Placeholder

Project Info

Team Members


Geoff
Team Placeholder thumbnail

Project Description


Placeholder combines multiple data sets, NSW spatial APIs, simple natural language processing and both web-based and physical visualization to help the ATO make better make decisions about where services to its more vulnerable clients should be focused in the future.

For our physical visualization, see below:
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Data Story


Placeholder helps the ATO target their tax help program by combining the following:
1. income tax return data from the GovhackATO dataset to target areas with the most eligible clients i.e. low income and net capital gain areas;
2. ABS demographic data from the GovhackATO dataset and geo-locations of retirement villages and technical schools from NSW spatial APIs to target vulnerable students and seniors;
3. personal insolvency data on tax related insolvencies from the AFSA non-compliance in personal insolvency dataset to identify key under-served areas; and
4. flood zoning data from SEED Environmental Planning Instrument - Flood API to ensure that ATO's most vulnerable clients keep their heads above water - literally.
There is clearly some existing strategies in place for centre placement, for example data showed moderate correlation between placement of centers and the numbers of retirement villages in the area. However, there was no correlation between areas with high levels of tax related personal insolvencies and the placement of the centres. This is likely due to the ATO not have access to such data and demonstrates the utility of data sharing between government departments.
See below:
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Evidence of Work

Video

Homepage

High-Res Image

Team DataSets

GovHackATO

Description of Use: Key income tax return data from the ATO in this dataset was used to target postal areas mostly likely to have eligible low income earners (i.e. pursuant to ATO policy under $60,000) and ABS demographic data from the this dataset was used to target vulnerable groups such as students and seniors.

Data Set

Enviromental Planning Instrument - Flood

Description of Use: Flood zoning data was extracted from the SEED Environmental Planning Instrument - Flood API to ensure that ATO's most vulnerable clients keep their heads above water - quite literally.

Data Set

Spatial Services - Administrative Spatial Programs - Technical College

Description of Use: ABS demographic data from the GovhackATO dataset and geo-locations of technical schools, that is Post secondary (TAFEs) educaton excluding University, from NSW spatial APIs was combined to target vulnerable students for ATO's tax help program. This dataset was combined with ATO Tax Help Centre data in the Govhack ATO dataset via mapping longitude and latitude geo-locations in this dataset to the ABS postal areas keyed data in the Govhack ATO dataset.

Data Set

Spatial Services - Administrative Spatial Programs - Retirement Village

Description of Use: ABS demographic data from the GovhackATO dataset and geo-locations of retirement village from NSW spatial APIs was combined to target vulnerable seniorsfor ATO's tax help program. This dataset was combined with ATO Tax Help Centre data in the Govhack ATO dataset via mapping longitude and latitude geo-locations in this dataset to the ABS postal areas keyed data in the Govhack ATO dataset.

Data Set

Non-compliance in personal insolvencies - Australian Financial Security Authority

Description of Use: Team Placeholder used simple natural language processing ("NLP") to identify and extract data from this dataset related to tax related insolvencies and used this to identify key under-served areas for the ATO's tax help program. This dataset was combined with ATO tax return data and ABS demographic data in the Govhack ATO dataset via mapping S3A keyed information in this dataset to the ABS postal areas keyed data in the Govhack ATO dataset.

Data Set

Challenges

Spatial data challenge

How can spatial data be leveraged to provide the best community outcome? How can this mapping data be used to deliver value to the people of NSW?

Go to Challenge | 14 teams have entered this challenge.

The Friendly ATO

How can the ATO use artificial intelligence or machine learning to better understand and develop ways to engage with our clients?

Go to Challenge | 15 teams have entered this challenge.

Bounty: Tax Help Centers

Looking at how the ATO could use artificial intelligence or machine learning to locate the best locations for Tax Help Centers

Go to Challenge | 21 teams have entered this challenge.

More than apps and maps: help government decide with data

How can we combine data to help government make their big and small decisions? Government makes decisions every day—with long term consequences such as the location of a school, or on a small scale such as the rostering of helpdesk staff.

Go to Challenge | 58 teams have entered this challenge.

Bounty: Mix and Mashup

How can we combine the uncombinable?

Go to Challenge | 61 teams have entered this challenge.

SEED - Open Data with a Purpose

We are seeking to challenge the status quo. Moving from open data as a bi-product of government business, to active management of open data to better support reuse and innovation – hence open data with a purpose. To achieve this we want to trigger a conversation between developers and the data custodians.

Go to Challenge | 11 teams have entered this challenge.

What do you want from government data challenge?

How should NSW government best provide data to the developer community? Show how our data can be made more usable for developers. What quality or format or standardisation issues does government need to fix or to consider? What developer community needs does the government need to support better?

Go to Challenge | 13 teams have entered this challenge.

Data4Good

How can open data be used to make a social impact, contributing to the betterment of society? How can we improve prospects for children, and education, using open data? What sort of impact can be made on homelessness, mental health outcomes, or the environment, using open data?

Go to Challenge | 19 teams have entered this challenge.

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