Considering spatial accessibility of health services is a critical part in the planning and management of health services. There is evidence that poor geographical locations can obstruct prompt basic health care services to some population sections. We developed a location-allocation P-median model for health centres after analysing their sites, demand location of health services and the road network in Jeddah, Saudi Arabia. This model attempts to optimize health care services network and to put forward location recommendations to maximise service coverage. Our model is shown to be useful as it provides a robust evidence base to urban planners and policymakers responsible for making spatial decisions for the development of the health sector. Besides, it follows the paradigm of new urbanism that encourages decentralisation of essential facilities including basic healthcare in cities, where emphasis is on offering all basic services within walkable distances of 15 min. or less.

Type-2 diabetes is a growing lifestyle disease mainly due to increasing physical inactivity but also associated with various other variables. In Saudi Arabia, around 58.5% of the population is deemed to be physically inactive. Against this background, this study attempts explore the spatial heterogeneity of Type-2 diabetes prevalence in Jeddah and to estimate various socio-economic and built environment variables contributing to the prevalence of this disease based on modelling by ordinary least squares (OLS), weighted regression (GWR) and multi-scale geographically weighted (MGWR). Our OLS results suggest that income, population density, commercial land use and Saudi population characteristics are statistically significant for Type-2 diabetes prevalence. However, by the GWR model, income, commercial land use and Saudi population characteristics were significantly positive while population density was significantly negative in this model for 70.6%, 9.1%, 26.6% and 58.7%, respectively, out of 109 districts investigated; by the MGWR model, the corresponding results were 100%, 22%, 100% and 100% of the districts. With the given data, the corrected Akaike information criterion (AICc), the adjusted R2, the log-likelihood and the residual sum of squares (RSS) indices demonstrated that the MGWR model outperformed the GWR and OLS models explaining 29% more variance than the OLS model, and 10.2% more than the GWR model. These results support the development of evidence-based policies for the spatial allocation of health associated resources for the control of Type-2 diabetes in Jeddah and other cities in the Arabian Gulf.

This study aimed to evaluate the impact of participatory urban planning on community well-being and quality of life. Urban planning is done to transform urban areas into sustainable cities with adequate open spaces and many features of smart cities. To achieve successful urban planning, active and involved participation of the stakeholders is necessary. Many aspects, like the social and cultural identities of the nation, its technological competencies, adequate knowledge and expertise in urban planning and sustainable development, are required. A survey of 150 Saudi professionals with expertise or interest in urban development and community well-being showed that the above results have only been partially achieved. These results were only partially and sometimes indirectly supported by the literature. Community participation in urban planning may be improved by using social media platforms and information and communication technologies. These methods will be useful to connect urban planning officials/local governments with concerned stakeholders. Although the specific social and cultural identities of Saudi Arabia need consideration in ensuring community participation, this should not be at the exclusion of non-Saudi communities.

Coworking spaces have become an increasingly important component of contemporary urban economies, yet their spatial organisation remains poorly understood in rapidly developing Gulf cities. This study examines the spatial clustering and urban correlates of coworking space locations in Jeddah, Saudi Arabia. A dataset of 53 coworking spaces was integrated with points of interest, built-up land, and road-network data within a GIS framework. Spatial pattern was evaluated using Monte Carlo nearest-neighbour analysis, kernel density estimation, and Ripley’s L-function. A 1 km grid was subsequently used to model coworking space presence through binary logistic generalised linear regression, with a 2 km grid used for sensitivity analysis. Coworking spaces exhibited pronounced spatial clustering: the observed mean nearest-neighbour distance was 1023 m compared with 2667 m under complete spatial randomness (nearest-neighbour ratio = 0.384, Monte Carlo p = 0.001). The observed centred Ripley’s L-function exceeded the upper pointwise 95% CSR simulation envelope throughout the evaluated 0.25–10 km range, indicating multiscale spatial concentration. In the final 1 km model, business density (OR = 2.13, p < 0.001), built-up proportion (OR = 1.042 per percentage point, p < 0.001), and road density (OR = 1.075, p = 0.008) were positively associated with coworking space presence. Business density and built-up proportion remained significant at the 2 km scale, whereas the road-density association weakened. These findings suggest that coworking spaces in Jeddah are primarily embedded within business-intensive, highly urbanised, and accessible parts of the metropolitan area while also demonstrating sensitivity to spatial analytical scale.