Determining Effects on Temperature Interpolations from Large Lakes using QGIS

Purpose

The purpose of this tutorial is to provide the steps necessary for creating an Inverse Distance Weighting (IDW) interpolation in QGIS, and then how to analyze the errors generated spatially. More specifically, you can interpret these errors with proximity to features (large lakes in this project) to see how the distance to a lake affects the error in the interpolation.

Introduction

An Inverse Distance Weighted (IDW) Interpolation uses surrounding known points (i.e. elevation) to estimate point values across an area, using the known points values. The IDW takes the distance away from the surrounding points to determine the estimated values. With the creation of this IDW interpolation, error is created because the interpolation is only estimated values across the area. It is known that lakes have a moderating effect on temperature, and so this project seeks to assess the impact on error of interpolations, rather than on temperature. This project has unlimited uses outside of lake effect as well, as you could analyze urban areas, forests, or any other type of feature/land type and its effect on quantitative values.

Software

The software used for this project was QGIS, which is a free and open-sourced GIS software. This tutorial was completed on Mac OS X, but will work with other operating systems as well. The software can be downloaded here https://www.qgis.org/en/site/forusers/download.html

Data

The data used in this tutorial includes a shapefile of large lakes within Ontario, a shapefile of Ontario, and temperature data for Ontario represented as point data. The data used can be found freely online to use in QGIS, and this tutorial will work for any point values wanted to use for interpolation.

Tutorial

Adding Data

After downloading the QGIS with the link posted above, the first thing that needs to be done is uploading the data into the program. This can be done by using the Add Raster Layer or Add Vector Layer tools, depending on the type of data being used. For the case of this specific project, the data is in vector format (polygons) and the vector tool will be used. (Layer/Add Layer/Add Vector Layer)

To add the point data (temperature) you will need to use the Add Delimited Text Layer instead of the Add Vector Layer (Layer/Add Layer/Add Delimited Text Layer). Add the file from your data, and select CSV for the file format (Note: Make sure the table file is set as CSV). For this file, there should be no header checked, and fields 3 and 4 are used for X field and Y field respectively.

Note: It is wise to keep all needed data in one folder, and save all of the following data to the same folder as well.

Interpolation

Now that the data is imported and in the proper format, we can add it to GRASS GIS for the interpolation methods. This is done (similarly to QGIS) using the add vector tool in GRASS. When the data is uploaded, we first need to separate the 1995 temperature points into validation and training sections to use. This is done using the select by attributes function (Vector/Feature Selection/Select by Attributes). In this tutorial, we used 20% points for validation, and 80% for training. Select the Temp1995 file for input, and label your output points for the validation. On the next tab (Selection), scroll to the bottom and put 20% of the points in the box (I used 74). Next, the temperature data needs to have the training points separated (Vector/Feature Selection/Select by Another Map). Use your Temp1995 for the ainput, your validation file for the binput, label the output, and select disjoint for the operator. This will create a new file for the training points.

Map

To create a visually appealing map with the interpolation, we will now clip the raster IDW with the Ontario shapefile. To do this, you will use the raster clipper function (Raster/Extraction/Clipper). Select the IDW for the input, and select mask layer with the Ontario shapefile. Select the correct output file, and click ok to create the clip.