Tag Archives: Education

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.

Tools to Analyze County Demographic-Economic Characteristics

.. demographic-economic characteristics of counties are essential for business development, market analysis, planning, economic development, program management and general awareness of patterns and trends. This section provides access to data and tools to examine these data for all counties in the U.S. This annual update includes geographic area characteristics based on ACS 2015 data.  The tools/data are organized into four related sections summarized below.

1. General Demographics
View interactive table at http://proximityone.com/us155dp1.htm
Patterns of School Age Population by County
Use GIS tools to visually examine county general demographics as illustrated below. The following view shows patterns of percent population ages 5 to 17 years of age by county — item D001-D004-D018 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

2. Social Characteristics
View interactive table at http://proximityone.com/us155dp2.htm 
Patterns of Educational Attainment by County
– percent college graduate
Use GIS tools to visually examine county social characteristics as illustrated below. The following view shows patterns of percent college graduate by county — item S067 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

3. Economic Characteristics
View interactive table at http://proximityone.com/us155dp3.htm 
Patterns of Median Household Income by County
Use GIS tools to visually examine county economic characteristics as illustrated below. The following view shows patterns median household income by county — item E062 in the interactive table. Create your own views.

… view developed using the CV XE GIS software.

4. Housing Characteristics
View interactive table at http://proximityone.com/us155dp4.htm 
Patterns of Median Housing Value by County
Use GIS tools to visually examine county housing characteristics as illustrated below. The following view shows patterns median housing value by county — item E062 in the interactive table. Create your own views.

… view developed using 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.

America’s Cities: Demographic-Economic Characteristics Annual Update

.. tools and data to interactively examine demographic-economic characteristics of America’s 29,321 cities/places .. understanding demographic-economic characteristics of cities and places is essential for business development, market analysis, planning, economic development, program management and general awareness of patterns and trends. This section provides access to data and tools to examine characteristics of all cities/places in the U.S. This annual update includes data for 29,321 cities/places based on ACS 2015 data.

Accessing the Data; Using Interactive Tables
Each of the four links below opens a new page providing access to U.S. by city/place interactive tables — by type of subject matter. Use tools and usage notes below table to select operations to perform queries, sort and select columns.
General Demographics
Social Characteristics
Economic Characteristics
Housing Characteristics

How the the Tables/Data Can be Used
The following table shows data derived from the Economic Characteristics table. The top 10 cities/places having the highest median household income ($MHI) are shown. The table also shows population, median family income ($MFI) and per capita income ($PCI). The $250,000 value is a cap; the actual value is $250,000 or higher. Use the interactive tables to create similar views for states of interest. Use the button below the table to select/view cities within a selected metro. Compare attributes of cities of interest to a peer group based on population size.

Visual Analysis of City/Place Population Patterns
Use GIS resources to visually examine city/place demographic-economic patterns. 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.
… click map for larger view and details.

Related Data
Cities/Places Main Section
Citie Population Estimates & Trends, 2010-15

More About Using These Data
Using ACS 1-year and 5-year data

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.

National Children & Education Statistics Program Updates

.. NCES Program updates .. tools, data & methodology to examine national scope children & education .. school, school district & extended geographic-statistical data with drill-down to school and intersection level. See more about the NCES Program below.

New this Week
ACS 2015 school district demographic-economic interactive tables
– view, compare, analyze selected/all U.S. school districts
– more focused blog updates coming soon.

School Districts with Highest Median Household Income
Use the interactive table to examine economic characteristics of school districts. Below is a list of the 10 school districts having the highest median household income developed using the Economic Characteristics interactive table. Develop similar views for metros and states of interest.

– ranked on item E062 — median household income.
– click graphic for larger view.

Use GIS tools to develop thematic pattern maps such as the one shown below with NCES GIS projects. Select from hundreds of statistical measures. Create your own regional;/district views. Integrate other data.

Patterns of Economic Prosperity by School District
– median household income (item E062 in table)

– view developed with CVGIS software & related GIS project and data.
– click graphic for larger view.

See the School Districts Economic Characteristics Interactive Table.

About the National Children & Education Statistics Program
The National Children & Education Statistics (NCES) Program provides access to tools, data & methodology to examine national scope children’s demographics & education-related characteristics. These resources enable stakeholders to view and analyze detailed geographic and statistical data at the school, neighborhood, community, attendance zone, school district and higher level geography. Integrate these data with drill-down demographic-economic data to the census block and intersection levels. Examine characteristics of schools, school districts and education data with related and higher level geography including urban/rural, cities, counties, metros, state and the U.S.

See NCES Main Section.

Contents: Summary of NCES Program Resources
Click a link to view more detail on a selected topic.
Updates: New Resources, Events & Related Topics
Analytics, Blogs, Studies
Using Software Tools & Datasets
01 Mapping & Visual Analysis Tools
02 School District Annual Demographic-Economic Data Resources
03 Children’s Demographics & Living Environment by School District
04 School District Enrollment & Operational Characteristics
05 School District Finances: Sources & Uses of Funds
06 School District Geographic Size & Characteristics
07 School District-ZIP Code Area Relationship Table
08 K-12 Public Schools
09 K-12 Private Schools
10 K-12 Public School Attendance Zones
11 K-12 Public Schools by Urban/Rural Status
12 Census Tract Demographic-Economic Characteristics
13 Metropolitan Area Situation & Outlook 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.

Personal Consumption Expenditures by State: Updates & Pattern Analysis

.. data and tools to develop insights into personal consumption patterns by state .. growth in state personal consumption expenditures (PCE) – the measure of goods and services purchased by or on behalf of households – decelerated to 3.6 percent on average in 2015 from 4.4 percent in 2014. In 2015, PCE growth ranged from 1.5 percent in Wyoming to 5.0 percent in Florida. PCE by state data for 16 expenditure categories are shown for the U.S. and by state in the interactive table. See related Web section for more detail.

Per Capita Personal Consumption Expenditures
  — Patterns & Characteristics by State

The following graphic shows patterns of percent change in total PCE 2010-2015 by state labeled with 2015 per capita total PCE. Use CVGIS project to examine PCE by types and different years. Integrate additional subject matter and types of geography. Click graphic for larger view with details.

– views developed with CVGIS and related GIS project & datasets.

In 2015, the fastest growing categories of expenditures across all states were food services and accommodations, health care and other nondurable goods. These categories along with housing and utilities were also the largest contributors to growth in total PCE by state.

Per capita PCE by state measures average PCE spending per person in a state. Across all states, per capita total PCE was $38,196. Per capita PCE by state ranged from a high of $49,717 in Massachusetts to a low of $29,330 in Mississippi.

Personal Consumption Expenditure by Category
PCE by state is the state counterpart of the Nation’s personal consumption expenditures (PCE). PCE by state measures the goods and services purchased by or on behalf of households and the net expenditures of nonprofit institutions serving households (NPISHs) by state of residence for all states and DC. PCE by state reflects spending on activities that are attributable to the residents of a state, even when those activities take place outside of the state. Per capita PCE by state measures average PCE spending per person in a state.

Interactive Analysis
The following two graphics illustrate use of the interactive PCE table. View 1 shows Texas by PCE type ranked in ascending order on percent change from 2010 to 2015 (ranked on far right column). View 2 shows Texas by PCE type ranked in descending order on percent change from 2010 to 2015 (ranked on far right column). Use the table to examine characteristics of states of interest. Click graphic for larger view.

Texas by PCE Type; Ranked Ascending on PCPCE Change 2010-15

Texas by PCE Type; Ranked Descending on PCPCE Change 2010-15

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.

Congressional District 2015 Demographic-Economic Characteristics

.. congressional districts vary widely in demographic-economic characteristics.  We have new data for 2015 providing insights to characteristics of the 114th Congressional Districts.  This section summarize a few of these characteristics and provides access to a wide range of data that you can use to view, sort, rank, and compare congressional districts using interactive tables.

Patterns of 2015 Educational Attainment
The following graphic shows patterns of educational attainment (percent college graduate) by congressional district in the Los Angeles area. White label shows the congressional district code; yellow label shows percent college graduate. Legend shows color patterns associated with percent college graduate intervals.

– View developed using CV XE GIS software and associated GIS project.

How Congressional Districts Compare
Reference items refer to items/columns shown in tables described below.

.. general demographics: congressional district UT03 has the smallest median age (27.5 years — item D017) and FL11 has the highest median age (53.5 years).

.. social characteristics: congressional district KY05 has the fewest number of people who speak English less than “very well” (2,676 — item S113) and FL27 has the largest number (281,053).

.. economic characteristics: congressional district ND00 has the lowest unemployment rate (2.6% — item E009) and MI13 has the highest unemployment rate (14.6%).

.. housing characteristics: congressional district MI13 has the lowest median housing value ($63,100 — item H089) and CA18 has the highest median housing value ($1,139,900).

Access the Detailed Interactive Tables
Click a link to view more thematic pattern maps and use the interactive tables.
.. General Demographics
.. Social Characteristics
.. Economic Characteristics
.. Housing Characteristics

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.

Powerful School District Maps

AASA Partners with ProximityOne to Share Powerful School District Maps

This post is a partial summary of this AASA blog … AASA, American Association of School Administrators, was pleased to partner with ProximityOne for our latest economic impact report, Unequal Pain: Federal Public Education Revenues, Federal Education Cuts and the Impact on Public Schools.

Goto ProximityOne  Using ProximityOne’s custom mapping tools, AASA’s report includes a national map showing the role that federal dollars play in schools district operating budgets. The national map is detailed, shading the various shares for school districts, also outlined at the state and congressional district level. The map is a huge asset to AASA’s report. Absent the map, the report is very data heavy. The map is a clear, concise representation of a very wonky discussion.

The map is also a very powerful tool: It shows, at the most local level, how any cut to federal education funding–regardless of how it may be described as ‘across the board’ or ‘uniform’–is anything but. Even the most modest of cuts (less than 2 percent) will be felt very differently in a district where federal dollars are upwards of 60 or 70 percent of their operating budget than in a district where less than 4 percent of the operating budget is federal dollars. The map is a very clear illustration of the unequal pain that stems from federal education cuts.

AASA first partnered with ProximityOne a year ago. ProximityOne uses geographic-demographic-economic data and analytical tools to inform discussions and explain current situation/area characteristics. They work with a wide variety of clients, including both private and public sector organizations.

This type of analysis may prove valuable to AASA members. As such, we will be partnering with ProximityOne to host a webinar on this map, the ProximityOne mapping tools, and what it can mean for schools.

See more about ProximityOne School District Decision-Making Information
… more about ProximityOne School District Finances