Content
Introduction to Micro Quiz 3 and Class Overview
The class began with the administration of the third micro quiz, which followed the same pattern as previous assessments. The instructor explained that the micro quiz serves as a proof of understanding from the assignment, with questions that are structurally similar but use different numerical values. Students were given until 10:10 a.m. to complete the quiz, with the instructor noting that they likely would not need the full allotted time.
After collecting the completed quizzes, the instructor announced that the final twenty minutes of class would focus on content covered in the previous session. The discussion would return to the types of data that were opened in QGIS during that prior class, with particular emphasis on land use land cover maps and the changes that occur within those maps. The instructor noted that there are various acronyms used in this field, including LUC versus LULC, and emphasized that understanding these concepts is important for the work ahead.
Logistical Setup and Data Access
Preparing to Work with Large Datasets
Before diving into the content, the instructor provided two important logistical instructions. First, students were asked to open QGIS if they had not already done so, with the caveat that the software can take approximately one minute to load. Second, students were directed to open a browser window to access the data folder where updated materials were available.
The instructor explained that while the Nicaragua folder had been used previously, the class now had access to a significantly expanded collection of organized data. This new data was compressed into a single zipped file for ease of download, but when extracted, students would find folders for their assigned country. The instructor strongly recommended that students begin downloading the data immediately, as some files were quite large. For example, Chile’s data set was approximately 1.29 gigabytes in size.
Understanding Big Data and Its Implications
The instructor disclosed that Nicaragua had been small enough to download quickly, but explained that the new datasets were substantially larger because they were based on satellite data, which produces an enormous wealth of information with high spatial detail. While this detail is valuable, it comes with the drawback of being “big data.” The instructor mentioned another class they teach at the PhD level that focuses specifically on big data, and shared that although their professional background is in economics, the work that has driven success in their career has primarily involved working with big data and writing programming code to handle it. On a daily basis, the instructor described themselves as really being a programmer, comparing it to playing video games but getting paid to do it.
To illustrate the scale of big data being discussed, the instructor noted that if one has a gigabyte of data, that represents more than a billion pixels. This helps contextualize why the download times and file sizes are significant considerations when working with spatial data.
Understanding GeoTIFF Files
What is a GeoTIFF?
The instructor explained that in the previous class, students had downloaded a TIFF file and placed it in a specific location before opening it in a blank QGIS project. However, the instructor noted that one important aspect had been skipped: what exactly is that file? The file type in question is called a GeoTIFF.
A GeoTIFF is essentially a two-dimensional array or matrix of numbers. The instructor used an Excel spreadsheet as an analogy to help explain this concept, since Excel is commonly used to display two-dimensional matrices or arrays of numbers. Each entry in a GeoTIFF has an associated color, but underneath that visual representation is simply a number that categorizes the land cover type at that specific location. For example, the number 11 by itself is relatively meaningless, but when working with land use land cover maps, there is always an accompanying legend. In this case, the number 11 corresponds to open water and is given the color blue in the visualization.
Scale and Accessibility of GeoTIFFs
Despite their apparent complexity, a GeoTIFF—even one that is a gigabyte in size with a billion grid cells—is essentially just an extremely large spreadsheet of numbers. One key advantage of GeoTIFFs is that they can be operated on very rapidly, far faster than would be possible with an Excel spreadsheet of equivalent size. This computational efficiency is one reason why they are the preferred format for working with large spatial datasets.
The instructor emphasized that looking at a TIFF in an ordinary image viewer does not make sense, which is why the proper procedure involves opening it in specialized software like QGIS.
Types of Geographic Data
Raster Data
The first major type of geographic data discussed is raster data, represented by TIF files. The land use land cover maps that the class has been using are TIF files. A raster is a grid or matrix of data organized in rows and columns. Each cell in the raster represents a specific geographic location and contains a value—in the case of land use land cover maps, that value indicates what type of land cover exists at that location. The primary advantage of raster data is that it represents something that covers a whole area. For example, it effectively represents phenomena like precipitation across a landscape.
Vector Data
The second major type of geographic data is vector data, which consists of polygons, lines, or points. Unlike raster data, vector data is not organized as a grid or matrix of rows and columns. Instead, it uses latitude and longitude coordinates to define geographic features. Vector data comes in several file formats, the most common of which is a shapefile, designated by the .SHP extension. Shapefiles are an older, deprecated file type that remains widely used despite their age. Shapefiles are a proprietary data type owned by Esri, the company that produces ArcGIS, which is the primary commercial competitor to QGIS, the free option that this class uses.
A more modern alternative to shapefiles is the GeoPackage format, designated by the .GPKG extension. GeoPackage is functionally similar to a shapefile but offers approximately ten times faster performance. GeoPackages are the recommended format for new work because they represent a significant technological improvement over the aging shapefile standard.
Comparing Raster and Vector Data
The fundamental difference between raster and vector data relates to what type of geographic phenomena they represent most effectively. Raster data represents phenomena that cover a whole area, such as precipitation or land cover across a landscape. Vector data typically represents distinct geographic features such as outlines—for instance, the boundary of a country represented as a polygon—or points, such as the exact geographic location where a specific observation was taken. Understanding this distinction is important for selecting the appropriate data type for different analytical purposes.
Working with TIFF Files in QGIS
Opening Raster Data
The instructor had previously asked students to open one TIFF file as part of the prior class assignment. The new task involved opening each student’s country-specific TIFF file. To accomplish this, students needed to open QGIS and create a new blank project. The instructor noted that the easiest method for adding data to QGIS is to use the drag-and-drop functionality.
To complete this task, students needed to look in the teaching folder, find their assigned country’s data folder, and extract it. On Windows systems, this involves right-clicking on the zip file and using 7-zip or another extraction tool that comes with Windows, or alternatively, copying the folder from inside the zip directory and pasting it in the desired location.
Adding Vector Data Layers
To complement the raster data, the instructor asked students to also work with vector data. Students were directed to go up one level in the folder structure and open the administrative boundaries folder. The instructor noted that this folder contains shapefiles, which can be confusing because a shapefile actually consists of five different files altogether. When working with shapefiles, it is necessary to find and open the correct file, specifically the one with the .shp extension. For users who do not have file extensions displayed in their file managers, they should look for the file labeled as a shapefile and drag it into QGIS.
The instructor explained that there are typically two versions of administrative boundary files available: a dash-0 version and a dash-1 version. The dash-0 version represents admin level 0, which corresponds to the country level. The dash-1 version represents admin level 1, which corresponds to provinces or states. Depending on the specific analytical purpose, users might be interested in either version.
Layering and Visualizing Multiple Data Types
Once both the raster and vector data are loaded into QGIS, their appearance on screen depends on the order in which they were added. If the appearance is not as desired, one can click checkboxes to control whether specific layers are visible on screen. Clicking the checkbox next to a layer name toggles its visibility.
If one layer is covering another in an undesirable way, the layers can be reordered by dragging them within the layers panel. When the raster layer goes away due to being below the vector layer, users can drag the vector layer in the panel to reposition it, and now the raster will be visible below the vector layer.
Modifying Layer Appearance
Often, the default appearance of polygon layers is not ideal for visualization purposes. Many users prefer to see just the boundaries of polygons rather than having them filled with a solid color. To modify layer appearance, one can double-click on a layer name to bring up a menu of options, and then select the Symbology option. From there, various adjustments can be made.
One useful adjustment is to turn down the opacity of the polygon fill, which allows both the boundary lines and the underlying raster data to be visible simultaneously. Additionally, QGIS includes several built-in templates that can improve the visual appearance of maps. For example, using green borders on the polygons makes it easier to see the underlying raster cells while simultaneously highlighting the country or administrative boundaries.
Alternative Data Loading Methods
The instructor mentioned an alternative method for loading data that does not rely on drag-and-drop functionality. Under the Layer menu, there is an “Add Layer” option that allows users to load data manually. However, this method is somewhat more complex because users must specify whether they want to add a raster or vector layer. For users having trouble with the shapefile drag-and-drop method, the instructor recommended using “Add Vector Layer” from this menu. The instructor indicated that a follow-up email would provide more detailed instructions for those experiencing difficulties.
Land Use Land Cover Maps as Environmental Modeling Inputs
The Central Role of LULC in Linking Economy and Environment
The instructor explained the reason for spending class time on land use land cover maps and their visualization. These maps serve as key inputs into all sorts of different environmental models. While the class has focused heavily on economic tools such as supply and demand curves and fisheries growth equations, linking the economy and the physical environment requires knowledge of physical models and how to use them. The land use land cover map is the critical connecting point between these two domains.
A land use land cover map represents multiple important aspects simultaneously. It represents the economy, because urban expansion, factories, and roads are all economically produced things that show up on the land use land cover map. These economic activities then become inputs into different environmental models. The land use land cover map is therefore one of the key ways we think about environmental impact and how human economic activity translates into environmental change.
Analyzing Change Over Time
Beyond just examining a single snapshot of land use land cover at one point in time, what is often particularly valuable is having a time series of different land use land cover maps. This approach enables a very accurate way of representing deforestation, because rather than just knowing the total area lost, one can identify the exact specific pixels that were lost between time periods. This level of spatial precision is not possible with many other methods.
Drivers of Land Use Change
Economic and Environmental Factors
There are numerous drivers of land use change, and many of them align with what one might expect based on economic theory and environmental principles. Economic factors that drive land use change include population growth, economic development, income growth, and agricultural expansion. Economic activities like the extraction of timber are also important drivers. Beyond purely economic factors, climate change is another significant driver that results in changing land use land cover maps.
The instructor noted that there is a comprehensive list of potential drivers, but students do not need to memorize these factors. Rather, it is more important to understand the general categories and to recognize that land use change is driven by a complex interaction of economic, social, and environmental factors.
The SSP and RCP Framework Applied to Land Use
Understanding Scenarios and Projections
The right way to think about future land use change involves the SSP and RCP framework that had been discussed earlier in the course. The SSPs, which are Shared Socioeconomic Pathways, now appear with assumptions specific to land use change. There is a comprehensive research paper on this topic, but the key questions that the SSPs address include considerations such as what type of regulation exists, what changes in productivity might occur, and what broader societal trends are unfolding. Within the SSPs framework, researchers develop projections into the future under different scenarios of what might happen to land use and land cover.
Projecting Crop Demand Under Different Scenarios
One illustrative example involves a chart showing change in crop demand, which is measured in millions of tons of dry matter per year. The baseline scenario from the historical period shows a single line representing observed data. Once one crosses from the present into the future, the chart displays five different lines, each representing one of the five SSPs.
The SSP3 scenario, which the instructor described as the “bad scenario,” shows a huge increase in millions of tons of dry matter of crops per year compared to other scenarios. The reasoning behind this projection is that in the SSP3 scenario, humanity is eating much more beef, which means that agricultural production must increase substantially to grow the grain needed to feed that additional livestock.
Using SSPs for Country-Specific Analysis
The SSPs provide a rich dataset of projections regarding what happens to different land use classes over time into the future. Going forward, when the class completes country-specific reports as part of their assessments, they will be expected to evaluate not just the current state of ecosystem services in their assigned country, but also to project and assess how things might change under different scenarios. This will provide a more complete picture of future environmental conditions and the implications for ecosystem services.
Installing Additional Software and Next Steps
Introduction to InVEST
The instructor announced that they would be sending a detailed announcement over the weekend. This announcement would include instructions for installing another piece of software called InVEST, which is one of the main biophysical models that the class will use throughout the course. The announcement would provide detailed step-by-step instructions for the installation process.
On Monday, when the class reconvenes, the plan is to begin hands-on work with InVEST and the country-specific data that students have now downloaded and become familiar with.
Classroom Logistics and Schedule
The instructor provided an important logistical note regarding classroom locations for the remainder of the semester. The class will be held in the current classroom for all sessions with one exception: on April 20th, when the regular classroom is not available, the class will revert to an older classroom that had been used previously. Apart from this one date, all classes will be held in the current location.
Supplementary Discussion: Shapefiles Versus GeoPackages
Why GeoPackages are Superior to Shapefiles
After thanking all students for their attention, one student asked a follow-up question about the difference between a shapefile and a GeoPackage file and inquired why GeoPackages provided much faster performance than shapefiles.
The instructor explained that a shapefile is an antiquated database technology that has remained in wide use despite its age and limitations. Shapefiles are based on the DBF file format variant, which was invented around 1980, and it was poorly thought out from a design perspective. Computing technology and database design principles have advanced tremendously since that era. In contrast, a GeoPackage is a much more modern data structure that takes advantage of contemporary database design principles and uses JSON, which is a nice and flexible data format that integrates well with modern programming workflows.
The fundamental reason for the performance difference is that GeoPackages benefit from decades of improvements in database design and are optimized for contemporary computing hardware, while shapefiles remain constrained by design decisions made in the 1980s that are no longer optimal for modern computing environments.