Rail Commodity Origin and Destination Statistics (RailOD)

Detailed information for 2015

Status:

Active

Frequency:

Annual

Record number:

2736

The survey collects data on railway commodities carried by Canadian National (CN) and Canadian Pacific (CP) Rail.

Data release - March 31, 2017

Description

The survey collects data on railway commodities carried by Canadian National (CN), Canadian Pacific (CP) Rail ), carriers that interline with CN and CP, as well as a number of regional and short-haul carriers that do not interline with either CN or CP. The data are used by Statistics Canada as input to the Canadian System of National Accounts, by Transport Canada, other federal and provincial departments, by transportation companies, consulting firms, universities and foreign governments. The information is used for the analysis of transportation activity, for marketing and economic studies, as well as industry performance measures.

Reference period: Annual

Subjects

  • Transportation
  • Transportation by rail

Data sources and methodology

Target population

The target population consists of Canadian railways such as Canadian National Railway (CN), Canadian Pacific Railway (CP), carriers that interline with CN and CP, as well as a number of regional and short-haul carriers that do not interline with either CN or CP.

Instrument design

This methodology does not apply.

Sampling

This survey is a census with a cross-sectional design.

Data are collected for all units of the target population, therefore no sampling is done.

Data sources

Data collection for this reference period: 2015-03-01 to 2016-02-29

Responding to this survey is mandatory.

Data are extracted from administrative files.

Administrative data provided by Transport Canada are used to collect the data. Commodity origin and destination statistics are provided to Transport Canada (TC) and represent an annual census of waybill records from the two major railways - the Canadian National and Canadian Pacific. Freight interlined with Class II (short haul) carriers is included while interline duplication between CN and CP is removed. Each record represents a freight movement and shows origin, destination, commodity code, tonnage and other related information.

Error detection

At the micro level, several checks are performed on the data to verify internal consistency and identify extreme values. At the macro level, the data are subjected to a detailed quality review process, including a comparative analysis to prior year. Material errors are thereby identified and corrected.

Imputation

This methodology type does not apply to this statistical program.

Estimation

This methodology type does not apply to this statistical program.

Quality evaluation

The combined survey results are analyzed before dissemination. In general, this includes a detailed review of the data, a review of general economic conditions as well as historic trends and comparisons with other data sources.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects which could identify any person, business, or organization, unless consent has been given by the respondent or as permitted by the Statistics Act. Various confidentiality rules are applied to all data that are released or published to prevent the publication or disclosure of any information deemed confidential.

If necessary, data are suppressed to prevent direct or residual disclosure of identifiable data. Data for a specific industry or variable may be suppressed (along with that of a second industry or variable) if the number of enterprises in the population is too low.

Revisions and seasonal adjustment

Annual estimates are provided for the reference year. The data for the previous reference year are revised if necessary. As this is an annual survey, seasonal adjustments are not applicable.

Data accuracy

The methodology of this survey has been designed to control errors and to reduce the potential effects of these. Since the survey is a census of the target population, only non-sampling errors are possible. Examples of non-sampling error are coverage error, data response error, non-response error and processing error. A discussion of these types of errors and the steps taken to address them follows.

Coverage errors can result from incomplete listing and inadequate coverage of the provinces and territories. For the survey, since the population is comprised primarily of CN and CP, coverage errors are unlikely to happen. Coverage error is minimized by keeping the frame up to date using survey and administrative sources. Coverage rates are monitored during sampling process.

Data response errors may be due to questionnaire design, the characteristics of a question, inability or unwillingness of the respondent to provide correct information, misinterpretation of the questions or definitional problems. For the survey, these errors are controlled through careful questionnaire design, the use of simple concepts and consistency checks.

Non-response error is related to respondents that may refuse to answer, are unable to respond or are too late in reporting. For the survey, this type of error is mitigated by the close contact Statistics Canada staff maintain with Transport Canada (the department responsible for data collection) and the respondents.

Processing error may occur at various stages of processing such as data entry, editing and tabulation. For the survey, various measures have been taken to minimize these errors. For instance, data entry and edit are performed simultaneously due to the spreadsheet design which allows errors to be quickly seen. As well, historical ratios aid in eliminating outliers created by data entry. Finally, tabulation is automated to eliminate human error.

Non-sampling error is not related to sampling and may occur for many reasons. For example, non-response is an important source of non-sampling error. Population coverage, differences in the interpretation of questions, incorrect information from respondents, and mistakes in recording, coding and processing data are other examples of non-sampling errors.

Non-sampling errors are controlled through a careful design of the questionnaire, the use of a minimal number of simple concepts and consistency checks. Coverage error was minimized by using multiple sources to update the frame. Measures such as response rates are used as indicators of the possible extent of non-sampling errors.

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