Canada's Core Public Infrastructure Survey (CCPI)

Detailed information for 2018

The purpose of this survey is to collect statistical information on the inventory, condition, performance and asset management strategies of core public infrastructure assets owned or leased by various levels of the Canadian government.

Data release - October 26, 2020 (First in a series of releases for this reference period)

• Questionnaire(s) and reporting guide(s)
• Description
• Data sources and methodology
• Data accuracy

Description

The purpose of this survey is to collect statistical information on the inventory, condition, performance and asset management strategies of core public infrastructure assets owned or leased by various levels of Canadian government. The following 9 core public infrastructure assets are assessed:

- Bridge and tunnel assets
- Culture, recreation and sports facilities
- Potable water assets
- Public transit assets
- Road assets
- Public social and affordable housing assets
- Solid waste assets
- Storm water assets
- Wastewater assets

Information from this survey will be used by analysts and policy-makers to better understand the current condition of Canada's core public infrastructure. This will enable all levels of government to develop policies to support Canada's core public infrastructure and help monitor and report progress on desired outcomes.

Reference period: The calendar year

Collection period: During the year following the reference year

Subjects

• Government

Data sources and methodology

Target population

The target population consists of local, municipal, regional provincial and territorial governments that own one or more core public infrastructure assets. Collectively, these various levels of government are referred to as "organizations".

Instrument design

The questionnaire content for this survey was developed jointly by Statistics Canada and Infrastructure Canada. Qualitative testing was performed by the Statistics Canada Questionnaire Design Resource Centre for 2016 iteration. Testing consisted of cognitive interviews with English and French respondents to assess their understanding of the concepts, terminology, questions and response categories, and their ability and willingness to answer the questions. Questionnaire testing was completed in January and February 2017, as part of the initial feasibility study for Canada's Core Public Infrastructure Survey, 2016.

Sampling

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

The objective of this survey is to produce estimates describing the inventory, condition, performance and investments related to Canada's core public infrastructure in 2018. Estimates are representative of the:

- national dimension;
- provincial and territorial dimension;
- urban and rural dimension for municipalities;
- size dimension for municipalities.

The core of the sample is represented at the municipal level. A census municipalities with at least 1,000 residents was carried out. Rural municipalities with at least 500 residents were sampled.
To ensure greater coverage of publicly owned assets, the frame also has representation from the following groups:

1. Regional governments: This group is an extension of municipalities as this level of government can own core infrastructure and provide services to more than one municipality. Some of the more prevalent core infrastructure owned by this group includes public transit, potable water, social and affordable housing, culture, sports and recreation facilities, roads, and bridges and tunnels.

2. Provincial and territorial governments: Provincial and territorial ministries that are responsible for the following core assets: roads; bridges and tunnels; social and affordable housing; culture, recreation and sports facilities; and public transit are included in the sample.

The final selection of units were subject to Statistics Canada's internal guidelines and practices.

Data sources

Data collection for this reference period: 2019-11-04 to 2020-02-21

Responding to this survey is mandatory.

Data are collected directly from survey respondents.

Data are collected primarily through electronic questionnaire, while providing respondents with the option of replying by telephone interview or using other electronic filing methods.

View the Questionnaire(s) and reporting guide(s) .

Error detection

Error detection is an integral part of data processing activities. Prior to imputation, a series of edits are applied to the collected data to identify errors and inconsistencies. Outlier detection is also performed on select variables to identify improbable or influential values. All outliers are further verified and those deemed to be outliers are imputed along with incoherent and missing values.

Errors and inconsistencies in the data are reviewed and resolved by referring to data for similar units in the survey and information from external sources. If a record can not be resolved, it is flagged for imputation.

Finally, edit rules are incorporated into the imputation system to detect and resolve any remaining errors, as well as to ensure that the imputed data are consistent.

Imputation

After error detection, two classes of units are created: total non-response cases and partial non-response cases. Total non-response units are treated through weighting, the weights of the responding units are modified to represent the non-responding units. Partial non-response units; either when respondents do not completely answer the questionnaire, or when reported data are considered incorrect during the error detection steps, are treated using imputation to fill in the missing information and modify the incorrect information. Many methods of imputation may be used to complete a questionnaire, including manual changes made by an analyst. The automated, statistical techniques used to impute the missing data include deterministic imputation, replacement using auxiliary information available from other sources, replacement based on known data relationships for the sample unit, and replacement using data from a similar unit in the sample (known as donor imputation). Usually, key variables are imputed first and are used as anchors in subsequent steps to impute other, related variables.

Imputation generates a complete and coherent microdata file that covers all survey variables.

Estimation

A complete file of weighted microdata is created for all sampled organizations in the survey population for which data were reported or imputed. Weights are adjusted to account for total non-response so that the final estimates are representative of the entire survey population. Weighted estimates are produced using the Generalized Estimation System.

Quality evaluation

Estimates are reviewed to ensure that the findings are logical and quality checks are carried out to ensure that estimates are consistent. Atypical results are flagged for investigation and are corrected as necessary. In addition, subject matter experts from outside Statistics Canada are given an opportunity to review the survey microdata and estimates, as well as provide feedback on their quality prior to their official release.

Disclosure control

Statistics Canada is prohibited by law from releasing any information it collects that 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.

In order to prevent any data disclosure, confidentiality analysis is done using the Statistics Canada Generalized Disclosure Control System (G-Confid). G-Confid is used for primary suppression (direct disclosure) as well as for secondary suppression (residual disclosure). Direct disclosure occurs when the value in a tabulation cell is composed of or dominated by few enterprises while residual disclosure occurs when confidential information can be derived indirectly by piecing together information from different sources or data series.

Statistics Canada is prohibited by law from releasing any information it collects that 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.

Revisions and seasonal adjustment

This methodology type does not apply to this statistical program.

Data accuracy

The Canada's Core Public Infrastructure Survey was separated into populations. A census was taken for some populations while a sample was taken for others. However, due to non-response, all of the populations were treated as samples for the purpose of estimation. As such, the estimates are subject to sampling error. This error can be expressed as a standard error. For example, the proportion of organizations in the target population that would respond YES to a given question is estimated to be 50%, with a standard error of 0.04. In repeated sampling, the estimate would be expected to fall between 46% and 54%, nineteen times out of twenty. The following rules based on the standard error (SE) are used to assign a measure of quality to all of the estimates of percentages.

SE
0.00 - 2.49 A - EXCELLENT
2.50 - 4.99 B - VERY GOOD
5.00 - 7.49 C - GOOD
7.50 - 9.99 D - ACCEPTABLE
10.00 - 14.99 E - USE WITH CAUTION
Greater than 15.00 F - UNRELIABLE

The following rules based on the coefficient of variation (CV) are used to assign a measure of quality to all of the estimates of counts and totals.

CV
0.00% - 4.99% A - EXCELLENT
5.00% - 9.99% B - VERY GOOD
10.00% - 14.99% C - GOOD
15.00% - 24.99% D - ACCEPTABLE
25.00 - 34.99% E - USE WITH CAUTION
Greater than 35% F - UNRELIABLE


Survey estimates may also contain non-sampling errors, which may occur for many reasons. Population coverage errors, differences in the interpretation of questions, incorrect information from respondents, and mistakes in recording, coding and processing data are examples of non-sampling errors. Non-response is an important source of non-sampling error. While the impact of non-sampling errors is difficult to evaluate, measures such as response rates and imputation rates can be used as indicators of the potential level of non-sampling error.