EMS Site Suitability Analysis Using Google Earth Engine

Spatial Analysis and GIS Techniques for Optimizing EMS Site Locations Using Google Earth Engine

Created byEarth's AI

Last updated 8/2025

English

What you'll learn

Understand the fundamentals of site suitability analysis and its importance for EMS (Emergency Medical Services) facility planning.
Learn how to use Google Earth Engine (GEE) to access, process, and analyze geospatial datasets such as roads, population density, slope, and land cover.
Develop skills to create and interpret suitability indices by combining multiple spatial criteria with weighted factors.
Gain hands-on experience in visualizing, mapping, and exporting suitability results to support decision-making in EMS site selection.

Course content

1 section • 6 lectures • 1h 3m total length

Lecture 1: Fundamentals of Remote Sensing13:19
This lecture introduces the core principles of remote sensing, including the science behind satellite imagery and aerial data collection. Students will learn about electromagnetic spectrum, sensor types, image resolution, and common applications. Emphasis is placed on understanding how remotely sensed data can be used to analyze land cover, environment, and urban infrastructure. This foundational knowledge prepares learners to work with spatial data for various geospatial analyses, including site suitability modeling.
Lecture 2: Site Suitability Mapping8:03
Site suitability mapping is a critical spatial analysis method used to identify the best locations for specific applications. This lecture covers concepts like criteria selection, weighting, normalization, and multi-criteria decision analysis (MCDA). Students will explore how to evaluate environmental, social, and infrastructural factors to create composite suitability maps. Practical examples include selecting optimal sites for facilities such as EMS stations, bus stops, or EV charging points, providing learners with skills to support data-driven urban planning and resource allocation.
Lecture 3: GIS Introduction7:39
This lecture provides an overview of Geographic Information Systems (GIS) technology and its fundamental components—data types, spatial databases, map projections, and key analysis tools. Students will understand how GIS enables the visualization, querying, and modeling of spatial phenomena. The lecture also introduces basic GIS operations like buffering, overlay, and spatial joins, which are essential for advanced geospatial analysis. This grounding enables students to harness GIS software for practical applications in urban planning, environmental management, and emergency services.
Lecture 4: Introduction to Google Earth Engine (GEE)9:25
Google Earth Engine (GEE) is a cloud-based platform for planetary-scale geospatial analysis. This lecture familiarizes students with the GEE interface, JavaScript API, and key data catalogs, such as satellite imagery and global datasets. Learners will explore how to access, filter, and visualize large geospatial datasets efficiently. The lecture also highlights GEE’s capacity for scalable processing, enabling users to perform complex analyses that would be difficult on local machines. This foundational knowledge sets the stage for hands-on geospatial programming and analysis.
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 in GEE — Suitability Mapping for EMS21:38
This hands-on lecture guides students through building a complete EMS site suitability model using Google Earth Engine. Students will learn to integrate multiple spatial layers—roads, population density, slope, and urban areas—applying weighted criteria to generate suitability indices. The session covers data preprocessing, normalization, visualization, and exporting results for reporting. By implementing real-world scenarios, learners develop practical skills in spatial decision support systems, preparing them to contribute effectively to emergency service planning and geospatial project workflows.

Requirements

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

Description

ffective placement of Emergency Medical Services (EMS) facilities is crucial to ensuring rapid response times and improving public health outcomes. This course equips learners with the knowledge and tools to perform comprehensive site suitability analysis for EMS locations using advanced geospatial techniques. The primary platform used is Google Earth Engine (GEE), a powerful cloud-based GIS tool that enables large-scale spatial data processing and analysis.

Students will start by understanding the fundamentals of site suitability and the importance of spatial factors such as proximity to roads for accessibility, population density to estimate demand, terrain slope for construction feasibility, and urban land cover to consider built environment constraints. The course covers how to access, filter, and preprocess relevant datasets within GEE.

A key focus will be on learning how to create normalized spatial layers representing individual criteria and combine them with appropriate weights into a composite suitability index. This weighted overlay approach allows for flexible decision-making tailored to real-world EMS planning needs.

Hands-on exercises will guide students through mapping, visualizing, and interpreting suitability results in GEE’s interactive environment. Additionally, learners will practice exporting geospatial outputs for further analysis or reporting.

By course completion, students will have practical expertise in leveraging cloud-based GIS technology for EMS site planning, enhancing their ability to make evidence-based decisions that can save lives and optimize resource allocation. This course is ideal for GIS professionals, urban planners, emergency management personnel, and anyone interested in applied spatial analytics for public health infrastructure.

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.