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This paper explores how participatory science can enhance building services on a higher education campus. The use of participatory science aims to involve students, faculty members, and technical teams in improving the management of the campus through their participation in data collection and evaluation of the building services. It represents a valuable alternative for campuses needing more building monitoring. The paper also shows how the performance of participatory science could be improved by combining digital technologies such as Building Information Modeling (BIM) and artificial intelligence (AI). The framework is applied to the Faculty of Engineering at An-Najah National University to improve the building services of the campus. A combination of users’ feedback and AI-generated synthetic data is used to explore the performance of the proposed method. Results confirm the high potential of participatory science for improving the services and quality of life on higher education campuses. This is achieved through students’ active participation and involvement in data collection and reporting on their individual experiences.

Assessing urban service locations is a key issue within city planning, integral to promoting the well-being of citizens, and ensuring effective urban development. However, many current approaches emphasize spatial analysis focused solely on physical attributes, neglecting the equally vital social dimensions essential for enhancing inhabitants’ comfort and quality of life. When social factors are considered, they tend to operate at smaller scales. This paper addresses this gap by prioritizing integrating social factors alongside spatial analysis at the community level. By employing space syntax theory, this study investigates urban service suitability in Hajjah, a Palestinian urban community, presenting a novel approach in the literature. The research identifies good spots for essential governmental facilities like health clinics and fire stations using axial map analysis. It also suggests reallocation for some schools. Additionally, it shows ways to improve the placement of community amenities, finding ideal park locations but suboptimal mosque placements. Commercial services also exhibit areas for enhancement including gas stations and shops. The insights from this research can offer policymakers and planners insights to create more efficient, equitable, and accessible cities. The research approach incorporates social behavior dynamics into spatial analysis, promoting inclusive urban planning.

This paper presents two complementary methods: an approach to compute a network data-set for indoor space of a building by using its two-dimensional (2D) floor plans and limited semantic information, combined with an optimal crowd evacuation method. The approach includes three steps: (1) generate critical points in the space, (2) connect neighbour points to build up the network, and then (3) run the optimal algorithm for optimal crowd evacuation from a room to the exit gates of the building. Triangulated Irregular Network (TIN) is used in the first two steps. The optimal evacuation crowd is not based on the nearest evacuation gate for a person but relies on optimal sorting of the waiting lists at each gate of the room to be evacuated. As an example case, a rectangular room with 52 persons with two gates is evacuated in 102 elementary interval times (one interval corresponds to the time for one step for normal velocity walking), whereas it would have been evacuated in not less than 167 elementary steps. The procedure for generating the customized network involves the use of 2D floor plans of a building and some common Geographic Information System (GIS) functions. This method combined with the optimal sorting lists will be helpful for guiding crowd evacuation during any emergency.