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. |
Application Guidelines - All Entries to be received by April 2, 2023
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:
- Project descriptions should be no longer than 500 words and include its purpose, data used, and the solution that it provides.
- The use of maps and other data visualization elements to describe project results are strongly encouraged.
Questions regarding the bursary should be sent to the attention of the Director of Education.
2021-22 Nathan adams STUDENT BURSARY WINNERMelissa Jade Greco - University of WaterlooHonours Planning, Faculty of Environment
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.
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Mapping Crime in Waterloo (2019), An Analysis of Total Crime Occurrences in the Region of Waterloo2020-21 STUDENT BURSARY WINNERXuyang Han - York UniversityGeomatics Engineering
Abstract: 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.
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3-D Datascape Mapping of Toronto
Studying Invasive Plant Distribution by Using GIS
About UsWe are recognized as the premier geospatial and information non-profit organization in Ontario. where members share their experiences through networking and program events for the use and integration of spatial data, information, applications and technologies that provide solutions and solve challenges in both the public and private sectors. | Become a MemberShow your commitment to the Geospatial Community by joining us. Be part of a dynamic geospatial and information community whose mandate is to advance the effective application of spatial and information to enable business services and decision making. MEMBER BENEFITS Education and research. Networking. Discounts and member-only events. |
