title: Countering gentrification in Tāmaki Makarau: Towards a framework for urban environmental justice

authors: Terruhn, Jessica

abstract: This policy brief argues for urban policies that actively aim to end gentrification.

Gentrification is intimately tied to state-led processes of urban renewal. Though historically understood as the displacement of low-income households as an unfortunate byproduct of socioeconomic and demographic neighbourhood change, contemporary gentrification is constituted by profit-driven investment decisions that erase undervalued neighbourhoods and their communities in the name of urban renewal (variously referred to as redevelopment, regeneration, renaissance, or revitalisation).

These processes of gentrification fundamentally contribute to environmental injustice. At the same time as creating ‘zones of abundance’, neighbourhood regeneration projects simultaneously (re-)create ‘zones of neglect and sacrifice’, which are burdened with environmental risks. As low-income and ethnic minority households are more likely to either reside in or to be displaced into these neighbourhoods, they are disproportionately exposed to environmental risk factors.

Moreover, gentrification enables and legitimises the exclusion of marginalised communities in multiple ways: 1) it devalues existing places and their residents in favour of regeneration, 2) it displaces longtime residents from regenerated neighbourhoods because of increasing property values, and/or 3) it alienates longtime residents as neighbourhood aesthetics, amenities and services change and become unaffordable and/or irrelevant.

As gentrification contributes to low-income households’ experiences of precarity and severs community ties and place attachments, it generates significant costs to personal and community health and wellbeing as well as social cohesion. The recommendations articulated in this brief emphasise the importance of transformative policy solutions that actively counter gentrification.
These include:
• Equitable investment in urban infrastructure
• Prioritising affordable housing
• Ensuring accessible transportation
• Empowering local communities
• Implementing a Te Tiriti o Waitangi approach to policy development

text: Countering gentrification in Tāmaki Makarau: Towards a framework for urban environmental justice This policy brief argues for urban policies that actively aim to end gentrification.

Gentrification is intimately tied to state-led processes of urban renewal. Though historically understood as the displacement of low-income households as an unfortunate byproduct of socioeconomic and demographic neighbourhood change, contemporary gentrification is constituted by profit-driven investment decisions that erase undervalued neighbourhoods and their communities in the name of urban renewal (variously referred to as redevelopment, regeneration, renaissance, or revitalisation).

These processes of gentrification fundamentally contribute to environmental injustice. At the same time as creating ‘zones of abundance’, neighbourhood regeneration projects simultaneously (re-)create ‘zones of neglect and sacrifice’, which are burdened with environmental risks. As low-income and ethnic minority households are more likely to either reside in or to be displaced into these neighbourhoods, they are disproportionately exposed to environmental risk factors.

Moreover, gentrification enables and legitimises the exclusion of marginalised communities in multiple ways: 1) it devalues existing places and their residents in favour of regeneration, 2) it displaces longtime residents from regenerated neighbourhoods because of increasing property values, and/or 3) it alienates longtime residents as neighbourhood aesthetics, amenities and services change and become unaffordable and/or irrelevant.

As gentrification contributes to low-income households’ experiences of precarity and severs community ties and place attachments, it generates significant costs to personal and community health and wellbeing as well as social cohesion. The recommendations articulated in this brief emphasise the importance of transformative policy solutions that actively counter gentrification.
These include:
• Equitable investment in urban infrastructure
• Prioritising affordable housing
• Ensuring accessible transportation
• Empowering local communities
• Implementing a Te Tiriti o Waitangi approach to policy development

year: 2025

title: AI: a new (r)evolution or the new colonizer for Indigenous peoples?

authors: Whaanga, Hēmi

abstract: We are often told that there is one constant in life and that is change; it is inevitable, inescapable. When the forces of power begin to blow and conditions are right, change will happen. As the planet undergoes a period of transformation brought about by the advances of data science and the convergence of technologies, the Internet of All Things and AI, the potential of AI to be change agent for Indigenous peoples is a thought-provoking, and to a certain degree, daunting proposition. The rapid progress of technology and innovation, in terms of its volume, complexity, and exponential growth in computing power, have drastically changed how we socialize, communicate, access, share, distribute and view knowledge and information. Is AI inevitable, inescapable, a fait accompli for Indigenous peoples?

text: AI: a new (r)evolution or the new colonizer for Indigenous peoples? We are often told that there is one constant in life and that is change; it is inevitable, inescapable. When the forces of power begin to blow and conditions are right, change will happen. As the planet undergoes a period of transformation brought about by the advances of data science and the convergence of technologies, the Internet of All Things and AI, the potential of AI to be change agent for Indigenous peoples is a thought-provoking, and to a certain degree, daunting proposition. The rapid progress of technology and innovation, in terms of its volume, complexity, and exponential growth in computing power, have drastically changed how we socialize, communicate, access, share, distribute and view knowledge and information. Is AI inevitable, inescapable, a fait accompli for Indigenous peoples?

year: 2020

title: Optimization and application of mixed regression models based on the expectation maximization algorithm

authors: Ding, Jiahui

subjects: clusterwise linear regression

abstract: This study further explores clusterwise linear regression (CLR), specifically the expectation maximization (EM) algorithm. The algorithm was quantitatively analyzed through the generated data set. Previous regression methods are integrated and improved to provide better performance in dealing with complex conditions. The model was tested under different independent variables and methodologies, conclusions were drawn from the quantitative results, and optimal solutions under different scenarios were summarized. In addition, the application of the EM algorithm is deduced. The core purpose of this study is to test the reliability and performance of these methods in different situations. By generating diverse data, simulating different environments, and testing various model combinations, we can obtain the optimal regression model combination under different environments. The contribution of this study is to provide an effective and flexible tool for the application of hybrid regression models and provide a reference for future improvements and optimizations. Through this research, we hope to provide new solutions for application scenarios such as market segmentation, medical data analysis, and financial risk management, and provide a theoretical basis for personalized medicine and precision marketing strategies.

text: Optimization and application of mixed regression models based on the expectation maximization algorithm This study further explores clusterwise linear regression (CLR), specifically the expectation maximization (EM) algorithm. The algorithm was quantitatively analyzed through the generated data set. Previous regression methods are integrated and improved to provide better performance in dealing with complex conditions. The model was tested under different independent variables and methodologies, conclusions were drawn from the quantitative results, and optimal solutions under different scenarios were summarized. In addition, the application of the EM algorithm is deduced. The core purpose of this study is to test the reliability and performance of these methods in different situations. By generating diverse data, simulating different environments, and testing various model combinations, we can obtain the optimal regression model combination under different environments. The contribution of this study is to provide an effective and flexible tool for the application of hybrid regression models and provide a reference for future improvements and optimizations. Through this research, we hope to provide new solutions for application scenarios such as market segmentation, medical data analysis, and financial risk management, and provide a theoretical basis for personalized medicine and precision marketing strategies.

year: 2024-09-12