Towards resilient and liveable neighbourhoods post Covid-19: Evaluating neighbourhood quality in Sydney (AUS) and Newcastle upon Tyne (UK)

Blog 16th November 2023

In this guest post Dr Ozgur Gocer, Dr Ayse Ozbil Torun and Dr Seraphim Alvanides write about their research project on changing ‘home environments’ after the pandemic. Their work was supported with a USF Pandemics and Cities grant.

Our research project aimed to address changing immediate home environments influenced by climate change, hybrid work arrangements, and the pandemic. It was based on the neighbourhood’s profound impact on the physical and mental well-being of its residents as well as its potential role in creating sustainable and resilient communities. Recognizing the lack of comprehensive tools for assessing neighbourhood quality (NQ), despite its acknowledged importance, the long-term vision of this research is to provide planners and policymakers with an integrated and data-led NQ evaluation framework that can provide actionable insights to assess and enact efficient plans and policies aimed to develop high-quality neighbourhoods. This framework relies on measurable, accessible, and reproducible environmental attributes to assess NQ in terms of liveability and resilience, aligning with post-pandemic priorities and addressing existing limitations.

The partnership effectively harnessed our strengths as researchers. Dr Gocer managed data collection and TOPSIS-EM analysis while Dr Ozbil Torun led urban form and street network evaluations, including the application of the framework. Dr Alvanides guided GIS-based and statistical analyses and Dr Kent provided insights based on her expertise in healthy built environments. Research partners contributed staff time, knowledge sharing, and data, ensuring project success and meaningful outcomes.

Team and partnerships

This study was intended to be a disciplinary and interdisciplinary research partnership between the Australian (Dr Gocer, University of Sydney) and UK teams (Drs Ozbil Torun and Alvanides, Northumbria University). This collaboration builds upon an ongoing relationship between the project leads. Drs Ozbil Torun and Gocer previously collaborated on a research project funded by the Scientific Research Council of Turkey (TUBITAK) from 2014 to 2017, resulting in co-authored articles and shared research endeavours. With the support of this USF grant, this collaboration is poised to expand further, incorporating new researchers while building upon the foundation laid by Drs Gocer and Ozbil Torun.

There were significant opportunities for cross-fertilisation of ideas and sharing of resources between this project and the wider People and Place research group at Northumbria University and Urban Housing Lab at the University of Sydney, which enhanced and extended the impact of this funding. Apart from forthcoming publications, future events where the project’s findings will be presented include the University of Sydney’s Lunch Time Research Seminar Series and Northumbria University’s Architecture and Built Environment Departmental Seminar Series in October 2023.


We have successfully accomplished the project application’s planned deliverables and associated outputs.

Stage 1. Establishing an assessment tool for NQI.

Defining the Neighbourhood

We employed areas previously defined statistical administrative units, namely MSOAs (Middle Super Output Areas) and SA2 (Statistical Area Level 2) for Newcastle and Sydney, respectively, as a proxy for neighbourhoods. We selected two pilot neighbourhoods in each city to apply our NQI framework and test its applicability in distinct urban environments (Figure 1). Benwell and Walker are from Newcastle, while Granville Clyde and Canterbury (South)-Campsie are from Sydney. These neighbourhoods were selected based on their similarities in terms of socio-economic status and geographic locations as well as because of their variation in street network layout, housing types and land-use so that they are diverse yet comparable.

Figure 1 with four map sections showing neighbourhoods in Sydney, Australia (top row), and Newcastle upon Tyne, UK (bottom row), with red line sections showing the neighbourhood parameters
Figure 1: Selected neighbourhoods in Sydney, AU (top row) and Newcastle upon Tyne, UK (bottom row).

Datasets (Defining NQI measures and datasets)

We extensively scoured open-access datasets to assess their relevance for measuring NQ environmental attributes. Employing various data collection techniques, we gathered information necessary for a pilot study testing our NQI framework’s viability across urban contexts with varying data availability levels. This included gathering secondary open-source data such as Open Street Maps and Points of Interest at different spatial scales, supplemented by street-segment level spatial data, evaluating street connectivity through space syntax techniques based on OSM street network data.

As a result of this in-depth data-scoping exercise, we categorised the common available datasets in both study areas under 4 categories of key neighbourhood attributes, along with their related quantitative measures and descriptions: (i) Urban green (e.g. publicly accessible green space); (ii) Amenities (e.g. service coverage); (iii) Accessibility & Urban mobility (e.g. street connectivity); and (iv) Socio-economic Status (SES) (e.g. crime rate).

Circle in grey and yellow with grouped enrionmental attributes of neighbourhood quality
Figure 2: Environmental attributes, their associated measures and methods of calculation underlying the proposed NQI framework.

To calculate each measure, we gathered and merged data from various sources and applied four different analytical techniques. These included Quantity Calculations to measure entities in terms of quantity within each neighbourhood, Published Statistics where existing statistics were used, Percentage calculations within a distance threshold to assess service coverage, and Connectivity Measurements for evaluating accessibility and urban mobility. Our calculations were rooted in the latest available census data from 2021, ensuring that our insights were as current as possible.

Weighting the Neighbourhood Attributes

We employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) in conjunction with the entropy method (EM) to objectively evaluate and rank neighbourhoods based on various measurable attributes. Expert opinion surveys were used to determine attribute weights, ensuring transparency and inclusivity in the evaluation process. The diverse panel of experts from various countries participated in the survey. EM was then used to rank attributes and assign weights based on survey data. Notably, Publicly Accessible Recreational Space and Access to key transit sites emerged as primary determinants of neighbourhood quality.

Stage 2. Testing the feasibility of the proposed assessment tool through a pilot study.

In the second stage, we tested our NQI framework in diverse urban contexts. This pilot study aimed to compare data availability and completeness between two countries with similar COVID-19 restrictions and post-pandemic urban trends.

Ranking Neighbourhoods

According to the TOPSIS analysis, Campsie-North was ranked the highest (Figure 3), indicating its status as the top-ranked neighbourhood among the four case studies, while Walker was at the opposite end of the spectrum.

Table with yellow coloured section at bottom with yellow star to the right hand side
Table 1: Si* and Si-; positive and the negative-ideal solution, respectively.

Ci Ranking score

Campsie’s number one position was attributable to its outstanding performance in key attributes such as access to green spaces, health services, and socio-economic status. More specifically, accessibility to public recreational space (i.e. % of land allocated to public playgrounds, neighbourhood parks, etc.) and access to key transit sites (i.e. total number of residents with access to metro and/or bus stops within a 5-minute walking distance) within a neighbourhood were identified as the key measures by experts identifying NQ during the post-pandemic era. The experts in urban architecture and planning also identified two more critical environmental measures that contribute to the quality of a post-pandemic neighbourhood. These include the Land Use measure of service coverage, specifically how well the residential population is served with food retailers, as well as the Accessibility and Urban mobility measure of street connectivity (i.e. how easy it is to access from one street to all others), both of which are within a 5-minute walking distance of residences. Hence, this comprehensive framework showcases the feasibility of data-driven evaluation in assessing NQ, particularly in the context of post-COVID considerations and urban planning.

Figure 3: two graphic visualisation maps in grey and red lines showing service coverage and angular integration for Campsie-North
Figure 3: Service Coverage (total amount of food retail that can be accessed within 400 meters of each street segment) and angular Integration (how accessible each street segment is from all others within 400 meters) visualised for Campsie-North.

Public-facing reflections

We are dedicated to sharing our findings with the public and the experts. We are planning to conduct a workshop with local stakeholders in the North of England (e.g. Newcastle City Council, North of Tyne Combined Authority, etc.) and community leaders in the 2 pilot study neighbourhoods in Newcastle. This active engagement workshop will enable us to present our developed NQ assessment framework and explore the potential/shortcomings of the applicability of the index in policy formulation and interventions. We will better grasp the multiple place-based specificities and needs/perspectives of various users across the case study locations as a result of this workshop, which will be the first step towards enhancing and optimising our NQI framework.

Future work

Following the successful completion of the current project, our next step is to apply for larger complementary funding, such as EU-framework programmes, the Economic and Social Research Council Standard grant and the Australian Research Council & AHURI Grant, to widen the scope of our approach and transfer the key learnings of this preliminary local-scale study to a global scale. Wider University support will be provided by both Institutes’ Research Funding and Policy Managers to identify appropriate funding sources for further development of the research collaboration. In the post-USF phase, we aim to optimize the proposed NQI by testing its validity against perceptual neighbourhood quality data (i.e. perception of safety, walkability) collected via participatory research (e.g. Volunteered Geographical Research). In addition, the proposed NQI will be enhanced by incorporating other datasets that are available mainly from official governmental data collections. For example, we are aware, based on our previous discussions with Local Authorities (LAs), that LAs have other more detailed and potentially useful datasets (e.g. residential survey data, pedestrian/traffic counts), which can potentially enhance the proposed NQI to inform policy formulation and interventions. Hence, our mission in the post-USF phase will be to work with stakeholders (including community groups) through focus groups and co-creation workshops to explore the potential of the NQI to be adopted more widely. From a policy point-of-view, introducing a data-led methodology which could be used and replicated by policymakers and others seeking to develop healthy and resilient neighbourhoods may help inform related policy.