Office of Planning, Environment, & Realty (HEP)
Penelope Weinberger, AASHTO, Pweinberger@aashto.org
The big news is that the data are out! Yes, the CTPP 5-year small area data product has been delivered, developed and disseminated. For a more robust discussion, see the Census Transportation Planning Products (CTPP) Data and Data Access Software hit the Streets article in this issue.
The new CTPP Ongoing Technical Service Program has issued a solicitation to the states for a $4.5 million five year funding package to cover 2015 - 2019. The solicitation was mailed to the states on July 11, 2013 with a request for a contact, a commitment and payment by the end of FY 2014 (July, 2014). The CTPP asks the states for funding in advance of data delivery, as the Census Bureau requires advance payment. As of November 15, 2013, 29 of 51 DOTs have committed to the program and 21 have paid in full. As in previous solicitations, the assessment is population based, can be paid with SP&R funds and the requirement for local match has been waived by Federal Highway Administration. All previous solicitations had full participation of the 50 states and DC, and we anticipate the same participation for this one.
The new Board, headed by Tracy Larkin-Thomason of the Nevada DOT, is busy revising the CTPP Oversight Board member roles to match the new ongoing status, developing the budget and organizing research and other activities to purchase the next datasets and run the program smoothly.
Penelope Weinberger, AASHTO, Pweinberger@aashto.org
AASHTO released the 2006-2010 CTPP on October 31, 2013 with Version 1.0 of a powerful data access tool. The United States Census Bureau delivered the special tabulation of five-year small area data to AASHTO in May 2013 with over 350 gigs of flat files. We are already working on a Version 1.1 with some fixes, although people are able to access and use the data now.
The dataset consists of almost 200 residence-based tables, 115 workplace-based tables and 39 flow tables (Home to work) for over 325,000 geographies. Demographic characteristics include age, race, sex, earnings, income, employment status, industry, occupation, household size, vehicle availability, and a host of others, not the least of which is means of transportation to work!
The data are derived from two American Community Survey microdata record sets based on the 2006 - 2010 ACS. One set is the original microdata; all tables derived from this set are designated A, the other set is a disclosure proofed microdata set; all tables derived from this set are designated B. That is to say, the first letter in the table name indicates the set from which the table was derived. For more information on disclosure proofing, please access the CTPP e-Learning module "Disclosure Proofing" at: http://ctpp.training.transportation.org/
The CTPP Data Access Software is a powerful tool, including a robust mapping tool, used for both selecting geography and rendering results. Results can be presented as various style charts and tables that can be rearranged to suit your analysis and display needs. Data from the tool can be downloaded as comma or semicolon delimited .csv, .xml, .shp, .tab.
But enough chatter, how about some pictures? Figure 1 shows what it looks like when you log in.
Figure 1 Software window after logging in
Let's look in detail (Figure 2):
Figure 2 Software window after logging in (Details)
Box 1 is the main menu: You can use it to log in, search the tables, by table number, phrase, keyword or variable, and find help, tutorials and the CTPP feedback form, which lets you report a bug or problem. To save selections, you must register with an email address and a password.
Box 2 is the table folder; the fully expanded folder structure for residence-based tables looks like this:
Box 3 is Geography selection; geography can be selected before or after a data table, by a list, through a map, or by drilling down into a table.
Here I'm selecting from the list (Figure 3). To get here, I clicked on the blue WORKPLACE; selected POW State-County on the left; and jumped to New Jersey on the right. To select all New Jersey Counties I just need to do two more things; on the left first hit the Clear full selection button, to clear the states that are selected by default, second, on the right, click the green checkmark to the right of New Jersey (Figure 4).
Figure 3 Expanded folder structure.
Figure 4 Selecting WORKPLACE using Selection list
Or, I can achieve the same result using with the selection map (Figure 5). Make sure my level is county, Zoom to the state, checking the box to automatically highlight the counties.
Figure 5 Selecting WORKPLACE using Selection map - map view
Figure 6 Selecting WORKPLACE using Selection map(2) - Add highlighted POW counties to selection
Click on the yellow Add button (Figure 6), and Voila! (Figure 7)
Figure 7 Selecting WORKPLACE using Selection map(3) - Highlighted POW counties are added
Now I want to see some data, how about workers at workplace? I hit Show CTPP Tables, and either scroll or search for A202100 (Figure 8).
Figure 8 Software window after clicking Show CTPP Tables
Wow, Bergen County has almost half a million workers! Where are the densest employment centers? I'm going to drill down, by clicking on it, choose tract level geography and then look at it on a map (Figure 9).
Figure 9 Map view after clicking Bergen County, New Jersey and choosing tract level
These three tracts have the most workers.
The data access software is a powerful tool once you learn to navigate it. There are tutorials, videos, cheat sheets and live help.
This article compiles three case studies of comparing CTPP and Longitudinal Employment Household Dynamics (LEHD) Origin-Destination Employment Statistics (LODES) commuting flows at the county level. It is often a good idea to examine data at a larger geographic scale before delving into small scale data analysis. Three case studies were independently conducted by Southern California Association of Government (SCAG), New York State Department of Transportation (NYSDOT) and Cambridge Systematics. All three studies show major differences between CTPP and LODES flows, especially for the intra-county flows. The internal county flows are often much lower in the LODES compared to the CTPP, even after adding in an estimated 10% of self-employed, who are assumed to be more likely to work in their own county.
Case Study 1: Southern California Association of Government
This section is contributed by Cheol-Ho Lee in SCAG and Liang Long in Cambridge Systematics Inc.
This study reviews county level flows from three data sources: CTPP 2006-2008, 2012-2013 California Household Travel Survey (CHTS) and LODES 2013. The study area is the SCAG region which consists of six counties with about 18 million population and 7.5 million jobs. The six counties are: Imperial (IMP), Los Angeles (LAX), Orange (ORG), Riverside (RIV), San Bernadino (SBN) and Ventura (VEN).
Table 1, Table 2, and Table 3 show commuting flows distribution by workplace counties for each residence county for CTPP 2006-2008, 2012-2013 CHTS and LODES 2013. For most counties (IMP, LAX, SBN and VEN), CTPP and CHTS demonstrate very similar flow distribution patterns by workplace counties. For example, both CTPP and CHTS show more than 94% of workers living and working in Imperial county, while LODES shows only 67% intra-county flows. Even after adding in an estimated 10% of self-employed, the internal county flows in LODES are still 15% lower compared to CTPP and CHTS. However, for Riverside County, CTPP shows 71% intra-county flows, and CHTS and LODES show only 52% and 47% respectively. Most differences between CTPP and CHTS should be attributed to flows for Riverside residents outside of the SCAG region that are observed in the CHTS. For Orange County, LODES and CHTS have more consistent patterns with about 65% intra-county flows and about 23% flows out to Los Angeles County. Figure 10 shows side-by-side comparison across three data sources for the proportion of workers living and working in the same county. For all counties except Orange and Riverside, LODES data shows a significantly lower proportion of workers living and working in the same county than CTPP or CHTS.
Table 1. Flows Proportion by Workplace Counties: CTPP 2006-2008
|County||IMP||LAX||ORG||RIV||SBN||VEN||Outside SCAG||Residence Total|
Table 2. Flows Proportion by Workplace Counties: 2012-2013 CHTS
|County||IMP||LAX||ORG||RIV||SBN||VEN||Outside SCAG||Residence Total|
Table 3. Flows Proportion by Workplace Counties: LODES 2013
|County||IMP||LAX||ORG||RIV||SBN||VEN||Outside SCAG||Residence Total|
Figure 10. Proportion of Workers Working and Living in the Same County
Case Study 2: New York State Department of Transportation
This section is an excerpt of "Commutation Flow: CTPP 2000, ACS & CTPP, and LEHD-OTM" authored by Nathan Erbaum. The full article can be accessed here: http://www.fhwa.dot.gov/planning/census_issues/ctpp/status_report/sr0111.cfm
NYSDOT conducted a review of the LODES 2006 and compared it with other sources, including the CTPP 2000, CTPP 2006-2008 and the 2001 NHTS.
A number of specific tests were done to look at the flow for each county from the top 5, 10 and 15 originating counties. Table 4 shows top five origin counties for Queens County, NY across CTPP 2000, LODES 2006, and CTPP 2006-2008. When comparing the LEHD-OTM to the CTPP, findings included:
Table 4: Journey To Work Flow Comparison - Queen County, NY
|Destination County: Queens County, NY|
|Origin County||CTPP 2000||LODES 2006||CTPP 2006-2008||CTPP 2006-2008 Margin of Error|
|Queens County, NY||367,825||215,397||446,035||7,046|
|Kings County, NY||62,255||74,265||70,965||2,632|
|Nassau County, NY||66,085||57,076||70,255||2,755|
|Suffolk County, NY||25,160||22,304||24,855||1,584|
|Bronx County, NY||17,645||29,837||20,690||1,669|
The complete comparison report can be accessed here: https://www.dot.ny.gov/divisions/policy-and-strategy/darb/dai-unit/ttss/repository/3compare.pdf
When comparing the LODES 2006 to the 2001 NHTS, the 2001 NHTS has about 15 percent of home-to-work trips exceeding 20 miles. The LODES has a much larger proportion of home-to-work pairs exceeding 20 miles.
Case Study 3: Dane County, Wisconsin
This section is contributed by Liang Long from Cambridge Systematics Inc.
This case study utilizes two different sources for commuting patterns for Dane County, Wisconsin: CTPP 2006-2008 and LODES 2008. Primary job flows are used from LODES 2008 to be consistent with CTPP job flows.
Similar to the previous two case studies, this Wisconsin case also reflects lower intra-county flows in LODES compared to the CTPP. Table 5 shows workers commuting to Dane County. The LODES results have a very large portion of flows from outside the region. Dane County is the home to the capital, Madison, so this difference may be attributed to most state employees being reported as working in the capital's county.
For Dane County residents, CTPP again shows a much higher percentage of intra-county flows than that of LODES (92% for CTPP and 82% for LODES). The fact that LODES is not including self-employed and military personnel certainly helps to explain some of the discrepancies. The higher proportion for workers commuting from Dane County to Milwaukee County observed in the LODES may be due to the accuracy of workplace location. In this case, businesses including Manpower Group which is headquartered in Milwaukee, Wisconsin may have employees who work in many different locations but are reported at the headquarters' location.
Table 5. Flow Distributions for Dane County, Wisconsin (WI)
|Destination County: Dane County, WI|
|Origin County||CTPP 2006-2008||LODES 2008||Percent Differences|
|Dane County, Wisconsin||84.1%||68.3%||15.7%|
|Rock County, Wisconsin||3.5%||3.7%||-0.2%|
|Columbia County, Wisconsin||2.9%||3.2%||-0.3%|
|Jefferson County, Wisconsin||0.2%||2.8%||-2.6%|
|Sauk County, Wisconsin||1.5%||2.0%||-0.5%|
|Waukesha County, Wisconsin||1.8%||1.9%||-0.1%|
|Milwaukee County, Wisconsin||0.5%||1.6%||-1.1%|
|Iowa County, Wisconsin||1.7%||1.4%||0.3%|
|Green County, Wisconsin||1.3%||1.3%||0.0%|
|Origin County: Dane County, WI|
|Destination County||CTPP 2006-2008||LODES 2008||Percent Differences|
|Dane County, Wisconsin||95.2%||81.8%||13.3%|
|Rock County, Wisconsin||0.8%||1.4%||-0.6%|
|Columbia County, Wisconsin||0.6%||0.8%||-0.2%|
|Jefferson County, Wisconsin||0.6%||0.8%||-0.2%|
|Sauk County, Wisconsin||0.6%||1.2%||-0.7%|
|Waukesha County, Wisconsin||0.3%||2.2%||-1.8%|
|Milwaukee County, Wisconsin||0.3%||3.4%||-3.1%|
|Iowa County, Wisconsin||0.3%||0.4%||-0.1%|
|Green County, Wisconsin||0.2%||0.4%||-0.2%|
The lure of LODES is that it provides data for home and work pairs synthesized down to the block level flow and is therefore very attractive for zonal analysis, especially since it is updated regularly. As with all data sources, users need to check for consistency and anomalies. Some recommendations for checking include:
Whatever you choose, you may have to adjust these public data sources with your best local knowledge.
This article is compiled by Liang Long from Cambridge Systematics. If you have any questions, please contact Dr. Liang Long by e-mail at firstname.lastname@example.org.
FHWA's 2015 National Household Travel Survey (NHTS) team is gearing up and fielding an energized team with both new and familiar faces. The NHTS Support Contract was awarded to MacroSys Inc. in October. This particular contract will not only continue to provide data user support but also assist the new Add-on participants in statistical and methodological survey research services.
The NHTS continues to sponsor the TRB Task Force on Exploring New Directions for the NHTS. The Task Force Team issued a report of their Phase 1 outreach activities report, TRB Circular E-C178 (http://www.trb.org/main/blurbs/169627.aspx). Some suggestions will be to investigate sample frame to an address-based frame, with the possibility of integrating some cellphone and landline listings into the sample. There are also plans for conducting a GPS subsample of the survey.
The 2009 NHTS data continues to serve the user community with over 55,000 visits to the NHTS website in 2013. About 4,500 downloads have been performed for NHTS data in this same year to be used towards various transportation-related studies: vehicle ownership, demographic distributions of bicyclists and pedestrians, commuting patterns, weekend travel, electric vehicle use, public health effects of walking, activity modeling, CO2 emissions, sustainability, ride sharing, flexible work schedules, and public transit use. As researchers and transportation agencies continue their analysis of previous datasets, the NHTS team maintains an open line of communication with the user community for suggestions to improve the upcoming 2015 survey.
As work continues to recruit potential Add-on states and MPOs for the 2015 survey, please contact NHTS Program Manager, Adella Santos (email@example.com or 202-366-5021) to learn more about joining the program.
Gregory Slater, Maryland State Highway Administration, firstname.lastname@example.org
Data driven decision support and performance-based planning and programming are vital components in achieving the mission within most transportation agencies. With a comprehensive data driven approach, you can truly tell the story of your transportation system, its condition, where it has been and where it is headed. Simply put, it is the use of data to guide your decisions to achieve the greatest benefits. Transportation agencies, like many other entities both public and private, have varied needs not all of which can be met, so finding balance among factors such as performance management outcomes and risk. Using quality and consistent data can help maximize results and achieve a balance to address congestion, safety, environmental responsibility, and transportation asset management programs. Consistent and quality data are critical to success.
By definition, these performance-based programs are guided by data, so the data have to adhere to some core data principles, have a well-defined method for data management, and be part of a guiding plan for an agency's data program.
State DOTs are using data to support and guide extremely important decisions, so that data better fit and are ready to use and have a full understanding of the level of reliability in the decision making process. Data development and the use of data in the transportation world is not new. With the emergence of both new data sources and performance-based planning and programming, the importance of quality, mature and consistent data sets within and across the state DOTs has been a hot topic with the AASHTO Standing Committee on Planning (SCOP) over the past few years. This was elevated with the inclusion of the performance-based planning elements that were written into MAP-21. With MAP-21, State DOTs began the development of a set of core data principles to help guide them in the data world. The development task began in SCOP's data sub-committee and was refined with input from across SCOP, the TRB data sections, and a combined data section stakeholder group that includes representation from USDOT.
To assist states and attempt to achieve some national consistency in the data used in transportation, SCOP, through its data sub-committee, develop a set of nine core data principles.
Principle 1: Data are an Asset-Data is a core business asset that has value and is managed accordingly.
Principle 2: Data must be Open, Accessible and transparent -Access to data is critical to performing duties and functions, data must be open and usable for diverse applications and open to all.
Principle 4: Data Quality and Extent is fit (or appropriate) for a variety of applications-Data quality is acceptable and meets the needs for which it is intended.
Principle 5: Data are Compliant with Law and Regulations
Principle 6: Data are Secure-Data are trustworthy and are safeguarded from unauthorized access, whether malicious, fraudulent or erroneous.
Principle 7: There is a Common Vocabulary and Data Definition -Data dictionaries are developed and metadata established to maximize consistency and transparency of data across systems.
Principle 8: Data are not duplicated -To the maximum extent feasible, data are collected once and used many times.
Principle 9: Decisions Maximize the Benefit of Data - Timely, relevant, high quality data are essential to maximize the utility of data for decision making.
Moving forward, there are a few options being explored for integrating these core data principles and research to help guide this initiative. The traditional route would be for AASHTO member states to adopt these principles, and then states would have them available as an official source of record, but as MAP-21 is a 2-year bill, and reauthorization discussions are already in full swing, these principles could be included into the reauthorization bill. These core data principles will help states ensure data are given the attention and documentation commensurate with the important decisions that are being made with them, and ensure a variety of stakeholders can benefit from their value. For questions comments or input, contact Gregory Slater, Maryland State Highway Administration email@example.com.
CTPP Contact List
CTPP Hotline - 202/366-5000
CTPP website: http://www.fhwa.dot.gov/planning/census_issues/ctpp/
FHWA website for Census issues: http://www.fhwa.dot.gov/planning/census_issues
AASHTO website for CTPP: http://ctpp.transportation.org
1990 and 2000 CTPP data downloadable via Transtats: http://transtats.bts.gov/
TRB Subcommittee on census data: http://www.trbcensus.com
Tracy Larkin Thomason, NVDOT
Susan Gorski, MI DOT
Census Bureau: Social, Economic and Housing Statistics Division
CTPP Listserv The CTPP Listserv serves as a web-forum for posting questions, and sharing information on Census and ACS. Currently, more than 700 users are subscribed to the listserv. To subscribe, please register by completing a form posted at: http://www.chrispy.net/mailman/listinfo/ctpp-news. On the form, you can indicate if you want e-mails to be batched in a daily digest. The website also includes an archive of past e-mails posted to the listserv.
 The table was provided by HBA Specto for the analysis of CHTS Data for California Statewide Travel Demand Modeling Calibration.