Where are Australian jobs growing or shrinking (2002 - 2014; over 100 regions; SA4)?

<h2>This data shows count of employees by 100 regions within Australia over 12 years (2002 - 2014). In 2002, there were 8.5M workers, rising to 10.3M in 2014. Maps show percent change in count of employees over preceding four years eg 2010-2014.</h2><h2>Parent data - Employee $ DATA by detailed occupation, by location (SA4), by year; </h2><div>#Changelog:</div><div>v6.1 - added jpg bubble plot - linked below at github.</div><div>v6 - added new csv file including SA4 names and States.</div><div>Data from:</div><div>v4.3 - added jpg combining three maps; 2006, 2010, 2014.</div><div>v4.2 - added link to full database of underlying data now added to figshare (m9.figshare.4522895)</div><div>v4.1 - add index.html, background20-12-16.pdf - Nectar.org.au archive of site at (now offline); </div><div>amended data totals to include AU GDP per ABS</div><div>v4 - add description of SQL to extract published data from parent DB.</div><div>v1 - 3. Minor edits.</div><div><br></div><div>#description</div><div>This dataset is an aggregation of all Australian Salaries and Wages by location and over 12 years in four year snapshots (2002, 2006, 2010, 2014). Some data excluded which was not allocated to a SA4 location. Source Data from ATO; Australian Tax Office.</div><div><br></div>#file_description<br><div>HeadcountRaw.csv provides total data (employee count). Includes total counts per SA4 location, and percent change between each of the years; 2002 - 6; 2006 - 10; 2010 - 14 eg 101 means 1% increase. This file also contains the SQL query to extract this file from the parent DB.</div><div><br></div><div>HeadcountRaw_display.csv provides data (employee count) to visualise at (1) National Map.gov.au or (2) Aurin.org.au. This only includes the data for SA4 regions which can be visualised. </div><div><br></div><div>See #datavis below for explanation of image files.</div><div><br></div><div>#Method</div><div>Parent DB CSV files loaded into MariaDB on Nectar Infrastructure (refer NCRIS). SQL to extract this subset of data from parent DB is included in the header of HeadcountRaw.csv. Access through <a href="http://areff2000.net16.net/" rel="nofollow">http://areff2000.net16.net</a> (now offline).</div><div><br></div>#sourcedata<br><div>ATO Data request at: <a href="https://datagovau.ideascale.com/a/dtd/Measuring-industry-growth-stable-fall-Employees-Sole-Traders/231857-26233#idea-tab-comments">data.gov.au Ideascale</a></div><div><br></div><div>Original data (parent data) at: <a href="https://data.gov.au/dataset/ad-hoc-data-requests/resource/d198f1da-614e-42cf-b480-5cee7d2ef752">data.gov.au</a></div><div>Parent data description: "Individuals data for 2001-02, 2005-06, 2009-10 and 2013-14 income years. Table 1: Salary and Wages income, by Occupation and SA4 location Table 2: Sole trader business income, by Industry and SA4 location." Sole trader data not included in this sub-collection.</div><div><div><p>#current_analysis</p><p>See analysis in progress for:</p><p>=> Individual income by occupation / location <br></p><p>at: <a href="http://areff2000.net16.net/" rel="nofollow">http://areff2000.net16.net</a> (offline) or (offline) (Updated: 11.11.16, 10.07.17)</p><p> #datavis -</p><p><u>How To </u><b>To view on National Map (data.gov.au mapping tool).</b> </p><p>1. Save <a href="">data</a> as csv. </p><p>Data (loaded here), currently at: - now available as html_backup[date].zip at ATO DATAbase - see Figshare link below.</p><p>2. Open http://nationalmap.gov.au. </p><p>3. Click 'Add Data'. <br>4. Drag csv file onto map. </p><p>5. Click Done. <br>6. Select Year in control panel (lower left of screen). Raw shows count of jobs. Year shows % change from four years earlier. <br>7. Click on region (SA4) to see data for that region.</p><div><br></div><div><p>#Data_format</p><p>Year | Occupation | Location (SA4) | Count of Workers | $ of Workers <br>* <u>Year</u>: [2002, 2006, 2010, 2014]<br><br>* <u>Salary and Wages</u>; 200,000 lines (summary only included here)<br>* Sole Proprietors; 100,000 lines (not included here)</p><p>* <u>Occupation</u>: Description at <a>Australian Bureau of Statistics</a>. (3,216 lines) (link below)</p><p>* <u>SA4 Location</u> descriptions at: <a href="http://stat.abs.gov.au/itt/r.jsp?databyregion#/">http://stat.abs.gov.au/itt/r.jsp?databyregion#/</a>. </p><p>SA4 definition/description at: http://www.abs.gov.au/ausstats/abs@.nsf/0/B01A5912123E8D2BCA257801000C64F2?opendocument </p><p>#dataTotals - Salary and Wages</p><table><tbody><tr><th>Year</th><th>Workers (M)</th><th>Earnings ($B)</th><th><a href="http://www.tradingeconomics.com/australia/gdp">GDP</a> USD($B)</th></tr><tr><td>2002</td><td>8.5</td><td>285</td><td>400</td></tr><tr><td>2006</td><td>9.4</td><td>372</td><td>746</td></tr><tr><td>2010</td><td>10.2</td><td>481</td><td>1142</td></tr><tr><td>2014</td><td><a href="">10.3</a></td><td><a href="">584</a></td><td>1450</td></tr></tbody></table><p>Table 1: Aust. Salary and Wages 2002 - 2014.</p><p>GDP info from: <a href="http://www.tradingeconomics.com/australia/gdp">T</a>rading Economics (link below).</p><p><br></p><p>#datavis</p><p>1. Three Chloro images made at aurin.org.au (AU researcher login required). eg Chloro12_2014 is 12 colour chloropeth, for 2010 - 2014, Chloro12_2010 is 2006 - 2010, Chloro12_2006 is 2002 - 2006.</p><p>Please cite images as: Ferrers, R., ATO - User uploaded data (2016) visualised in AURIN portal (map visualisation chloropeth) on 25.8.2016. Viewed online at: <a href="https://dx.doi.org/10.6084/m9.figshare.4056282.v2">https://dx.doi.org/10.6084/m9.figshare.4056282.v2</a></p><div><div></div></div><p>2. Red/Orange (year.tiff) images made at nationalmap.org.au (NM), where 2014.tiff is percent difference 2010 - 2014, 2010.tiff is 2006 - 2010, 2006.tiff is 2002 - 2006. Three scale files explain colours on each year.tiff, where related scale is [year]NM_scale.tiff.</p><p>3. A new #datavis - scatter plot - is available (linked below) at github: http://areff2000.github.io/2017/03/28/plotly.html (Mar '17).</p><p>#usage</p><p>This #datavis was used in a University of Melbourne Library Hackathon - Hack for Good (25.8.16) - https://twitter.com/ValueMgmt/status/769041449862168577</p><p>Slides attached below: (see Canva link; Ferrers, Li, Kreunen and Lindsay (2016). L^2 Local Livability Index. Online at: https://www.canva.com/design/DAB8-48tlEw/view)</p><p>https://twitter.com/ValueMgmt/status/770144651953135616</p> </div></div><ul></ul></div>