Tapestry

Project Info

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Project Description


Tapestry

Draw the future!

In order to visualise the future, first we must be able to understand the relationships between our data sets. Tapestry enables us to visualise exactly how changes in data will affect the entire tapestry of information, available to city planners of the future. How do trends in information affect trends in the same time frame?

Tapestry enables you to visualise these relationships as they travel into the future.


Data Story


Tapestry allows anyone to explore, search for and experiment with their own data stories, based on correlations which have not been considered in the past.
Although correlation is not causation, perhaps trends in information over time have profound, and yet undetermined effects on other datasets.


Evidence of Work

Video

High-Res Image

Team DataSets

OpenNEM Energy data

Description of Use: Correlating total demand for NSW region (which includes ACT) to make predictions

Data Set

ACT AQI (Air Quality Index) Live Data

Description of Use: Used to correlate air quality with other values for the same time period, and provide predictions

Data Set

Births, by year and month of occurrence, by state

Description of Use: To group by month and year, to find trends to match with other data sets

Data Set

Average Passenger Boardings By Service Type 2017-18

Description of Use: Used to work out the average per month of people catching the bus over a year

Data Set

Challenges

🌟 Canberra 2029 – Inclusive; Progressive; Connected

How do we use data from the past to predict a better future for Canberra? How do we best support the diversity of our community? Optimise the way we travel and transport goods throughout our city? Predict the jobs of the future – and the skills needed for them? Connect our citizens with their environment?

Go to Challenge | 21 teams have entered this challenge across all checkpoints that have passed.

Australia’s Future Employment

Choose one of the following questions to address: 1. How can recent and future changes in the labour market help prepare young people for job opportunities? 2. What can we learn from case studies of regional labour markets? For example, what does rapid change in the industries or occupations within a region tell us about the needs of employers/workers in other regional labour markets

Go to Challenge | 38 teams have entered this challenge across all checkpoints that have passed.

🌟 Pedestrian and Air Quality Sensor Data

How might we improve users’ experience of their city by using data from pedestrian and vehicle counters and/or air quality sensors?

Go to Challenge | 15 teams have entered this challenge across all checkpoints that have passed.

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