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.
Mississauga is a booming metropolis located on the Northern Shore of Lake Ontario. It has a population of over 757,000 people and is home to over 59,000 businesses. Mississauga is a part of the Greater Toronto Area (GTA), the financial heart of Canada and the third largest financial centre in North America with over 6 million residents.
For this analysis I used open data available from the Mississauga Data website (http://www.mississauga.ca/portal/residents/mississaugadata). The open data contains a land use polygon file in the form of a kml. Using Esri ArcMap the kml was converted to a shapefile. The area calculator tool was then used to calculate the area of each polygon. The results were grouped into categories and aggregated based on wards. The results are several maps and tables breaking down the city of Mississauga by ward and top land use.
The predominant three land uses in Mississauga are transportation, detached residential housing and industrial. Mississauga is a very well balanced city with high amount of residential land throughout. The north-east corner (Home of Lester B. Pearson Airport, the largest in Canada) contains some of the largest concentrations of industry. The north-west boundary towards also contains a high proportion of industrial and institutional land. The southern half of Mississauga towards Lake Ontario has the highest amount of green space.
Here is a link to the pdf.
Mississauga has a diverse and complex transportation network including roadways, highways and public transportation. The road network in Mississauga is over 5,500 lane kilometres of road and 2,400 lane kilometers of sidewalks. From Mississauga you can access three 400 series highways including the 401, 401 and 407 ETR. Mississauga is serviced by MiWay Ontarios third largest municipal transit provider. They provide services with over 930 transit operators, 460 buses and over 3,600 bus routes.
With all of these options how are residents of Mississauga getting to work, and are there any special trends for transportation methods. Three methods of transportation were examined in this study: driving, public transportation and walking. The census data used in this analysis was obtained from Esri Business Analyst. The analysis was conducted using the Esri ArcMap hot spot analysis tool. This tool uses the Getis-Ord Gi statistic method and identifies statistically significant hot spots. A hot spot indicates a statistically significant high area and a cold spot indicates a low area.
The hot spots for driving and public transportation show opposite trends. The majority of public transportation is centered on the downtown core and the western edge of the city. The north-west corner with ample highway access was identified as a hot spot. The analysis of walking to work shows that walking is heaviest in the southern edges of the city towards the lake shore. There is a walking cold spot that coincides to a high driving low public transportation zone.
Click here for the pdf.
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.