The article examines the content, essence, types and possibilities of using new pedagogical technologies in organizing independent education in geography, in particular the “Case Study” technology, in the educational process. Also, examples of independent educational tasks developed on the basis of problem situations based on the characteristics of geography in order for the teacher to present certain problems to students and direct them to search for solutions, collect additional information from students in addition to the knowledge they have acquired in class, and analyze them are presented.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

Chapter 2 of this research includes an online survey on the use of ICTs and visualization tools in real undergraduate human geography fieldwork programs. The survey was executed around the year of 2014-2015 through Survey Monkey.The invitation of this survey was sent through emails to approximately 80 human geography fieldwork leaders as well as several authors of geography-fieldwork-related published papers. In the end, there were 40 respondents and 34 of them answered that they have ever been involved in or carried out a human geography fieldwork and then they were continuously exposed to the rest of the survey. But the response rates of most subsequent questions were around 50% or less. Date: 2018-04-26 The uploaded data relates to an online survey. The original online survey link (html), the email invitation to this survey (pdf), and the responses data exported from the survey (xlsx) are included..csv and .ods conversions are provided by DANS.

Volunteered geographic information and citizen science have advanced academic and public understanding of geographical and ecological processes. Videos hosted online represent a large data source that could potentially provide meaningful results for studies in physical geography—a concept we term volunteered geographic videos (VGV). Technological advances in image-capturing devices, computing, and image processing have resulted in increasingly sophisticated methods that treat imagery as raw data, such as resolving high-resolution topography with structure from motion or the calculation of surface flow velocity in rivers with particle image velocimetry. The ubiquitous nature of recording devices and citizens who share imagery online have resulted in a vast archive of potentially useful online videos. This article analyzes the potential for using YouTube videos for research in physical geography. We discuss the combination of suitability and availability that has made this possible and emphasize the distinction between moderately suitable imagery that can directly answer research questions and lower suitability imagery that can indirectly support a study. We present example case studies that address (1) initial considerations of using VGV, (2) topographic data extraction from a video taken after a landslide, and (3) data extraction from a video of a flash flood that could support a study of extreme floods and wood transport. Finally, we discuss both the benefits and complicating factors associated with VGV. The results indicate that VGV could be used to support certain studies in physical geography and that this large repository of raw data has been underutilized.

Explore Geography-Study and teaching (Secondary)-Problems, exercises, etc through data from visualizations to datasets, all based on diverse sources.

A GeoJSON file with polygons of every _location studied. The City of Seattle Department of Transportation (SDOT) is providing data from the public life studies it has conducted since 2017. These studies consist of measuring the number of people using public space and the types of activities present on select sidewalks across the city, as well as several parks and plazas. The data set is continually updated as SDOT and other parties conduct public life studies using Gehl Institute’s Public Life Data Protocol. This dataset consists of four component spreadsheets and a GeoJSON file, which provide public life data as well as information about the study design and study locations: 1 Public Life Study: provides details on the different studies that have been conducted, including project information. https://data.seattle.gov/Transportation/Public-Life-Data-Study/7qru-sdcp 2 Public Life Location: provides details on the sites selected for each study, including various attributes to allow for comparison across sites. https://data.seattle.gov/Transportation/Public-Life-Data-Locations/fg6z-cn3y 3 Public Life People Moving: provides data on people moving through space, including total number observed, gender breakdown, group size, and age groups. https://data.seattle.gov/Transportation/Public-Life-Data-People-Moving/7rx6-5pgd 4 Public Life People Staying: provides data on people staying still in the space, including total number observed, demographic data, group size, postures, and activities. https://data.seattle.gov/Transportation/Public-Life-Data-People-Staying/5mzj-4rtf 5 Public Life Geography: A GeoJSON file with polygons of every _location studied. Please download and refer to the Public Life metadata document - in the attachment section below - for comprehensive information about all of the Public Life datasets.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

This qualitative data collection comprises eight interviews and two group conversations collected as part of the doctoral study: Museum of Contemporary Commodities (MoCC): a research performance. The project worked with partners in London and Exeter between 2015-17 to co-produce a series of site responsive, digitally interactive and relational encounters that were devised and staged between commodities, 'consumers' and everyday retail spaces. These resulting qualitative data examine how these events and encounters were realised and to what effects ie. how the people participating perceived both the event and contemporary commodity cultures and their consequences through the lens of their encounter with the project.

Accompanying documentation for this data set includes a ‘MoCC zine’ that documents the journey of the project and the process and methods involved in its co-production.

The social and environmental consequences of what has been termed the 'prolific present' are increasingly well documented. Overflowing and abundant, material and intangible commodities are arriving from factories, onto container ships, into warehouses, onto screens, into shops and through homes, into charity shops, recycling yards and waste dumps. An important part of managing and growing this flow of 'stuff' is the capturing, measuring and computational valuing of our consumption practices. How these data processes are constructed, with whose values and to whose profit, and with what impact and to whose detriment, is often obsfuscated or hidden from public scrutiny. If these processes are unknown to us, how can we make informed decisions on how we participate in them? Understanding and challenging the deeply connected and largely invisible relationships between data, trade, places and values has thus become an urgent matter of concern.

My practice-led PhD 'Museum of Contemporary Commodities (MoCC): a research performance', combined social art practice with geographic methods to investigate connections between data, commodities, and perceptions of value in contemporary capitalism. The primary aim of this ESRC funded fellowship was to consolidate the knowledge and insights gained in this doctoral study and share them with academic audiences. This included using MoCC as a case study to develop a series of conceptual and methodological accounts of the research design, delivery and impacts in order to contribute to advancing knowledge in human geography, and to work with other scholars, artists and technologists to create a series of more broadly accessible outcomes from research findings.

Literature review dataset

This table lists the surveyed papers concerning the application of spatial analysis, GIS (Geographic Information Systems) as well as general geographic approaches and geostatistics, to the assessment of CoViD-19 dynamics. The period of survey is from January 1st, 2020 to December 15th, 2020. The first column lists the reference. The second lists the date of publication (preferably, the date of online publication). The third column lists the Country or the Countries and/or the subnational entities investigated. The fourth column lists the epidemiological data utilized in each paper. The fifth column lists other types of data utilized for the analysis. The sixth column lists the more traditionally statistically-based methods, if utilized. The seventh column lists the geo-statistical, GIS or geographic methods, if utilized. The eight column sums up the findings of each paper. The papers are also classified within seven thematic categories. The full references are available at the end of the table in alphabetical order.

This table was the basis for the realization of a comprehensive geographic literature review. It aims to be a useful tool to ease the "due-diligence" activity of all the researchers interested in the spatial analysis of the pandemic.

The reference to cite the related paper is the following:

Pranzo, A.M.R., Dai Prà, E. & Besana, A. Epidemiological geography at work: An exploratory review about the overall findings of spatial analysis applied to the study of CoViD-19 propagation along the first pandemic year. GeoJournal (2022). https://doi.org/10.1007/s10708-022-10601-y

To read the manuscript please follow this link: https://doi.org/10.1007/s10708-022-10601-y

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed.This project used data from multiple sources-the area-identified National Crime Victimization Survey (NCVS, 2008-2012), and data from other public data sources such as the American Community Survey (ACS) and the decennial Census data-to study how the changing geography of American immigration has influenced violent victimization among different racial and ethnic groups, particularly Blacks, Hispanics, and Whites.This collection includes three Stata data files:"Data_File1_county_foreignborn_1980_2010.dta" with 6 variables and 3,103 cases"Data_File2_county_variables_2007_2012.dta" with 19 variables and 18,618 cases"Data_File3_tract_variables_2007_2012.dta" with 16 variables and 440,083 cases.The area-identified NCVS data are only accessible through the Census Research Data Centers and could not be archived.

Students will explore the spatial patterns of the Human Development Index (HDI) to identifyregional patterns and causal factors in the data. The activity uses a web-based map and is tied to the AP Human Geography benchmarks. Learning outcomes: Students will be able to analyze development statistics and see how development correlates with other APHG topics (for example, fertility and mortality).Find more advanced human geography geoinquiries and explore all geoinquiries at http://www.esri.com/geoinquiries

This activity will no longer be maintained after June 16, 2025. Current lessons are available in the K-12 Classroom Activities Gallery.

This activity uses Map Viewer and is designed for intermediate users. We recommend MapMaker when getting started with maps in the classroom - see this StoryMap for the same activity in MapMaker.ResourcesMapTeacher guideStudent worksheetVocabulary and puzzlesSelf-check questionsGet startedOpen the map.Use the teacher guide to explore the map with your class or have students work through it on their own with the worksheet.New to GeoInquiriesTM? See Getting to Know GeoInquiries.AP skills & objectives (CED)Skill 2.E: Explain the degree to which a geographic concept, process, model or theory effectively explains geographic effects in different contexts and regions of the world.SPS-7.C: Describe social and economic measures of development.SPS-7.D: Explain how and to what extent changes in economic development have contributed to gender parity.Learning outcomesStudents will analyze and use development statistics to identify and explain correlations between development and other APHG topics (for example, fertility and mortality).More activitiesAll Human Geography GeoInquiriesAll GeoInquiries

Spatial synchrony, defined as correlated temporal fluctuations among populations, is a fundamental feature of population dynamics, but many aspects of synchrony remain poorly understood. Few studies have examined detailed geographical patterns of synchrony; instead most focus on how synchrony declines with increasing linear distance between locations, making the simplifying assumption that distance decay is isotropic. By synthesising and extending prior work, we show how geography of synchrony, a term which we use to refer to detailed spatial variation in patterns of synchrony, can be leveraged to understand ecological processes including identification of drivers of synchrony, a long-standing challenge. We focus on three main objectives: (1) showing conceptually and theoretically four mechanisms that can generate geographies of synchrony; (2) documenting complex and pronounced geographies of synchrony in two important study systems; and (3) demonstrating a variety of methods capable of revealing the geography of synchrony and, through it, underlying organism ecology. For example, we introduce a new type of network, the synchrony network, the structure of which provides ecological insight. By documenting the importance of geographies of synchrony, advancing conceptual frameworks, and demonstrating powerful methods, we aim to help elevate the geography of synchrony into a mainstream area of study and application.

Explore Geography-Study and teaching(Secondary) through data • Key facts: number of authors, number of books, books, authors, publication dates, book publishers • Real-time news, visualizations and datasets

This repository contains the geodemographic classifications obtained through different setups of our graph convolutional neural network framework for spatial geodemographic classification (forthcoming), along with three baseline models created using spatial fuzzy c-means and the London Output Area Classification by Singleton and Longley (2015).

Contains data from CDRC LOAC Geodata Pack by the ESRC Consumer Data Research Centre and data derived from data available from Chris Gale's repository. Contains National Statistics data Crown copyright and database right 2015.

References

Singleton A D, Longley P A (2015) The Internal Structure of Greater London: A Comparison of National and Regional Geodemographic Models. Geo: Geography and Environment. Available from: dx.doi.org/10.1002/geo2.7

This repository contains the data used for the book chapter by De Sabbata et al (2025), including the Free Gazetteer Data made available by GeoNames under CC BY 4.0, a file containing the names of the Italian provinces was made available by Michele Tizzoni under CC BY 4.0, and data derived from them using Mistral-7B-Instruct-v0.2, which was made available by the Mistral AI Team under Apache License 2.0. The code used to process the data is available via our related GitHub repository under MIT Licence.The Author Accepted Manuscript of "Geospatial Mechanistic Interpretability of Large Language Models" available on arXiv (arXiv:2505.03368).De Sabbata, S., Mizzaro, S. and Roitero, K. (2025) “Geospatial mechanistic interpretability of large language models,” in Janowicz, K. et al. (eds.) Geography according to ChatGPT. IOS Press (Frontiers in artificial intelligence and applications).Abstract: Large language models (LLMs) have demonstrated unprecedented capabilities across various natural language processing tasks. Their ability to process and generate viable text and code has made them ubiquitous in many fields, while their deployment as knowledge bases and ``reasoning'' tools remains an area of ongoing research. In geography, a growing body of literature has been focusing on evaluating LLMs' geographical knowledge and their ability to perform spatial reasoning. However, very little is still known about the internal functioning of these models, especially about how they process geographical information.In this chapter, we establish a novel framework for the study of geospatial mechanistic interpretability -- using spatial analysis to reverse engineer how LLMs handle geographical information. Our aim is to advance our understanding of the internal representations that these complex models generate while processing geographical information -- what one might call "how LLMs think about geographic information" if such phrasing was not an undue anthropomorphism.We first outline the use of probing in revealing internal structures within LLMs. We then introduce the field of mechanistic interpretability, discussing the superposition hypothesis and the role of sparse autoencoders in disentangling polysemantic internal representations of LLMs into more interpretable, monosemantic features.In our experiments, we use spatial autocorrelation to show how features obtained for placenames display spatial patterns related to their geographic location and can thus be interpreted geospatially, providing insights into how these models process geographical information. We conclude by discussing how our framework can help shape the study and use of foundation models in geography.

20 May 2025

Some counts in the ‘Train’ category were incorrectly suppressed when actual figures should have been available. We have republished the 2018 data based on the 2023 Census meshblock pattern. 2023 Census main means of travel to work by statistical area 2 provides updated data.

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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) 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 census usually resident population count who are studying (part time or full time) 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.

Compares percent distribution of STEM (science, technology, engineering and math and computer science) and BHASE (non-STEM) fields of study between census divisions.

Apostasy is defined as the abandonment or renunciation of a religious or political belief. This article describes the apostasy of a professor of geography with respect to their initial hostility toward the utility of learning outcomes assessments. This apostasy motivated the development of assessment instruments that could provide evidence that graduating geography and environmental science majors possessed more skills and knowledge and confidence in their skills and knowledge than they did as incoming first-year students. The instruments we developed for learning outcomes assessment are described and presented. Qualitative and statistical analyses of several years of data demonstrate statistically significant improvements in the objective quizzes and self-assessments of the graduating students. The results provided a satisfying body of evidence suggesting that the teaching and learning taking place in our department are effective while also identifying some issues we need to address. These data provide a mechanism for the faculty to reflect on our curriculum and teaching practices to identify ways to improve them. These instruments are used on an on-going basis to inform departmental program reviews, to field inquiries from accreditation teams, and to promote the department within the university.

Dataset shows an individual’s statistical area 2 (SA2) of usual residence and the SA2 of their place of study, for the census usually resident population count who are studying (part time or full time), by main means of travel to education from the 2018 and 2023 Censuses.

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The main means of travel to education categories are:

• Study at home

• Drive a car, truck, or van

• Passenger in a car, truck, or van

• Bicycle

• Walk or jog

• School bus

• Public bus

• Train

• Ferry

• Other.

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Main means of travel to education is the usual method a person used to travel the longest distance to their place of study.

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Educational institution address is the physical location of the individual’s place of study. Educational institutions include early childhood education, primary school, secondary school, and tertiary education institutions. For individuals who study at home, their educational institution address is the same as their usual residence address.

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Educational institution address is coded to the most detailed geography possible from the available information. This dataset only includes travel to education information for individuals whose educational institution address is available at SA2 level. The sum of the counts for each region in this dataset may not equal the census usually resident population count who are studying (part time or full time) for that region. Educational institution address – 2023 Census: Information by concept has more information.

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This dataset can be used in conjunction with the following spatial files by joining on the SA2 code values:

• Statistical area 2 2023 (generalised)
• Statistical area 2 2023 (Centroid Inside)

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Download data table using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data).

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Educational institution address time series

Educational institution address time series data should be interpreted with care at lower geographic levels, such as statistical area 2 (SA2). Methodological improvements in 2023 Census resulted in greater data accuracy, including a greater proportion of people being counted at lower geographic areas compared to the 2018 Census. Educational institution address – 2023 Census: Information by concept has more information.

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Rows excluded from dataset

Rows show SA2 of usual residence by SA2 of educational institution address. Rows with a total population count of less than six have been removed to reduce the size of the dataset, given only a small proportion of SA2-SA2 combinations have commuter flows.

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About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Main means of travel to education quality rating

Main means of travel to education is rated as moderate quality.

Main means of travel to education – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Educational institution address quality rating

Educational institution address is rated as moderate quality.

Educational institution address – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Percentages

To calculate percentages, divide the figure for the category of interest by the figure for ‘Total stated’ where this applies.

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Symbol

-999 Confidential

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.