2018 Census Main means of travel to education by Statistical Area 2
The 2018 Census commuter view dataset contains the census usually resident population count who are studying, (part time or full time) by statistical area 2 for the main means of travel to education variable from the 2018 Census. The geography corresponds to 2018 boundaries. This dataset is the base data for the ‘There and back again: our daily commute’ competition. This 2018 Census commuter view dataset is displayed by statistical area 2 geography and contains from-to (journey) information on an individual’s usual residence and educational institution address* by main means of travel to education. * Educational institution address is coded from information supplied by respondents about where they study. Where respondents do not supply sufficient information, their responses are coded to ‘not further defined’. The 2018 Census commuter view datasets excludes these ‘not further defined’ areas, as such the sum of the counts for each region in this dataset may not be equal to the total employed census usually resident population count aged 15 years and over for that region. It is recommended that this dataset be downloaded as either a CSV or a file geodatabase. This dataset can be used in conjunction with the following spatial files by joining on the statistical area 2 code values: · Statistical Area 2 2018 (generalised) · Statistical Area 2 2018 (Centroid Inside) The data uses fixed random rounding to protect confidentiality. Counts of less than 6 are suppressed according to 2018 confidentiality rules. Values of -999 indicate suppressed data.. Data quality ratings for 2018 Census variables, summarising the quality rating and priority levels for 2018 Census variables, are available. For information on the statistical area 2 geography please refer to the Statistical standard for geographic areas 2018.
StatsNZ combined data from the census forms with administrative data to create the 2018 Census dataset, which meets Stats NZ’s quality criteria for population structure information.
StatsNZ added real data about real people to the dataset where we were confident the people should be counted but hadn’t completed a census form. We also used data from the 2013 Census and administrative sources and statistical imputation methods to fill in some missing characteristics of people and dwellings.
An independent panel of experts has assessed the quality of the 2018 Census dataset. The panel has endorsed Stats NZ’s overall methods and concluded that the use of government administrative records has improved the coverage of key variables such as age, sex, ethnicity, and place. The panel’s Initial report of the 2018 Census External Data Quality Panel (September 2019) assessed the methodologies used by Stats NZ to produce the final dataset, as well as the quality of some of the key variables. https://www.stats.govt.nz/reports/initial-report-of-the-2018-census-external-data-quality-panel
Its second report, 2018 Census External Data Quality Panel: Assessment of variables (December 2019) assessed an additional 31 variables. https://www.stats.govt.nz/reports/2018-census-external-data-quality-panel-assessment-of-variables.
In its third report, Final report of the 2018 Census External Data Quality Panel (February 2020), the panel made 24 recommendations, several relating to preparations for the 2023 Census. https://www.stats.govt.nz/reports/final-report-of-the-2018-census-external-data-quality-panel
Along with this report, the panel, supported by Stats NZ, produced a series of graphs summarising the sources of data for key 2018 Census individual variables, 2018 Census External Data Quality Panel: Data sources for key 2018 Census individual variables. https://www.stats.govt.nz/reports/2018-census-external-data-quality-panel-data-sources-for-key-2018-census-individual-variables
2018 Census data user guide provides information about collecting, processing, interpreting, and ways of using 2018 Census data. https://www.stats.govt.nz/methods/2018-census-data-user-guide
Statistical Area 2s are based on the meshblock pattern. Non-alignment of meshblock and cadastral boundaries are one of a number of reasons for meshblock boundary adjustments. Other reasons include requests from local authorities, Local Government Commission, Electoral Representation Commission and to make census enumeration processes easier.
From the meshblock pattern, higher geographies, including the 2018 Statistical Area 2 pattern, were dissolved using the dissolve tool in the Arc GIS suite.
Creative Commons Attribution 4.0 International (CC BY 4.0)