Category Archives: Honolulu, Hawaii

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.

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.

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.

Examining Health Characteristics by Census Tract

.. new data, new ways to examine health characteristics at the city and census tract/subcounty level.  For example, among the 500 largest U.S. cities in 2014, the incidence of high blood pressure ranged from 22.5% (Longmont, CO) to 47.8% (Gary, IN). Use the interactive table to view, rank, compare this and other new wide-ranging health statistics for the 500 largest U.S. cities and associated census tracts. See the related Web section for more detail.

At the census tract/neighborhood level, 937 tracts have more than 10% of the population ages 18 years and over with coronary heart disease. What are characteristics of health-related factors in your city, neighborhood and census tracts of interest? Use tools reviewed in this section to access/analyze a wide range of health-related characteristics (see items list below) — not available at the city or census tract level before.

Patterns of High Blood Pressure: Honolulu, HI by Census Tract
This graphic illustrates visual analysis and analytical potential for tracts in cities covered.

– Click graphic for larger view with high blood pressure %population label
– View developed with CV XE GIS software and related GIS project/fileset.

Accsss/analyze these data for approximately 28,000 tracts (of a total approximate 74,000) on topics including chronic disease risk factors, health outcomes and clinical preventive service use for the largest 500 cities in the U.S. These small area data enable stakeholders in cities, local health departments, neighborhoods and study areas to better understand the characteristics and geographic distribution of health-related measures and how they might impact health-related programs and other demographic-economic issues.

Scope of 500 Cities
The following graphic shows the 500 cities (green areas) included in project. Data for these cities and intersecting tracts are available. Click graphic for larger view providing county visibility and city name labels. Expand browser to full window for best quality view.

– View developed with CV XE GIS software and related GIS project/fileset.

The 500 Cities data have been developed as a part of the CDC 500 Cities project, a collaboration between the Centers for Disease Control (CDC), the Robert Wood Johnson Foundation and the CDC Foundation. These data are being integrated into the Situation & Outlook (S&O) database and included in the S&O metro reports. Examine health-related characteristics of metro cities and drill-down areas in combination with other demographic-economic measures.

Analytical Potential
These data provide only the health characteristics attributes. They are a small, but important, subset of a larger set of key health metrics. These data are estimates subject to errors of estimation and provide a snapshot view of one point in time.

The value of these data can be leveraged by linking them with other demographic-economic data from the American Community Survey (ACS 2015) tract and city data. Integrate and analyze these data with related data and alternative geography. See related health data analytics section.

Patterns of Heart Disease; Charlotte, NC-SC Area by Tract
This graphic illustrates coronary heart disease patterns by census tract for cities included in the database. Gray areas are census tracts not included in the 500 cities database. Click graphic for larger view.
– View developed with CV XE GIS software and related GIS project/fileset.

Using the Interactive Table
Use the interactive table to view, rank, compare, query these health measures by city. The following graphic illustrates how the table can be used to examine patterns of Texas cities. Table operations are used to selected Texas cities then rank the cities based on the “Access” column — “Current lack of health insurance among adults aged 18-64 Years”.

Try it yourself. Use the table to examine a set of cities in a state of interest.

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.

New ACS 2015 1-Year Demographic-Economic Data

.. essential data to assess where we are, how things have changed and how things might change in the future down to the sub-neighborhood level. The American Community Survey (ACS) is a nationwide survey designed to provide annually updated demographic-economic data for national and sub-national geography. ACS provides a wide range of important data about people and housing for every community across the nation. The results are used by everyone from planners to retailers to homebuilders and issue stakeholders like you. ACS is a primary source of local data for most of the 40 topics it covers, such as income, education, occupation, language and housing. ProximityOne uses ACS to develop current estimates on these topics and 5-year projections. This section is focused on ACS 2015 data access, integration and use and is progressively updated.

New ACS 2015 1-year estimates are available as of September 15, 2016.

Importance of ACS: Assessing Demographic-Economic Change
Oil prices plummeted in late 2014. How has this affected people and households in areas hardest hit? Find out for wide-ranging geographies using the ACS 2015 1-year estimates. Compare to ACS 2014 1-year estimates. Use the ACS 2016 1-year estimates (September 2017) to see how the impact has continued. Demographic-economic conditions change for many reasons; oil price changes are just one.

Keep informed about ACS developments and related tools and applications:
• Updates are sent to ProximityOne User Group members (join here).
… access special extract files and GIS projects available to members.
• ACS updates and applications are covered in the Data Analytics Blog.
• ACS data access, integration & use … join us in a Data Analytics Lab session.

In the weeks ahead, the following ProximityOne information resources will be updated with new ACS 2015 1-year data:
U.S.-State-Metro Interactive Tables
• Demographic component section of Metro Situation & Outlook Reports .. example for Dallas metro
• Housing characteristics component section of Metro Situation & Outlook Reports .. example for Dallas metro
Demographic-Economic Trend Profiles
• Special study reports.

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.

Real Purchasing Power by State & Metro

.. how does the real purchasing power in metros of interest compare to other metros? Use data and tools reviewed here to examine the purchasing power of the incomes in different metros and states … this section provides access to regional price parities (RPPs) estimates developed compare regions within the U.S. RPPs are regional price levels expressed as a percentage of the overall national price level for a given year. The price level is determined by the average prices paid by consumers for the mix of goods and services consumed in each region. See about these data. See example about using RPPs below in this section.

• Use the interactive table to view, rank, compare the RPPs
.. for all states and metropolitan statistical areas (MSAs).
• Use GIS tools described here to develop RPP thematic pattern maps.
.. add your own data & geography, select different HPI measures or criteria.
.. zoom to different geographic extents, label and modify colors as desired.

Patterns of Regional Price Parities by Metro: 2014
The following graphic shows patterns of 2014 all items Regional Price Parities by metro (MSAs). The color patterns/intervals are shown in the inset legend. In additional views (below this graphic) metros are labeled with the 2014 all items RPP. Click graphic for larger view. Expand browser to full window for best quality view. Use the GIS tools described here to develop thematic pattern maps for a range of data and criteria.

.. view developed using the CV XE GIS software.
.. click map for larger view and details.

Additional Views — install GIS project (see steps here) and create your own custom maps
Georgia & Region
Missouri & Region
Texas & Region

Using the RPP — Illustrative Examples
1. Comparing real purchasing power:
  — Houston, TX metro compared to Waco, TX metro.
The the all items RPP for the Houston metro in 2014 was 100.3 while the all items RPP for the Waco, TX metro in 2014 was 91.5. (from RPP table). On average, prices are 0.3 percent higher and 8.5 percent lower than the U.S. average for the Houston metro and the Waco metro, respectively. The per capita personal income (PCPI) for the Houston metro in 2014 was $54,820 and the per capita personal income for the Waco metro was $35,340 (get from the table at http://proximityone.com/reis.htm). The RPP-adjusted PCPI values are $53,223 ($54,820/1.03) and $38,622 ($35,340/0.915), respectively. The gap between the purchasing power of the two metro PCPIs is reduced when adjusted by their respective RPPs.

2. Comparing real purchasing power:
  — Washington, DC metro compared to Columbia, MO metro.
• Washington, DC metro 2014 all items RPP: is 119.4 (from RPP table); 2014 PCPI: $62,975 (from this table)
• Columbia, MO metro 2014 all items RPP: 93.0 (from RPP table); 2014 PCPI: $41,418 (from this table)
• The RPP-adjusted PCPI values are $52,742 ($62,975/1.194) and $44,535 ($41,418/0.93), respectively.

Using the RPP Interactive Table
Use the interactive table to examine the RPP by state and metro. The following graphic illustrates use of the table to show the 10 metros having the highest 2014 all items RPP. Click graphic for larger view. Examine metros and states of interest with more detail using tools below the table.

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.

State and Regional Decision-Making Information

Organized on a state-by-state basis, use tools and geographic, demographic and economic data resources in these sections to facilitate planning and analysis. Updated frequently, these sections provide a unique means to access to multi-sourced data to develop insights into patterns, characteristics and trends on wide-ranging issues. Bookmark the related main Web page; keep up-to-date.

Using these Resources
Knowing “where we are” and “how things have changed” are key factors in knowing about the where, when and how of future change — and how that change might impact you. There are many sources of this knowledge. Often the required data do not knit together in an ideal manner. Key data are available for different types of geography, become available at different points in time and are often not the perfect subject matter. These sections provide access to relevant data and a means to consume the data more effectively than might otherwise be possible. Use these data, tools and resources in combination with other data to perform wide-ranging data analytics. See examples.

Select a State/Area

Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
D.C.
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming

Topics for each State — with drill-down to census block
Visual pattern analysis tools … using GIS resources
Digital Map Database
Situation & Outlook
Metropolitan Areas
Congressional Districts
Counties
Cities/Places
Census Tracts
ZIP Code Areas
K-12 Education, Schools & School Districts
Block Groups
Census Blocks

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.