This repository contains the first version of the BikAIr Logger. It allows visualizing the data collected from the BikAIr project.
Author: Bikair team
Email Address: [email protected]
Bikair is a project developed by students from the 4th Generation EAGLE graduate program. It focuses on testing low-cost Arduino-based sensors in an urban environment such as the city of Würzburg. Eventually, the project aims to correlate in-situ data with relevant Remote Sensing-based parameters. It takes advantage of the recent advancements in the Internet of Things (IoT), which allows the collection of data on specific locations and its further transportation using wireless network protocols. Bikair team designed static and mobile devices using sensors that measure environmental parameters like temperature, humidity, UV radiation, particulate matter (PM1, PM 2.5, PM 10), and soil moisture. These measurements could strengthen a wide range of applications such as monitoring the relationship between urban green and temperature, identifying air pollution hotspots, or supporting the downscaling of satellite remote sensing products.
To visualize the logger, you could follow the next steps:
- Download the repository
- Unzip the file
- Open the project
Bikair_Logger_V1.Rproj - Run the code
Main_App.R
After running the code, a new window with the logger will open.
The following step is uploading the data. This is possible from the Data Import tab. In the left corner, there is a Browse button. By clicking it, you can select the file with the data. It is located in the folder of the repository \Bikair_Logger_V1-main\Data
After loading the file, a table with the data will be displayed. In the left panel you can select variables and observe some statistics such as the median, quartiles, and the minimum and maximum value.
The variable selected would work as a starting point for the next steps. It could be changed afterward.
In the Interactive Map tab, you can visualize the spatial distribution of the data collected. Initially, it would be clear, but you can change the type of visualization to points, lines or raster.
In the Points View, you could visualize each measurement as a point. In the right panel, the display could be adjusted to your preferences by the following options:
- Choose the range of points
- Select a variable
- Pick a color palette
- Change the size of the points
- Reduce the opacity
- Modify the legend
By clicking Export, the map could be saved as a png file.
When switching to Line View, the direction of movement can be observed through an animation. In the right panel, you can adjust the following options:
- Choose the range of points
- Select a base color
- Pick a pulse color
By clicking the Raster View option, tha app will show an interpolation of the measurements. The preferences that could be adjusted are the following:
- Choose the range of points
- Select a variable
- Pick a color palette
- Change the resolution (depends on the density of the data collected)
- Reduce the opacity
- Modify the legend
By clicking Export, the map could be saved as a png file.
The Plots tab contains a variety of graphs that allow a better understanding of the variables.
