Discussion and visualisation of data should make an issue more understandable
Housing is a fraught topic in New Zealand, as it is in many places. Too many people lack access to a decent place to live at a price they can afford. On the other hand, people who have lived in neighbourhoods they love for decades may be reluctant to see them change through intensification. That is a hard problem, and one that’s helpful to consider in light of good available data.
An article in the most recent edition of The Sunday Star-Times missed an opportunity to do that. The article describes results of a survey conducted by Freshwater Strategy on behalf of The Sunday Star-Times. The survey measured attitudes of eligible voters toward new housing and increased housing density. Ideally the survey results would have helped to inform the debate around these issues, but the results were communicated poorly, reducing their ability to make a constructive contribution to the debate.
Reproduced from page 6 of the Sunday Star-Times from 22 Feb, 2026 for purposes of education, commentary, and criticism.
Show the things that are most important for the audience to know
One data communication choice in the article that limits its likely use and usefulness relates to which results were featured in visualisations and which were not. The headline of the article says that ‘voters back extra houses, but not in their backyard’ and the text of the article describes density in people’s own local area as ‘often a lightning rod in the debate over growth’. Given those things, it’s surprising to see that even though the text of the article discusses attitudes toward increased density in survey respondents’ own local areas, those results don’t feature in either of the two charts shown in the article. The two charts that are shown illustrate fairly similar responses to fairly similar questions (about increased housing intensification around transport infrastructure and in existing urban areas, which in practice are often likely to be the same places).
Lesson: When using a combination of text and visualisations to communicate insights from data, the data visualisations should be used to illustrate the most important points.
Show (and tell) your audience about your results at a level of granularity required to inform their decision making
A second problem with the way the data from the survey is communicated in the article is that the charts that are shown present more granular results than the text descriptions that accompany them. That makes it difficult to understand the results in detail – particularly for the crucial question for which there is no visualisation.
That is problematic because for emotive issues such as housing there is a big difference between being strongly supportive versus slightly supportive or being strongly opposed versus slightly opposed. Those who are strongly supportive or opposed are much more likely to take actions such as contacting their elected representatives, signing petitions, participating in consultation processes, and sharing their views formally or informally. Their votes are also more likely to be influenced by the issue.
Showing more granular results with all six possible responses to each question would have made it easier to see the differences in attitudes when increased density is discussed in the abstract versus in a way that could have an immediate effect on the people expressing the attitudes. Obviously data communication almost always involves choices about what to include and what to exclude or present in an aggregated manner, but in this case a relatively small change in the type of chart used would have enabled much more information to have been communicated to help readers understand the issue being discussed.
Both of the charts shown are bar charts, with each bar representing the proportion of survey respondents who gave each response to the question shown at the bottom of the chart. Because they are mutually exclusive proportions (each person can give only one answer to each question, and all respondents are represented if only to say they are neutral or unsure) then these results could have been shown using a stacked bar chart. That is where there is a bar sliced into segments – in this case based on which response people selected for each question. One stacked bar chart with three bars could have been used to compare overall results for the three questions discussed in the article.
The text of the article also discusses variation in responses to all three questions based on location, age, voting preferences, and home ownership. There are no charts for any of those things, which makes it somewhat difficult to follow the text-based discussion about how results vary by group. Additional stacked bar charts would have helped to show key differences based on location, age, voting preferences, and home ownership.
Lesson: When possible – and especially when details are important to truly understanding an issue – try to preserve granularity when communicating data and only aggregate when doing so helps the viewer understand the data more clearly.
Define your metrics
Given the previously described issues with what was shown in visualisations, the text of the article had to carry most of the burden of communicating the survey results; however many readers would probably struggle to fully understand the text-based descriptions. To see why, let’s take a look at an excerpt from the text of the article.
“… while 49% of voters support more medium and high-density housing in existing urban centres (with 27% of voters opposed and 22% neutral), Auckland residents aren't so impressed.
Some 39% of those polled were opposed to the proposition, and 36% in favour. Only 3% are unsure, while 22% are neutral. That compares with a net result of +28% in Wellington and a +42% result across the rest of the North Island.”
The last sentence in the excerpt shown discusses ‘net results’ of +28% in Wellington and +42% across the rest of the North Island. The subsequent discussion also uses the ‘net’ terminology. People who regularly review survey data are likely to know that ‘net results’ in this context mean the total percentage in support (slightly + strongly) minus the total percentage in opposition (slightly + strongly); however the article does not say that anywhere and it seems unlikely that most readers of the Sunday Star-Times are familiar with that convention. Calculated metrics like that should be explained when viewers may be unfamiliar with them.
Lesson: If you are using a calculated metric, you should explain how it’s calculated if more than a very small percentage of the target audience are unlikely to know that.
Between the most salient question not being shown in a chart or described in full, group differences not being shown in charts or described in full, and many people not understanding the concept of net results in this context I suspect this article may leave many readers no better informed than they were before they read it. That is a lost opportunity for data to play an important role in helping people understand and make decisions about this important issue for our society. A detailed understanding of how attitudes vary by group and by form of intensification is likely to be necessary for finding housing solutions with enough community support to be implemented, and for identifying the specific challenges that must be overcome to make that happen.