The data that we usually generate from sensors are in raw form and generally in CSV format. It’s hard to visualize it just by staring it in the long list separated by a comma.
The traditional form of data visualization is in Pi-chart, graphs, and another different visual format. This is still not enough for us to find the pattern within the data and visualize the data in the pictural form. We usually lost the valuable information from the blind spot of those current data visualization.
We experienced the mixed reality in the field of data visualization with various sets of combinations. This series will go from the start to the bottom of the mixed reality data visualization.
- Developing the sensor and collect the data from it:
We design, assemble and develop the sensor for the air pollution data collection.
We then deployed our sensors in various parts of the university. The sensor is designed in such a way that it will collect data every 30 minutes. The sensor contains different urban pollution data like pm 2.5, pm 1.0, pm 10, and 9 more. The assembled sensor also has humidity, GPS, and temperature sensors, which also collect data simultaneously.
The collected data looks like below.
After cleaning, re-arranging the data, the sensor data is ready to use for the visualization.
2. developing the city model for the overlay of data in Mixed reality data visualization.
i used the same model that i prepared for the virtual reality data visualization. the process is in the link below.
Using data cubes in city model by using rhino to visualize in virtual reality.
3. making the data surface by using sensor location and sensor data.
By using the GPS location, we plot the sensor in the city model. The x and the y value are generated from the GPS location to put the exact sensor data in the sensor location.
the boarder points are decided by the mean value of the total sensor data of one period of time.
the final edited sensor values look like the figure below:
since we want to visualize the rate of changing the pollution in the particular area, we can find the pattern in which area the pollution spread fast and how the pollution spread in a specific area.
We use rhino python to plot the data in the rhino in the form of points. Then the data points are used to make the data surface by utilizing the patching option in rhino.
import Rhino
import Rhino.Geometry as rg
import rhinoscriptsyntax as rs
import csv
def readFile():
filename = rs.OpenFileName("Open CSV file","*.csv|", None, None, None)
file = open(filename, 'r')
# f = open('only differencws for data plott.csv', 'r')
rd = file.readlines()
ptlist = []
for line in rd:
x=float(line[1])
y=float(line[2])
z=float(line[3])
sensor1 = float(line[4])
scalled = sensor1 * 10000
finalpt = z + scalled
pt = (x,y, finalpt)
ptlist.append(pt)
filename.close()
for point in ptlist:
rs.AddPoint(point)
readFile()The height of the developed surface represented the value of pollution change in the ideal time. But the data surface is very different than the site topography. So I use the color, which represented the value of the pollution change in that area. For that, I use the grasshopper and heat-map generation option. The flow chart of the grasshopper is shown below.
After making the heat-map, the model look like this,
We use various way to present data with model in the Mixed reality.
After making the rhino file ready, we export the fbx file format to import it into the unity.
The steps need to follow to prepare the unity file is explained here.
Setting Up in unity file to develop for magic leap glass.
After finishing the unity development, we need to make magic leap app which then install in the magic leap.
The final output look like this:
Dibash Adhikari 