Urban climate affects how people move through cities, but its influence is difficult to capture with models based on generalized comfort indices that ignore individual experiences of climate. This thesis instead explores a bottom-up approach that uses daily Global Navigation Satellite System (GNSS) traces of people traversing an urban environment, which inherently contain each individual's personal influences on their mobility. A machine learning model was developed and trained using this dataset with the purpose of predicting future mobility values, while assessing the role that climate played in such predictions. The model employed a Spatio-Temporal Graph Neural Network (STGNN) architecture to capture both potential spatial dependencies between visited locations and temporal patterns in their activity.

The work draws on the Seoul Cozie dataset, which recorded six weeks of GNSS location data from wearable devices of 22 participants in Seoul during autumn 2023. Positions were aggregated into a graph structure with road intersections as nodes and transitions between them as edges. Climate features (temperature, humidity and PM10) were interpolated from over 1,000 weather stations using a Triangulated Irregular Network method and added as dynamic node features. STGNN variants were trained and compared based on whether they included climate node features.

Results show forecasts of node visits with low Mean Square Error of around 0.12 . However, precision and recall values for visited/unvisited node detection are low, peaking at 56.51%, reflecting strong class imbalance in the input. Adding climate attributes produced only minor and inconclusive improvements, in part due to the dataset’s short time span. The thesis proposes a reproducible framework linking climate and mobility, while underlining the need for richer datasets and for more flexible model architectures, capable of addressing class imbalances and representing personal mobility datasets.

This report details the development process of an open-data-based tool, an extension of the original interface created by Royal HaskoningDHV. The objective was to bridge the gap between geographical data and Architectural, Engineering, and Construction (AEC) industry applications. The tool aimed to transform spatial data for architects, facilitating contextual analysis in Rhinoceros and Grasshopper, ultimately aiding architects and engineers in enhancing designs based on environmental impact.

The initial tool focused on Netherlands data, but the ultimate goal was to make it applicable to other countries/regions. The research involved evaluating data availability for different regions, acquiring and aligning relevant data for Grasshopper, and implementing these data workflows into wind and solar analyses.

The data evaluation stage revealed challenges due to varying data availability and accessibility across countries. For example, Germany's fragmented data required navigating different portals, while Hong Kong's centralized data via API was more accessible. The lack of standardization hindered automation, necessitating manual data retrieval strategies that could be challenging for non-geomatics experts.

Data alignment methods varied, introducing complexities. For instance, Italy required 3D extrusion from 2D shapefiles, leading to unavoidable errors. Spain used a different method, showcasing the difficulty of a universal solution due to data standardization and interoperability issues.

Two techniques were envisioned for the open-data tool: TIN-based and Voxel-based methods, each with distinct qualities and limitations. The TIN-method offered high-quality analyses but required rigorous data alignment, while the Voxel-based method allowed flexibility but risked issues with resolution.

Limitations of exploratory analysis included a focus on five countries/regions and inherent constraints of Rhinoceros, limiting tool accessibility and requiring alternative approaches. Additionally, language barriers and data platform permeability might have led to overlooked datasets.

In conclusion, the report acknowledges the need for future work. Optimization of code for readability and performance is suggested, and the inclusion of additional data types (vegetation, land use, transport) in data workflows is proposed. Input from AEC professionals through methods like questionnaires or testing is recommended for further improvement. This report emphasizes the evolving nature of the tool and the importance of ongoing refinement to meet the needs of diverse AEC professionals.