This year's GIScience course projects


Welcome to another year of GEOG 506 projects, ranging from LBS to GIS implementation. This year we had eight. Here are the abstracts


Over time the function of geovisualizations has shifted from visualization that solely display spatial information to interactive tools that facilitate the exploration of data with the goal of knowledge building. In order for this function to be fully realized, effective geovisualizations adhere to classic cartographic design principles that allow for the clear visualization of geospatial data. The advent of Web 2.0 and the development of advanced mobile devices has brought map- making and geovisualization into the public domain, an era in which both ordinary citizens industry professionals can disseminate geospatial data through the creation of geovisualizations. By examining the products of a web-based geovisualization tool, this project measures the extent of clutter in visualizations with changes in cartographic design and screen size with the goal of better understanding the implications of these new geovisualization technologies on knowledge building.. The visualization can be viewed at the following link.


Location-based services (LBS’s) are computer programs that use spatial knowledge of the user (usually, but not necessarily on a mobile device) and then uses this information in conjunction with other spatial data from the surrounding environment to facilitate spatio-temporal decision making. Over the last few decades, LBS's have increasingly become an important part of daily life. They are a ubiquitous tool that serve various functions, from helping emergency services locate victims to helping shoppers find the nearest retail outlet. A growing use of LBS's is to provide an increase in the quality of service offered by public transportation companies. Applications which provide real-time bus schedules are a way of improving this service quality. For this project, the goal was to create an application using the Societé de Transport de Laval’s (STL) transit data that was capable of determining the users’ location, determining the location of the nearest bus stop to the user and then returning the time of the next bus for that stop. While this result was not fully achieved, the resulting code, analysis and discussion is given below. Implications on users’ privacy and the benefits to the STL that this application have are discussed.



Traditional geographic information systems represent spatial data at a fixed point in time. Despite the analysis of space through time being central to many fields of geography, modern geographic information systems have yet to develop the ability to model temporal data. The problem lies in the historical roots of GIS — cartography, the geographical matrix and relational database management systems — which disregard the temporal characteristics of data. Spatiotemporal analysis is necessary for the understanding of any dynamic processes, such as trends, flows and changes.

The aim of this project is to develop a functional temporal geographic information system data model to analyse employment clusters in the Greater Toronto Area. Clusters of employment were first located using employment data from the Transportation Tomorrow Survey, aggregated in Traffic Analysis Zones. The clusters were modelled using an adaptation of the Event-based Spatiotemporal Data Model (ESTDM). In this model, every event change is recorded in the database and time stamped. This model limits the redundancy in the data and allows the querying of specific temporal intervals and of specific changes in values within the interval. The model is easily replicable and adaptable. It works best with simple event changes, such as urbanization or forest fire analysis.


Geospatial metadata are a subcategory of metadata pertaining to the unique geographic and spatial content and context of a dataset. Metadata facilitate the discovery, access, retrieval and sharing of geospatial datasets, thus creating new opportunities for scientific inquiry. These functions are afforded by the adoption of metadata standards, which outline a specific set of structural and descriptive practices for dataset producers to follow. Thus, understanding the way in which geospatial datasets are structured and described vis-à-vis metadata is essential to the advancement of GIScience research.

This project assesses how different metadata standards vary in the way they structure and describe geospatial datasets. It explicates the advantages and disadvantages of metadata standards with respect to the affordances they provide. For this purpose, five metadata standards were examined. The subjects were found to vary greatly in their functional affordances. Amongst the subjects, standards developed specifically for geospatial datasets provided the most detailed abstractions, however, this came at the expense of simplicity. Generalist standards conveyed information about distribution, but ignored the idiosyncratic attributes, obscuring origin and use implications.


Within the context of a growing demand for government transparency, accountability and openness, open data initiatives are increasingly being implemented locally, nationally and internationally 2012). Open data initiatives in governments face initial barriers and continual challenges that are technical, managerial and institutional in nature. These impediments parallel those of Geographic Information Systems (GIS) implementation, experienced a few decades earlier. The aim of this study is to identify best practices for current and incipient open data implementation initiatives in the public sector, drawing on lesson learnt from GIS implementation efforts. Determinants of implementation will be examined. The approach is three-fold: 1) conduct a realist review of the GIS implementation literature; 2) perform a realist review of the open data implementation literature; and 3) compare and contrast findings, identifying best practices for open data implementation. Results suggest that defining the role and scope of the innovation being introduced is critical for adoption. Drawing on the conceptual underpinnings of GIS implementation may be appropriate to study and explicate the organizational challenges of open data implementation.


Big Data has become an extremely popular topic over the past few years – meriting the term ‘buzzword’ in the truest sense. However, even through gaining increasing popularity through the media, there is still a lot of mystery surrounding the subject. Specialized data analytic companies have a variety of resources, which allow them to process Big Data. This project aimed to determine whether or not a person with limited technological and GIScience knowledge would be able to perform analysis and retrieve meaningful results with a subset of Big Data. By performing spatial, temporal, and spatiotemporal sub-analysis it was determined that sufficient challenges and limitations that are often mentioned in Big Data literature arise.


Geospatial ontologies are formalizations of geographic concepts, properties, and relationships. Geospatial ontologies are designed to formalize knowledge of geographic entities so that computers can understand the semantics (the meaning) behind terms used to describe real world geographic phenomena. This study approached the issue of geospatial ontologies by attempting to construct an ontology of rapids, also known as whitewater. As this is a study in Geographic Information Science, this paper examines the topic of Geospatial Ontology as it applies to the geographic concept of rapids. What are rapids? What is whitewater? What are the ontologically significant concepts, properties, and relationships within this specific domain? Is there an ontology for this particular geographic concept presently? Can it be improved or approached using a different framework? These are questions addressed by this study. I selected three methodologies, each with a different approach to ontology creation, and tried to formalize the concept of Rapids in an ontology. After completing these three methodologies I compiled my rapids ontology on the online ontology platform web protégé hosted by Stanford University Online.