

Join the Congressional District-State Legislative District (CDSLD) Group .. be a part of the community. .. click here to join .. there is no cost.

Coming up … mapping/analyzing school district finances in context of the 115th Congressional Districts (June 2017).

See the related section on Making/Using 113th Congressional District Maps.
.. view different congressional district vintages in same map.

115th Congressional Districts by Incumbent Party Affiliation
This view and related GIS project/data update when changes are made to the 115th Congressional Districts incumbents (last updated 5/10/17). Party affiliation shown in this view is also available in the related interactive table. Click graphic for larger view. Expand browser window for best quality view.

– View developed using CV XE GIS and related GIS project.
– see below in this section about using this GIS project.

Use the Geographic Information System (GIS) tools and data to view/show congressional district in context with roads, landmarks and other geography. Flexibly add labels. Create pattern views. Add your own data.

Patterns of Economic Prosperity by 115th Congressional District
The following graphic shows patterns of ACS 2015 median household income (MHI) by 115th Congressional District. Click graphic for larger view. Expand browser window for best quality view.

– View developed using CV XE GIS and related GIS project.
– use the GIS project and tools see below to create different views.

Examine Characteristics of any Congressional District
The following graphic shows patterns of ACS 2015 median household income (MHI) by census tract in context of 115th Congressional Districts in a region of North Carolina. CD 3712 (Charlotte area) is shown with bold boundary. It is easy to see which areas/tracts have different levels of economic prosperity.

– View developed using CV XE GIS and related GIS project.
– use the GIS project and tools see below to create different views; add other layers.

Creating congressional district maps is often specific to a particular analysis, zoom-view, labeling, combination of different geographies or other considerations. While there are no estimates of unemployment by congressional district, using GIS tools it is possible to view/geospatially analyze patterns of unemployment within congressional district by county, census tract, block group and other geography.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Life Expectancy Change by County, 1980-2014

.. data and tools to examine changing life expectancy by county. Use the interactive table to examine life expectancy characteristics and related demographics for counties and regions of interest. Use the related GIS project and datasets to examine life expectancy contextually with other geography & subject matter. See details below. These data and tools are part of the ProximityOne health data analytics resources.

Life expectancy is rising overall in the United States, but in some areas, death rates are going in the other direction. These geographic disparities are widening.

Life Expectancy Change by County, 1980-2014
The following graphic shows patterns of the change in life expectancy change from 1980 to 2014. Click graphic for larger view. Expand browser window for best quality view.

– View developed using CV XE GIS and related GIS project.
– see below in this section about using this GIS project.

Life expectancy is greatest in the high country of central Colorado, but in many pockets of the U.S., life expectancy is more than 20 years lower. These data are based on research and analysis by the University of Washington Institute for Health Metrics and Evaluation.

Examining life expectancy by county allows for tracking geographic disparities over time and assessing factors related to these disparities. This information is potentially useful for policymakers, clinicians, and researchers seeking to reduce disparities and increase longevity.

Life Expectancy Change by County, 1980-2014 — drill-down view
— South Central Region
The following graphic shows patterns of the change in life expectancy change from 1980 to 2014. Click graphic for larger view. Expand browser window for best quality view. The larger graphic shows counties labeled with change in life expectancy from 1980-2014.

– View developed using CV XE GIS and related GIS project.
– see below in this section about using this GIS project.

Additional Views — use the GIS project to create your own views
.. click link to view
Alaska
Hawaii
Minneapolis metro

Using the Interactive Table
Use the interactive table to view, rank, compare life expectancy characteristics. This graphic shows California counties ranked on life expectancy change 1980-2014 in descending order. Select states or metros of interest. Click graphic for larger view.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

County 5-Year Trends: Income & Income Inequality

.. tools and data to examine how the U.S. by county household income and income inequality are changing … how is household income changing in counties of interest? What are the trends; what is causing the change? What are the characteristics of income inequality and how is it changing? How might this change impact your living environment and business?

This section provides access to tools and data to examine U.S. by county measures of household income and income inequality between two 5-year periods (2006-10 and 2011-2015). These data can provide insights into how household income and income inequality are changing for one county, a group of counties and the U.S. overall. Use the interactive table to view median household income and measures income inequality for all counties. See more detail about these topics here. Measures of income inequality can be estimates/examined using the Gini Index.

The Gini Index & Measuring Income Inequality
The Gini Index is a dimensionless statistic that can be used as a measure of income inequality. The Gini index varies from 0 to 1, with a 0 indicating perfect equality, where there is a proportional distribution of income. A Gini index of 1 indicates perfect inequality, where one household has all the income and all others have no income.

At the national level, the 2015 Gini index for U.S. was 0.482 (based on 2015 ACS 1-year estimates) was significantly higher than in the 2014 ACS Index of 0.480 (based on 2014 ACS 1-year estimates). This increase suggests that income inequality increased across the country.

Examining Household Income & Income Inequality Patterns & Change
The following two graphics show patterns of the GIni Index by county. The first view is based on the American Community Survey (ACS) 2010 5-year estimates and the second is based on the ACS 2015 5-year estimates. The ACS 2010 estimates are based on survey respondents during the period 2006 through 2010. The ACS 2015 estimates are based on survey respondents during the period 2011 through 2015. One view compared with the other show how patterns of income inequality has changed at the county/regional level between these two 5-year periods.

Following these Income Inequality views are two corresponding views of median household income; using data from ACS 2010 and ACS 2015. Use CV XE GIS software with the GIS project to create and examine alternative views.

Patterns of Income Inequality by County; ACS 2010
The following graphic shows the patterns of the Gini Index by county based on the American Community Survey 2010 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Patterns of Income Inequality by County; ACS 2015
The following graphic shows the patterns of the Gini Index by county based on the American Community Survey 2015 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Patterns of Economic Prosperity by County; ACS 2010
The following graphic shows the patterns of median household income ($MHI) by county based on the American Community Survey 2010 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Patterns of Economic Prosperity by County; ACS 2015
The following graphic shows the patterns of median household income ($MHI) by county based on the American Community Survey 2015 5-year estimates (ACS1115). The legend in the lower left shows data intervals and color/pattern assignment

.. view developed with ProximityOne CV XE GIS and related GIS project.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.

Examining County Migration: 2010-2016

.. tools and data to examine U.S. by county migration 2010 to 2016 … is the population moving away or into your counties of interest? What are the trends; what is causing the change? What are the characteristics of the population moving in and out? How might this impact your living environment and business?

The total net international migration among all counties 7/1/2010 – 7/1/2016 was 5,641,260, an annual average of 940,432. The sum of net domestic migration among counties is zero by definition, but domestic migration among counties varies radically by size and direction. This section is focused on U.S. by county migration from 2010 to 2016. Migration is one component of change used to develop population estimates. See more about county population estimates and components of change in this related Web section.

Largest 10 Counties Based on 2016 Population
This table shows how domestic migration varies widely among the most populated counties. Use this interactive table to develop your own custom views for counties of interest.

Patterns of Population Change by County, 2010-2016
– the role and impact of migration
The following graphic shows how counties have gained population (blue and green) and lost population (orange and red) during the period 2010 to 2016. Click graphic for larger view; expand browser window for best quality view.

.. view developed with ProximityOne CV XE GIS and related GIS project.

Examining Population Components of Change
– net migration and natural change
Population change can be examined in terms of components of change. There are three components of change: births, deaths, and migration. The change in the population from births and deaths is often combined and referred to as natural increase or natural change. Populations grow or shrink depending on if they gain people faster than they lose them. Examining a county’s unique combination of natural change and migration provides insights into why its population is changing and how quickly the change is occurring.

Using the Interactive Table
– examining migration by county
Use the interactive table to examine characters of counties by states, metro or peer group. The following graphic illustrates use of the interactive table to view net migration for the Houston metro by county. The net migration button was used to select only the net migration columns, FindCBSA button used to show only counties in this metro and the final step was to sort the resulting table on 2016 population. Click graphic for larger view.

Join me in a Data Analytics Lab session to discuss more details about accessing and using wide-ranging demographic-economic data and data analytics. Learn more about using these data for areas and applications of interest.

About the Author
— Warren Glimpse is former senior Census Bureau statistician responsible for innovative data access and use operations. He is also the former associate director of the U.S. Office of Federal Statistical Policy and Standards for data access and use. He has more than 20 years of experience in the private sector developing data resources and tools for integration and analysis of geographic, demographic, economic and business data. Contact Warren. Join Warren on LinkedIn.