Author: Abdul-Rahman, Mohammed
Title: A community resilience assessment framework for university towns
Advisors: Chan, H. W. Edwin (BRE)
Wong, Man-sing Charles (LSGI)
Degree: Ph.D.
Year: 2022
Subject: University towns
Community and college
Sustainable urban development
Cities and towns -- Data processing
Hong Kong Polytechnic University -- Dissertations
Department: Department of Building and Real Estate
Pages: xxiii, 233 pages : color illustrations
Language: English
Abstract: Due to the high global rate of urbanization in the 21st century and the global increase in the world's population in the last few decades, Higher Educational Institutions (HEIs) can no longer house their students within their campuses due to the increased number of enrolments and the unavailability of land for spatial expansion, especially in urban areas. Often, these HEIs' students have to live off-campus either in Purpose-Built Students Accommodations (PBSAs) or Housing with Multiple Occupancies (HMOs), preferably within the university town for ease of commuting to the HEIs. In most cases, the HEIs also act as pull factors for migration into the university towns as people who take up jobs in the HEIs often prefer to live closer to their places of work too. This increases the population and density of the university towns.
Over time, the HEIs take over the identities of their towns and almost everything in those towns become tailored, directly or indirectly, to cater to the university, its staff, and the students. This leads to "studentification", a term used to describe the contradictory social, economic, cultural, and spatial transformations of urbanism resulting from an influx of students into neighbourhoods around HEIs. Although studentification is not always a negative phenomenon as portrayed by the global media, extant literature shows that the negative impacts of studentification often outweigh its benefits. For example, in new and developing towns, HEIs become agents of development by fast-tracking governments supports to provide urban basic services in the area and attracting direct and indirect investments into the town in terms of service provision, real estate investments, etc, as well as providing a market for local businesses and a cheap and skilled workforce (fresh graduates and students who seek for part-time jobs and internships). On the other end, most studentification researchers posited that the negative impacts of studentification as the towns grow and the HEIs expand, are detrimental to the towns' sustainability. These include the gentrification of the old residents and the slumification of the towns. According to literature, every university town has its unique studentification challenges. To develop sustainability in university towns, the towns need to be made resilient against the challenges of studentification. This involves the assessments of university towns to identify their unique challenges and developing resilience. However, there is no known Community Resilience Assessments (CRA) methodology specifically developed for this purpose.
In line with the United Nations Sustainable Development Goals (SDG 11) which aims to make human settlements inclusive, safe, resilient, and sustainable by 2030, this study aimed at filling the above gap by developing an Artificial Intelligence-based CRA framework for identifying and assessing community challenges and developing resilience in university towns. This was achieved through the following objectives: 1. reviewing the existing literature to understand the nature of community resilience challenges in university towns, understand concepts and theories related to studentification and community resilience nexus, as well as assessing the available CRA methodologies; 2. identifying the Critical Success Factors (CSFs) for CRA; 3. developing an Artificial Intelligence-based data pre-processing framework that identifies and assesses community resilience challenges in university towns using location-based User-Generated Contents (UGC), and; 4. developing a Composite Resilience Index (CRI) for university towns, using Akoka, Lagos – Nigeria, as a case study.
Contents and meta-analysis carried out in objective one showed that none of the existing CRA methodologies was designed to assess or develop the resilience of university towns. A few of the existing CRA methodologies use big data (mainly census, sensors and Geographic Information System-based data), measure cross-scale relationships or temporal dynamism. About half of the methodologies also only assess resilience but do not provide action plans. None of the existing CRA methodologies was designed to harness the potential of location­based textual big data generated from microblogs using Artificial Intelligence (AI) tools like Machine Learning (ML) or National Language Processing (NLP).
Every CRA is often seen as a multistakeholder and a complex project which needs efficient management of resources to achieve success. Objective two employed contents analysis to explore the community resilience literature for success factors for CRA and used an expert survey for measure the criticalities of the factors. 28 Critical Success Factors (CSFs) were found to be important for achieving success in carrying out CRA in university towns in both developed and developing countries.
Building on the outcomes of objectives 1 and 2 above, objective three was used to develop an AI-Based Data Pre-Processing Framework that simplifies pre-processing location-based user-generated big data using the Twitter Application Programming Interface (API). This framework combines three ML and NLP programmatic algorithms that help in mining and cleaning the big data, modelling the topics, and analysing the sentiment polarities. The framework helps communities to identify and analyse their studentification impacts (community challenges), and it was used to assess the community challenges of six university towns, one each from the six continents (Loughborough -UK, Akoka -Nigeria, Ann Arbor ­USA, Hung Hom – Hong Kong, Sydney – Australia, and Aguita de la Perdiz – Chile).
Due to the complex nature of human communities, community resilience is best captured as a socio-ecological concept and therefore, apart from the Resilience Theory itself, the Socio-Ecological Systems Theory and Complex Adaptive Systems Theory were often used in the literature to deconstruct scenarios such as studentification. The above theories were used as meta and grand theories to drive this study. However, to better frame this study theoretically, Grounded Theory was used as a mid-range theory to drive the methodology. The AI-Based Data Pre-Processing Framework was designed to automate the steps and principles of Grounded Theory for big data analysis. Action Theory was then used as a micro-theory to design the resilience action plans of the proposed CRA framework.
Building on the outcomes of objectives 1 – 3, and using Akoka as a case study, the last objective developed a Composite Resilience Index (CRI) using Delphi and Analytic Hierarchy Process (AHP) modelling. The CRI is the last part of the Community Resilience Assessment Framework for University Towns, and it helps university towns to assess the existing level of community resilience against studentification, develops localized solutions for the university towns and helps in reviewing, assessing, and rating the performance of initiatives (outcome indicators). In general, the proposed CRA framework would help professionals and decision-makers in developed and developing countries to harness UGC big data and use new technologies such as ML and NLP to assess community challenges in existing university towns and develop strategies to improve their resilience to the negative impacts of studentification, thereby making university towns inclusive, safe, and sustainable. This project also contributes immensely to the resilience, studentification and the artificial intelligence body of knowledge.
Rights: All rights reserved
Access: open access

Files in This Item:
File Description SizeFormat 
6243.pdfFor All Users4.52 MBAdobe PDFView/Open

Copyright Undertaking

As a bona fide Library user, I declare that:

  1. I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
  2. I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
  3. I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.

By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.

Show full item record

Please use this identifier to cite or link to this item: