# Data Science for Geographic Information Systems

arXiv: https://arxiv.org/abs/2404.03754 · HTML: https://mlsanity.com/paper/2404.03754/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 87 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 4 of 4)

- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Flood Prediction Using Machine Learning Models: Literature Review](https://mlsanity.com/paper/1908.02781/) (2019)
- [Enabling Cognitive Smart Cities Using Big Data and Machine Learning: Approaches and Challenges](https://mlsanity.com/paper/1810.04107/) (2018)
- [Multispectral Indices for Wildfire Management](https://mlsanity.com/paper/2309.01751/) (2023)

## Abstract (arXiv)

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 Tr'oia, 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.