Nathan Adams Student Bursary
The BeSpatial Student Bursary was renamed in 2022 in honour of Nathan Adams, a Master’s candidate studying Geography and GIS at the University of Guelph. Nathan had graduated from the University’s undergraduate Geography program and was working on his thesis to develop a new way of using AI for extraction of objects from a LiDAR point cloud when he died due to complications of Type 1 Diabetes at the age of 24 in 2021.
There is a strong connection to the Adams family and BeSpatial Ontario. Nathan's cousin, Sarah Brooke, was the first student to win the award and Nathan’s mother, Dianne Adams, served on the board throughout her career. The Adam’s family is honoured that Nathan’s name will be associated with the annual bursary for other students working towards a degree in the GIS field.
Bursary value: $500
The BeSpatial Student Bursary is an annual award that recognizes the contributions of students studying at Ontario post-secondary institutions to the geospatial and information community. This is a great opportunity for students to get name recognition and begin participating in industry events.
To be eligible for this award, students must be currently enrolled in a certificate, diploma, post-graduate diploma, undergraduate, or graduate studies program in Ontario.
Broad topics for consideration can include the application of GIS in the following areas: Education, Geomatics Engineering, Environment, Natural Resources, Public Safety, Transportation, Facility and Asset Management, Business, and Analytics.
To be considered for this award, eligible students are asked to submit a story map and short paper detailing their project completed as a requirement for their studies in Ontario. Additional details are as follows:
Questions regarding the bursary should be sent to the attention of the Director of Education.
2021-22 Nathan adams STUDENT BURSARY WINNER
Melissa Jade Greco - University of Waterloo
Honours Planning, Faculty of Environment
Mapping Crime in Waterloo (2019), An Analysis of Total Crime Occurrences in the Region of Waterloo
The purpose of this project is to provide a snapshot of crime statistics based on all cases reported to the Waterloo Regional Police Service (WRPS) in 2019. Graduated symbols and choropleth maps are used to represent the geographic distribution of the total crime occurrences in the Region of Waterloo, as well as the distribution of three specific types of occurrences of interest, including breaking and entering, assault, and drug related cases. The analysis may be used to illustrate which areas of the city have the highest total crime occurrences, and thus, which may require the most police resources.
The project uses crime occurrence data published by the WRPS in CSV format as well as open data from the Region of Waterloo (e.g., regional boundary shapefile). ArcMap and ArcGIS online were used for data processing and map production. As mentioned, graduated symbols maps and choropleth maps are used to identify patterns in the data and identify crime hot spots. Numerous geoprocessing tools were used in the creation of these maps, such as the XY table to point, generate tessellation, select by location/attribute, collect events, and spatial join geoprocessing tools.
2020-21 STUDENT BURSARY WINNER
Xuyang Han - York University
Clustering Marine Automatic Identification System (AIS) Data Using Optimized Density-Based Spatial Clustering of Applications with Noise (DBSCAN)
Today, maritime transportation represents substantial international trade. Sustainable development of marine transportation requires systematic modeling and surveillance for maritime situational awareness. In this research, we present an enhanced density-based spatial clustering (DBSCAN) method to model vessel behaviours. The proposed methodology enhances the DBSCAN clustering performance by integrating the Mahalanobis Distance metric that considers the correlations of the points representing the locations of the vessels. The clustering method is applied to historical Automatic Identification System (AIS) data by proposing an algorithm for generating a clustering model of the vessels' trajectories and a model for detecting vessel trajectories anomalies such as unexpected stops, deviations from regulated routes, or inconsistent speed. Besides, an automatic and data-driven approach is proposed to choose the required initial parameters for enhanced DBSCAN. Two case studies present outcomes from the openly available Gulf of Mexico AIS data and Saint Lawrence Seaway and Great Lakes AIS licensed data acquired from ORBCOMM (a maritime AIS data provider). This work's findings demonstrate the applicability and scalability of proposed method for modeling more water regions, contributing to the situational awareness, vessel collision prevention, safe navigation, route planning, and detection of vessel behaviour anomalies for auto-vessels development towards the sustainability of marine transportation.
2019 Student Bursary Winner
Devon Kleinjan - University of Guelph
3-D Datascape Mapping of Toronto
Devon Kleinjan is entering his final year at the University of Guelph, studying Landscape Architecture. He is supplementing his professional degree with a minor is GIS. He is currently working as a Landscape Intern with the Hamilton Conservation Authority. His BeSpatial project submission stood at the intersection of Landscape Architecture, Geography and Technology. He is looking forward to being a part of BeSpatial for the professional and personal connections it can provide.
2018 Student Bursary Winner
Nebyu Woldeyohanes - Nipissing University, Ontario
Nebyu Daniel Woldeyohanes is a 3rd year Environmental and physical Geography student at Nipissing University, focusing in GIS and Environmental Management. He was born and raised in Ethiopia, coming to Canada when he was 15. He aspires to be a GIS specialist to be able to solve real life problems.
His project studied Invasive Plant Distribution through the Identification and Impacts of Water Hyacinth (Eichhornia crassipes) on Lake Tana, Ethiopia.
2017 Student Bursary Winner
Kyle Wittmaier - Nipissing University, Ontario
This project was completed as part of the requirement for 2016 Esri GIS Scholarship. The objective was to identify the most suitable locations for setting up solar panel farms in the Niagara Region using GIS mapping techniques.
In recent years, Canada has invested heavily in renewable energy and the completion of this project would provide useful information in determination financial feasibility for establishing solar energy generation facilities. Location of suitable sites was determined using GIS multi-criteria evaluation analysis tools. The three main criteria were: accessibility/boundaries, topographic factors, and existing land cover/use. All criteria were individually analyzed and then combined using the Weighted Sum tool before Boolean logic was applied to remove restricted development areas. The results show that there are three clusters of suitable locations present in the Niagara Region near the municipalities of Lincoln, Niagara Falls, and Fort Erie. These are the areas where future solar panel farm development should be considered.