Tag Archives: New York City

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.

New Monthly Residential Construction by Metro Updates

.. tools, data & methods to assess the housing situation, examine housing supply and demand market conditions, and how metros of interest are changing.  New July 2016 building permits (new housing units authorized) and over-the year monthly data are now available for each metropolitan statistical area (MSA).

Use the interactive table to view, query, rank, compare data by metro. Map and geospatially analyze construction patterns with the CV XE GIS software and ready-to-use GIS project/datasets – see details.

Updated Resources to Examine Residential Construction Patterns
Metro Situation & Outlook Reports
.. metro by metro … examples: Houston, Los Angeles, Chicago, Atlanta.
County Annual U.S. by county
County & City/Place Monthly

Patterns of New Authorized Residential Units by Metropolitan Area
The following graphic shows value of single unit structures units authorized  by metro. Larger view shows more details including a mini-profile of housing units authorized detail. Create similar views for preferred time periods and different residential unit attributes using the GIS project.  Zoom-in to areas of interest.  Label the geography as desired.  Add your own data.

View created with CV XE GIS. Click graphic for larger view.

The time lag from reference date to access date of these data is one month, contributing to both the freshness of the data and importance of the data as a leading economic indicator. The importance of these data transcends issues concerning housing market conditions alone.  These data are one part of a mix of demographic-economic factors required to understand housing market conditions and the local/regional economy. These data are a part of the process to develop the ProximityOne county and sub-county demographic-economic estimates and projections.

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.

Neighborhood Analysis: Block & Block Group Demographics

.. steps to analyze NYC Chelsea area demographics that can be applied to any neighborhood … demographic characteristics of the Chelsea area in New York City, an area west of Avenue of the Americas between 14th and 34th Streets, is radically different from adjacent areas. This topic was covered in a “great wealth divide” New York Times story. This section reviews how census block and block group demographic-economic data can be used to examine these patterns. A GIS project is used that associates census block and block group data for visual analysis Methods summarized here can be applied to any area. Use the tools described in this section to obtain demographic-economic profiles for any neighborhood based on an address. See related Web page for more detail.

See related post on Most Populated New York City Census Blocks.

Study Area in Context of Broader Area
The study area, a group of selected census tracts, is shown as the red cross-hatched area in context of lower Manhattan in the view below.

  — view created using CV XE GIS and associated GIS Project

Zoom-in View of Study Area
The next view shows a zoom-in to the study area. Block groups are shown with a red boundary. Chelsea Park is visible as the green area above the pointer south of 29th street.

  — view created using CV XE GIS and associated GIS Project

Census Block Demographics in Context of Block Groups
The next view shows a further zoom-in showing census blocks with black boundary and block groups with red boundary. Census blocka are shown with a semi-transparent yellow fill pattern (population greater than 4) and gray fill pattern (blocks with population less than 5). The block group containing Chelsea Park (green area above pointer) contains three census blocks, 2 with no population and one with 1,010 population. Block data are from Census 2010; there are no post-Census 2010 block level demographics available. The analysis could be extended to shown wide-ranging demographics at the block level.

  — view created using CV XE GIS and associated GIS Project

Examining Socioeconomic Attributes
In this further zoom-in, Chelsea Park (green area) is shown near the pointer. Census block population labels are turned off for blocks with 5 or more population to help show a less cluttered view. Block groups are labeled with two values. The yellow upper label shows the median housing value (MHV). The green lower label shows median household income (MHI). Both data items are based on the American Community Survey 5-year estimates (ACS 2013) are centric to 2011. The ACS data are updated annually; as of October 2015, the latest data are from ACS 2013; the ACS 2014 data become available December 2015. The ACS 2013 5 year estimates are top-coded at $1,000,001 for MHV and $250,001 for $MHI.


  — view created using CV XE GIS and associated GIS Project

The block group containing Chelsea Park has a median household income of $26,440; the median housing value estimate is not available (too few owner-occupied units to develop MHV estimate). The Chelsea Park block group code is “360610097002” — this code uniquely identifies this block group among all other block groups in the U.S.

The block group immediately to the south of the Chelsea Park block group median household income of $21,750; the median housing value estimate is $1,000,001 (top-coded). The code for this block group code is “360610093006”.

While the MHI for BG 360610093006 might seem like it should be higher, a look at the number of households by income interval explains this number. Almost half of the households in the BG have a household income below $20,000. Analytical options that might be considered include using mean household income or mean family income instead of median.

Compare number of households by household income intervals for these two block groups.

Compare Your Block Group of Interest to Chelsea Park BG
Compare the above BG attributes to any BG of interest:
1. Copy and paste this string into text editor (eg, Notepad) window (do not press enter after paste):
http://factfinder.census.gov/bkmk/table/1.0/en/ACS/13_5YR/B19001/1500000USXXXXXXXXXXX|1500000US360610097002

2. Click here, key in an address then click Find to locate the 11 character BG code.
— scroll down to “2010 Census Blocks” and then further to “GEOID”
— copy the first 11 digits of the GEOID value to clipboard see illustrative graphic.

3. Paste those 11 characters into the URL, replacing the “XXXXXXXXXXXX”; this modification must be exact.

4. Press Enter. A profile appears comparing your BG to the Chelsea Park BG 360610097002.

Data Analytics Lab Session
Join me in a Data Analytics Lab session. There is no fee. Discuss how tools and methods reviewed in this section can be applied to your situation.

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.

New York City: Census Block Demographics

.. census blocks are the smallest geographic areas for which decennial census data (and data from any Federal statistical program) are tabulated. Census blocks are geographically defined by the Census Bureau in coordination with local agencies. For Census 2010, there were a total of 11,078,297 census blocks covering the U.S. wall-to-wall. 541,776 of these blocks are water blocks, mainly located in coastal areas. Approximately one-third of all census blocks have zero population. See more about accessing and using census block demographics in this related Web section

Related New York City posts:
Manhattan Financial Sector Earnings – monthly/quarterly attributes
    .. examine establishment characteristics by type of business
        for any New York City borough or the metro
NYC Chelsea area and demographic analysis
Patterns of Block Group Income Inequality
    .. illustrative applications in Pelham, NY vicinity; north of NYC & NYC overall

Census block data are important to demographic/market analysis in part due to the data being counts of population and housing units rather than estimates (subject to errors of estimation). Block data are also important due to their geographic granularity, very detailed geography. Block data provide a good way to aggregate small area demographics into territories, markets and service areas using GIS tools. We have not only demographic data for blocks but also their geographic attributes: location/boundary and area. Make maps and perform geospatial analysis using census block shapefiles. Use census block geography with non-census data for wide-ranging analyses.

Largest Population New York City (NYC) Census Blocks
The following graphic shows the NYC Census 2010 census block having the largest Census 2010 population that is not a group quarters population block.
The Lincoln Center census block shown in the graphic (red boundary) has 4,067 population and 2,922 housing units.

– click graphic for larger view; view developed using CV XE GIS

This block (36 061 015500 6000) occupies 0.033 square miles. It has a population density of 122,333 (population per square mile).

The NYC block with the largest population is on Rikers Island and has a group quarters population of 8,634 and 0 housing units.

For Census 2010, there were 350,169 census blocks covering the state of New York; 13,356 census blocks were water blocks. For the State of New York, as of Census 2010 the average census block population was 55 (57 excluding water blocks).

More about census blocks. In built-up urban areas, a census block often shares a boundary with a conventional 4-sided city block. Census blocks are normally bounded by roads and in some cases other types of physical boundaries. For Census 2010, each census block is coded as urban or rural; this is the basis for defining urban or rural population and geographies such as urbanized areas. See urban population and urban/rural ZIP Codes. Census block geography nest within block groups and census tracts.

Upcoming sections will focus on accessing, integrating and using New York City block group and census tract demographic-economic data. Unlike census blocks, annually updated demographic-economic data are available for block groups and census tracts from the American Community Survey.

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.

Patterns of Block Group Income Inequality

Income inequality exists in an area where there is a mix of households that have very high incomes coexisting with a set of households with very low income. The high heterogeneity of income inequality among households typically extends to other demographic, social, economic and housing attributes. A neighborhood with high income inequality is unlikely to be homogeneous in most respects.

This section reviews data and methods to examine/analyze income inequality at the block group level of geography. See more detail in related Web section. Block groups (BGs) average 1,200 population and cover the U.S. wall-to-wall. Using higher level geography, even census tracts, may tend to mask the existence of income equality. By using BGs, we are able to examine income inequality patterns for other geography such as cities, counties and school districts (by looking at BGs that intersect with these areas). The next map graphic illustrates how these patterns can be examined using GIS resources.

Patterns of Income Inequality by Neighborhood & School District
The following view shows patterns of income inequality by block group within school districts in the Pelham, NY vicinity just north of New York City. K-12 public schools are shown as yellow markers. The Gini Index, based on ACS 2013, is used as the measure of income inequality. Colors/values of the Gini Index are shown in the legend as the left of the map. See more about the Gini Index below.

View created with CV XE GIS. Click graphic for larger view.
The larger view shows BGs labeled with the Gini Index value.

The following related view shows patterns of median household income (MHI) by census tract for the same area as above. This view shows the high median income for the census tracts in the southern section of Pelham school district. Compare patterns in the MHI by tract view with the Gini Index by BG view above.

View created with CV XE GIS. Click graphic for larger view.
Tracts labeled with percent population 25 years and over who are high school graduates.

Patterns of Income Inequality by Block Group; New York City area

View created with CV XE GIS. Click graphic for larger view.

Block Group Income Measures
Block group income measures are only available from the American Community Survey (ACS). Block groups are the smallest geographic level for which data are tabulated from the annually updated ACS. In the applications reviewed here, the ACS 2013 5-year estimates are used.

Using the block group income inequality measures, enables us to examine characteristics in the vicinity of schools and how neighborhood inequality might exist across school districts. Neighborhods with high inequality might directly impact on K-12 student opportunities and educational outcomes.

Gini Index of Income Concentration
The Gini Index can be used as a measure of income concentration/inequality. The Gini Index is based on the Lorenz curve The Gini Index varies from 0 to 1, with a value of 0 indicating perfect equality, where there is a proportional distribution of income across all households. A value of 1 indicates perfect inequality, where one household has all the income and all others have no income.

In the graphic shown below, the Gini Index represents the area (A) between the diagonal, or line of perfect equality, and the Lorenz curve, as a percentage of the total area lying beneath the diagonal (A + B). When income inequality rises, the Lorenz curve bows further downward and the area (A) between it and the diagonal increases in size. The result is that the Gini Index increases.

The Gini Index for the U.S. in the 2013 ACS (0.481) was significantly higher than in the 2012 ACS (0.476). This increase suggests that income inequality increased nationally. Examine state-by-state patterns of income inequality using the interactive table in this related section.

The annually updated ACS 5-year estimates can tell us how income inequality is changing at the block group level. Appealing reasons for using the ACS data include the availability of related subject matter, such as educational attainment, that are relevant to extended analyses.

Analyzing Patterns for Areas of Interest
Use the existing state K-12 schools GIS projects to examine income inequality based on block groups. Project datasets include a block group layer/shapefile that contains the Gini Index and several related income attributes. More about the state K-12 GIS projects.

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.