Data Science

PLAN9075 Urban Data and Science of Cities Unit Coordinator: Somwrita Sarkar Assessment 1 Nayonika De 490204054

Are bigger cities better?

‘WE CAN’T SOLVE PROBLEMS BY USING THE SAME KIND OF THINKING WE USED WE USED WHEN WE CREATED THEM’ –Albert Einstein

Introduction When it comes to globalization and urbanization, Australian cities are no behind. On one hand the global cities like Sydney and Melbourne, act like engines that power the country’s economy, but on the other hand they also contribute to conspicuous income inequality (Sarkar, Phibbs, Simpson & Wasnik, 2016). Research shows that global cities tend to agglomerate to provide with socio-economic opportunities that result in higher GDP and also reducing the per capita investment to maintain the infrastructure. We can state that the proportion of the cities have a plausible correlation to the population distribution. Increase in the population demands for more social infrastructure and facilities.

The purpose of this assessment is to scientifically and statistically comprehend how individual income categories (as one of the variables) of Significant Urban Areas influence the sizes of the cities. With the expansion of economy and population in the Significant Urban Areas in Australia, there comes a question to be argued about whether bigger cities accommodate people of different income groups and provide them with equitable economic opportunities and social infrastructure. Fair number of research exhibits that bigger cities are both economically and socially unfavourable to the marginal income groups which infers inefficiency of the functioning of the cities. Significant cities on one hand create job opportunities for the people and on the other hand creates imbalance in terms of housing, community infrastructure and transit affordability. As urban planners and city scientists we need to apprehend and develop cities that accommodates different income groups thus making it resilient and sustainable.

Hypothesis My hypothesis is to prove or disprove whether lower income categories scale sub-linearly with respect to the size of the cities. It will assist to further comprehend the scaling for the individual household income in the Significant Urban Areas in Australia. Larger cities are usually identified having higher income disparity. In terms of market economy, we can justify that larger Significant Urban Areas provide more diverse income opportunities compared to smaller Significant Urban Areas. But also, global cities especially the CBDs have high land cost, which automatically segregates the higher and the lower income categories in terms of housing and amenities. The issue discussed in this paper is a major concern as the future economic variations will widen the gap between the higher and the marginal income groups in Significant Urban Areas. On one hand higher income categories will tend to opt for luxurious goods and services and on the other hand lower income groups have to sustain themselves with basic necessities. It will be quite intriguing to understand the connection with the growing Significant Urban Areas with ABS data.

Descriptive Statistics The pie chart on the left shows the thirteen

Income Distribution Large Cities

large cities in Australia with the thirteen different income categories (Expert & Expert,

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2018). And it shows that there is a huge disparity between the two largest cities/ Significant Urban Areas i.e. Sydney and Melbourne shows the concentration of population and wealth. On the other hand, cities like Hobart, Geelong, Central Coast which are smaller in terms of economic Sydney Perth Adelaide Gold Coast - Tweed Heads Sunshine Coast Central Coast Hobart

Melbourne Brisbane Canberra - Queanbeyan Newcastle - Maitland Wollongong Geelong

opportunities show the drastic change in population and income concentration.

The pie chart 2 showing the income

Income Distribution Small Cities

distribution of the thirteen smaller cities in Australia presents a very interesting visual

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description.

It depicts the uniformity in terms of population concentration and income distribution in the smaller cities/ Significant Urban Areas. This descriptive visual will further help us to understand the complex Grafton

Ulladulla

Port Pirie

Parkes

relationship between income distribution in

Port Augusta

Kingaroy

Portland

Colac

larger and smaller cities with that of thirteen

Ulverstone

Swan Hill

Warwick

Kempsey

income categories through scatter plots and

Murray Bridge

line graph.

Scaling Analysis The 2016 data gathered from Australian Bureau of Statistics show the following thirteen scatter plots.

$150-$299 ($7,800-$15,599)

$1-$149 ($1-$7,799)

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15

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0

5

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15 y = 0.9852x - 2.2723

y = 1.0144x - 3.166

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$300-$399 ($15,600-$20,799)

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y = 0.9624x - 1.7716

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10

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$500-$649 ($26,000-$33,799)

$400-$499 ($20,800-$25,999)

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$650-$799 ($33,800-$41,599)

15

15

y = 0.9661x - 1.8341

y = 0.9807x - 2.1012

y = 0.9886x - 2.2076

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10

10

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$800-$999 ($41,600-$51,999)

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0

5

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10

10

5

5

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0

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0 0

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15

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1.15 1.1 1.05 1 0.95 0.9 0.85

y = 1.114x - 5.0094

0

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Chart Title

$3,000 or more ($156,000 or more)

14 12 10 8 6 4 2 0

5

y = 1.0657x - 3.7879

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0

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15 y = 1.0498x - 3.8832

y = 1.0639x - 3.6728

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$2,000-$2,999 ($104,000-$155,999)

15

15

15

5

$1,750-$1,999 ($91,000-$103,999)

$1,500-$1,749 ($78,000-$90,999)

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y = 1.0537x - 3.3642

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5

5

5

15 y = 1.0357x - 2.7377

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0

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$1,250-$1,499 ($65,000-$77,999)

15 y = 1.0159x - 2.4859

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0

$1,000-$1,249 ($52,000-$64,999)

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The scatter plots after the study of the (variables) population in the Significant Urban Areas and the income categories they show that, the low and middle income categories show a sub linear patter and in case of higher income categories, we see super linear pattern. The scatter plots from income categories between $150-$299 and $650-799 per week show sublinear growth pattern and the income categories between $1000-$1249 and above show superlinear pattern of growth. The line graph shows the dip indicating the sub linear pattern and the climb indicates the super linear behaviour. In case of global cities like Sydney and Melbourne, the income disparity is more compared to the small Significant Urban Areas like Grafton and Parkes. It is very interesting to find that income group between $1$149 and $800-$999 show linear pattern of growth in the Significant Urban Areas in Australia.

The above scatter plots show a strong and intriguing relationship between the Significant Urban Area population and the thirteen individual household income categories.

The household per capita income has been studied against the population spread across the Significant Urban Areas (SUAs) in Australia. Instead of selecting the total income distribution in the SUAs, the study aims to find the shifting dynamics of individual income categories with that of changing city size to capture the income inequality in details.

Discussion The hypothesis has been proved on the basis of scatter plot and the descriptive analysis that the lower income categories depict income disparity in larger SUAs than compared to the smaller SUAs. The income categories are uniformly distributed in the smaller cities in Australia. From this study we can infer that a strong relation is established between the increase in the sizes of the Significant Urban Areas and the increasing income disparity.

Limitations This paper like several other research looks at the income composition as the main factor which builds the relation between the wage distributions city size (Alperovich, 1995). This paper does not consider the structure of socioeconomic facilities which can also influence the income categories in the SUAs in Australia.

Policy and Conclusion The quantitative based research of cities is lagging behind in case of Australia. The scientific and statistical results are of great value that will inform the strategic urban planning policies and socio-economic policies, particularly apprehending how the city size impact income inequality and other issues. Income disparity can result in urban gentrification thus displacing low income groups and altering the local neighbourhood characteristics, lack of affordable housing and socio-economic and spatial exclusion. The national urban policy document ‘Our Cities- The Challenge of Change’ 2010 has included the elements productivity, sustainability, liveability and governance which will help the Significant Urban Areas to keep pace with the global urbanization trends. But also to make cities smart in terms of investment, policy and technology as mentioned in the 2016 national urban policy document, all people need to be a part of it. Income disparity segregates the different income groups who aim to achieve different socioeconomic goals. Also constructing new infrastructure as a part of city development is unlikely a solution to improve our functioning of the significant areas. Utilizing the existing resources can also be a way to improve the performance of the cities in Australia in terms of economy. Australia has been facing issues in terms of affordable housing, socioeconomic inequalities, mass transit connectivity and renewing existing suburban areas. A national suburban policy in can help in improving the housing affordability, transit connectivity and equitable access to socio-economic infrastructure. National urban policies in Australia tend to focus on infrastructural developments and on long terms planning strategies for the communities but at the same time, the socio-economic impacts on the different communities are not contemplated. For e.g. the Cross City Tunnel was a major transport infrastructure failure because of the non-consideration of public interest evaluation and public consultation. In case of Sydney, the Greater Sydney Regional Plan, a non-statutory document also focuses proving affordable housing, better connectivity and liveability

but it lacks rigorous quantitative data analysis to show why is it that we have 5-10% reservation for affordable housing. And this is where data plays a massive role in pointing at the issues scientifically and mathematically and provide with sustainable options based on quantitative data analysis. This study can contribute to further impacting the urban policies for cities in Australia.

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