Volunteer provided advanced QGIS training to American Leprosy Missions

Volunteer: Mohammad Rajabi, Ph.D.

Introduction

American Leprosy Missions (ALM) is a global organization that serves persons affected by leprosy and related diseases, helping them to be healed in body and spirit and restored to lives of dignity and hope.  ALM works to cure and care for people suffering from leprosy and end this ancient disease.

As relatively new GIS users, ALM staff have started to build an international GIS community to support their projects around the world.  Currently ALM (USA), Universidade Federal do Pará (Brazil), the Damien Foundation (Belgium) and NLR (Netherlands) are part of this international GIS community and meet every other month to share ideas and support each other.  In these meetings it had been determined that training in advanced QGIS would be beneficial to the group and aid in the fight against leprosy. Therefore, ALM reached out to GISCorps for a volunteer with advanced expertise in teaching QGIS. The volunteer, Mohammad Rajabi, Ph.D., a faculty member at the Geomatics Department of BCIT in Vancouver, Canada was selected for this project and taught several virtual classes to five students located in the US, Brazil, Belgium, and Netherlands.

ALM Team and the Instructor, Mohammad Rajabi, PhD

Sample Project

To make the teaching process more meaningful, the training focused on a real dataset that ALM was already working on, including Landsat satellite imagery along with the corresponding shapefile file of Bihar, India, that contained demographic/census data of the region. The goals pursued in the training through this sample project were as follows:

  1. Locating the houses/dwellings through analyses (classification) of the satellite imagery;
  2. Locating the high-density (more than 100 persons) residential areas;
  3. Locating clusters of high-density residential areas (villages/hamlets);
  4. Assigning population to the clusters based on the district/town census data using normal distribution, and finally;
  5. Using “R” to extract data from shapefiles and process them statistically.

Training

The training started with a quick review of the fundamental concepts of Remote Sensing and GIS. Figure 1 shows the topics covered in this part.

Figure 1: Topics of Theoretical Discussions

The training continued with downloading a satellite image from Landsat website and classifying it using supervised and unsupervised classification methods. For the supervised and unsupervised classifications, the Semi-Automatic Classification Plugin (SCP) and built-in unsupervised function of QGIS were used, respectively. Figure 2 shows a screenshot of supervised classification process using SCP plugin of QGIS.

Figure 2: Supervised Classification of a Satellite Imagery Using SCP

Using supervised classification, the locations of houses/dwellings were identified. The next step was to determine the clusters of homes/dwellings, i.e., villages/hamlets. Figure 3 shows the result of this analysis.

Figure 3: Points with High Concentration of People

Next step was to cluster these high-density points using K-Means Clustering and assigning random population to each cluster based on the already available census data. Figure 4 shows the result of this process.

Figure 4: K-Means Clustering of High-Density Population Points

The last step of training was to use “R” for statistical processing of the spatial data. Figure 5 shows a sample R code for extracting data from shapefiles and plotting them.

Figure 5: Statistical Analysis of Spatial Data Using R

Final Remarks:

The training mentioned above was completed over seven two-hour sessions. The training dates and concepts covered during each session are as below:

  1. March 16 – Basics of Remote Sensing and GIS
  2. March 30 – Supervised Image Classification (using SCP Plugin in QGIS)
  3. April 13 – Supervised Image Classification (Cont’d)
  4. April 27 – Supervised and Unsupervised Image Classifications
  5. May 12 – Spatial Data Analysis (using QGIS)
  6. May 26 – Spatial Data Analysis (Cont’d)
  7. June 10 – Spatial Data Analysis, Statistical Analysis (using R), and Concluding Remarks.

“Taking part in this activity was a very rewarding opportunity for me and I sincerely thank GISCorps and ALM for giving me this chance. While I had the pleasure of meeting and knowing some great people practically all over the world, the project expanded the horizon of my technical knowledge on how GIS and Remote Sensing technologies are used in public health domain. The volunteer position I assumed with GISCorps completely fulfilled my desire and passion for learning more and putting my knowledge into practice.”

– Mohammad Rajabi, Ph.D., faculty member at the Geomatics Department of BCIT in Vancouver, Canada

“Mohammad, thank you for all of your time and expertise, I know that everybody learned a lot and enjoyed your teaching.”

– Leslie Zolman, American Leprosy Missions 

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