Geospatial

Geospatial need-to-knows

By Nicci Potts 20 November 2023
Geospatial need-to-knows

The key things you need to know when working with geospatial data.

Introduction

I recently sat on a panel for geospatial data practitioners and a couple things occurred to me. First, geospatial data can mean different things to different people and secondly, geospatial data can have a lot of barriers to entry (mostly acronyms!).

Geospatial data is becoming an increasing utilised data source, but for some maybe it feels like it’s a data type only accessible to certain cliches or those with access to paid software. In 2023, I think this is simply not true. I believe that after a small investment of time most data savvy people can find themselves getting to grips with this data type.

And this is good because geospatial opens up a world (pun intended) of possibilities to disaggregate data, which is particularly important in local decision making! It can also be visually evocative and easy to engage with for non-data experts, making it a key tool for the data scientist arsenal.

So in this blog post, I’ve put together what I think are the key concepts and terms you need to learn for entry into the world of geospatial analysis.

Contents

What is geospatial data?

Geospatial data is anything that contains information related to locations on Earth (or other Planets) surface, meaning geographical locations (i.e. postcodes, GPS coordinates) or surface characteristics (i.e. satellite imagery, chemical spectra).

Importance and Applications

Some of the uses of geospatial data can include:

  • Urban Planning: Assessing land use, infrastructure planning, and resource allocation.
  • Environmental Science: Monitoring changes in ecosystems, analyzing climate patterns, and managing natural resources.
  • Logistics and Transportation: Optimizing routes, tracking assets, and improving delivery efficiency.
  • Public Health: Mapping disease outbreaks, understanding healthcare access, and planning healthcare facilities.

If you are interested in the use of geospatial data to monitor reforestation in Uganda then check out this blog post.

Key Concepts

Coordinate Reference System (CRS)

A Coordinate Reference System (CRS) is a framework used to precisely define locations on the Earth’s surface. It consists of a coordinate system, datum, and projection, enabling accurate representation of spatial data. Two primary types of CRS include:

  • Geographic CRS: Utilizes latitude and longitude to define locations on a spherical or ellipsoidal surface.

  • Projected CRS: Involves the transformation of geographic coordinates onto a flat, 2D plane, commonly used in maps and visualizations.

Popular CRS standards include WGS84 for GPS coordinates and UTM for more localized measurements.

EPSG (European Petroleum Survey Group) Codes

EPSG codes are unique identifiers assigned to specific coordinate reference systems, datums, and transformations. These codes simplify the referencing of various CRS standards, aiding interoperability among different geospatial software and datasets. For instance, EPSG:4326 represents the WGS84 geographic coordinate system.

Data Types

Geospatial data comes in two primary forms:

  • Vector Data: Utilizes points, lines, and polygons to represent spatial features. Points indicate specific locations, lines represent linear features (roads, rivers), and polygons define areas (boundaries, regions).

  • Raster Data: Comprises a grid of cells or pixels, each with a value representing information about an area’s characteristics (satellite imagery, elevation models).

Sources of geospatial data

Remote Sensing

Remote sensing involves acquiring information about the Earth’s surface without direct physical contact. This technique utilizes satellites, aircraft, drones, or ground-based sensors to gather data in various spectral bands. Types of remote sensing data include:

  • Optical Imagery: Captures visible and near-infrared light, beneficial for land cover classification, crop monitoring, and urban planning.

  • Radar and LiDAR: Utilizes active sensors to measure distance, terrain elevation, and vegetation structure even in adverse weather conditions or at night.

GPS (Global Positioning System)

GPS technology uses satellites to determine precise geographic locations on Earth. GPS receivers receive signals from these satellites to calculate coordinates, aiding in the collection of accurate geospatial data. This technology is vital for navigation, surveying, and mapping applications.

Data formats

Common Geospatial File Formats:

  • Shapefiles: A widely used format containing geometric and attribute data. They consist of multiple files (.shp, .shx, .dbf) that store information about spatial features.

  • GeoJSON: A lightweight format for encoding various geospatial data structures using JavaScript Object Notation (JSON). It’s commonly used for web mapping applications due to its simplicity and readability.

  • KML/KMZ: Keyhole Markup Language (KML) and its compressed variant (KMZ) are XML-based file formats used to display geographic data in Google Earth and other geobrowsers.

  • GeoTIFF: Embeds georeferencing information within TIFF image files, allowing storage of raster data with spatial information.

Popular geospatial software

  • ArcGIS: Esri’s ArcGIS is a comprehensive platform offering tools for mapping, spatial analytics, and data management, widely used in various industries due to its versatility and extensive functionalities.

  • QGIS: An open-source Geographic Information System (GIS) software offering similar capabilities to ArcGIS. QGIS is user-friendly, free to use, and supported by a vibrant community.

  • Google Earth Engine: A cloud-based platform for planetary-scale geospatial analysis. It provides access to a vast repository of remote sensing data and computational capabilities for large-scale analysis.

  • GDAL (Geospatial Data Abstraction Library): A library for reading and writing raster and vector geospatial data formats. It offers a set of command-line tools and APIs for data transformation and manipulation.

Programming languages

  • R: R, along with specialized packages like {sf} and {terra}, provides extensive functionalities for geospatial analysis. Its flexibility in statistical computing, visualization, and geospatial modeling makes it a preferred choice for researchers and analysts dealing with spatial data.

  • Python: Python offers powerful libraries such as {GeoPandas}, {Shapely}, and {Rasterio}, making it a robust tool for geospatial analysis. Its simplicity, readability, and wide adoption in data science and GIS communities make it a versatile language for handling geospatial data.

Both R and Python support a rich ecosystem of geospatial libraries and tools, empowering users to perform complex analyses, create visualizations, and develop custom geospatial applications.

Challenges

  • Data Volume and Complexity: Geospatial datasets can be massive and intricate, posing challenges in storage, processing, and analysis, especially when dealing with high-resolution imagery or large-scale mapping projects.

  • Accuracy and Precision: Ensuring the accuracy and precision of geospatial data is crucial, as inaccuracies can lead to flawed analyses and decision-making.

  • Interoperability: Integrating and sharing geospatial data across different platforms, software, and organizations remains a challenge due to varying standards and formats.

Future trends

  • AI and Machine Learning in Geospatial Analysis: Integration of AI algorithms and machine learning techniques is enhancing geospatial analysis, enabling automated feature extraction, pattern recognition, and predictive modeling from vast datasets.

  • Cloud-based Geospatial Solutions: Cloud computing offers scalable storage, processing power, and collaboration capabilities, allowing users to access and analyze geospatial data more efficiently and cost-effectively.

  • Internet of Things (IoT) and Geospatial Integration: The fusion of geospatial data with IoT devices enables real-time tracking, monitoring, and analysis of spatially distributed phenomena, benefiting fields like smart cities, agriculture, and logistics.

Summary image by Jean-Luc Benazet from pexels