Insight diagram

MKT563 | Ashleigh Summers

Student ID: 11719557

Assignment Four | Big Data & Marketing Analytics 


Introduction 

The town of Bourke is located approximately north-west of New South Wales in Australia, set on the banks of the Darling River.

According to the 2016 Census QuickStats report from the Australian Bureau of Statistics (ABS), there were 2,634 people in the local government area (LGA) of Bourke. This including, 49.5% were male, and 50.5% female. Aboriginal and Torres Strait Islander people made up 31.5% of the population.

The LGA of Bourke provides many schools for preschool children, primary and high school students. According to the (ABS) education report, 46.9% of children are educated, and 53.1% are uneducated.  The Bourke–Walgett school of distance education offers exemplary education programs which allows preschool to year 12 children to be schooled from home.

Labelled the ‘most dangerous in world’ the town of Bourke has built a horrific reputation when it comes to crime and youth related crime. According to The Sydney Morning Herald, since 2013 the town of Bourke have been focused on improving the incarceration rates and improve prospects of their Aboriginal inhabitants.

Aboriginal leaders including, Indigenous leader Alistair Ferguson, in 2013 partnered with an independent group, Just Rienvest NSW, who advocates for the reallocation of public funds from prisons to early intervention programs to prevent youth crime.

A report assessment by KPMG in 2018 found the new approach had saved $3.1million in 2017 alone. From here, Police began involved in employment strategies for youth to prevent idleness that leads to crime.

This focus has since moved from law and order of youth behaviour to the social and economic decisions that motivate these children to begin with. Since this change and extra support from the Aboriginal community, the crime rate in Bourke began to drop.

The Model

The model is designed to identify the key indicators of crime in Bourke town and how it is linked with the Youth education/Drop outs. The model is further developed to identify the crime rate from school dropouts with a break up of repeated crime history details. Later, the model is strengthened by identifying the development areas to reduce the crime numbers. The major programs considered are Back to school and employment opportunities which are backed up by community funds. Finally, the result is indicated of reduction in crime rate by providing the opportunities to youth at all stages.

Assumptions

  1. Bourke town population 1000
  2. Youth population is assumed at 30% out of total population.  
  3. Youth are either employed or unemployed. 
  4. Further the population is divided into 2 main categories: Adult and Youth population. 
  5. The model is represented by taking stocks, variables and flows/transitions to connect. 
  6. The list of stocks and variables are represented as follows:

 Stocks

  1. Bourke Population
  2. Adult Population
  3. Youth population
  4. Educated population in youth segment
  5. Dropouts in youth segment
  6. Unemployed in educated segment
  7. Crime population from Dropout segment
  8. Crime population from Offender segment
  9. Convicted
  10. Repeat crime from the convicted
  11. New crime
  12. Released youth from detained
  13. Community programs for youth
  14. Employment (opportunities program)
  15. Educated (Back to school program)

Variables

The Crime and Community Funds support are the variables created to show the impact on the stocks

  1. Crime commits
  2. Crime Rate
  3. Community funds

Conclusion

The model is created to show the importance of the Community fund support to Bourke youth by providing the employment opportunities and back to school program to reduce the crime and help the youth to build the better future.

 

References

ABS, A. B. (2016). Bourke, NSW. Retrieved from Australian Bureau of Statistics, ABS: https://quickstats.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/LGA11150?opendocument

Alexander, H. (2019, May 29). How NSW town labelled 'most dangerous in world' changed its destiny. Retrieved from The Sydney Morning Herald : https://www.smh.com.au/national/nsw/how-nsw-town-labelled-most-dangerous-in-world-changed-its-destiny-20190527-p51ri6.html

Bourke, V. (2020). Visit Bourke. Retrieved from Visit Bourke : http://www.visitbourke.com.au/

Education, N. D. (2020). Learning from home. Retrieved from Bourke-Walgett School of Distance Education: https://bourkewalg-d.schools.nsw.gov.au/learning-at-our-school/learning-from-home.html

KPMG. (2016). Unlocking the Future, Maranguka Justice Reinvestment Project in Bourke, Preliminary Assessment. NSW: KPMG.

KPMG. (2018). Maranguka Justice Reinvestment Project, Impact Assessment. NSW: KMPG; Just Reinvet NSW, Maranguka Community Hub. Retrieved from https://www.justreinvest.org.au/wp-content/uploads/2018/11/Maranguka-Justice-Reinvestment-Project-KPMG-Impact-Assessment-FINAL-REPORT.pdf

NSW, J. R. (2020). KPMG Report shows changes in Bourke had economic impact of $3.1m in 2017 & estimates $7m additional over 5 years. Retrieved from Just Reinvest NSW: https://www.justreinvest.org.au/landmark-report-demonstrates-economic-impact-of-3-1-million-in-2017-and-estimates-additional-impact-of-7-million-over-five-years-through-justice-reinvestment-in-bourke/

Conceptual model of youth alienation, police and community development in the Town of Bourke, NSW | Assignment Four | MKT563
Insight diagram

Adapted from Fig 12.1 p.476 of the Book James A. Forte ( 2007), Human Behavior and The Social Environment: Models, Metaphors and Maps for Applying Theoretical Perspectives to Practice; Thomson Brooks/Cole Belmont ISBN 0-495-00659-9

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Using the reading assignment from El-Taliawi and Hartley on using a SSM for COVID-19 follow the steps for SSM to include: 1) Describe the Problem (unstructured). 2) Develop a Root Definition for the COVID-19 problem space by identifying the three elements: what, how, why. A System to do X, by (means of) Y, in order to achieve Z. X - What the system does Y - How it does it Z - Why is it being done (see slide 33 in the Systems Thinking Workshop reading)Download Systems Thinking Workshop reading) 3) Identify the Perspectives (CATWOE) 4) Develop a basic Systemigram / Rich Picture to tell the story. Submit your assignment as a Word document or PDF that addresses #1-4.
1) Problem Situation (Unstructured)

The COVID‑19 pandemic represents a complex, ill‑structured problem characterized by uncertainty, rapidly changing conditions, and conflicting stakeholder perspectives. As El‑Taliawi and Hartley emphasize, COVID‑19 is not merely a biomedical crisis but a socio‑technical system failure involving public health, governance, economics, social behavior, and global interdependence. There was no single agreed‑upon definition of “the problem.” For some actors, the problem was viral transmission and mortality; for others, it was economic collapse, civil liberties, misinformation, or institutional trust.

Key features of the unstructured problem include:

  • High uncertainty about the virus’s behavior, transmission, and long‑term effects.

  • Multiple stakeholders with competing values and priorities (health vs. economy, freedom vs. safety).

  • Nonlinear dynamics, where interventions (lockdowns, travel bans, vaccination campaigns) produced unintended consequences.

  • Fragmented governance, with responses varying across nations, states, and institutions.

  • Information overload and misinformation, complicating sense‑making and public compliance.

This ambiguity and plurality make COVID‑19 unsuitable for purely “hard” systems approaches and well suited for Soft Systems Methodology (SSM), which focuses on learning, interpretation, and accommodation rather than optimization.

2) Root Definition (What–How–Why)

A system to coordinate societal responses to the COVID‑19 pandemic (X), by integrating public health expertise, policy decision‑making, communication, and stakeholder engagement under conditions of uncertainty (Y), in order to reduce harm to human life and societal functioning while maintaining legitimacy, trust, and resilience (Z).

  • What (X) — Coordinating societal responses to COVID‑19.

  • How (Y) — Through adaptive governance, expert input, communication, and stakeholder engagement.

  • Why (Z) — To minimize health, social, and economic harm while sustaining trust and resilience.

This root definition reflects SSM’s emphasis on purposeful human activity systems, not technical control systems.

3) Perspectives (CATWOE Analysis)
  • Customers — General public, vulnerable populations, healthcare workers, businesses, future generations affected by long‑term consequences.

  • Actors — Governments, public health agencies (e.g., WHO, CDC), healthcare providers, scientists, media organizations, community leaders.

  • Transformation — A society facing uncontrolled viral spread and systemic disruption is transformed into one that manages risk, adapts behavior, and mitigates impacts.

  • Worldview (Weltanschauung) — COVID‑19 is a complex, evolving crisis requiring learning, adaptation, and pluralistic decision‑making rather than linear control.

  • Owners — National governments, international bodies, and political authorities with the power to authorize or terminate interventions.

  • Environmental Constraints — Scientific uncertainty, political polarization, economic limitations, cultural norms, global interdependence, time pressure.

CATWOE highlights how different worldviews lead to different “valid” problem definitions, reinforcing the need for SSM.

4) Systemigram / Rich Picture (Narrative Description)

The rich picture would depict COVID‑19 at the center as a disruptive force interacting with multiple subsystems. Public health institutions generate evolving scientific knowledge, feeding into government decision‑making. Governments issue policies (lockdowns, mandates, economic relief), which influence public behavior. Public response is shaped by trust, media narratives, misinformation, and social norms. Economic systems react to restrictions, feeding back political pressure on leaders. Healthcare systems experience strain, influencing policy urgency and public fear.

Feedback loops dominate the picture:

  • Policy decisions affect public behavior, which affects transmission rates.

  • Media narratives influence trust, which affects compliance.

  • Economic impacts influence political decisions, sometimes undermining health goals.

The systemigram emphasizes learning loops, delays, conflicts, and unintended consequences rather than linear cause‑effect chains.

If you want, I can translate this directly into a Word‑ready format, tighten it to a specific page limit, or sketch a labeled rich‑picture layout you can redraw by hand or digitally for submission.

translate this directly into a Word‑ready format 5 pages and sketch a labeled rich‑picture layout you can redraw by hand or digitally for submission.
Soft Systems Methodology (SSM) Analysis of the COVID‑19 Pandemic

Based on El‑Taliawi and Hartley

Introduction

The COVID‑19 pandemic represents one of the most complex global crises of the modern era. Traditional “hard” systems approaches, which assume clearly defined problems and measurable objectives, proved insufficient for addressing the pandemic’s intertwined health, social, political, and economic dimensions. El‑Taliawi and Hartley argue that COVID‑19 is best understood as a messy, ill‑structured problem situation, making it well suited for analysis using Soft Systems Methodology (SSM).

SSM does not attempt to “solve” the problem in a technical sense. Instead, it provides a structured learning process to explore multiple perspectives, clarify assumptions, and identify purposeful human activity systems that can improve the situation. This document follows the core SSM steps required in the assignment: (1) describing the unstructured problem, (2) developing a root definition, (3) identifying perspectives using CATWOE, and (4) developing a systemigram / rich picture narrative.

1. Description of the Problem Situation (Unstructured)

The COVID‑19 pandemic emerged rapidly and evolved unpredictably, creating a situation characterized by ambiguity, disagreement, and uncertainty. At the outset, there was no shared understanding of the nature or scale of the threat. Scientific knowledge about transmission, severity, and long‑term effects developed incrementally, often changing public guidance and policy decisions. This uncertainty undermined confidence and complicated coordinated action.

The problem situation extended far beyond public health. Governments faced competing pressures to protect lives, preserve economic stability, and maintain civil liberties. Healthcare systems experienced surges in demand, shortages of personnel and equipment, and moral distress among frontline workers. Businesses and workers faced closures, unemployment, and financial insecurity. Social isolation measures disrupted education, mental health, and community cohesion.

Multiple stakeholders framed the “problem” differently. For public health officials, the primary concern was reducing transmission and mortality. For political leaders, the challenge included maintaining legitimacy and public compliance. For citizens, the problem often centered on personal risk, economic survival, and trust in institutions. Media organizations and social platforms amplified both accurate information and misinformation, shaping public perception and behavior.

Covid-19 HW
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Insight diagram
The model here shows the COVID-19 outbreaks in Burnie Tasmania, which has impacted in the local economy. the relationship between COVID-19 and economic situation has been shown in the graph. Based on the susceptible analysis, people who usual go out are might have chance to meet susceptible people and have a high rate to be infected. The period of spreading can be controlled by keeping social distance and Government lockdown policy. 

Susceptible can be exposed by go out.  resident has a possibility to infect and be infected by others. people who might be die due to the lack of immunity. and others would recover and get the immune. 

Beside, the economy situation is proportionate to the recovery rate. If there are more recovery rate from the pandemic, the employment rate will be increased and the economy situation will recover as well.   
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Insight diagram
Introduction:
This simulation model demonstrates the outbreak of Covid-19 in Burnie, Tasmania and how the corresponding government’s responses affect the spreading of Covid-19. Meanwhile, this model also shows how the economy in Burnie is influenced by the impacts of both Covid-19 and government policies.

Variables: 
This simulation contains some relevant variables as follow:

Variables in Covid-19 outbreaks: (1) Infection rate, (2) Recovery rate, (3) Death rate, (4) Immunity loss rate

Variables in Government policies: (1) Vaccination rate, (2) Lockdown, (3) Travel ban, (4)Quarantine

Variables in Economy: (1) E-commerce business, (2) Unemployment rate, (3) Economic growth rate.

Assumption:
Government responses would be triggered when reported Covid-19 cases are at least 10.

The government policies reduce the spreading of Covid-19, but they would also limit economic development at the same time due to the negative impact of the policies on the economy is greater than the positive impact.

The increase in reported Covid-19 cases would negatively affect economic growth.

Interesting Insights:
The first finding is that the death number would keep increasing even though the infection rate has decreased, but with stronger government policies (such as implementing a coefficient over 25%), no more death numbers will occur caused by Covid-19.

The second finding is that as government policies limit business activities, with the increasing number of reported Covid-19 cases, economic growth will suffer a severe blow even if e-commerce grows, it can’t make up for this economic loss.
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