What is Spatial Data Analysis?

Minerva Singh
A free video tutorial from Minerva Singh
Bestselling Instructor & Data Scientist(Cambridge Uni)
4.4 instructor rating • 43 courses • 77,021 students

Lecture description

In this section the students are going to be introduced to spatial/geo-spatial data and how they differ from GIS. The students will then be introduced to the project they are going to work with, along with an introductions of the different questions they can answer through spatial data analysis. We will talk about the important applications of spatial data analysis, sources of free data and tools to be used in this course (R and QGIS). Briefly present the different softwares and packages that need to be installed.

Learn more from the full course

Core Spatial Data Analysis: Introductory GIS with R and QGIS

Become Proficient In Spatial Data Analysis Using R & QGIS By Working On A Real Project - Get A Job In Spatial Data!

02:22:07 of on-demand video • Updated November 2021

  • You will have a greater clarity of basic spatial data concepts and data types
  • Carry out practical spatial data analysis tasks in freely available software
  • Learn about the kind of questions that are answered through spatial analysis and where to obtain free spatial data.
  • Analyze spatial data using both R and QGIS
  • Process raster and vector data in both R and QGIS
  • Show off your skills & gain experience by working on a real life conservation related spatial data analysis project
  • Start analyzing spatial data for your own projects using two powerful freeware tools
  • You'll have a copy of all the data and R scripts used in the course will be provided to students for their reference and to use in their own analysis.
  • You'll also have plenty of handy hints and tips will be provided alongside the code to prevent glitches
English [Auto] In this lecture, I will quickly take you through some of the concepts associated with spatial data analysis. I will talk you through the rationale underpinning project based learning, the actual project. We will work through some of the themes we will cover in the course, some of the questions we can hope to answer from spatial data, some of the sources of free spatial data and the weapons of choice. First, some basic definitions, spatial data or geospatial data refers to data that have a geographic component to them. That is data with geographic coordinates or information of geographic location. These data are widely available on Google Earth. SatNav or even your Fitbit. Geographic information systems are typically software where geographic information or spatial data are stored in less spatial data, geospatial data and geographic information systems. These items get used interchangeably. This is incorrect because spatial data are the data themselves and geographic information systems. They are a system of software we use to process these example archaeologist's software. And here we store spatial data from different sources as liers or in geo databases killed. This is a three year software. All can be used as a gift tool as well. The latter are free, less formalized, more intuitive and very powerful. The rationale behind this course is very simple project based format work and work much better than theory on legal basis. That is why the course follows a project based format. We are going to work with real spatial data collected from the dumbed down national park in Vietnam. In my number of years as a researcher, I have realized the most effective way of gaining proficiency in spatial data analysis is to step back and understand the context of the spatial system. We want to study. That is what or where is my study area? What am I hoping to learn about my study area? What are some of the facts already known about my study area? What are the possible data and tools available to help me answer the questions about my study area? In this course, we will work with real life data straight away. Real life data are complicated and messy. Working through an actual problem gives one hands on training, helps in integrating concepts and the most important thing, learning how to apply those concepts for an actual project. For a number of years as a student, I would attend Terreri heavy lectures and we would start off with concepts, formula, so on. Frankly, that feels a bit like being hit on the head with a hammer. But when you work through a project, all these things, they become very intuitive. In the next bit, we are going to learn about the study area. We are going to work on the different teams. We are going to cover in this course my teams. I mean, what are the different things we can learn about our study area? Possible questions to ask with spatial data are needed for it. And which tools can help us understand our study area better and answer our questions? These are some of the effects associated with our study area, the Camden National Park in Vietnam. I suggest you go through the list. This is an IUCN Category two national park in Vietnam. Vietnam itself is a country in Southeast Asia. It's not just a war with a very topography. It has black areas. It has mountains. This particular park is located in northern Vietnam and is very important for regional biodiversity, as it were. Vietnam has more than 150 protected areas and these have different IUCN categories. Two, four, five. Many beers have no categories or the categories have not been reported. Vietnam and its protected areas suffer from forest degradation, forest loss, and this is a very serious issue. So now, as you've gone through all these facts with me by now, some questions would have started popping in your mind. And that is the whole point of a course like this, that at the end of it, you should be able to think about the areas of research, the questions you can ask. This is a very important but slightly non tangible skill. So here goes some of the possible questions we can ask. Where exactly is that the national park located? How big is it? What does the protected area network of Vietnam look like? OK, we can answer this straight on. The gray boundary is the territorial boundary of Vietnam located in black. Within it are the protected areas. And this map was created by downloading data from these two sources. Livaditis is an excellent source for free spatial data. Further, what is the average size of a protected area in Vietnam? How are protected areas distributed across the different management categories? What is the most common IUCN management category in Vietnam? Of course, there are several more questions one could ask, and there are several other teams we are going to cover in the course which will enable you to ask more questions and answer them as well. One of the most important themes in spatial data analysis, and this is something we will cover our topography products. This is because the evaluation of topography is a very important component in most spatial data analysis projects, and this includes projects which are not ecology related. So important are these data that NASA launched the shuttle radar topography mission as RTM, and this has a near global scale collection of the MS digital elevation models or the 3-D representation of Earth's terrain, in turn can help derive a number of topography products such as slope aspect, hill, shade. Digital elevation model and the derive product play an important role in things like infrastructure, planning, precision agriculture, to name a few. They can also help answer important ecological questions in the forest degradation of an area linked with that slope by the end of this course. These are some of the things that you will be able to answer. Another important theme, which is quite specific to ecology related work and we will cover in the course, is basic vegetation mapping. For this, we start out by driving vegetation indices as they help quantify the state of vegetation and the state of vegetation can be linked with ecological properties such as forest cover, moisture degradation. These mostly make use of optical bands such as those from Landsat arithmetic operations to obtain a composite vegetation index band. In this course, we are going to focus on and DBI, which is one of the most commonly applied industries for basic vegetation mapping. We obtain it through the near infrared and the red band of optical data. Values of this index range from zero to one. The closer we are to one and the HILDUR is our vegetation, lower values indicate degradation or even bare areas. This is the end of the term down national park. The LANSAT data were taken from Earth Explorer. I suggest you spend some time looking at this particular map. As you can see straight on, we can derive a few inferences about the state of vegetation in the thumbnail national park. On the extreme right hand side, we have very low values. And why this to me indicates that these areas have undergone severe deforestation or even maybe bear on the extreme left hand side. The vegetation is rather yellowish. So it possibly has values around zero point four, which may indicate severe degradation or ongoing forest loss. In the middle, you can see the vegetation is rather green, quite close to point eight, indicating that these areas still retain a healthy, intact forest structure. So straight on, we can start visualizing the condition of a given area by calculating a very simple index. Now, the weapons of choice for spatial data analysis in this course will be Cutie's, I'm using Yuji's despiser, but you can download the most latest version of our programming language. I would suggest straight on if you want to do spatial data analysis and ah, please install and load these packages. We are going to use this almost every time. In case you don't know how to install packages, please type these two things in your console and it will take you through the process in the next lecture. I'm going to briefly touch upon some of the basic theoretical concepts associated with spatial data analysis. After that, we will be good to get started with the real thing.