Category Archives: Health Data Analytics

Examining Health Care Infrastructure by ZIP Code

.. small area data providing information on sub-county and sub-city/place geographies are challenging to locate and use — particularly in context of demand for healthcare services and demographic attributes of associated neighborhoods. Develop insights into the healthcare infrastructure by ZIP code using the two related resources reviewed here — 1) individual ZIP code demographic-economic profiles and 2) ZIP code demographic-economic interactive tables. One way to examine the healthcare infrastructure for an area is to view/analyze the number and attributes (employment, earnings) of healthcare establishments by types of business/industry (such as physicians office or hospitals). Data and tools reviewed here provide insights into characteristics and patterns of national scope ZIP code areas — examine your ZIP codes of interest.

Option 1 — ZIP Code Profiles
.. examining the healthcare infrastructure in context of the related demographic-economic situation … the following graphic shows ZIP code 10514 (Westchester County, NY) with a bold red boundary.  Census tracts are shown with black boundaries with tract codes as white labels. See more about ZIP-Tract relationships. Cities/places are shown with blue cross-hatch pattern.

– view created with CV XE GIS software and related GIS project.

The above map graphic is part of a ZIP Code 10514 profile (click link to view complete profile). Section 3.1. of the profile shows the number healthcare establishments in the ZIP Code as partly shown in this graphic:

The portion of the table shows the NAICS/type of business code at left, followed by type of business description and the number of establishments at the right.

Examine other characteristics of this ZIP code profile and in context of others via this related Web section. These profiles update in May 2018.

Option 2 — ZIP Code Health Care Sector comparative analysis
.. examining the healthcare infrastructure for a set of ZIP codes in a state, metro, county or peer group … use the interactive table located here to view/rank/compare health care business establishments by type of business for a selected set of ZIP codes. This table shows a query placed on the table to show the total number of offices of physicians for ZIP codes in the vicinity of ZIP 10514. It shows that there are 14 offices of physicians establishments and 13 have 1-4 employees.

About These and Related ZIP Code Data
Data used to develop the tools/resources described above are based in part on the Census Bureau County Business Patterns program. These establishment data update annually.

ZIP code demographic-economic interactive tables
Use the following tables to examine a wide range of ZIP code demographic-economic conditions:
  • General Demographics
  • Social Characteristics
  • Economic Chacteristics
  • Housing CHaracteristics

Data Analytics Web Sessions
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 America’s Cities: Demographic-Economic Updates

.. of the approximate 29,500 U.S. cities and places — geographic areas of population concentration — 301 had an ACS 2016 5-year estimated population of 100,000 or more. The median household income among these places, one measure of economic prosperity, ranged from $26,249 (Detroit, MI) to $117,642 (Frisco, TX).

What are the demographic-economic characteristics of your cities/places of interest? How do these compare to peer groups or a metro/state of interest. Learn more using the new city/place demographic interactive tables. Its about more than economic prosperity — using these data provide otherwise unknowable attributes about the demographic, social, economic and housing characteristics of individual cities/places.

Visual Analysis of City/Place Population Dynamics
The following view shows patterns of population percent change by city in the Charlotte, NC/SC metro area.

… view developed using the CV XE GIS software.
… more about above view in City/Place Economic Characteristics section.

Patterns of Economic Prosperity ($MHI) by City/Place
— Northern Virginia, DC, Maryland; part of the Washington, DC metro.

… view developed using the CV XE GIS software.
… click graphic for larger view with places labeled by name and $MHI.

Interactive Tables — new January 2018
Use these interactive tables to get answers, build insights:
• General Demographics
• Social Characteristics
• Economic Characteristics — used to develop data at top of section
• Housing Characteristics
Related:
• City/Place GeoDemographics Main Section
• Annual City/Place Population Estimates & Trends
• Similar ACS tables: Census Tracts | ZIP Codes | State, Metro & County

More About City/Place GeoStatistical Data and Data Analytics
The term “places” as used here refers to incorporated places and Census Designated Places (CDPs). Incorporated places are political areas having certain governmental powers designated by the corresponding state. Unincorporated places, or Census Designated Places (CDPs), are statistical areas having no official standing and no governmental powers but are recognized as being areas of population concentration. Wide-ranging demographic-economic estimates are developed annually for the approximate 29,500 incorporated cities and CDPs based on the American Community Survey 5-year estimates. See more about the ACS 2016 5-year estimates.

Many cities have planning and data development operations that develop important local data including tax parcel data, building permit data, transportation and infrastructure data … bit generally not the data reviewed in this section. Many cities have no planning department to develop, organize and analyze geographic, demographic, economic data … making these data even more essential.

Increasingly in core sections of metropolitan areas, as shown in the above graphics, a large number of cities/places are contiguous. Many retain their own character evolving over many years. Having the detailed ACS demographic-economic data makes it possible to compare places side by side. Use the same data for related drill down geography such as census tracts and block groups to examine neighborhoods and market areas.

Data Analytics Web Sessions
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.

TractWatch — Examining Small Area Change

Understanding the demographic-economic landscape for small area geography and how it is changing is vital for many stakeholders. Businesses and other organizations need to know how their market/service areas are changing … getting answers to questions like knowing about recent trends, where we are now and the how/where/how much things might change in the future.

Examining Tract Change
The following view shows census tracts (black boundary) located in the northeast Houston, TX area. Tracts are labeled with 2017 population estimates and percent population change from 2010 to 2017. Tract geography and characteristics are shown in context of three cities/places — Houston (orange cross-hatch), Humble (blue) and Atascocita CDP (green). It is easy to see what census tracts intersect with what cities and where. The pointer/hand is located in census tract 48-201-240902, partly intersecting with Humble city. The tract 2017 population of 12,984 reflects an increase of 10.4% since 2010. The dark brown bold boundary at the top of this tract is the Harris County, TX boundary.

.. view developed with ProximityOne CV XE GIS and related GIS project.
.. create views like this for any area in the U.S.; add your own data.

TractWatch tells us which tracts in a region of interest changed during the past year based on quarterly observable data with only a one quarter lag.

Census Tracts & TractWatch
TractWatch is a new tool/service focused on examining recent demographic-business change for each census tract. These approximate 74,000 geographic areas cover the U.S. wall-to-wall and averaged 4,000 population as of Census 2010. Tracts have a generally stable geography between decennial censuses and are coterminous with county boundaries. Tracts cover the U.S. with more than a 2-to-1 ratio compared to ZIP code areas (see tract-ZIP relationship table).

Integrated with Situation & Outlook
TractWatch insights are developed through the use of the ProximityOne Situation & Outlook (S&O) database and information system — a part of S&O demographic-economic estimates and projections developed and updated annually. The 2017 vintage tract estimates and projections (annual data) cover the period 2010 through 2022 (5-year projection).

TractWatch – Monitoring Change
As a part of the S&O annual estimates and projections development, a range of measures are updated quarterly at the census tract level. Quarterly data are developed that include population, housing units, vacant units, households and business establishments.

There is only a one-quarter lag in the availability of observable census tract data. For example, observable 2017Q1 data can be added to the S&O database in July 2017. Data are analyzed and converted into a TractWatch national dataset.

Situation & Outlook Reports
The Situation & Outlook Reports (S&O Reports) are updated weekly, for the U.S. and each county, metro and state. TractWatch is a part of the “Recent Change and Outlook” S&O Report section and updated quarterly. See schedule of updates the shows when TractWatch is updated.

The S&O Reports (metro and county) Recent Change and Outlook section includes a list of census tracts which have shown significant change over the past year for that geography. A table of typically 10-to-25 key tracts are listed in a table with selected demographic-business change attributes.

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.

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.