A presentation at the 2016 New Zealand Association of Gerontology annual conference.

1. A Dynamic Spatial Dashboard For Ageing
and Dementia in New Zealand: Active
Data For a Changing World
Authors
Hamish ROBERTSON, Nick NICHOLAS,Joanne TRAVAGLIA

2. Contents
• Introduction
• Why spatial dashboarding?
• Information visualisation and change
• New Zealand demography and dementias
• Geography matters
• Connecting the dots
• Building a dashboard
• Conclusion

3. Introduction
• Population ageing is the major demographic event of the 21st century so far
• Population ageing is dynamic – we cannot expects patterns to be static
over time e.g. compression, cohort changes, compositional changes in
cohorts for dementias, ethnicity etc.
• Rates vary by social and biological factors, as do individual trajectories –
not a single condition, disease, event or experience
• Communities are not uniformly resourced to address the dementias and
their consequences – location matters, distribution matters, preferences
matter
• Space, time, scale and responses (policy, science, knowledge base,
communities, individuals) are important factors in outcomes

4. Why spatial dashboarding?
• Dashboards are common in clinical and administrative information
environments – concept already exists and is growing in popularity
• Information visualisation and visual literacy are growing programmes in
industry – ITC and elsewhere
• Spatial component is integral to modern digital technologies but
conventional systems often low on spatial literacy and cognition
• Combining existing, accepted strategies with new, emergent ones has value
in promoting acceptance and application
• ‘Big data’ paradigm is growing fast the rise
• Potential to provide a common platform for discussion, inquiry, analysis
and action – democratise data and engage stakeholders

5. Statistics New Zealand Projections
Population projections middle scenario
0
1000000
2000000
3000000
4000000
5000000
6000000
7000000
2013 2018 2023 2028 2033 2038 2043
High Medium Low

6. Statistics New Zealand Latest Projections
Population projections aged 65+ years
0
200,000
400,000
600,000
800,000
1,000,000
1,200,000
1,400,000
1,600,000
2013 2018 2023 2028 2033 2038 2043

7. Projected Population Aged 65+ in 2043
0
50,000
100,000
150,000
200,000
250,000
300,000
350,000
400,000
450,000
Auckland
Christchurchcity
Taurangacity
Wellingtoncity
Hamiltoncity
Dunedincity
Whangareidistrict
NewPlymouthdistrict
Waimakariridistrict
LowerHuttcity
Hastingsdistrict
Waikatodistrict
KapitiCoastdistrict
Selwyndistrict
Tasmandistrict
PalmerstonNorthcity
Napiercity
FarNorthdistrict
Nelsoncity
Rotoruadistrict
Waipadistrict
WesternBayofPlentydistrict
Marlboroughdistrict
Timarudistrict
Invercargillcity
Poriruacity
UpperHuttcity
Wanganuidistrict
Queenstown-Lakesdistrict
Gisbornedistrict
Thames-Coromandeldistrict
Horowhenuadistrict
Taupodistrict
Matamata-Piakodistrict
Ashburtondistrict
Whakatanedistrict
Manawatudistrict
Kaiparadistrict
CentralOtagodistrict
Mastertondistrict
Southlanddistrict
Haurakidistrict
Waitakidistrict
SouthTaranakidistrict
SouthWaikatodistrict
Tararuadistrict
Cluthadistrict
Hurunuidistrict
CentralHawke'sBaydistrict
Greydistrict
Rangitikeidistrict
Goredistrict
Cartertondistrict
SouthWairarapadistrict
Bullerdistrict
Waimatedistrict
Westlanddistrict
Ruapehudistrict
Otorohangadistrict
Stratforddistrict
Opotikidistrict
Waitomodistrict
Wairoadistrict
Mackenziedistrict
Kaikouradistrict
Kaweraudistrict
ChathamIslandsterritory

8. Estimated Prevalence Rates
Source: Deloitte Access Economics/Alzheimers New Zealand 2012

9. Estimated Prevalence by Age and Sex 2011-2050
Source: Deloitte Access Economics/Alzheimers New Zealand 2012

10. Dementia Estimates 2013-2038
0
20000
40000
60000
80000
100000
120000
140000
160000
2013 2018 2023 2028 2033 2038

11. Histogram of 2038 Estimates by TA and Local Boards

12. Heat Map of 2038 Estimates

13. Dementia
Estimates
New Zealand
2038

14. Auckland Boards and
Surrounds
Dementia Estimates
2038

15. Spatial Dashboard
0
20000
40000
60000
80000
100000
120000
140000
160000
2013 2018 2023 2028 2033 2038

16. Conclusion
• Visual methods are increasingly common in health and social policy
domains – e.g. qualitative software and Google Earth
• Accessible to multiple audiences of varying levels of numeracy, skill
and familiarity
• Integrating more visual methods, such as maps and spatial analytics
• Permit variability in modelling and discussion – multiple scenarios in
the one framework
• Changes in one data set can be accessed in all visuals utilised
• Potential for democratising data access, modelling and proposed
responses
• Make complex data visualisation a practical instrument for living in an
increasingly quantified world