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.
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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.
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The purpose of this project was to use exploratory data analysis to examine a data set and chose the most appropriate categorization scheme. The data set selected for presentation in this poster was population per dwelling in western Nova Scotia. The classification schemes examined included standard deviation, equal interval, quantile and natural breaks.
The purpose of this project was to practice using exploratory data analysis in ArcGIS. Data was collected on 6 topics from the World Bank (http://www.worldbank.com) for countries in Europe.
Retail Market Potential
Watch out video games, a new (or old) contender in back in town. The market for board games (not just MonopolyTM and ScrabbleTM any more) has exploded in recent years. The technological age has created a renaissance for this classic form of entertainment and sales have never been bigger. Amazon reported that the percentage of board game sales increased by double digits from 2012 to 2013. In 2013 the online crowd funding service Kickstarter reported raising $52.1 million dollars for board games ($6.8 million more than raised for video games. The popularity of board games is still increasing and these trends are expected to continue.
A business that is ready to capitalize on this rapidly expanding market is a board game café. A board game café is a combination a traditional café/bistro (drinks and light snacks), a cozy lounge area to play games and retail space for selling board games.
Target Market Analysis
For this site location analysis three market characteristics were examined. The target age group for the board game café is ages 10-39. We plan to have the café open to everyone during the day and then switch to 19+ patronage in the evening. The target population ratio was calculated by dividing population ages 10-39 by total population. We would like to target the store in an area with high proportions of this population group.
The café will also feature food, beverages and alcohol. To target this market we analyzed spending on food in restaurants. This was calculate by dividing spending on food in restaurants by total spending on food. We would like to target areas where spending on food in restaurants is elevated.
The café will also serve alcohol and hopes to be a hot spot for board gamers who enjoy the social aspect of going out for the night while still enjoying games with friends. To find this market we looked for areas where spending on drinks at establishments is high. This was calculated as the ratio of spending on alcohol in restaurants over total spending on alcohol. We would like to target the café in an area where there is elevated spending on alcohol at restaurants.
The purpose of this project was to use exploratory data regressions to analyze spending patterns. The target variable to analyze selected was total spending on education and the study area was western Nova Scotia. Twenty explanatory variables were initially selected for analysis from census and household spending datasets. Each exploratory variable was then plotted against the target variable and those with linear trends were selected. The explanatory variables were then plotted against each other and those with non-linear trends were selected. This selection of variables was then used for the analysis and is shown in the data dictionary below.