Tag Archives: GIS

Examining Appalachia City Characteristics & Trends

.. using tools and data to examine geographic, demographic, economic characteristics of the Appalachia Region .. Appalachia is a region that includes parts of 13 states, 420 counties, and has long been challenged with poverty. This section is part of a series focused on Appalachia.  See related more detailed Web section.

The Appalachia Region; Lay of the Land
The population of Appalachia increased from 25.1 million in 2010 to 25.5 million in 2016, an increase of 289,806. The following graphic shows how Appalachia region counties have gained population (blue and green) and lost population (orange and red) during the period 2010 to 2016. Click graphic for larger view; expand browser window for best quality view.

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

Cities in Appalachia
In 2016, there were 2,393 cities in Appalachia. Seven cities had population over 100,000; 16 cities had over 50,000 population and 213 cities had 10,000 or more population.

The following graphic shows cities (red markers) with 2016 population of 10,000 or more in the Appalachia region in context of counties (yellow fill pattern). Click graphic for larger view; expand browser window for best quality view. Larger view shows city names except where labels could overlap.

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

Growing Cities
The following view shows cities as green markers having 5,000+ 2016 population with growth of 500+ or more population, 2010-2016.

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

Cities & Metros in Appalachia
The following graphic shows Metropolitan Statistical Areas (green fill pattern) that intersect with Appalachia region counties. Note that some metros only partly intersect with Appalachia. County boundaries are shown as overlay on metros. For example, only northern counties of the Atlanta metro (see pointer) are Appalachia counties. “Edge” Appalachia metros create opportunities for nearby Appalachia counties. Cities within Appalachia and having 50,000+ 2016 population are shown with orange markers. Click graphic for larger view; expand browser window for best quality view.

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

Characteristics of Metros, Cities and School Districts
• Demographic-economic profiles for selected cities
Examples (click link above to view other cities; click links below for specific city profiles):
.. Cumberland, MD [2421325] (19,978)
.. Frostburg, MD [2430900] (8,676)
Access any/all U.S. city(s) — http://proximityone.com/places15dp1.htm
• Demographic-economic profiles for selected school districts
Examples (click link above to view other districts; click links below for specific district profiles):
.. Allegany County Public Schools, MD [2400030]
.. Pittsburgh School District, PA [4219170]
Access any/all U.S. school district(s) — http://proximityone.com/sd15dp1.htm
• S&O metro reports

Examining Characteristics of All Cities/Places
Use these resources to examine all U.S. cities/places.
• Cities/Places Main Section
• America’s Communities Program — city profiles
• All Cities/Places — 4 Web section/tables
• City Population Estimates & Trends 2010-2016 interactive 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.

Analyzing Block Group Demographics

.. tools & data to analyze sub-census tract households, education, income, housing, more … Block Groups, subdivisions of census tracts, are the smallest geographic areas for which “richer demographics” are developed by the Census Bureau. Block group demographic-economic estimates, based on Census 2010 geography, are annually updated beginning with American Community Survey (ACS) 2010. The latest ACS estimates for these 217,740 areas covering U.S. wall-to-wall are from ACS 2015. The ACS 2016 update will be released in December 2017.  See the related Web section for more detail about accessing and using block group geography and demographic-economic data.

Patterns of Economic Prosperity by Block Group
The following graphic shows patterns of median household income by block group in the Houston, TX area. Markers show block groups with 10 or more housing units having value of $2 million or more. Markers are labeled with the number of housing units having value of $2 million or more in that block group. Click graphic for larger view, more detail and legend color/data intervals. This map illustrates the geographic level of detail available using block group demographics and the relative ease to gain insights using geospatial data analytics tools.

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

Block Group Demographic-Economic Data & Shapefiles
… selection of key demographic-economic attributes; annual update
… subject matter categories include:
  • Total population>
  • Population by gender iterated by age
  • Population by race/origin
  • Households by type of household
  • Educational attainment by detailed category
  • Household Income by detailed category
  • Housing units by owner/renter occupancy
  • Housing units by units in structure
  • Housing units by detailed value intervals

See the related Web section for a detailed list of items.

Use these Data on Your Computer
Use the above U.S. national scope dataset with your own software or in ready-to-use GIS projects with the CV XE GIS software.

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

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

City Population Characteristics & Trends: 2010-2016

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

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

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

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

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

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

– View developed using the CV XE GIS software.

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

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

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

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

Click graphic for larger view.

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

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

Relating ZIP Codes to City/Places

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

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

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

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

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

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

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

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

Click graphic for larger view.

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

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

Making & Using Custom 115th Congressional District Maps

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Life Expectancy Change by County, 1980-2014

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

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

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

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

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

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

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

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

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

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

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

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

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.