The purpose of this assignment was to analyze point patterns. Data used in this presentation was obtained from the City of Vancouver Open Data Catalogue. The data sets included fire halls, disability parking, schools, street lighting panels and sewer manholes. This collection of data sets contained a varied number of points as well as dispersion patterns. Two methods were used, quadrat and average nearest neighbours.
A script tool was created in ArcMap using python and ArcPy. The user inputs a point data set, a study area, quadrat size (user selected or optimal size calculated automatically) and the test significance level. The quadrat analysis generates a fishnet (a square grid) covering the study area and then calculates how many points fall within each grid. The variance between the quadrats is calculated as well as a test statistic value to determine the pattern of the points. The output is a summary of the results and the point pattern type. In addition a fishnet grid is generated to visualize the spacing and the distribution of the points relative to the grid. A very small quadrat size can results in a large number of empty quadrats. As the quadrat size is increased more points will be located in each quadrat and the pattern will disperse with increasing sizes.
Nearest Neighbour Analysis
The average nearest neighbour tool looks at proximity of points. The nearest neighbour index is determined based on the average Euclidean distance between each feature and its nearest neighbour and its compared to the total study area. The output includes the z-score and the p-value associated with the pattern
The pdf poster presentation for this project can be viewed here.
The purpose of this project was to examine and apply spatial statistical methods. The data set used was educational attainment divided by dissemination area (DA) for the Western Greater Toronto Area. The population with a bachelors degree or higher and no certificate diploma or degree were examined. These variables were normalized to total population aged 15 or older.
Several statistical methods were examined. Spatial autocorrelation was examined using Moran’s I. This method can be used to examine the similarity of nearby continuous features. This method calculates the difference between the target feature and the mean for all features, and the difference between each neighbour and the mean. The results can then be used to determine if the pattern is dispersed or clustered.
The General G-Statistic examines for clusters and then identifies if they are low or high values. The result does not identify the location of clusters, only that high or low values tend to cluster near another.
The hot spot analysis was also used in order to identify clusters and outliers. The tool used for this analysis was the Anselin Local Morans I tool. This tool identifies the location of clusters as well as if the clusters are of high or low values. The ArcMap Hot Spot Analysis (Getis-Ord Gi*) and the Optimized Hot Spot Analysis tool was also used. These tools identify both clusters as well as identifying if the clusters are of high or low values.
Geographic distribution was also measured using the standard distance and standard deviational ellipse. These tools determine the dispersion fo values around the mean centre. These tools show directional trends within the data.
Click here to view the pdf poster presentation of this project.
The purpose of this project was to examine the Esri ArcMap Geostatistical Analysis Tool. The input data set contains soil sample data from a field site located within Northern Ontario. The soil was sampled for gold (Au [ppm]) as part of a geological exploration project. Due to the nature of the terrain and outcrop the samples were not collected within a regular grid. Due to the localized nature of gold mineralization the samples can be considered to suffer from the nugget effect. Various geostatistical methods will be evaluated and a predictive surface will be generated for the Au mineralization to guide future exploration.
View the complete pdf poster here.
This is a student project completed as part of the Advanced GIS Diploma requirements at the Centre of Geographic Sciences (COGS). All data was analyzed with Esri Business Analyst using a dataset compiled Duns and Bradstreet Solutions. The figures presented are estimates only and should not be used for any decision making purposes.
Coffee or ‘black gold’ as it is referred to by those working in the industry is big business. It was the highest selling hot beverage in Canada and in 2011 with over 14 billion cups consumed. Approximately 64% of Canadians drink coffee every day which is the equivalent to 12.7 pounds of roasted coffee per capita. The predominant market for coffee is age driven. It is the drink of choice for Canadians aged 25 to 49 (after water). Coffee consumption is the highest in the 50+ age category and lowest in those below 24 years of age (Source: Coffee and Tea Industry Trends from the Canadian Coffee and Tea Show (2011) Agri-Food Canada).
The purpose of this study is to examine the coffee shop industry in Mississauga, Ontario. The focus of the study is the Tim Hortons coffee shop chain which will be compared to its competitors. A combination of estimated sales volume and census data will be used to rank stores and compare Tim Hortons performance to competitors. All data used in this study was obtained from Esri Business analyst.
Mississauga is Canada’s 6th largest and fastest growing major city (Source: City of Mississauga Website). Mississauga is home to over 720,000 residents with a work force of over 425,000 people. Mississauga is also a part of the Greater Toronto Area (GTA) which is the largest population centre in Canada.
In this study 154 coffee shops were assessed of which Time Hortons had the highest number of stores at 68 locations. The total sales volume of the Mississauga coffee shop market was estimated at over 10 million dollars. The market share and sales volume of the coffee shop market in Mississauga is shown in Figure 1.
Click here to view the pdf of the poster.
I was fortunate to participate in the International Platinum Symposium hosted in Sudbury. I was able to present the preliminary findings of my research project and discuss them with experts from all around the world. It was an excellent way to get a lot of research ideas to work on. I was also able to participate in an around the lake field trip visiting sites in Ontario, Minnesota and Michigan.
Here is a link to my poster.
I presented some of my research findings at the Geological Society of America annual conference in Minnesota. This was a great chance to improve my public speaking in front of a large and well informed audience. Although I was a little nervous it went off without a hitch. Afterwards I was able to get feedback from the audience and even had the author of one of the papers I was referencing came up to tell me how excited he was that I was using his research.
Here is a link to a pdf of my presentation.
This is a poster I presented at a GAC-MAC (Geological Association of Canada, Mineralogical Association of Canada) conference in Ottawa. It was towards the end of my research and my presentation skills had really come a long way. I won an award for having a top student poster at the conference.
Here is a link to the poster.
MSc – Characterization of High-PGE low Sulphur mineralization at the Marathon PGE-Cu Deposit, Ontario
I completed a Masters degree in geology at the University of Waterloo. This program involved both course work and a thesis research deposit. My research topic was focused on a unique zone of mineralization at the Marathon PGM-Cu deposit located on the north shore of Lake Superior in Ontario. The research was done in conjunction with Stillwater Canada Inc., the Canadian division of the successful Stillwater Mining Inc. out of Billings, Montana. The mineralization zone I studied was coined the ‘W-Horizon’ and it was a lens containing high grades of platinum and palladium mineralization. For my research project I collected detailed drill core samples (over 200 samples) which intersected W-Horizon which I analyzed for lithogeochemistry. I created a data base of detailed chemistry, physical rock descriptions and thin section descriptions. I combined this data set with the Stillwater data set and analyzed the 3D spatial distribution of the W-Horizon. My research involved developing a mathematical model of the W-Horizon to help explain its origin and that could be used to target future exploration work. Here is a link to a digital copy of my thesis, ‘Characterization of High-PGE Low-Sulphur Mineralization at the Marathon PGE-Cu Deposit, Ontario’.
This research position was very interesting and challenging. My research work was guided by Dr. Robert Linnen (University of Western Ontario), Dr. Dave Good (Stillwater VP Exploration during the project) and Dr. Iain Samson (University of Windsor). This excellent team allowed me to present ample times and to integrate ideas from the group into the research project. During the course of this project I presented research at several conferences and meetings. I also regularly attended and presented at the ‘Hard Rock Cafe’ an informal geological research consortium at the University of Waterloo.
I used GPS tracks from a rock climbing trip to Red River Gorge in the summer of 2014. I used ArcGIS Online to display the routes we drove from our campground at Lago Linda’s. I also show points for each of the craigs we went to. Nice and easy way to share the trip.
Click here to launch the Map App.
For this personal project I used ArcGIS Online to prepare a travel journal for a trip I took to New Zealand. Using a story map journal is an amazing way to share your trip. It provides a great and easy to use interface to prepare your journal and makes it easy to integrate spatial information about your trip. I love it because what part about a vacation isn’t spatial? You are traveling far from home and usually in a new place. What better way to share it than also showing people not only what you saw but where you saw it.
Click this link to launch the map app.
The purpose of this project was to use the Erdas Imagine Software to conduct a supervised image classification. The study area for this project was the
Annapolis Valley. The image used for classification was a Landsat 8 combined image taken on September 5, 2014. In addition students from the Centre of Geographic Sciences (COGS) conducted a “ground truthing” exercise in early October. Students visited a collection of field sites and took detailed notes describing the locations. Ground truthing locations are labelled on the Landsat true color image.
The purpose of this project was to use SPOT satellite imagery in an unsupervised classification. The SPOT satellite image was first orthorectified using secondary sources (national road and hydro vector files). The unsupervised classification was run on the orthorectified data, and then grouped into appropriate land cover categories. Validation files were provided to the students to assess their classification and to enable a quantitative analysis of the performance of various classification, grouping and filtering methods. After a satisfactory classification was complete, the image was transformed into a vector image for use within the ESRI ArcMap software package. Each student in the class selected a different ‘scene’ and the combined class covered almost all of Nova Scotia. An important part of this assignment was keeping data organized and cohesive so that all the classified images could be combined for a final project. The DEM and SPOT imagery was accessed through the GeoBase website http://geobase.com, an excellent source for free imagery.
This was a fun project using labeling and overlays in ArcMap to create a fun cartographic map. This is a fictitious historical map of Viking lords and lands in the Lawrencetown Region, Nova Scotia in the year 806 AD. In this time period Lords controlled vast tracts of land surrounded by forests and wilderness. The Lords of the valley constantly battled for land and boarders were short lived.
Link to a pdf of the map.
The purpose of this project was to explore cartographic elements, projections and style. Using a instructor provided data set a map outlining the course of the ill-fated 1914 Shackleton Expedition was created. In addition to the map a web-version of the map was also created with images linked along the travel path of the crew.