The poker-machine map of disadvantage: where gambling losses and machine exposure overlap


An interactive analysis of pokies losses across Victorian local government areas

Introduction

The Victorian local government data presents a more place-based picture, despite the fact that poker machine losses are frequently addressed as an issue of personal preference. Socioeconomic disadvantage, machine density, and poker machine losses are not equally distributed throughout Victoria. Certain communities bear a far greater burden than others.

The Victorian Gambling and Casino Control Commission LGA gaming statistics, ABS SEIFA disadvantage data, and ABS local government borders are used in this visual story to investigate the areas where losses are concentrated. The argument that underprivileged people bet more is not the main topic. Rather, the graphs demonstrate the spatial concentration of gaming infrastructure and losses in areas already under more socioeconomic strain.

1. The Scale

Victoria’s losses at the poker machine are not isolated incidents. They are a regular monthly outflow from nearby settlements. By displaying monthly player losses throughout Victorian LGAs, this first graphic establishes the scope of the problem.

To alternate between the average loss per LGA and the overall losses, use the buttons. You can zoom into shorter time periods using the range slider. This helps demonstrate that the problem is a widespread and recurring phenomenon throughout the state rather than just a few extreme spots.

2. Where Victoria’s poker-machine losses concentrate

The map illustrates how poker machine losses are not distributed equally throughout Victoria. The geography of gambling harm is more visible in darker LGAs, where losses per inhabitant are higher.

Because it changes the narrative from a statewide total to a local pattern, this chart is helpful. The reader can view overall losses, losses per resident, machine count, venues, and SEIFA disadvantage data by hovering over or clicking on an LGA. Because it links losses, exposure, and disadvantage in one perspective, the map becomes more than just a location chart.

3. The loss burden is concentrated in a small number of places

The regions in Victoria with the highest percentage of poker machine losses are displayed on the treemap. Each rectangle’s color indicates the loss per inhabitant, while its size indicates the overall loss.

Compared to the map, this chart offers an alternative viewpoint. Whereas the treemap illustrates how concentrated the losses are, the map displays the locations of the losses. The LGAs and areas that contribute most to the overall loss are shown by larger blocks. Then, by indicating if those losses are likewise high in relation to the population, the color scale adds a second layer.

The reader can navigate from the statewide image to the regional and LGA-level details by clicking into the treemap.

4. Where machine exposure rises with disadvantage

LGAs are summarized by SEIFA IRSD decile in this chart. More socioeconomic disadvantage is shown by lower deciles, whilst less disadvantage is indicated by higher deciles.

The graph contrasts median losses and machine density over disadvantage deciles. The reader can add Metro and Regional lines through the legend after starting with the statewide pattern. Instead of displaying too much at once, this makes the chart easier to understand step-by-step.

The purpose of this chart is to test whether the pattern is structural. The problem goes beyond individual gambling behavior if machine density and losses are higher in more disadvantaged deciles. It implies that the geography of harm includes local exposure to poker machines.

5. The double-burden communities

The story is completed in the last chart. An LGA is represented by each bubble. The SEIFA IRSD score is displayed on the x-axis; lower values indicate more disadvantage. The poker machine loss per resident is displayed on the y-axis. Machine density is indicated by bubble size.

LGAs that experience both greater socioeconomic disadvantage and greater losses per resident are highlighted in the darkened region. These locations combine two threats, which makes them significant. In addition to having greater gambling losses in comparison to the population, they already face more disadvantages.

The LGA name, loss per resident, total loss, SEIFA score, and machine density are displayed when you hover over each bubble. In order to allow the reader to concentrate on the communities most pertinent to the policy argument, the “Double burden only” button streamlines the view.

What the charts display

When taken as a whole, these five charts demonstrate the strong place-based pattern of poker-machine injury in Victoria. Larger LGAs losing more money is not the only problem. Even after accounting for population, a number of localities report significant losses, and several of these places also face greater socioeconomic disadvantage.

The double-burden pattern is the most compelling result. larger disadvantage, larger losses per person, and increased machine exposure are all combined in some LGAs. From a public interest standpoint, these are the communities where gambling losses are most worrisome.

The policy implication is therefore place-based. Harm-reduction policy should focus not only on statewide totals, but also on where machines and losses are concentrated. Areas with high losses, high machine density and higher disadvantage should be treated as priority locations for stronger venue regulation, community support and gambling-harm prevention.

Data, method and limitations

This analysis uses public government datasets to examine the relationship between poker-machine losses, machine exposure and socio-economic disadvantage across Victorian local government areas. Poker-machine losses, machine counts and venue counts were sourced from the Victorian Gambling and Casino Control Commission. Socio-economic disadvantage was measured using the Australian Bureau of Statistics SEIFA Index of Relative Socio-economic Disadvantage, and geographic boundaries were sourced from the ABS Australian Statistical Geography Standard.

The analysis joins these datasets at the local government area level. Poker-machine losses are shown both as total losses and as losses per resident, allowing large and small LGAs to be compared more fairly. Machine exposure is measured as electronic gaming machines per 1,000 residents. SEIFA IRSD scores and deciles are used to compare gambling patterns with area-level disadvantage.

The visual methods were chosen to show different parts of the story. The line chart shows the scale of losses over time, the map shows where losses are concentrated, the treemap shows which areas account for the largest share of losses, the decile chart compares exposure across disadvantage levels, and the quadrant chart identifies “double-burden” LGAs with both higher disadvantage and higher losses per resident.

There are several limitations. This is an area-level analysis, so it cannot show the behaviour or circumstances of individual people. A disadvantaged LGA with high losses does not mean every resident gambles or experiences harm. SEIFA is based on 2021 Census data, while the gambling data covers later reporting periods, so disadvantage is treated as a background indicator rather than a yearly measure. Losses per resident are also an imperfect measure because people may gamble outside the LGA where they live. Finally, the charts show patterns and associations; they do not prove that socio-economic disadvantage directly causes poker-machine losses.

Despite these limitations, the data is useful for identifying place-based patterns. It highlights where gambling losses, machine exposure and socio-economic disadvantage overlap, which is important for harm-reduction planning and public policy.

References

Australian Bureau of Statistics. (2021). Socio-Economic Indexes for Areas (SEIFA), Australia, 2021: Local government area indexes [Data set]. https://www.abs.gov.au/statistics/people/people-and-communities/socio-economic-indexes-areas-seifa-australia/2021/Local%20Government%20Area%2C%20Indexes%2C%20SEIFA%202021.xlsx

Australian Bureau of Statistics. (2024). Regional population by age and sex, 2024: Local government areas [Data set]. https://www.abs.gov.au/statistics/people/population/regional-population-age-and-sex/2024/32350DS0003_2024.xlsx

Australian Bureau of Statistics. (2024). Australian Statistical Geography Standard (ASGS) Edition 3: Local government area 2024 digital boundary files [Data set]. https://www.abs.gov.au/statistics/standards/australian-statistical-geography-standard-asgs/edition-3-july-2021-june-2026/access-and-downloads/digital-boundary-files/LGA_2024_AUST_GDA2020.zip

Victorian Gambling and Casino Control Commission. (2025). Density release: November 2025 [Data set]. https://www.vgccc.vic.gov.au/sites/default/files/2025-11/Density-Release-Nov25.xlsx

Victorian Gambling and Casino Control Commission. (2025). Historical yearly electronic gaming machine data by local government area, 2002–2023 [Data set]. https://www.vgccc.vic.gov.au/sites/default/files/2025-08/historical_yearly_egm_data_by_lga_2002-2023-(1).xlsx

Victorian Gambling and Casino Control Commission. (2026). Current monthly local government area gaming expenditure data [Data set]. https://www.vgccc.vic.gov.au/sites/default/files/2026-05/current_monthly_lga_data_release-(5).xlsx

GenAI acknowledgement

I used ChatGPT to assist with data-cleaning strategy and R code debugging. I also used ChatGPT to help identify and access relevant public dataset sources from the Victorian Gambling and Casino Control Commission and the Australian Bureau of Statistics.

The chart concepts, visual story structure and final design decisions were developed by me. I reviewed, tested and edited the final code, charts and written interpretation before submission.