Design and Implementation of a Geo-Spatial Data Collection and Analysis System

Thesis Abstract

The advent of geospatial technologies has significantly transformed spatial data collection and analysis, addressing critical challenges in geographic information systems (GIS) by enhancing data accuracy, efficiency, and integrative capabilities. Despite the proliferation of geospatial data, many organizations and research institutions lack robust, integrated systems capable of streamlining data collection, processing, and analytical workflows. This study aims to design and implement a comprehensive geo-spatial data collection and analysis system tailored to urban planning and environmental monitoring applications, with the specific objectives of developing a modular system architecture, integrating mobile and desktop data collection tools, and evaluating system performance in real-world scenarios. The research adopts a mixed-methods approach, combining quantitative and qualitative techniques. The study population comprises 150 field data collectors, GIS professionals, and urban planners within a metropolitan municipality. A stratified random sampling technique selects 60 field data collectors, ensuring representation across different urban zones. Data collection instruments include structured questionnaires to assess system usability and efficiency, observational checklists during field application, and semi-structured interviews with key stakeholders to gather in-depth insights. The system prototype development follows an iterative design methodology aligned with user-centered design principles, incorporating open-source geospatial frameworks such as QGIS and PostGIS for backend data management, complemented by mobile data collection applications built using Angular and Cordova. Data analysis involves descriptive statistics to summarize user experiences, and inferential procedures such as paired sample t-tests to compare data collection efficiency before and after system deployment. Thematic analysis is employed on interview transcripts to extract qualitative insights regarding system usability, data quality, and operational challenges. The system’s performance is further evaluated through a series of field tests measuring data accuracy, collection speed, and integration capability, with results analyzed via regression analysis to establish correlations between system features and operational improvements. Expected findings include a significant enhancement in data collection accuracy and speed, with the reduction of manual errors by at least 35%, and improved spatial data integration capabilities demonstrated through seamless GIS workflows. The developed system is anticipated to exhibit high usability scores (mean SUS score > 75), and stakeholders are expected to report increased confidence in data quality and decision-making support. These results will validate the system’s effectiveness and scalability for broader geographic applications. The study makes a notable contribution to knowledge by presenting an integrated framework for geo-spatial data collection and analysis that leverages open-source technologies to provide affordable, customizable solutions adaptable to diverse contexts. It offers a practical model for urban planning and environmental management agencies seeking to modernize spatial data workflows, with a clear methodology for system development, deployment, and evaluation. The main conclusion emphasizes that the proposed system significantly improves operational efficiency, data quality, and analytical capacity in geospatial workflows. It advocates for broader adoption of integrated geo-spatial systems in urban and environmental planning sectors to foster evidence-based decision-making. Recommendations include expanding system functionalities to incorporate real-time data processing, developing training modules for users, and exploring the integration of IoT sensors for enhanced environmental monitoring. Future research should investigate the deployment of such systems within different geographic and socio-economic contexts, alongside the incorporation of emerging technologies like artificial intelligence and machine learning to further augment geospatial analysis capabilities.