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Data Science for Geographic Information Systems

Afonso Oliveira, Nuno Fachada, João P. Matos-Carvalho

TL;DR

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.

Abstract

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.

Data Science for Geographic Information Systems

TL;DR

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.

Abstract

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.
Paper Structure (15 sections, 2 figures)

This paper contains 15 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Emergence of Data Science
  3. Evolution of Geographic Information Systems
  4. Key Components of Data Science in GIS
  5. Spatial Data Handling
  6. Machine Learning
  7. Big Data
  8. Data Visualization and Interpretation
  9. Applications
  10. Urban and Infrastructure Planning
  11. Environmental Analysis and Conservation
  12. Public Health and Safety
  13. Agricultural Optimization
  14. Case Study
  15. Conclusions

Figures (2)

  • Figure 1: Examples of data representation, centered around the continental United States of America. (a) Choropleth maps: thematic maps with areas shaded in proportion to variable values; (b) Heat Maps: represent intensity or concentration of data with colors; (c) Flow maps: visualize movement between locations using lines or arrows; (d) 3D models: represent surfaces in three dimensions, including man-made structures and topographic features.
  • Figure 2: Case study results, with a scale of 1 pixel per square meter: a) RGB representation of area of study, in Tróia, Portugal; b) slope in degrees; c) aspect (orientation of slope) in degrees; d) elevation in meters above sea level; e) canopy height in meters; f) vegetation enhanced; g) water features enhanced, with attention drawn to a subtle disturbance at the lower left corner, near the coastline; h) man-made structures; i) fuel map with fuel codes corresponding to a model developed based on fernandes2021modelos; and, j) burning Index map generated through simulations using an additional tool and historical weather data. The multispectral data was obtained from data_ms_2021vong_thermal_2022.