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Medical Center Suitability Mapping Using GIS and GEE
8 students

Medical Center Suitability Mapping Using GIS and GEE

Learn how to apply spatial data, in Google Earth Engine to identify optimal locations for medical facilities
Created byEarth's AI
Last updated 8/2025
English

What you'll learn

  • Understand core GIS and remote sensing concepts to analyze spatial data, including raster and vector formats, imagery interpretation, and geospatial tools.
  • Perform suitability mapping using multi-criteria analysis like slope, roads, land cover, and population to find ideal sites for medical centers.
  • Learn GEE for spatial modeling: access datasets, process imagery, apply formulas, and create automated, cloud-based suitability models.
  • Visualize and export suitability maps with color-coded layers, and export GeoTIFFs for reports, GIS software, or stakeholder presentations.

Course content

1 section • 6 lectures • 59m total length

  • Lecture 1: Fundamentals of Remote Sensing13:19

    This lecture introduces the principles of remote sensing, focusing on satellite imagery, spectral bands, resolutions, and platforms like Landsat and Sentinel. You’ll learn how remote sensing helps analyze Earth’s surface for planning purposes, including land cover, vegetation, and terrain features—critical inputs for medical facility planning. The session sets the foundation for understanding spatial data that feeds into suitability models.

  • Lecture 2: Site Suitability Mapping Concepts8:03

    You’ll explore multi-criteria decision analysis (MCDA) and the logic behind suitability mapping. Learn how to define and combine key criteria—such as population density, slope, road access, and land cover—using weights to rank ideal locations. This lecture will help you build a theoretical framework for spatial analysis used in site selection for healthcare infrastructure.

  • Lecture 3: Introduction to GIS7:39

    This lecture covers core GIS concepts: spatial data types (vector vs raster), coordinate systems, layers, and basic map analysis. You'll understand how GIS supports decision-making and how layers interact in a spatial environment. Tools like QGIS and ArcGIS are briefly introduced to demonstrate how GIS complements cloud-based platforms like Google Earth Engine.

  • Lecture 4: Introduction to Google Earth Engine (GEE)9:25

    Dive into the GEE platform: its interface, JavaScript API, and powerful datasets. You'll learn how to load, filter, visualize, and manipulate remote sensing and vector data from global sources. By the end, you’ll be ready to use GEE’s cloud computing tools to automate large-scale geospatial analyses.

  • Getting Started with the Google Earth Engine Interface3:38

    This section introduces the Google Earth Engine platform, guiding learners through accessing the Code Editor, understanding its interface, and exploring key panels like the script editor, map viewer, inspector, and data catalog. It helps beginners get comfortable navigating GEE before writing any code or performing analysis.

  • Lecture 5: Implementation of Suitability Mapping in GEE (Medical Center)17:16

    This hands-on lecture walks you through building a suitability model in GEE to identify optimal medical center locations. You’ll process layers like roads, population, slope, and land cover; normalize and weight them; and combine them into a suitability index. Learn to visualize outputs and export results for real-world use.

Requirements

  • No prior experience with Google Earth Engine is required — the course will guide you step-by-step.

Description

Identifying optimal sites for medical centers is a critical task that ensures healthcare accessibility, equity, and emergency readiness. In this course, you’ll explore how to use Remote Sensing, GIS, and Google Earth Engine (GEE) to build a spatial model for Medical Center Suitability Analysis.

The course begins with the fundamentals of remote sensing, teaching how satellite imagery is captured, processed, and applied to land use analysis. You’ll also explore GIS concepts, including spatial layers, vector and raster data, and map algebra.

Next, you’ll learn the core principles of site suitability analysis, including how to select criteria (like road proximity, terrain slope, population density, and land cover) and assign appropriate weights. These theoretical concepts are then brought to life using GEE, where you'll access satellite datasets, perform distance transforms, normalize variables, and calculate a composite suitability index.

Through a hands-on lab focusing on Los Angeles, you’ll develop a fully functional suitability model for locating medical centers. You’ll also learn to visualize results, export GeoTIFFs, and interpret outcomes for real-world planning.

This course is ideal for:

  • Urban planners and public health analysts

  • GIS students and professionals

  • Environmental and civil engineers

  • Policy makers and researchers

By course end, you'll have the tools to conduct suitability mapping projects for healthcare, disaster response, education, or other infrastructure using open-source geospatial platforms.

Let me know if you want the lecture titles, learning outcomes, or a step-by-step GEE script explanation next!

Who this course is for:

  • Students, researchers and professionals in agriculture, environmental science, geography, or remote sensing looking to apply satellite data in real-world scenarios.

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