Getting start with Geospatial Analysis

Geospatial analysis involves examining data that have a location element. By using geographic information systems (GIS) and related technologies, one can see patterns and relationships that are not obvious through tables or charts alone. For example, mapping the spread of a disease can help public health officials decide where to send resources (Zhao et al., 2022). Similarly, businesses can use geospatial analysis to find the most efficient delivery routes or to identify new areas for expansion (Løwe, 2023).

Generated using Google’s NotebookLM based on Geospatial data: the really big picture

Why Geospatial Analysis Matters

As more information becomes linked to places, understanding how to work with spatial data is essential. Governments, non-governmental organisations, and private firms all rely on geospatial analysis to make decisions. For instance, urban planners use GIS to design new roads, public health experts use it to monitor outbreaks, and conservationists use it to map habitats (Løwe, 2023). In Sri Lanka, ministries and local councils have begun using GIS to manage land use and respond to natural hazards. By learning geospatial analysis, you gain skills that are in high demand and that can help solve local and global challenges.

Geospatial analysis allows us to see the world in a different light, uncovering hidden patterns and connections that can lead to valuable insights and informed decision-making.

Anon

Key Concepts in Geospatial Analysis

1. Spatial Data Types
   • Vector Data: Points, lines, and polygons that represent features such as cities, roads, and administrative boundaries (Longley et al., 2005).
   • Raster Data: Gridded data where each cell has a value, for example satellite imagery or digital elevation models (Longley et al., 2005).
2. Coordinate Reference Systems (CRS)
   To combine datasets correctly, one must use the same spatial reference system. Common systems include WGS84 for global data and local projected systems (Longley et al., 2005).
3. Geoprocessing and Spatial Analysis
   Geoprocessing refers to operations such as buffering, overlay, and spatial joins, which help reveal relationships among features (Løwe, 2023). For example, one can buffer a river to find areas at risk of flooding or use spatial joins to attach population data to administrative zones.
4. Remote Sensing
   Remote sensing uses satellite or aerial imagery to measure land cover, temperature, or vegetation indices. The global dataset of annual urban extents (1992–2020) by Zhao et al. (2022) is one example where nighttime lights data helped map urban growth.

Tools and Software

1. QGIS
   • Free and open-source GIS software.
   • Supports vector and raster analysis, geoprocessing tools, and plugin extensions (QGIS.org, 2025).
   • Good for beginners because of active online forums and tutorials.
2. ArcGIS Pro
   • Commercial software by Esri, widely used in industry and government.
   • Provides advanced tools for spatial analysis, 3D mapping, and remote sensing integration (Esri, 2024).
   • Offers ArcGIS Online for sharing maps and collaborating.
3. Python with Geospatial Libraries
   • Python packages such as geopandas, rasterio, and folium help automate workflows (Longley et al., 2005).
   • Ideal for reproducible analysis and handling large datasets.
4. Google Earth Engine
   • Cloud-based platform for large-scale analysis of remote sensing data (Zhao et al., 2022).
   • Useful for change detection over big areas and long time series, for example studying deforestation or urban expansion.

Datasets and Sources

1. DataWorld: A platform hosting various spatial datasets. One can search for global or regional data on land use, demographics, or environmental variables (DataWorld, 2023).
2. USGS DIS Data Download: Provides free satellite imagery (Landsat, Sentinel) and elevation models (USGS, 2025).
3. Esri Open Access National Datasets: National-scale data for land cover, demographic statistics, and environmental variables (Esri, 2025).
4. Free GIS Data: OpenStreetMap and other community-driven sources offer vector layers such as roads, buildings, and points of interest (OpenStreetMap contributors, 2025).
5. Columbia University – Urbanization and Human Settlements: Offers datasets on urban extents, population, and infrastructure globally (Columbia University, 2025).
6. Global Nighttime Lights (DMSP-VIIRS): Used by Zhao et al. (2022) to map urban areas. Data are available via Google Earth Engine or government portals.

For each source, always note the date of access because online data may change. For example: (DataWorld, 2023, accessed 5 June 2025).

Recommended Reading

• Zhao, M. et al. (2022): A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights. Earth System Science Data, 14, pp. 517–534. Useful for understanding how remote sensing and nighttime lights can show urban growth over decades.
• Longley, P.A., Goodchild, M.F., Maguire, D.J. and Rhind, D.W. (2005): Geographic Information Systems and Science. Wiley. A foundational textbook covering GIS theory, methods, and applications.
• Løwe, V.J. (2023): GIS in Sustainable Urban Development. Atlas. An online article explaining how GIS supports sustainable cities, useful for understanding real-world use cases.
• Guo, J. et al. (2023): Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework. Available at: https://doi.org/10.5281/zenodo.7859205 (Accessed: 5 June 2025). Describes methods to improve resolution of nighttime light data.

Getting Started: Step-by-step

1. Choose a GIS Platform
   • If you cannot afford commercial software, start with QGIS (QGIS.org, 2025). Install it on your computer and familiarise yourself with the interface.
   • If possible, get a trial of ArcGIS Pro to learn industry-standard workflows (Esri, 2024).
2. Learn Basic Concepts
   • Study vector and raster data, coordinate systems, and spatial statistics. Use chapters from Longley et al. (2005).
   • Practice simple tasks: load a shapefile of administrative boundaries, calculate areas, and produce basic maps.
3. Explore Sample Datasets
   • Download a Landsat image from USGS (USGS, 2025) and import it into QGIS or ArcGIS. Visualise true colour composite and try basic classification.
   • Load OpenStreetMap layers to see how data such as roads and buildings are structured (OpenStreetMap contributors, 2025).
4. Follow Tutorials
   • Many free tutorials exist online (e.g., QGIS documentation, 2025; Esri training videos, 2024).
   • Replicate a small project: map urban growth in Colombo using nighttime lights data from Google Earth Engine (Zhao et al., 2022).
5. Practice Geoprocessing
   • Use buffer tools (e.g., buffer 1 km around water bodies).
   • Perform overlay: find where protected areas and planned infrastructure intersect.
6. Learn Remote Sensing Basics
   • Understand how to process raster data, calculate vegetation indices (e.g., NDVI), and detect change over time.
   • Use free tutorials on Google Earth Engine to analyse a time series of NDVI for a region (Guo et al., 2023).
7. Apply to Real-World Problems
   • For students in Sri Lanka, try mapping flood-prone zones by combining elevation data (USGS, 2025) with recent rainfall records.
   • Use urban extents data (Zhao et al., 2022) to study how Colombo’s built-up area changed between 2000 and 2020.

Practical Tips for Learners

• Keep an Organised File Structure: Store raw data, processed data, and scripts in separate folders.
• Record Metadata: Note when and where data came from, coordinate systems, and processing steps.
• Use Version Control: For code and scripts, use Git to track changes.
• Join Online Communities: QGIS and GIS Stack Exchange forums help when you face issues.
• Collaborate: Work with peers or join local GIS user groups to share knowledge.

References

Columbia University, 2025. Urbanization and Human Settlements Datasets. Available at: https://hipurban.org/ (Accessed: 5 June 2025).

DataWorld, 2023. DataWorld. Available at: https://data.world/ (Accessed: 5 June 2025).

Esri, 2024. ArcGIS Pro: Getting Started. Esri Press.

Esri, 2025. Esri Open Access National Datasets. Available at: https://www.esri.com/data (Accessed: 5 June 2025).

Guo, J. et al., 2023. Reconstructing Three-decade Global Fine-Grained Nighttime Light Observations by a New Super-Resolution Framework. Available at: https://doi.org/10.5281/zenodo.7859205 (Accessed: 5 June 2025).

Løwe, V.J., 2023. GIS in Sustainable Urban Development. Atlas. Available at: https://atlas.co/blog/gis-in-sustainable-urban-development (Accessed: 5 June 2025).

Longley, P.A., Goodchild, M.F., Maguire, D.J. and Rhind, D.W., 2005. Geographic Information Systems and Science. Wiley.

OpenStreetMap contributors, 2025. Free GIS Data. Available at: https://www.openstreetmap.org/ (Accessed: 5 June 2025).

QGIS.org, 2025. QGIS Documentation. Available at: https://qgis.org/en/docs/ (Accessed: 5 June 2025).

USGS, 2025. USGS DIS Data Download. Available at: https://earthexplorer.usgs.gov/ (Accessed: 5 June 2025).

Zhao, M., Cheng, C., Zhou, Y., Li, X., Shen, S. and Song, C., 2022. A global dataset of annual urban extents (1992–2020) from harmonized nighttime lights. Earth System Science Data, 14, pp. 517–534.