Getting Started with CyberGIS

Go to Course: https://www.coursera.org/learn/cybergis

Introduction

### Course Review and Recommendation: Getting Started with CyberGIS In our digital age, the importance of geospatial data science cannot be overstated. It underpins everything from urban planning to disaster response, and accessing this vast realm of information often requires a sophisticated technical understanding. The **Getting Started with CyberGIS** course on Coursera is thoughtfully designed to bridge the gap between traditional Geographic Information Systems (GIS) and the rapidly evolving field of cyberinfrastructure-enabled geospatial data science. This review delves into the course’s offerings, strengths, and why you should enroll. #### Course Overview The **Getting Started with CyberGIS** course is structured to give students a comprehensive introduction to CyberGIS and the principles of geospatial data science. It leverages advanced cyberinfrastructure, high-performance computing, big data, and cloud computing, ultimately aiming to equip students with expertise in handling geospatial data effectively. Ideal for both newcomers and those looking to deepen their understanding, the course emphasizes the latest developments in CyberGIS and geospatial data science principles. #### Syllabus Highlights 1. **Course Orientation** - This section lays the groundwork, familiarizing students with the interface and essential tools needed for the course. It's an excellent entry point for anyone new to online learning. 2. **Module 1: What is CyberGIS?** - The first module provides an introduction to CyberGIS, explaining key concepts and components. Students gain insights into the complexities of geospatial big data, its challenges, and the role of Geospatial Data Science in resolving these issues. The emphasis on real-world applications demonstrates how theoretical knowledge translates into practical solutions. 3. **Module 2: Geospatial Visualization using Python** - This module is particularly engaging as it introduces students to geospatial visualization techniques using Python. Hands-on experience with libraries like Matplotlib, Basemap, and Folium allows students to create interactive maps and visualizations, making the learning experience both practical and enjoyable. 4. **Module 3: Geospatial Object Manipulation and Hadoop** - This module dives deeper into manipulating geospatial data objects using the Shapely and RasterIO libraries. The introduction of Hadoop for big geospatial data processing is a key highlight, empowering students with the tools to manage large datasets effectively—a skill in high demand across various industries. 5. **Module 4: Theoretical Foundations and Future Trends** - The course concludes with a look at the theoretical underpinnings of CyberGIS and its future trends. After exploring computational intensity calculations and applying them to real-world scenarios, students will be equipped with a forward-thinking perspective on the evolving landscape of geospatial science. #### Course Strengths - **Comprehensive Approach**: The course covers theoretical and practical aspects of CyberGIS, ensuring a well-rounded understanding. - **Hands-On Learning**: Practical assignments using tools and libraries reinforce the theoretical knowledge, allowing students to apply what they've learned immediately. - **Future-Ready Skills**: With a focus on tools used in the industry (like Hadoop), students gain skills that are relevant and sought after in the job market. #### Who Should Take This Course? The **Getting Started with CyberGIS** course is ideal for: - Students and professionals in geography, urban planning, environmental science, and related fields. - Individuals looking to expand their skill set in geospatial analysis and big data. - Anyone interested in the intersection of technology and geospatial science, particularly in understanding how data can be leveraged for decision-making. #### Final Recommendation If you are eager to develop your expertise in geospatial data science while grappling with real-world challenges and solutions, **Getting Started with CyberGIS** is an outstanding course choice. The blend of theoretical foundations and practical application, alongside the use of leading technologies, ensures that you will walk away with valuable skills and knowledge. Enroll today to start your journey into the dynamic world of CyberGIS and discover how it can impact various sectors and communities.

Syllabus

Course Orientation

You will become familiar with the course, your classmates, and our learning environment. The orientation will also help you obtain the technical skills required for the course.

In this module, we will get introduced to the basics of CyberGIS and Geospatial Data Science. First, we'll learn about the definition of Geographic information science and systems, and related concepts. Next, we'll get introduced to the basics of advanced cyberinfrastructure and its components. Then we will see how CyberGIS combines Cyberinfrastructure and GIS to produce a sum that is greater than its parts. We will see the components of CyberGIS and the community and sciences it supports. Then, we look at geospatial big data, specifically the complexity and challenges it presents in terms of data representation, sharing, and privacy. We then look at how Geospatial Data Science provides tools to resolve the challenges posed by big geospatial data. Finally, we conclude the lesson by looking at scientific applications and drivers that require CyberGIS and Geospatial Data Science to address the problems posed by them.

In this module, students will get introduced to techniques for geospatial visualization and Web mapping using Python. First we'll learn about the basics of plotting geospatial data and creating maps using Matplotlib, Basemap, and Cartopy. Next, we will learn techniques to create and share our Web maps using Mplleaflet and Folium libraries. Lastly, we will see a brief introduction to GeoPandas and how to use it to do simple plot, simple geometry, and conduct basic spatial operations.

In this module, students will get first get introduced to techniques for manipulating geospatial objects using geospatial libraries in Python. Specifically, we will learn how to manipulate both vector and raster data objects using Shapely and RasterIO libraries. Next, students get introduced to using the Hadoop paradigm for taming big geospatial data. Specifically, we will learn the fundamentals of how to process big spatial data with Hadoop. Students will get a brief introduction to the Hadoop framework, its major components, and its characteristics, and will learn about Hadoop Distributed File System (HDFS), its architecture and simple commands to interact with it. We will also learn about the MapReduce computing paradigm and see an example of how it may be applied using Hadoop streaming API to process New York City taxi data.

In this module, we will learn about the theoretical underpinnings of CyberGIS. We will start the module by looking into theoretical foundations of cyberGIS, specifically looking at the computational intensity calculations. Then we will apply the theoretical concepts to an application case study learning how to calculate this computational intensity. Lastly, we will conclude the module and course by looking at some future trends.