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iNZight is a free and accessible statistical visualisation software. Founded by Chris Wild of the University of Auckland, development of iNZight is now lead by Tom Elliott of iNZight Analytics.
The software was initially designed for New Zealand high schools, allowing students to quickly and easily explore data and understand basic statistical ideas using the companion program VIT. However, iNZight now extends to multivariable graphics, time series, and generalised linear modelling, including modelling of data from complex surveys.
iNZight is free. That means you can download and use it however you want, for whatever you want. There are absolutely no restrictions. You can download for yourself or redistribute it. You can even modify it if you are so inclined! However, it is important to note that iNZight comes with absolutely no warranty.
Dr. Tom Elliott is the recipient of a Royal Society Te Apārangi Ngā Puanga Pūtaiao Fellowship, facilitating his work to create open-source data science tools and a state-of-the-art data analytics platform, designed with full te reo Māori capability for use in Māori data environments.
Funded by: Royal Society of New Zealand Te Apārangi
From the Royal Society Te Apārangi: Data has become a crucial resource in modern society, so democratising access to data and the information it contains is a critical role of data science. This involves building and updating data tools and platforms that make it easier for non-technical users to learn about their world.
In Aotearoa, the analytics software ‘iNZight’ has become a core component of not only our statistical education curriculum, but more recently research organisations and government agencies have recognised its value in their own data environments. However, ‘iNZight’ is now facing technical barriers to meeting the increased demands of its users, which presents an opportunity to completely redesign the system for our new and expanding audience. During this redesign, we have the chance to incorporate Māori data sovereignty principles into the core of our framework, ensuring that the platform is suited to diverse Māori data environments.
This project will develop open-source tools for building flexible, data-driven web applications and use them to create a next-generation data analytics platform that rebuilds and extends the existing iNZight software. In partnership with Tūhono Trust, these these tools will be trialled in Māori data environments with te reo Māori capability. It will also provide secure funding to support Dr Elliott’s development as an emerging Māori data science research leader of international significance.
Te Rourou Tātaritanga: Informatics for Social Services and Wellbeing aims to address New Zealand's critical need for better linking of data and access to datasets, leveraging administrative and other data resources to advance excellence in the use of social data.
Andrew Sporle is a co-leader on the project, alongside researchers from the University of Auckland and Victoria University of Wellington.
Funded by: Ministry of Business, Innovation and Employment Endeavour Fund
Grant number: 62506 ENDRP
Te Rourou Tātaritanga aims to improve data standards, support secure and ethical access to data, and promote Māori data sovereignty, while addressing privacy, legal, and security considerations surrounding the use of social data. By strengthening how social data is linked, accessed, and analysed, the programme will support more informed decision-making in the development of social policy.
Calculating and comparing health or social statistics for populations can be tricky, time consuming and usually requires advanced statistical skills.Mātau is an easy to learn tool that enables rapid calculation and comparison of robust population statistics without additional software or specialist statistical skills. No statistical calculation or coding are required to produce tables, graphs and even maps.
Funded by: iNZight Analytics
It can be used for any outcome statistics in any geographical area or time period using either aggregate data (counts) or unit record file data. Comparisons can be made between population outcomes over time, between regions or by ethnicity, age or sex. Pull down menus and interactive graphics are used to producing statistically robust calculations and comparisons including confidence intervals. Multiple options for analysis can be selected from the menu if required but the default setting on menus is the standard approach allowing Mātau produces results in the form of tables, graphs and maps with all output exportable in multiple formats so they can be easily included in offline reports.
An early version of Mātau was used for estimation of regional ethnic- and age-specific COVID-19 outcomes near the beginning of the pandemic in early 2020, and presented to the New Zealand Government for policymaking. The Mātau app is currently being redeveloped using updated Ministry of Health data.
