The primary objective of this study was to evaluate the physical accessibility of existing mental health facilities for international students and to propose a framework for geographical infrastructure enhancement. This framework is intended for implementation by TUM to ascertain the optimal locations for new facilities, informed by the concentration of student housing.
The Journey to Your Wellbeing
"This project develops a geospatial framework and an interactive dashboard to optimise student access to mental health facilities in Munich, demonstrating a strategic increase in accessibility from 21% to 60%."
The findings show that 21% of facilities are currently accessible within 30 minutes (red heart). Proposed Mental Health facilities are projected to increase accessibility by 60% (blue heart).
Assessment of the physical accessibility of International students at TUM to Mental Health Facilities
How we built it
Dataset: International student private residence data (ArcGIS Survey123), Studentenwerk Halls of Residence (OSM API), TUM existing mental health facilities (OSM API), Oberbayern Street Network data(OSM), General Transit Feed Specification – Munich public transport Schedules.
Software Stack: Python, Jupyter Notebook, ArcGIS PRO, ArcGIS Online, Experience Builder, SURVEY 123, GitHub.
Module Stack: r5py, Geopandas, TravelTimeMatrix, Kernel Density(KDE), pandas, os(operating system), datatime.
The first phase of the study involved a baseline analysis to assess the current physical accessibility of Existing Facilities for students living in Private Residences and Studentenwerk halls of residence. We used the r5py routing engine ingested with OSM network, GTFS, Student survey data, and Studentenwerk halls data to build a navigable transport network and calculated the reachable existing mental health facilities for 15, 30, 45, and 60 minutes, and this analysis revealed a gap of only ~21% of the residences being within 30 minutes of the existing facilities.
Additionally, Kernel Density Estimation (KDE) Clustering was used to perform a spatial density analysis of students’ residences to identify hotspot zones, and 3 optimal locations were proposed based on this analysis to maximise facility coverage. Using the proposed locations as a baseline, another network routing similar to the existing facilities was performed, and the validation showed approximately a 40% increase in coverage, i.e., 60% of the students living in private residences and studentenwerk halls of residence are now within 30 minutes of the necessary help.
Furthermore, Isochrones were generated as vector polygons around the existing and proposed facilities using 15, 30, 45, and 60-minute time thresholds, also based on the Bavaria street network, and public transport schedules were generated as vector polygons using multi-modal service areas. These isochrones are the final export ingested into Experience Builder to create a web map that can be used by students to choose the closest mental health facility to them, and by TUM stakeholders to identify locations to site new mental health facilities for easier accessibility to their students.
Software Stack: Python, Jupyter Notebook, ArcGIS PRO, ArcGIS Online, Experience Builder, SURVEY 123, GitHub.
Module Stack: r5py, Geopandas, TravelTimeMatrix, Kernel Density(KDE), pandas, os(operating system), datatime.
The first phase of the study involved a baseline analysis to assess the current physical accessibility of Existing Facilities for students living in Private Residences and Studentenwerk halls of residence. We used the r5py routing engine ingested with OSM network, GTFS, Student survey data, and Studentenwerk halls data to build a navigable transport network and calculated the reachable existing mental health facilities for 15, 30, 45, and 60 minutes, and this analysis revealed a gap of only ~21% of the residences being within 30 minutes of the existing facilities.
Additionally, Kernel Density Estimation (KDE) Clustering was used to perform a spatial density analysis of students’ residences to identify hotspot zones, and 3 optimal locations were proposed based on this analysis to maximise facility coverage. Using the proposed locations as a baseline, another network routing similar to the existing facilities was performed, and the validation showed approximately a 40% increase in coverage, i.e., 60% of the students living in private residences and studentenwerk halls of residence are now within 30 minutes of the necessary help.
Furthermore, Isochrones were generated as vector polygons around the existing and proposed facilities using 15, 30, 45, and 60-minute time thresholds, also based on the Bavaria street network, and public transport schedules were generated as vector polygons using multi-modal service areas. These isochrones are the final export ingested into Experience Builder to create a web map that can be used by students to choose the closest mental health facility to them, and by TUM stakeholders to identify locations to site new mental health facilities for easier accessibility to their students.
Challenges we ran into
The main challenge we encountered was in the process of automating the analysis using Python. We were exposed to some libraries/modules we hadn't used before, and to ensure the spatial accuracy of the output was consistent, we ran many trials and errors before we finally got a satisfying output. During the trial and error stage, we thought “what if we end up not meeting the deadline? ”´, but thankfully, everything worked out, and as we envisioned.
What we're proud of
We are proud that this project tackles the critical issue of mental health through a distinct geographic perspective. The core of our research was a vital question: do existing mental health facilities truly offer accessibility to students with a maximum commute of 30 minutes? Our interactive web map is the answer, a solution that directly addresses these accessibility gaps. It serves as a crucial strategic resource for the university while simultaneously offering support and relief to every student managing their mental health journey.
What we learned
The project not only enhanced our existing expertise but also introduced new knowledge, particularly regarding the Python and ArcGIS products utilised. Beyond technical growth, we acquired new managerial competencies and cultivated trust with our partner. The successful completion of this project is a direct result of effective teamwork with a reliable partner.
What's next
Our survey data include information on international students' nationalities and preferred languages. Although some facilities offer English services, many TUM international students in distress, particularly those from non-English speaking countries, would prefer to communicate in their native language. Consequently, our future strategy will focus on addressing the language gap among service providers and reducing wait times, which are significant obstacles for mental health services in Germany.
The findings show that 21% of facilities are currently accessible within 30 minutes (red heart). Proposed Mental Health facilities are projected to increase accessibility by 60% (blue heart).
Visualisation of the Near Me tool around the nearest mental health facilities. The green box shows the closest while the pink box shows the farthest away.
Visualisation of the Direction tool showing the driving path from point A - point B
Sources
Ansari Lari, S., Zumot, M. S., and Fredericks, S. (2025). Navigating mental health challenges in international university students: adapting to life transitions. Frontiers in Psychiatry, 16(1574953). https://doi.org/10.3389/fpsyt.2025.1574953
Xie, Q., Zhu, Y., Lin, T., Yin, Z.., and Goldberg, S. (2024). Bridging the Mental Health Care Gap for International Students via Digital Interventions [Preprint]ResearchGate. https://doi.org/10.31234/osf.io/8b3j9
Shek, C. H. M., Chan, S. W. C., Stubbs, M. A., and Lee, R. L. T. (2024). Promoting International Students' Mental Health Unmet Needs: An Integrative Review. International Journal of Mental Health Promotion. https://doi.org/10.32604/ijmhp.2024.055706
Xie, Q., Zhu, Y., Lin, T., Yin, Z.., and Goldberg, S. (2024). Bridging the Mental Health Care Gap for International Students via Digital Interventions [Preprint]ResearchGate. https://doi.org/10.31234/osf.io/8b3j9
Shek, C. H. M., Chan, S. W. C., Stubbs, M. A., and Lee, R. L. T. (2024). Promoting International Students' Mental Health Unmet Needs: An Integrative Review. International Journal of Mental Health Promotion. https://doi.org/10.32604/ijmhp.2024.055706
Students
Aderonke Adetoro and Erika Pazmino
15th intake
Supervisor
Juliane Cron, M.Sc.
Keywords
Mental Health, Network Analysis, WebMap, TUM International Students
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