Vector data analysis is a fundamental component of geoinformatics and disaster management, enabling the representation, management, and interpretation of discrete geographic features such as roads, rivers, buildings, administrative boundaries, evacuation routes, shelters, and hazard zones. The analysis follows a systematic sequence of steps to transform raw spatial data into meaningful information for disaster risk reduction, preparedness, response, and recovery. The first step is data acquisition. Vector data are collected from multiple sources, including Global Navigation Satellite Systems (GNSS), total stations, remote sensing image interpretation, field surveys, cadastral maps, topographic maps, government databases, and volunteered geographic information. Features are represented as points, lines, and polygons depending on their spatial characteristics. For disaster management, examples include hospitals as points, road networks as lines, and flood-prone areas as polygons. The second step is data input and digitization. Paper maps or satellite images are converted into digital vector formats through manual or automated digitization. Existing digital datasets are imported into Geographic Information Systems (GIS). During this process, coordinate systems and map projections are standardized to ensure compatibility among different datasets. The third step is data editing and preprocessing. Raw vector datasets often contain errors such as missing features, duplicate entities, overshoots, undershoots, sliver polygons, and incorrect attribute entries. Editing involves correcting geometry, removing errors, updating missing information, and validating feature locations. Data cleaning improves the accuracy and reliability