# Module 1. Geographic Information Science ## Learning Objectives * Define and differentiate Geographic Information Systems and Geographic Information Sciences. * Identify key areas of Geographic Information Science Research. * Compare and Contrast the elements of GISystems and CyberGIS. ## Lecture Slides {% embed url="" %} Lecture 1. Geographic Information Science {% endembed %} ## Assignments You must log into the [University of Illinois Urbana-Champaign Moodle](https://learn.illinois.edu/course/view.php?id=71249\§ion=4) to complete assignments. You can access the following resources through Moodle. * [ ] Lecture Video * [ ] Lab 1. Discussion: What is Geographic Information Science? * [ ] Quiz 1 ## Overview **Geography** is a field of science concerned with people, places, and spatial phenomena. Geography has a long history. Modern-day geographers work in government, industry, and academia to develop solutions to some of society's biggest challenges. From climate change to the COVID-19 pandemic, geographers have been at the forefront. To solve geographic problems, we need geographic data. This course will introduce you to some central topics around geographic data that you must consider when performing geographic work. Let's begin by exploring the topic of Geographic Information Science. ### Geographic Information Science Dr. Michael Goodchild first popularized Geographic Information Science in his seminal paper in 1992 ([link](https://www.tandfonline.com/doi/abs/10.1080/02693799208901893)). Dr. Goodchild suggested the term **Geographic Information Science** in contrast to the term **Geographic Information Systems**, computer systems used for capturing, storing, managing, and displaying geographic data. Geographic Information Science refers to the basic scientific questions of how we use geographic data and the research questions we develop. Some of the issues that Goodchild raises are: 1. Data collection and measurement 2. Data capture 3. Spatial Statistics 4. Data modeling & theories of spatial data 5. Data structures, algorithms, and processes 6. Display 7. Analytical tools 8. Institutional, managerial, and ethical issues Since Goodchild's (1992) paper, GIScience, as it is often abbreviated, has evolved to reflect many technological innovations that have occurred during the past decades. Throughout this course, we will come back to some of these topics and look at how technological development has changed the face of GIScience. ### Geographic Information Systems Geographic Information Systems are essential tools for addressing many of the questions brought about by Geographic Information Science. There are two main flavors of GIS systems, proprietary and open-source. Proprietary GIS is those that a company opens. A famous GIS company is ESRI, which makes many geospatial tools. Their [ArcGIS](https://www.esri.com/en-us/arcgis/products/index) products are some of the most widely used GIS tools available and one that you will use in this course. In contrast, open-source GIS systems also exist. A popular open-source GIS solution is [QGIS](https://www.qgis.org/en/site/). QGIS is licensed under the Create Commons Attribution-ShareAlike 3.0 license ([link](https://creativecommons.org/licenses/by-sa/3.0/)). You may download and use QGIS without a licensing fee, but you are required to give proper credit if you share or adapt the software. {% embed url="" %} Example of the use of Geographic Data {% endembed %} ### CyberGIS While GIS systems are excellent options for geospatial analysis on your local desktop, geographers are increasingly facing the need to analyze massive datasets. This need is driven not only by the complexity and size of individual datasets but interest in addressing complex geospatial problems that require the integration of multiple data. Voluminous, complex, and often dynamic data, often called **big data**, have become popular for spatial analysis. For example, many geospatial scientists have leveraged social media data, such as Twitter, to study social phenomena and linkages to physical geographic patterns. For example, analyzing Twitter textual content to forecast flooding ([link](https://link.springer.com/chapter/10.1007/978-3-030-80458-9_2)). An article by Li et al. ([2016](https://www.researchgate.net/publication/312550275_How_Much_Data_Do_You_Need_Twitter_Decahose_Data_Analysis)), suggests that an individual user of Twitter can create between 500-700 million tweets daily. Additionally, this data Dr. Shaowen Wang's CyberGIS utilizes geospatial information, science, and systems based on advanced computing and cyberinfrastructure ([link](https://www.tandfonline.com/doi/full/10.1080/00045601003791243)). **CyberGIS** utilizes distributed, high-performance computing to overcome computational challenges arising from large geospatial datasets. You will learn more about this facet of Geographic Information Science in the course GGIS 407 CyberGIS & Geospatial Data Science and GGIS 507 High-Performance Geospatial Computing. ### Geospatial Intelligence You may hear the term Geographic Information Intelligence or GEOINT. GEOINT refers to intelligence about human activity, including social, political, and environmental components, on Earth derived from geospatial data. The National Geospatial-Intelligence Agency (NGA) is the U.S. government agency tasked with geospatial intelligence (l[ink](https://www.nga.mil/)). Work done by the NGA provides policymakers, military service members, intelligence professionals, and first responders with geospatial intelligence for making decisions. ## Readings You may need to obtain these from the [University of Illinois Library](https://www.library.illinois.edu/search-tools/). * Goodchild, M. F. (2019). Geography and geographic information science: An evolving relationship. *The Canadian Geographer*, 63(4), 530– 539. * (2006). Spatial Analysis in Geography. In: Spatial Analysis and GeoComputation. Springer, Berlin, Heidelberg . [https://doi.org/10.1007/3-540-35730-0\_2](https://link.springer.com/chapter/10.1007/3-540-35730-0_2)