- Gained practical knowledge working with the Canadian Census Data by downloading spatial and tabular data, then combining them, the visualizing that data extracted from the Canadian Census Data Collection.
- Used different quantitative data classifications to create different maps that emphasized different ways of dividing up the data, and how they are presented. (Using natural breaks, equal intervals, standard deviation, and manual breaks)
- Gained practical knowledge on how to independently download datasets online, and filtering out the data based on different analytical objectives.
Final Project
Our final project primarily focused on earthquakes specifically around the Los Angeles county area. We wanted to investigate the dangers of the aftermath of an earthquake. Physically we focussed on the location of faults, and the potential of mass wasting by examining the types of soil, as well as the hydrology of the different areas. We also added an infrastructure aspect by looking at the location of hospitals with emergency services and the areas that lacked proximity to these emergency services. Below is a flowchart that was created for the final project report, about how the layers were manipulated and the different types of tools used to create the final map.
Our final goal for the final project was to put together a danger potential map on the aftermath of an earthquake, essentially where you should not be, or the areas that are at a higher risk.
For this project, we had 3 other people to work with. We divided ourselves into two groups, 2 of us focussed on the map, and the other 2 worked side by side with the others to make the flowchart, and we each had a part on the final project report. On this project we’ve learned a lot new skills on the ArcGIS program, especially some cool tools in the analysis toolbox!
Planning a Ski Resort: Environmental Assessment
This lab was about assessing the environmental impacts to the local area if a ski resort was built. This lab focussed on protected areas, as well as the total area of where snow would be present. The protected areas in focus are ecosystems, with red listed species, fishery habitats, old growth forest areas, as well as habitats for winter animals. (See map below)
MAP: protected-areas
Second map that was made was to highlight the hill shade areas
MAP: hillshade
This map shows the elevation variation of the proposed project area. With recent projections of global warming, the snow line is rising, this map shows the projections along with the first map shows the recent snow line at 600m. The snow line will always rise, which will decrease the area that is prime for skiing.
Below is a memo regarding the environmental assessment of the project.
Personally after the project, and examining the environmental assessment, I do not believe the project should continue as it is fairly close to the other ski resorts like Whistler and Big White, we do not need another resort. As well, there are major influences on the ecosystems that house certain red listed species which will influence the biodiversity in the high mountainous areas. This is the same opinion that I stand behind in the memo for the environmental assessment of the project.
Housing Affordability
The first part of lab 4 was all about using the different methods of dividing up the data present in an area, using different classification methods. The map can be found below. (“Dataclass”)
MAP: dataclass
From the maps generated using different methods of classification with the same data, we can see the variation of what we see want other people to see, against the reality.
For example,
If I were a journalist, I would use the manual break classification as it shows a significant emphasis on the housing prices in certain areas, for example, around UBC, and the North Shore area.It shows concentration areas of high housing costs. This will have more of a story to write about, as well having able to manually input the intervals will be easier to read for the intended audience.
If I were a real estate agent preparing a presentation for a potential homebuyer near UBC, I would choose to use the Natural Breaks, as it shows that the area around UBC is not the most expensive area to purchase a house. As it is in the same colour as a lot of other areas around the lower mainland. This will give the illusion that housing prices are not as high around UBC as everywhere else. This creates some implications as it somewhat skews the data, as it can show what you would want it to show depending on the different classification methods used. Which may be unethical especially concerning this case of drawing in potential homebuyers and changing the views of the housing prices to influence them to buy houses.
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The second part of the lab was about housing affordability, comparing Ottawa, and Vancouver. Affordability in this lab is measured by comparing the average income to the average housing costs. This is a better indicator of housing affordability than just housing cost alone, as some areas where housing costs are low, income may be low as well, which makes the hosing unaffordable even though cost is low. The map below shows the link between income, and housing costs in both Ottawa, and Vancouver, we can see from the maps that Vancouver does not have any areas with affordable housing, whereas in Ottawa, there are more areas with affordable housing.
The housing affordability rating categories are affordable, moderately unaffordable, seriously unaffordable, and severely unaffordable. Personally, I feel like affordability is not a good indicator of a city’s “livability” alone. Taking Vancouver as an example, housing can be seen from the results of this lab that it is fairly unaffordable, yet it is one of the most livable cities in the world. Livability should not be defined by housing affordability itself but rather taking into account factors like weather, public transit systems, etc.
MAP: affordability-2
Planning for a Tsunami
For this lab, we studied the risk areas and the danger zones in Vancouver if a 15 meter tsunami were to strike.
From this lab we’ve learned that 15.5% of Vancouver is at risk and in danger if a 15 m tsunami strikes. From the attribute table of the layer for Vancouver, we can find out the total area of Vancouver from the area column in the table of the area of the polygon. Then we added up the areas from the Vancouver land use combined with the Vancouver danger table to account for all the areas in danger. Then with the number calculated with the information above, divided by the danger zone with the total area of Vancouver to get that 15.5%.
We’ve also discovered in this lab, some health care and education facilities that are in the danger zone of the 15 m tsunami. They are listed below.
Education Facilities in Danger
- ST ANTHONY OF PADUA
- ECOLE ROSE DES VENTS
- HERTIAGE 3R’S SCHOOL
- VANCOUVER MONTESSORI SCHOOL
- FALSE CREEK ELEMENTARY
- EMILY CARR INSTITUTE OF ART & DESIGN
- HENRY HUDSON ELEMENTARY
- JOHN’S INTERNATIONAL
- FRANCIS XAVIER
- INSITUTE OF INDIGENOUS GOVERNMENT
Health Care Facilities in Danger
- FALSE CREEK RESIDENCDE
- BROADWAY PENTECOSTAL LODGE
- COAST WEST COMMUNITY HOME
- YALETOWN HOUSE SOCIETY
- VILLA CATHAY CARE HOME
By combining the Vancouver education and Vancouver Healthcare layers with the Vancouver Danger layer using the tool “intersect”, the facilities that are now showing up will be only of those when the two layers are both present which means it will show the facilities that will be in the danger zone.
MAP: mylab3
Coordinate Systems and Spatial Data Models
In the software that we are using in class, ArcGIS, there are a lot of factors that contribute to the inaccuracy of the data displayed to make the maps. In the second lab that we did, it was mostly focussed on coordinate systems, and spatial data models.
The first part of the lab was determining the distance between two cities in Canada, Vancouver and Montreal. It was important to figure out the prime projected coordinate system for the region being studied as we’ve learned in lectures that some projections of the maps will distort different factors of a map, for example, distance, area, shape, and direction. It was important to keep in mind the features of ArcGIS as well. For example, the one that we’ve studied in this particular lab, Projection-on-the-Fly.
Projection-on-the-fly changes the image automatically, correcting to the existing projection already selected, whereas using the project and transform commands, the projection could be more suitable for the particular case, as it will be defined according to what you want. In the Projection-on-the-fly featured, the spatial data coordinates are not changed by the process, rater it just makes everything look like it’s under the same coordinate system, which will create a problem regarding accuracy.
Part two of the lab was about remote sensing Landsat data. Landsat is basically a satellite that goes around the earth, that captures images for elevation from sensors on the satellite. The advantages for Landsat is that is it a relatively cheap and efficient method of acquiring up to date information, as the satellite makes it around the earth every 18 days. Data can be collected at inaccessible places like Antarctica, or the middle of a dessert. And some of the data is easily accessible for everybody. What we did in the lab was assessing the difference on the forest covers after the Mount. St. Helens eruption. (See Below)
Landsat data would also be useful for analyzing landslides. The potential research question could be about how secondary succession and the changes in land use progressed after the landslide, the geographic location could be similar to the study done on Mt. St. Helen’s, where the area surrounding the landside as well where the landslide area has occurred could potentially be studied. The time interval could be done at bigger interval if measuring for plant and forest growth, preferably around the first year to the next decade. If studying land use changes, intervals could be smaller, around a month to a year, as it will take less time to change what the land is used for. Preferably for measuring forest growth after the landslide, measuring during the same time of year, or during growing season would be more favorable.
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