Annual Survey of Service Industries: Surveying and Mapping (SMAP)

Detailed information for 2023

Status:

Active

Frequency:

Annual

Record number:

4715

This survey collects the financial and operating data needed to develop national and regional economic policies and programs.

Data release - October 18, 2024

Description

This survey uses administrative data to produce economic statistics for the Surveying and Mapping Services industry in Canada.

Administrative data from businesses are aggregated with information from other sources to produce official estimates of national and provincial economic production for this industry.

Survey estimates are made available to businesses, governments, investors, associations, and the public. The data are used to monitor industry growth, measure performance, and make comparisons to other data sources to better understand this industry.

Statistical activity

The survey is administered as part of the Integrated Business Statistics Program (IBSP). The IBSP has been designed to integrate approximately 200 separate business surveys into a single master survey program. The IBSP aims at collecting industry and product detail at the provincial level while minimizing overlap between different survey questionnaires. The redesigned business survey questionnaires have a consistent look, structure, and content.

The integrated approach makes reporting easier for firms operating in different industries because they can provide similar information for each branch operation. This way they avoid having to respond to questionnaires that differ for each industry in terms of format, wording and even concepts. The combined results produce more coherent and accurate statistics on the economy.

This survey is part of the Service Industries Program. The survey data gathered are used to compile aggregate statistics for over thirty service industry groupings. Financial data, including revenue, expense and profit statistics are available for all of the surveys in the program. In addition, many compile and disseminate industry-specific information.

Reference period: The calendar year, or the 12-month fiscal period for which the final day occurs on or between April 1st of the reference year and March 31st of the following year.

Subjects

  • Business, consumer and property services
  • Business performance and ownership
  • Financial statements and performance
  • Professional, scientific and technical services

Data sources and methodology

Target population

The target population consists of all establishments classified to the Surveying and Mapping Services industry according to the following North American Industry Classification System (NAICS) 2022 codes during the reference year: 541360 - Geophysical surveying and mapping services; 541370 - Surveying and mapping (except geophysical) services. This industry comprises establishments primarily engaged in gathering, interpreting and mapping geophysical data. These establishments often specialize in locating and measuring the extent of subsurface resources such as oil, gas and minerals. These establishments are also engaged in providing surveying and mapping services of the surface of the earth, including sea floor.

The observed population consists of all establishments classified to the Surveying and Mapping Services industry according to the NAICS 2022 codes 541360 - Geophysical surveying and mapping services, and 541370 - Surveying and mapping (except geophysical) services, found on the Statistics Canada Business Register as of the last day of the reference year (including establishments active for a part of the reference year).

Instrument design

This methodology type does not apply to this statistical program.

Sampling

The Business Register is a repository of information reflecting the Canadian business population and exists primarily for the purpose of supplying frames for all economic surveys in Statistics Canada. It is designed to provide a means of coordinating the coverage of business surveys and of achieving consistent classification of statistical reporting units. It also serves as a data source for the compilation of business demographic information.

The major sources of information for the Business Register are updates from the Statistics Canada survey program and from Canada Revenue Agency's (CRA) Business Number account files. This CRA administrative data source allows for the creation of a universe of all business entities.

This survey is a pseudo-census, and all financial variable values are primarily sourced from tax data (specifically, the T1 and T2 files from the CRA). For all other survey variables, imputation techniques are used.

SAMPLING AND SUB-SAMPLING
In a census, all sampling units are selected and are self-represented in the subsequent processing steps.

Data sources

Data are extracted from administrative files.

Administrative data
A strategy to replace survey data with tax data has been introduced to reduce the response burden and survey costs. The strategy involves using tax data instead of survey data for all of the units.

As part of the Integrated Business Statistics Program (IBSP), T1 tax data are used for unincorporated businesses and T2 tax data for incorporated businesses. Data replacement may be used to correct outliers or to replace partially or completely missing data.

Data integration combines data from multiple data sources, including administrative data from the CRA and other forms of auxiliary data, when applicable. During the data integration process, data are imported, transformed, validated, aggregated and linked from the different data source providers into the formats, structures and levels required for IBSP processing. Administrative data are used with a data replacement strategy for financial variables for all small, medium and large enterprises to avoid collecting these variables.

Error detection

Error detection is an integral part of data processing. Automated edits are applied to data records to identify reporting and capture errors. These edits identify potential errors based on year-over-year changes in key variables, totals and ratios that exceed tolerance thresholds, and they identify problems in the collected data (e.g., a total variable does not equal the sum of its parts). During data processing, other edits are used to automatically detect errors or inconsistencies that remain in the data. These edits include value edits (e.g., Value > 0, Value > -500, Value = 0), linear equality edits (e.g., Value1 + Value2 = Total Value), linear inequality edits (e.g., Value1 >= Value2), and equivalency edits (e.g., Value1 = Value2). When errors are found, they are corrected via imputation. Extreme values are also flagged as outliers, using automated methods based on the distribution of the information. Following their detection, these values are reviewed in order to assess their reliability. Manual review of other units may lead to additional outliers identified. These outliers are excluded from use in the calculation of ratios and trends used for imputation, and during donor imputation. In general, every effort is made to minimize the errors of duplication, misclassification and processing.

Imputation

When there are non-reported tax data, or when reported data are considered incorrect during the error detection steps, imputation is used to fill in the missing information and modify the incorrect information. Many methods of imputation may be used to complete the administrative data, including manual changes made by an analyst. The automated, statistical techniques used to impute the missing data include deterministic imputation, replacement using historical data (with a trend calculated, when appropriate), 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

In a pseudo-census approach, all units in the surveyed population are included in survey estimates. Administrative files and values coming from imputation steps are used for all units in the population. For all financial data, this strategy involves using tax data instead of survey data. T1 tax data are used for unincorporated businesses and T2 tax data for incorporated businesses. The remaining survey variables are imputed using automated statistical techniques (discussed in the imputation section) that result in a complete and coherent microdata file.

Estimation of totals is then done by simple aggregation of the values of all estimation units that are found in the domain of estimation. Estimates are computed for several domains of estimation such as industrial groups and provinces/territories, based on the most recent classification information available for the estimation unit and the survey reference period. Estimation of totals is then done by simple aggregation of the values of all estimation units that are found in the domain of estimation. Changes in classification are reflected immediately in the estimates.

When some enterprises have reported data combining many units located in more than one province or territory, or in more than one industrial classification, data allocation is required. Factors based on information from sources such as tax files and Business Register profiles are used to allocate the data reported on the combined report among the various estimation units where this enterprise is in operation. The characteristics of the estimation units are used to derive the domains of estimation, including the industrial classification and the geography.

Quality evaluation

Before the data release, combined survey results are analyzed for comparability; in general, this includes a detailed review of general economic conditions and coherence with results from related economic indicators, historical trends and information from other external sources (e.g., associations, trade publications or newspaper articles).

Disclosure control

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

Revisions and seasonal adjustment

There is no seasonal adjustment. Data from previous years may be revised based on updated information.

Data accuracy

The methodology of this survey has been designed to control errors and to reduce their potential effects on estimates. However, the survey results remain subject to errors. Errors can arise from the various phases of a survey. For example, these types of errors can occur when incorrect information is reported or missing in the tax files; when a unit in the target population is omitted or covered more than once; when GST data for records being modeled for a particular month are not representative of the actual record for various reasons; when a unit that is out of scope for the survey is included by mistake or when errors occur in data processing, such as coding or capture errors.

Prior to publication, combined survey results are analyzed for comparability; in general, this includes a detailed review of individual responses (especially for large businesses), general economic conditions and historical trends.

NON-SAMPLING ERROR
Non-sampling error is not related to sampling and may occur for various reasons during the collection and processing of data. For example, non-response is an important source of non-sampling error. Under coverage or overcoverage of the population, differences in the interpretations of questions and mistakes in recording, coding and processing data are other examples of non-sampling errors.

COVERAGE ERROR
Coverage errors consist of omissions, erroneous inclusions, duplications and misclassification of units in the survey frame.

The Business Register (BR) is the common frame for all surveys using the Integrated Business Statistics Program model. The BR is a data service centre updated through a number of sources including administrative data files, feedback received from conducting Statistics Canada business surveys, and profiling activities including direct contact with companies to obtain information about their operations and Internet research findings. Using the BR will ensure quality, while avoiding overlap between surveys.

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