Category Archives: Counties

Metropolitan Area New Residential Construction in 2017

.. understanding the housing situation; examining housing supply and demand market conditions; assessing trends for metropolitan areas … and how metros of interest are changing .. tools and data to examine patterns and change.

During 2017, cities and counties in permit issuing places authorized the construction of 1,281,977 new privately owned housing units with a total valuation of $258.5 billion. This was 1.4 percent above the annual estimate of 1,264,051 housing units and is a 6.2 percent increase from the 2016 total of 1,206,642.

Patterns of New Residential Construction by Metropolitan Area
The following graphic shows the 20 largest metropolitan statistical areas (MSAs) based on the number of new residential housing units authorized in 2017. Click graphic for larger view showing MSAs labeled with rank and name.

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

Residential Construction Data Analytics — Using Tools & Data
Visit the related Web section to access interactive table and GIS/GeoSpatial analytical tools and data.

Data Analytics Web Sessions
Join me in a Data Analytics Web Session, every Tuesday, where we review access to and use of data, tools and methods relating to GeoStatistical Data Analytics Learning. We review current topical issues and data — and how you can access/use tools/data to meet your needs/interests.

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.
 

Insights into County/Metro Business Establishment Patterns

.. new data, released this past week, enable us to better assess county and metro business establishment patterns .. these data help businesses understand how sales of their products and services align with markets .. what counties might be underserved? .. where should marketing and sales operations be ramped up or reduced in scope/reallocated? .. what do these tell us us about organization of market territories? how are these characteristics trending by county? Use the this interactive table to examine business establishments characteristics by county and metro by type of business. See more detail in the related Web section about topics covered in this post. 

• U.S. establishments rose 1.2% from 7,663,938 in 2015 to 7,759,807 in 2016.
• First quarter employment was up 2.1% from 124,085,947 to 126,752,238
• Annual payroll was up 2.9% from $6.3 trillion in 2015 to $6.4 trillion in 2016.

Based on the 2016 data (new April 2018), there are 7.3 million establishments in Metropolitan Statistical Areas (MSAs). In these MSAs ..
• 5.3 million establishments have fewer than 10 employees.
• 6,522 of these establishments have 1,000 or more employees.
• 374 of these establishments have 5,000 or more employees.

MSAs by Number of Establishments with 1,000 or more Employees
The following graphic shows Metropolitan Statistical Areas (MSAs) by number of establishments with 1,000 or more employees. Click graphic for larger view view showing metros labeled with number of these establishments. Expand brower to full window for best quality view.
.. View developed with CV XE GIS software and related GIS project.

Business Establishment Characteristics Updated in Metro Reports
Examine 2014, 2015, 2016 mini trend profiles for establishments by 2-digit sector in Metro Situation & Outlook Reports Choose any MSA by clicking column 3 in this table .. then view section 6.3. Examples:
New York .. Los Angeles .. Miami .. Chicago .. Dallas .. Houston .. Denver .. Seattle

Gaining Insights
Gain insights into these types of patterns by county by detailed type of business (NAICS). Use the interactive table below to examine business establishment characteristics for counties and metros of interest. Data reviewed in this section are based on the Census-sourced County Business Patterns released in April 2018. We have integrated current population estimates with the establishment data in the interactive table.

Tools You Can Use
• create maps and geospatially analyze business establishments at 6-digit industry detail with ready-to-use GIS project/tools .. see related section for details.
• Use the APIGateway tools to build custom business establishment datasets.
• Use the interactive table to query/view sort business establishment characteristics by county and metro.

Data Analytics Web Sessions
Join me in a Data Analytics Web Session, every Tuesday, where we review access to and use of data, tools and methods relating to GeoStatistical Data Analytics Learning. We review current topical issues and data — and how you can access/use tools/data to meet your needs/interests.

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.

How & Why State Demographics Are Changing

.. to examine how and why state demographics are changing, we look at the state as the sum of its parts — counties. Here we review tools and data to examine how and why state/county population is changing … is the population moving away or into your areas 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? See related Web section for more detail on topics covered here and access interactive table.

Patterns of Population Change by County, 2010-2017
The following graphic shows how counties have gained population (blue and green) and lost population (orange and red) during the period 2010 to 2017. Click graphic for larger view; expand browser window for best quality view.

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

The above graphic provides a visual summary of how and why demographics are changing from 2010 to 2017 in terms of components of change: births, deaths and migration. See the underlying data in this interactive table.

Change in the population from births and deaths is often combined and referred to as natural increase/change. The other way an area population changes is through migration (net international, net domestic, net migration). Examining an area’s unique combination of natural change and migration provides insights into why its population is changing and how quickly the change is occurring.

Examine States of Interest
Click a state link to view details about specific states …
Alabama .. Alaska .. Arizona .. Arkansas .. California .. Colorado .. Connecticut .. Delaware .. 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

Situation & Outlook Briefing Sessions
Join me in a Situation & Outlook Briefing Session, every Tuesday, where we review the where, what, how, and when of demographic-economic-business change – and how change might impact you.  We review current topical issues and data — and how you can access/use tools/data to meet your needs/interests.

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.

 

Creating Custom Demographic Datasets with API Tools

.. develop national scale spreadsheet files with virtually no learning time .. easy-to-use API operations to create national scope demographic-economic datasets based on American Community Survey 2016 1-year estimates .. custom subject matter selections. See more detail in related web sections ACS2016 and ACS2016_API.

Benefits and utility … how to acquire a spreadsheet showing the population of all cities with population estimates based on the ACS 2016 1-year data? … or, housing units, median household income, median housing value, etc.? Variations of this need frequently arise — what is the list of largest California counties sorted on total population: What are the 25 metros having the highest median household income? Which 10 congressional districts have the highest poverty incidence? Which urban areas have the highest educational attainment?

Use simple API calls described below to get answers to these types of questions — and more.  Create files that can be used for recurring applications. An example …

Urban Areas with 2016 Population 65,000+ Population
… results from using the API downloaded data … the following graphic shows urban areas with 65,000 or more 2016 population; zoom-in to Texas. The full national scope GIS project is available as described below; examine U.S. or any region. The file used to develop this view was created using the results of the API call reviewed below (requires integration of those data into the urban areas shapefile). Click graphic for larger view; expand browser window. Larger view shows urban areas labeled with name and mini profile for Dallas UA showing all subject matter items downloaded (via API) as described below.

… View developed using CV XE GIS.
… See more about Urban Population & Urban Areas.

Access ACS 2016 1-Year Data Using API Tools
Here are the API links … use these API calls to access/download selected items for selected geographies. See more about using API tools. Click a link and receive a return page with CSV-like structured data. See usage notes below. As these are ACS 2016 1 year estimates; geographies are only available for areas 65,000+ population.
Click a link:
• All U.S. cities/places
• All U.S. counties
• All U.S. CBSAs
• All U.S. Urban Areas
• All 115th Congressional Districts
• All U.S. states
• U.S. only

The following data retrieval operations are by state. These are examples using Arizona (FIPS state code 04).
• All [within state] Elementary School Districts
• All [within state] Secondary School Districts
• All [within state] Unified School Districts

API Call Returned Data Usage Notes
Clicking the All U.S. cities/places link above generates a new page with content very much like a CSV file. Try it .. click an above link.

See the related ACS2016_API web section for more details.

Items Retrieved in the API Calls
The sample header record above shows the subject matter item listed at the left in the following set of items. Modify API call and use other subject matter items. See full array of subject matter – xlsx file.
.. B01003_001E – Total population
Age
.. B01001_011E — Male: 25 to 29 years (illustrating age cohort access)
.. B01001_035E — Female: 25 to 29 years (illustrating age cohort access)
Race/Origin
.. B02001_002E – White alone
.. B02001_003E – Black or African American alone
.. B02001_004E – American Indian and Alaska Native alone
.. B02001_005E – Asian alone
.. B02001_006E – Native Hawaiian and Other Pacific Islander alone
.. B02001_007E – Some other race alone
.. B02001_008E – Two or more races
.. B03001_003E – Hispanic (of any race)
Income
.. B19013_001E – Median household income ($)
.. B19113_001E – Median family income ($)
Housing & Households
.. B25001_001E – Total housing units
.. B25002_002E – Occupied housing units (households)
.. B19001_017E — Households with household income $200,000 or more
.. B25003_002E — Owner Occupied housing units
.. B25075_025E — Housing units value $1,000,000 to $1,499,999
.. B25075_026E — Housing units with value $1,500,000 to $1,999,999
.. B25075_027E — Housing units with value $2,000,000 or more
.. B25002_003E – Vacant housing units
.. B25077_001E – Median housing value ($) – owner occupied units
.. B25064_001E – Median gross rent ($) – renter occupied units

The rightmost fields/columns in the rows/records contain the area name and geographic codes.

Using API Tools for Data Analytics
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 L

Low & Moderate Income Census Tracts; 2017 Update

..  data and tools to analyze characteristics and patterns of census tract geography with a focus on low and moderate income.   See related Web page for more detail.

Of the total 75,883 census tracts for which low and moderate income data were tabulated in the HMDA 2017 data, 6,023 (8.7%) were low income, 16,873 (24.5%) were moderate income, 32,509 (47.1%) were middle income and 19,159 (27.8%) were upper income. See more about these classifications. Find out about your tracts/neighborhoods of interest and how they compare to others using data and tools provided in this section.

Analysis of the low, moderate, middle, and upper income of the population and households by small area geography is important to housing market stakeholders, lenders, investors, cities/neighborhoods and others. Low and moderate income data by block group and census tract are used for compliance, eligibility determination and program performance in many Federal programs and agencies.

• Use the interactive table below to view, query, compare, sort census tracts.
• Use tract estimates & projections to examine changing characteristics.
– extended demographic-economic measures, annual 2010-2022

Low & Moderate Income by Census Tract
The following view shows census tracts designated as low and moderate income (orange fill pattern) in the the Houston, TX MSA (bold brown boundary) area. These are tracts having income level with codes 1 and 2 in the interactive table. A wide range of market insights can be created zoom-in views for counties, cities and neighborhoods and linking these with other data. Make variations of this view using ProximityOne data and tools described in this section.

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

View similar maps for these areas:
.. Atlanta metro
.. Chicago, IL metro
.. Dallas, TX metro
.. Knoxville, TN metro
.. with drill-down views for Knoxville city
.. Los Angeles, CA metro
.. San Francisco, CA metro

Using the Interactive Table
  – Examining LMI Tracts in Your Metro

Use the interactive table to view, query, sort compare tracts based on various demographic and LMI characteristitcs. The following graphic illustrates how the table can be used to view low and moderate income tracts for the Charlotte, NC-SC metro.
– click ShowAll button below table.
– enter a CBSA code in the edit box at right of Find CBSA LMI>.
– click the Find CBSA LMI button.
Resulting display of Charlotte metro LMI tracts only.

– 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.

Creating & Using Location Shapefiles

.. GIS tools and methods to develop and update location shapefiles .. location shapefiles are essential to most GIS applications. Location shapefiles, or point shapefiles, enable viewing/analyzing locations on a map and attributes of these locations such store or customer ID, street address, city, date updated, value, ZIP code and wide-ranging attributes about the location. This section reviews tools and methods to develop and use location shapefiles. See more detail about topics covered in this section in the related Web page.

Viewing/Analyzing Store Locations in the Dallas, TX Area
The following graphic illustrates how store locations can be shown in context of other geography and associated demographic-economic attributes. This view shows store locations (red markers) in context of Dallas city (blue cross-hatch pattern) and broader metro area. Markers shown in this view are based on a location shapefile created using steps described below. The identify tool is used to click on a location and show attributes in a mini-profile.

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

View the locations contextually with thematic patterns by tract or other geography. Combine views of store, customer, agent, competitor and other location shapefiles.
The following view shows patterns of median household income by census tract.

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

Development of location shapefiles often starts with a list of addresses. Locations are not always address-oriented; they might be geographically dispersed measurement or transaction locations — having no address assigned. In applications reviewed here, locations are organized as rows in a CSV file. Each CSV file contains like-structured attributes for each location. The example used in this section uses store locations located in the Dallas, TX area.

There are two basic methods used to create location shapefiles: 1) geocoding address-data contained in the source data file or 2) using the latitude-longitude of the location included in the source data file record. The focus here is on option 2 — using the latitude-longitude of the location already present in the source data file.

Creating a Location Shapefile
The process of creating a location shapefile uses the CV XE GIS Manage Location Shapefile feature. With CV running, the process is started with File>Tools>ManageLocationShapefile. The following form appears.

.. ManageLocationShapefile feature/operation in ProximityOne CV XE GIS.

CV XE GIS provides other ways to create location shapefiles:
• Tools>AddShapes>Points — click points on the map window canvas.
• Tools>FindAddress — creates a single point shapefile based on specified address.
• Tools>FindAddress (Batch) — creates a point shapefile based on specified file of address records.
See details in User Guide.

Steps to Create a Location Shapefile
The process of creating the shapefile “C:\cvxe\1\locations1pts.shp” can be viewed by clicking the Run button on the form (with CV running). Two input CSV structured files are required:
• data definition file
• source data file

There are two sets of illustration location input files included with the CV installer:
• locations1_dd.csv and locations1.csv (7 locations in Johnson County, KS)
• locations2_dd.csv and locations2.csv (252 locations in Dallas and Houston)
These files are located in the \1 (typically c:\cvxe\1) folder. The marker/location shapefile used in the map shown above was created using the lcoations2 input files.

Data Definition File
The Data Definition (DD) file is an ASCII/text file structured as a CSV file. It may created with any text editor. The DD file is specific to the source data file. But in the case of recurring source data files for different periods the same DD file might apply to many source data files. There are several rules and guidelines for development of the DD file:
• there is one line/record for each field in the source data file.
• each line/record must be structured in an exact form:
.. each line/record is comprised of exactly 4 elements separated by a comma:
.. 1 field name for subject matter item
– must consist of 1 to 10 characters and include no blanks or special characters
.. 2 field type: C for character, N for numeric
.. 3 field length: an integer specifying the maximum with of the field
.. 4 maximum number of decimals for field (value is 0 for character fields)
The DD File must include three final fields:
LATITUDE,n,12,6
LONGITUDE,n,12,6
GEOID,c,15,0
The structure of these three DD file records must be as shown above. The source data file, described below, must have the LATITUDE and LONGITUDE fields populated with accurate values. The GEOID field may populated with either an accurate value of placeholder value like 0.

Example. Data for each store for the default DD file name “C:\cvxe\1\locations1_dd.csv” include the following fields/attributes:
  NAME,C,45,0
STORE,c,15,0
ADDRESS,c,60,0
CITY,c,40,0
LATITUDE,n,12,6
LONGITUDE,n,12,6
GEOID,c,15,0

Optionally create a DD File using the Create DD File button on the form. Clicking this button will create a DD File containing attributes of the dBase file specified in the associated edit box. The DD File name is created from the dBase file name. If the dBase file name is “c:\cvxe\1\locations1pts.dbf”, the DD File will be named “c:\cvxe\1\locations1pts_dd.csv”.

About the GEOID
The GEOID is a 15 character code which defines the Census 2010 census block containing each location. The GEOID is generally assigned by the ManageLocationShapefile operation and is one of the important and distinctive features of this tool. The GEOID is used to uniquely determine, with the GIS application, any of the following: state, county, census tract, block group, or census block.

The GEOID, as used in this section, is the 15 character Census 2010 geocode for the census block. The GEOID value 481130002011012 (see in location profile in map at top of section) is structured as:
state FIPS code: 48 (2 chars)
county FIPS code: 113 (3 chars)
census tract code 000201 (6 chars)
census block code: 1012 (4 chars) (block group code: 1 — first of 4 characters)

About the Source Data File
The Source Data File is an ASCII/text file structured as a CSV file. It is typically developed by exporting/saving an Excel or dBase file in CSV structure. There are several rules and guidelines for development of the source data file:
• fields must be structured and arranged as defined in the DD File.
• character fields must not contain embedded commas.
• final items in record sequence must be:
.. LATITUDE – must have accurate decimal degree value; 6 digit precision suggested.
.. LONGITUDE- must have accurate decimal degree value; 6 digit precision suggested.
.. GEOID – this may be 0, not assigned or the accurately assigned GEOID value.
– optionally create/rewrite the GEOID used in the new shapefile.

Updates; Combining Vintages of Location Attributes
Location based data might update frequently, even daily. The recommended method to add, update and extend the scope of location-based data is to create new address shapefiles corresponding to different vintages or dates covered. The structure of the files must be the same so that they files can be used together or separately. Suppose there is one set of data covering year to date and a second set of data covering the following month. The ManagePointShapefile operation would be run once for each time period. Two shapefiles would be created. These shapefiles may be added to a GIS project and used separately or in combination to view/analyze patterns.

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.