Dr Jimi Adebayo

This report presents the summary of a research project undertaken under the auspices of Innovate UK Regulatory Science and Innovation Networks – Discovery Phase. The project focused on creating a network of higher education stakeholders including students, educators, HE managers, regulators and artificial intelligence experts. Its primary goal is to collaboratively explore and develop a regulatory framework for the adoption of Generative AI (GenAI) within the UK's higher education sector.

Spatial layouts help to shape retail consumer movement, which in turn plays a role in determining the distribution of retailers and performance of retail space on city network. Spatial configuration can be understood through street segment analysis, computing to-movement (integration) and through-movement (choice) metrics within a given set of connecting street networks, making it possible to assign syntactic values to individual street segments (space). In this paper, such syntactic values for the cities of Leeds and York have been established to indicate a spatial accessibility index that can be used to understand potential human (consumer) movement on spatial layouts. Other studies have established relationships between computed syntactic values and ranges of socio-economic activities, including land uses and urban value distributions. However, little is known about how configured (movement) metric outputs relate to changes in retail space’s rental values (as proxy for retail space performance) across different city network scales. In response, this study investigates the relationship between retail space performance and consumer movement patterns (CMP) within sampled spatial layouts. The CMP are defined as spatial configuration metric outputs of integration, choice and normalised angular choice (NACH) metrics, computed at macro (city) and meso (city centre) scales. Street segment analysis on spatial layouts at city (macro) and city-centre (meso) scales were computed using DepthMapX tool to obtain the CMP variables. The computed syntactic values of CMP variables were then exported as point features into QGIS for analysis with the retail space performance within the sampled spatial layouts. Rental value data for years 2010 and 2017 were obtained from the Valuation Office Agency VOA datasets for York and Leeds. The two datasets were linked through a common key variable (Unique Address Reference Number) to compute rental value changes using MS Access and MS Excel tools. The rental value change table was also exported as point features into QGIS for geospatial analysis with the computed syntactic values of CMP variables. To achieve this, the study utilises vector grid (developed at 500m X 500m at city scale, and 200m X 200m at city centre scale for both cities) to a create uniform platform for all variables per grid. The relationship outputs between variables were investigated at macro city scale and meso city-centre scale for the two cities. The study reveals that there are variations in relationships between retail space performance and computed movement syntax across different scales of spatial layouts. The variables exhibit significant positive relationships at mesoscale (city centre), while variables exhibit weak correlation at the macroscale (city) for both cities. It further reveals that the integration (to-movement) metric has the most significant impact on retail space performance, with the through-movement metric having the least impact across all spatial layouts. On this basis, the study conclude that integration metric has the capability of signalling future of retail space (rental value) performance at city mesoscale layouts.

The Nigerian real estate market is among the least transparent globally, characterized by undocumented transactions, unreliable market data, and inadequate property registration systems. This lack of transparency fosters corruption, reduces market efficiency, hinders socio-economic development, and deters foreign direct investment in the property sector. This study addresses these challenges by exploring the potential integration of Geographic Information Systems (GIS) and Artificial Intelligence (AI) for real estate data capture, storage, analysis, and management to enhance market transparency in Nigeria. It evaluates the current state of transparency in Nigeria's property market and identifies key factors contributing to its opacity. Additionally, the study examines the opportunities that GeoAI presents for improving market transparency. Insights from this research will be valuable for implementing GeoAI technology in other countries facing similar challenges related to real estate market transparency.

This study explored retail location performance of cities by investigating relationships between changes in retail property stock (supply), changes in retail rental value (demand), and spatial accessibility (retail consumer movement) across three UK cities, namely, Leeds, Newcastle, and York. This is to understand how retail locations and assets can be managed sustainably. In this sense, sustainability was considered through a dual focus in this paper: (1) the efficient use of retail property assets for economic purposes and (2) the impact of these physical retail assets on the local environment in terms of carbon footprint. The study relied on space syntax ideology in computing spatial accessibility index and adopted business rate datasets in computing changes in retail rental value and stock. Findings showed that spatial accessibility across retail locations could predict the performance of retail rental value (but not stock) across the sampled cities. The study further showed that extent of city analysis (scale) is significant in estimating retail location performance and understanding the influence of accessibility. This evidence has the potential to facilitate better decision-making concerning the planning, design, and management of retail locations and spaces. The study is significant because it can serve as a reference for promoting an urban sustainability agenda, especially in ensuring that urban land and properties are used optimally to maximise their social, economic, and environmental values.

This study explores the potential of GIS to map and analyse the distribution, stock and value of commercial and industrial property using rating data compiled for the purposes of charging business rates taxation on all non-residential property in the UK. Rating data from 2010, 2017 and 2019, comprising over 6000 property units in the City of York, were filtered and classified by retail, office and industrial use, before geocoding by post code. Nominal rateable values and floor areas for all premises were aggregated in 100 m diameter hexagonal grid and average rateable value calculated to reveal changes in the distribution and value of all employment floorspace in the City over the last decade. Temporospatial analysis revealed polarisation of York’s retail property market between the historic city centre and out-of-town locations. Segmenting traditional retail from food and drink premises revealed growth in the latter has mitigated the hollowing out of the city core. This study is significant in developing a replicable and efficient method of using GIS, using a nationally available rating dataset, to represent changes in the quantum, spatial distribution and relative value of employment floorspace over time to inform local and national land administration, spatial planning and economic development policy making.

This study explores how sustainable retail locations can be achieved within the urban areas vis-a-vis the applications of GeoAI. The study develops conceptual framework on the integration of relevant datasets, tools, algorithms, and techniques for developing smart geo-spatial tools capable of aiding smart decisions on various activities including, retail real estate development, investment, occupation, and efficient urban centre planning. The study rests on three (3) broad underlying principles. That is: (1) retail consumers (directly or indirectly) control the retail property markets and retail property location performance (2) spatial behaviour of retail consumers can be scientifically assessed and scored based on interconnectedness of streets (that is, space syntax theory) through spatial configuration analysis and (3) historical data on retail property performance could help in predicting retail location performance and resilience index using predictive machine learning algorithms. The study argues that a rethinking of the distribution of physical retail spaces would be appropriate to ensure various classes of retail real estate are optimally positioned in locations that best meet the present needs of the stakeholders whilst considering the future implications of their actions.

One of the characteristics of a healthy town centre is the presence of a vibrant retail offer and low vacancy rates. Not all town centres are dominated by retail outlets, however, the importance of retailing to the socio-economic wellbeing of town centres (places and people) and national developments cannot be overemphasized (Dixon, 2007; Oshea, 2017). Despite the increasing consumer preference for online retailing, market share of in-store (bricks & mortar) retailing is still well above 70% (ONS, 2017). In other words, shoppers still like to visit shops, they just don’t necessarily carry out transactions at the till. Although the nature of retailing continues to change, retail outlets and town and city centres alike, remain reliant on the footfall and spending characteristics of shoppers. Key to understanding this situation, is discerning, and then interpreting, the various existing data sets and software tools that can be used to measure and analyse retail space performance. This article sheds light on this situation by exploring existing datasets in the UK – its final output is a taxonomy of retail relevant data and software packages. This output can be used as a reference for anyone considering measuring and analysing performance of town and city centre retail spaces using geo-spatial techniques.

The data include street segments, rental values, stock, and clusters of retail properties across the cities of Leeds, Newcastle, and York. This forms part of PhD research undertaken at Northumbria University, with research outputs presented in the Journal of European Real Estate Research, titled Investigating Retail Property Market Dynamics through Spatial Accessibility Measures (2019). The publication is available online at: [https://doi.org/10.1108/JERER-01-2018-0009]. Further research outputs are also published in the Sustainability journal, titled Towards Attaining Sustainable Retail Property Locations: The Relationships between Supply, Demand, and Accessibility of Retail Spaces (Sustainability, 14(7): 3846, 2022). This publication is available online at: [https://doi.org/10.3390/su14073846]

Purpose The retail property market is constantly adopting to the continuous demand of retailers and their consumers. This paper aims to investigate retail property market dynamics through spatial accessibility measures of the City of York street network. It explores how spatial accessibility metrics (SAM) explain retail market dynamics (RMD) through changes in the city’s retail rental values and stock. Design/methodology/approach Valuation office agency (VOA) data sets (aspatial) and ordnance survey map (spatial) data form the empirical foundation for this investigation. Changes in rental value and retail stock between 2010 and 2017 VOA data sets represent the RMD variables. While, the configured street network measures of Space Syntax, namely, global integration, local integration, global choice and normalised angular choice form the SAM variables. The relationship between these variables is analysed through geo-visualisation and statistical testing using GIS and SPSS tools. Findings The study reveals that there has been an overall negative changes of 15 and 22% in rental value and retail stock, respectively, even though some locations within the sampled city (York, North Yorkshire, England) indicated positive changes. The study further indicated that changes in retail rental value and stock have occurred within locations with good accessibility index. It also verifies that there are spatial and statistical relationship between variables and 22% of RMD variability was jointly accounted for by SAM. Originality/value This research is first to investigates changes in retail property market variables through spatial accessibility measures of space syntax. It contributes to the burgeoning research field of real estate and Space Syntax.

This presentation was delivered online on 28th February 2025 as part of the 4th Land Management Network Conference, hosted by the University of the West of England, Bristol, UK.

The understanding that property value, street connectivity, accessibility, and peoples’ wellbeing are related could help in achieving sustainable environment. Urban and property decision makers including developers, investors, planners, and occupiers are now challenged with need to balance economy, society, and environmental needs to achieve sustainability. In a practical sense, a given city centre space containing retail property (dominantly) should have physical retail shops positioned in optimum (prime) locations that will attract the high footfall of consumers, reduces carbon footprint of consumers/suppliers, and generate optimum return for landlords and the property occupiers. This research maps out optimum locations of real shops (retail property) to achieve sustainable retail location within Leeds city centre. This study explores how sustainable retail locations could be achieved viz-a-viz interconnectivity of street network and its relationships with retail property market data. The study explores the spatial relationships between retail property value, consumer movement and shopping destinations. The work rests on the assumptions of space syntax that the interconnectivity of street network influence influx of people movement, property value and people wellbeing. The study analyses the spatial layouts of Leeds city centre (UK) using DepthMap and QGIS to better understand how sustainable retail locations can be achieved base on existing theory of spatial configuration. The work shows that reconfiguration and repositioning of certain numbers retail property could enhance property value, consumer movement and wellbeing, thereby increasing the chance of achieving a sustainable retail environment. The study recommends similar investigations into other urban centres to better understand how sustainability could be achieved in an urban setting.

The data contains processed datasets at various stages for an experimental research on application QGIS in estimating real estate market performance and resilience capacity of locations with Bracknell town centre.

Understanding real estate location performance is crucial for effective urban decision-making. This study develops an innovative GIS-based approach to measure and visualise market dynamics at the intra-city level by analysing locational changes in the demand and supply of real estate over a decade, mapping performance and resilience before and after the COVID-19 shock. The results indicate that the shock did not significantly disrupt market elasticity, and the technique produces 2D and 3D visual outputs that support smart decision-making in investment, development, management, and urban planning. Although the findings may be influenced by the specific real estate type examined, the study calls for broader application across various asset classes and spatial layouts, and highlights opportunities for further development using GeoAI algorithms.

Seminar Presentation at the 19th European Real Estate Society Education Seminar

Purpose Embracing digitisation within the building surveying profession will enhance its practices and, of course, improve productivity. However, the level of digitisation within the building surveying profession is very low. Thus, this study aims to identify factors impacting technology adoption within the building surveying professions and provide practical ways of improving the adoption of technology. Design/methodology/approach This study employed a convergent mixed-methods approach to identify digital technologies applicable to building surveying professions. The study also investigates factors influencing technological adoptions and provides ways of improving their adoption. The data collected were analysed using thematic analysis and ordinary least squares regression. Findings The study found that business communication platforms and smartphone applications are frequently used, while digital survey equipment and in-house developed applications are less commonly utilised by building surveyors. The influencing factors identified are economy, technical knowledge, culture, efficiency and regulatory factors. The study recommends increased education and training for building surveyors, promotional opportunities from manufacturers and government intervention in the form of subsidies or tax breaks to promote further digitisation within the building surveying profession. Originality/value This study provides valuable insight into strategies for the digitalisation of the building surveying profession. Application of the findings would promote further utilisation of digital technologies.

Suboptimal management of healthcare waste poses a significant concern that can be effectively tackled by implementing Internet of Things (IoT) solutions to enhance trash monitoring and disposal processes. The potential utilisation of the Internet of Things (IoT) in addressing the requirements associated with biomedical waste management within the Kaduna area was examined. The study included a selection of ten hospitals, chosen based on the criterion of having access to wireless Internet connectivity. The issue of biomedical waste is significant within the healthcare sector since it accounts for a considerable amount of overall waste generation, with estimates ranging from 43.62 to 52.47% across various facilities. Utilisation of (IoT) sensors resulted in the activation of alarms and messages to facilitate the prompt collection of waste. Data collected from these sensors was subjected to analysis to discover patterns and enhance the overall efficiency of waste management practices. The study revealed a positive correlation between the quantity of hospital beds and the daily garbage generated. Notably, hospitals with a higher number of beds were observed to generate a much greater amount of waste per bed. Hazardous waste generated varies by hospital, with one hospital leading in sharps waste (10.98 kgd-1) and chemical waste (21.06 kgd-1). Other hospitals generate considerable amounts of radioactive waste (0.60 kgd-1 and 0.50 kgd-1), pharmaceuticals, and genotoxic waste (16.19 kgd-1), indicating the need for specialised waste management approaches. The study sheds light on the significance of IoT in efficient waste collection and the need for tailored management of hazardous waste.

The study examines the influence of Generative Artificial Intelligence (GenAI) technology on teaching and learning within the built environment discipline from the perspective of academics. It explores the relationships between academics’ experience, AI knowledge, willingness to adopt AI technologies and their capacity to detect student use of AI. A mixed-methods approach was employed, incorporating qualitative interviews and quantitative surveys with built environment academics. A web scraping technique was used to obtain the contact details of potential research participants for purposive sampling, resulting in a sample of 56 participants from 42 UK universities offering built environment education. Cramér’s V coefficient was applied to analyse the relationships between the variables. The findings suggest that academics’ experience significantly affects their adoption of AI, their preparedness to adapt assessments and their ability to detect AI use by students. Academics with broader subject expertise are more inclined to embrace GenAI and adjust teaching practices. These insights contribute to policy development for integrating GenAI into built environment pedagogy and support its wider adoption in higher education.

The relevance of traditional dissertation models is increasingly being challenged by the rapid evolution of generative AI technologies such as ChatGPT and similar tools. This study explores the implications of these technologies for dissertation practice within built environment pedagogy. Specifically, it examines the positive and negative impacts of generative AI on students’ dissertation work and the influence of these technologies on dissertation assessment processes. Dissertation or thesis writing remains a core requirement for degree completion at both undergraduate and postgraduate levels, serving as evidence of students’ academic competence and disciplinary understanding. Within built environment disciplines, the dissertation has long been a key component of pedagogical practice. However, the emergence of generative AI necessitates a critical reassessment of how dissertations are conceptualised, undertaken, and evaluated. This study aims to: 1. Evaluate existing models for assessing dissertations within built environment pedagogy; 2. Examine the potential impacts of generative AI on the quality and authenticity of dissertations; 3. Investigate the influence of generative AI on dissertation assessment practices; and 4. Propose revised assessment models suited to the evolving educational landscape. Using a focus group research approach involving selected undergraduate and postgraduate students, as well as academics in the built environment disciplines, initial findings highlight the urgent need to reconsider and adapt dissertation assessment frameworks in response to the transformative effects of generative AI. The study underscores the need to rethink dissertation supervision and assessment within built environment pedagogy. It calls for the development of ethical and process-oriented assessment frameworks. These insights aim to guide the evolution of dissertation practices that remain both relevant and credible in the AI era. Keywords: Built environment pedagogy, AI, dissertation, assessment

The resilience capacities of commercial property markets and locations came to light with the market shock brought about by COVID-19 pandemic. The occurrence led to different property classes and locations responding ununiformly to the economic shock. As such, proceeds from investing and demanding (occupier and investment) of retail premises varies across locations. As property stakeholders continue to deal with the challenges influencing the demand and supply of retail properties, including, increasing vacancy rates, reduction in absorption rates and increasing store closures. It is important to have better understanding of how retail locations respond to the impact of COVID 19 (market shock) with a view of establishing retail location optimisation for better utilisation and management of urban resources. This study investigates resilience capacity (and reactions) of retail locations within sampled city (Leeds) with a view of developing optimum retail location model. The study explores and develop digital model that identifies, scores, classifies, and predicts optimum retail location base on resilience and performance of urban retail locations. The study adopts geospatial variables of rental value (at different dates) and accessibility metrics to grade performance and resilience index of retail locations before and after COVID 19. The study finds that a strong relationship exists between estimated retail location performance and the resilience index distribution of retail property locations in Leeds. That is, the market shock has high impact on retail property location after COVID-19 but less altercating impact on the prediction of commercial property location performance (as measured by rental value and accessibility). The result suggests that optimum localisation of retail property can be achieved viz-a-viz geospatial estimation of retail rental value through accessibility index. This pilot study calls on similar investigation on other urban settings to achieve comprehensiveness in modelling optimum retail location.