Spatial and Modal Patterns of Traffic Law Enforcement in Barcelona: Insights from Open Government Data

Suggested by: Martin Loidl

Short description: Barcelona has implemented a comprehensive Open Government Data (OGD) strategy, making a wide range of urban data publicly accessible. Among these datasets is a detailed, geo-located record of traffic fines issued by law enforcement, including contextual information such as time, location, type of infraction, and road user category. This dataset presents a unique opportunity to explore the spatial, temporal, and modal dimensions of traffic law enforcement in an urban context.

Despite the central role of law enforcement in traffic management and safety, there is a notable research gap regarding the patterns and equity of traffic fines. This thesis aims to fill that gap by addressing questions such as:

• Modal equity: Are traffic fines proportionally distributed across different road user groups (e.g., pedestrians, cyclists, motorists) relative to their mode share?
• Spatial and temporal hotspots: Are there identifiable hotspots or time windows where specific road users are disproportionately fined?
• Safety correlation: Is there a spatial or temporal correlation between enforcement activity and traffic crash occurrences?
• Infrastructure linkages: Can fines issued to vulnerable road users (e.g., pedestrians and cyclists) be linked to deficiencies in infrastructure quality or design?
• Policy implications: What do the patterns of enforcement suggest about the priorities and effectiveness of traffic law enforcement strategies? 

The dataset is available at https://opendata-ajuntament.barcelona.cat/data/en/dataset/denuncies_sancions_transit_bcn_detall. Research carried out can be co-supervised by local experts based in Barcelona.

References, suggested reading:

• Chaloux, N., & El-Geneidy, A. (2019). Rules of the Road: Compliance and Defiance among the Different Types of Cyclists. Transportation Research Record, 2673(9), 34-43. https://doi.org/10.1177/0361198119844965 
• Yasanthi, R. G. N., Wickens, C. M., Jonah, B., Mehran, B., & Suggett, B. (2024). Determinants of traffic safety enforcement behaviour among police officers: A narrative review. Case Studies on Transport Policy, 16, 101206. https://doi.org/10.1016/j.cstp.2024.101206
• Castillo-Manzano, J. I., Castro-Nuño, M., López-Valpuesta, L., & Pedregal, D. J. (2019). From legislation to compliance: The power of traffic law enforcement for the case study of Spain. Transport Policy, 75, 1-9. https://doi.org/10.1016/j.tranpol.2018.12.009 

Start/finish: anytime

Prerequisites/qualifications: Interest in planning and mobility research as well as in advanced spatial analysis. Data management and analysis skills are ultimately required. Scripting and coding skills are benefitial.

 

BEAM: Extending the European Assets Map methodology

Suggested by: Johannes Scholz, Daniel Hölbling and Manuel Strohmaier (Joanneum Research Life)

Keywords: Risk Assessment, BEAM Methodology, Geospatial Data Generation

Objective:

1. Extend the Basic European Assets Map (BEAM) methodology, which estimates asset values in €/m², to additional European countries using publicly available data sources such as OpenStreetMap, CORINE Land Cover, Urban Atlas, and Eurostat.
2. Develop a consistent, Europe-wide dataset to support improved and comparable assessments of potential damages from natural hazards, enabling more robust risk analyses at the continental scale.
3. Compare and evaluate the resulting dataset against a dataset developed by JOANNEUM RESEARCH, and explore enhancements through the integration of geographically localized building data.

Short Description:

Natural hazards pose varying levels of risk across Europe, and consistent, spatially resolved asset data are essential for effective risk assessment. The Basic European Assets Map (BEAM) provides monetary asset values per unit area (€/m²) and has already been implemented and validated for Germany (Copernicus Emergency Management 2020). This thesis aims to generalize and apply the BEAM methodology to other European countries using harmonized, openly available data sources. The result will be a comprehensive and standardized dataset for estimating damage potentials across Europe. To ensure reliability and usability, the new dataset will be compared to a reference dataset developed by JOANNEUM RESEARCH and further improved by incorporating detailed, geographically referenced building information. This work contributes to the foundation for large-scale, comparative risk assessments in the context of natural hazards.

Start: ASAP

Copernicus Emergency Management Service Risk and Recovery Mapping 2024: https://mapping.emergency.copernicus.eu/download/EMSN076/EMSN076_Final-Report_v1_20082025.pdf

Developing Calibration-Free Webcam Eye Tracking for Web Map Interaction

 How can we interact with web maps without using our hands? Can webcams replace traditional input devices and make map-based tools more inclusive? How can we estimate and refine a user’s gaze without a calibration phase? 

This MSc thesis will focus on developing a browser-based, calibration-free eye tracking system for hands-free web map interaction. The student will build a gaze-aware map prototype using open-source libraries (e.g., WebGazer.js) and apply statistical modeling (e.g., least squares) to continuously refine gaze accuracy without requiring a manual calibration phase. Key features such as gaze-based pan, zoom, and select interactions will be integrated and tested on a custom-built web map interface. 

The research contributes to digital accessibility for users with upper-limb impairments. The student will have the opportunity to contribute to a broader interdisciplinary initiative on inclusive cartographic design and collaborate with researchers in human-computer interaction, eye tracking, and web mapping. 

For more information: 
Contact: Dr. Merve Keskin, merve.keskin@plus.ac.at 
Start: As soon as possible 
Prerequisites/qualification: Basic experience with JavaScript, web development, or eye tracking libraries (e.g., WebGazer.js) is an advantage but not necessary. 
Keywords: Eye tracking, gaze-based interaction, web mapping, accessibility, user-centered design 

Setting up "Colouring Austria" platform

Thesis Type: Bachelor

Suggested by: Please contact Dr. Merve Keskin, merve.keskin@plus.ac.at 

Keywords: Building data, open platforms, data visualization, citizen science 

Objective: How many buildings are there in a city? What are their characteristics? Where are they located and how do they contribute to the city? How adaptable are they? How long will they last, and what are the environmental and socio-economic implications of demolition?  

This Bachelor thesis will focus on setting up the "Colouring Austria" platform, gathering building footprints for Salzburg, Linz, and Graz and enriching this building information with diverse thematic attributes. The platform will be based on the open code available at https://github.com/colouring-cities. The research is conducted in collaboration with the University of Salzburg, UniGraz and I:TU and will provide the Bachelor student with the opportunity to work in a multi-disciplinary team. 

Short description/Info about the project: The "Colouring Austria" aims to support Austrian cities in their transition towards climate-neutral and sustainable urban environments through active citizen participation and the crowdsourced data. Building upon the international Colouring Cities Research Programme (CCRP) and successful implementations in cities including London, Dresden, and Sydney, among others (Roper et al., 2022; Danke et al., 2024; Hecht et al., 2023), this research-led initiative will establish Austria's first comprehensive open-data platform dedicated to urban sustainability and facilitates transparent and inclusive data collection and visualization with a thematic focus on energy performance, building typologies, and mobility patterns. 

Start: As soon as possible  

Prerequisites/qualification: Familiarity with GIS, PostgreSQL, github 

POI Recommendation Based on "Sense of Place" extracted from LLMs

 Suggested by: Omid Reza Abbasi, Johannes Scholz and Dominik Kowald (KnowCenter)

Keywords: POI Recommendation, Sense of Place, LLMs

Objective:

1. Develop a methodology for extracting features that capture "sense of place" from textual and visual data using LLMs. This involves exploring techniques such as sentiment analysis, topic modeling, style extraction, and image captioning to identify emotional, cultural, and atmospheric attributes of locations. 

2. Evaluate the impact of "sense of place" features on the performance of location-based recommender systems. This includes comparing the performance of recommender systems with and without these features, using metrics such as user satisfaction, engagement, and discovery of unique experiences. 

3. Investigate the applicability of "sense of place" feature engineering across different location types and user demographics. This involves testing the proposed methodology on various datasets, including restaurant reviews, travel blogs, and social media posts related to tourist attractions and local events. 

Short Description: Location-based recommender systems play a crucial role in helping users discover and experience places that align with their interests. However, traditional approaches often focus on objective attributes like distance, ratings, and popularity, neglecting the subjective and emotional dimension of "sense of place." This thesis proposes to investigate how Large Language Models (LLMs) can be utilized to extract features that capture the unique atmosphere, cultural significance, and emotional resonance of locations, thereby enhancing the personalization and relevance of recommendations. The core challenge addressed in this thesis is the inability of existing location-based recommender systems to understand and incorporate the intangible qualities that contribute to a location's "sense of place." User reviews, social media posts, and geotagged photos often contain valuable insights into the emotional and cultural aspects of a location, but these are frequently overlooked by conventional methods. This thesis aims to explore how LLMs can be leveraged to extract these features, enabling a more thorough understanding of user preferences and place characteristics. 

Start: Anytime

Relevant Studies:

Wan, Zhizhong, et al. "LARR: Large Language Model Aided Real-time Scene Recommendation with Semantic Understanding." Proceedings of the 18th ACM Conference on Recommender Systems. 2024.