Data Science for Geographic Information Systems
It addresses the integration of data science into GIS for sectors like disaster management, but is incremental as it reviews existing trends without introducing new methods or results.
This work traces the historical and technical evolution of data science and GIS, highlighting their convergence and presenting a case study in disaster management using aerial data from Tróia, Portugal, to extract insights from raw data.
The integration of data science into Geographic Information Systems (GIS) has facilitated the evolution of these tools into complete spatial analysis platforms. The adoption of machine learning and big data techniques has equipped these platforms with the capacity to handle larger amounts of increasingly complex data, transcending the limitations of more traditional approaches. This work traces the historical and technical evolution of data science and GIS as fields of study, highlighting the critical points of convergence between domains, and underlining the many sectors that rely on this integration. A GIS application is presented as a case study in the disaster management sector where we utilize aerial data from Tróia, Portugal, to emphasize the process of insight extraction from raw data. We conclude by outlining prospects for future research in integration of these fields in general, and the developed application in particular.