This course (GEOB 270) contributed to my future career plans of becoming a landscape architect. I came out of this course with basic knowledge of GIS analysis tools and improved skills in spatial thinking. But I learned more than just theories and instructions; I also learned more about myself. I realized applying my knowledge to projects teaches me more than reading page after page and memorizing theory after theory.
For our final project of the course we worked in groups of four. I worked with three other girls: Hannah, Linnea and Karmina. My goal for this project was to get more comfortable with finding data sets appropriate for GIS analysis and also to learn more about Agricultural Land Reserves (ALR). I was able to achieve my goals with help from my teammates by the end of the project. We prepared a thorough report of our analysis and here’s a brief summary of our project:
The British Columbia Agricultural Land Reserve (ALR) is a system that classifies and preserves potential agricultural land. However, the ALR is not as accurate as necessary for this system to be beneficial. For example, features such as residential homes, water features, protected forests, and oil wells at times fall into the ALR and are not removed from the overall area. Issues such as these make the ALR less accurate than it should be.The goal of this project was to analyze the ALR within BC, in particular the Cariboo region, and calculate how much of the ALR is actually used for its purpose. From our analysis, we estimate that the actual area of land suitable for agriculture is 1.29 ha, and is 0.00002% of the entire Cariboo region. We determined this value by eliminating parks, buildings, aboriginal land reserves, and land cover that is not annual or perennial crops and pastures. To do so, we made about a dozen maps of the areas we eliminated and showed the suitable area for agriculture in the final map (Figure 1) in addition to the steps we took for each map in our flowchart (Figure 2).
Figure 1 – Final map
Figure 2 – Flowchart
Producing about a dozen maps was a very tedious job. My teammates and I spend hours and hours to find accurate data and to fix them for our analysis. Sometimes we found the data that were incomplete and we had to look even more. It was very very very frustrating but we managed to get all the data we need with a lot of help from our instructors and TA . By our third meeting we realized it’s more efficient to work in pairs so we split for the remaining maps. We communicated through Facebook and Google Documents. All my team members worked very hard to get our results and I thank every single one of them.
I learned a lot from this project from facts about ALR’s to GIS tools and techniques. Here are 5 most important / interesting things I learned from this projects:
- All the area reserved for ALR is not actually suitable for farming
- The ALR areas are not just dedicated to agriculture. They contain various land uses
- I worked with some new GIS tools like slope analysis and reclassify
- I understood the difference between union and merge
- I realized most of the work in a GIS project is finding appropriate data and not the analysis
- I realized more people in the team means better results and that communication is key in teamwork
This project was designed to assess the environmental impacts of the Garibaldi at Squamish project. Map 1, which is retrieved from our lab instructions, shows the location of the project in BC. This proposed project would become a year-round destination mountain resort on Brohm Ridge on Highway 99, 15 km north of Squamish and 45 km south of Whistler. In case of approval, this resort will consist of 124 ski trails of various difficulties in addition to 23 lifts and a resort accommodation and commercial developments. Even though it would provide many jobs, it is important to assess whether the positive economic impacts are worth the negative environmental impacts. To start, Northland Properties and Aquilini Investment Group of Vancouver submitted the proposal for approval under Environmental Assessment Act in 1997. Eventually, in 2010, the BC Environmental Assessment office replied back that the proposal is not complete. They stated that the project was lacking important information on effects on vegetation, fish and wildlife ecosystems. Five years later, project developers submitted an additional application addressing these environmental issues. In the next two months, after public consultations, Whistler opposed the project in a letter. Most importantly, they referred to a 1974 report that ruled out elevations below 555 meter for skiing. My task was to act as a natural resource planner who has been retained by British Columbia Snowmobile Federation, who initially opposed the project. I had to examine Environmental Assessment’s recommendations and Whistler’s criticism to figure out if there still is sufficient evidence to oppose the project, or if the concerns can be addressed as part of the project.
Map 1 – Project location in relation to communities, highways and provincial parks | source: lab instructions
Here are steps I took to analyze data and assess environmental impact of the project:
1. Acquiring Data: Searched in DataBC for data and downloaded two data layers in addition to getting data that was provided by our instructor from G:Drive.
2. Parsing Data: Created a geodatabase to organize my data in.
3. Filtering Data: Clipped vector and raster data to fit my project boundaries.
4. Mining Data: Reclassified the DEM, merged polygons and created buffers for more accurate and easier calculations.
5. Representing Data: Produced a final map in ArcGIS, adding basic map elements.
Map 2 shows the areas that should be protected from this project in addition to a 3D surface visualization in Map 3. My analysis showed that there are still reasons to oppose this project. Approximately 30% of the area has below 555 meter elevation and according to Whistler’s letter this area is useless for ski trails. Moreover, 55.5% of the proposed project area falls under protected areas and nothing could be built in those areas. It is important to note that these two criteria overlap in some cases; however, they still cover a significant area within the project boundary. Therefore, there’s barely has enough space for developments.
Map 2 – Environmental impact assessment of the project
Map 3 – 3D surface
Having studied ecology, I believe that humans should preserve environmentally sensitive areas not only for managing resources but also because of our moral obligations to other species. Most important ethical concerns in project development is 1) destruction of natural habitats since it could change the ecosystem significantly; and 2) destroying endangered species could result in irreversible ecological consequences. These two are both seen in the future of this project and the only way to avoid them is to not build anything in these areas.
Applied the 7 stages of data visualization to my project: acquiring, parsing, filtering, mining, representing, refining and interacting of the data
Looking at shelter costs and household income separately is not a good measure for comparing housing affordability in different cities, because for instance, people in larger cities might have higher incomes than smaller cities but same dwelling costs. To get a more accurate measure, we can use a ratio of shelter cost over median income. This ratio is called the “Multiple Median” also called price-to-income ratio, which is used in 11th Annual Demographia Housing Affordability Survey. This ratio is reliable and easily understood. It is “essential structural indicator for measuring the health of residential markets and facilitates meaningful and transparent comparisons of housing affordability” (2015). This survey was done by experts and professionals in the field of urban planning and should be trusted. These experts provided housing affordability ratings. In other words, they have indicated what ratios are affordable and which ones are not. Here’s a table that summarizes their findings:
I have used the ratio and the ratings from this survey to compare housing affordability in the cities of Vancouver and Montreal using GIS. Figure1, shows that there’s no affordable area in Vancouver, whereas, most of Montreal is affordable. However, it’s important to note that affordability does not mean liveability. Affordability simply mean the ability of the population to afford housing, while liveability is a complex measure of various factors such as natural environments, social stability, economic prosperity, educational opportunity and etc. Therefore, Vancouver might not be an affordable city but that doesn’t mean it has low levels of liveability.
Figure 1 – Affordability Maps
Compared datasets by appropriate classification of quantitative data retrieved from Canadian Census Data
In GIS there are 4 main data classification methods: 1) Natural Breaks, 2) Equal Intervals, 3) Standard Deviation, and 4) Manual Breaks. Natural Breaks is the default classification in GIS, however, it’s not always the most appropriate. When choosing classifications, we have to be careful and think of how to represent data so it’s most ethical and representative of the message we want to deliver with our map. Figure 1 shows all 4 methods used to show Vancouver dwelling costs. Even though the data is identical for all 4, each map represents Vancouver dwelling costs in a different way.
HOW TO CHOOSE A METHOD
We should consider 6 questions when choosing a method:
- Does the method take into consideration the distribution of data?
- Does it make it easier to understand data?
- Does make computations easier?
- Does it make the legend easier to read?
- Is it appropriate for our selected number of classes?
- Is this method the most ethical?
Taking into consideration the 6 questions above, the selected method might be different in different fields. For example, if both a journalist and a real estate agent used the same data of dwelling costs for Vancouver they would probably choose different methods. If I were a journalist, I would choose natural breaks. The data is skewed and the most accurate method for analysis would be natural because natural breaks method generates homogeneitywithin classes and heterogeneity between classes. Frankly, as a real estate agent, I would use manual breaks for 2 reasons: 1) To round up the numbers and make it easier for customers to read and find the best fit for their budget 2) For higher sales and advertisement, I could manipulate data in a way that the agency’s properties fall into the cheaper priced areas. In that way, customers would think they’re buying a reasonably priced property relative to the rest of the city. However, the latter is far from ethical and data classification should not be used to manipulate any decisions. Therefore, a reasonable and ethical manual break method would be best.
In an event of a tsunami in city of Vancouver, 5.87% of the total area is under danger. To find this percentage, I created a layer, in GIS, of areas within the 1 km proximity of shorelines as well as a vector layer of elevation areas 10 meters and under. Then, I used the ‘overlay tool’ under “Analysis tools’ in ArcToolbox to find the intersection of these two layers, which would show the danger zones in city of Vancouver. To calculate what percentage is under danger, I found the area of danger zone in the layer’s attribute table (using the statistics command) and divided it by the total area of the city.
There are a number of healthcare and education facilities that lie on danger areas. To find these facilities, I used the overlay tool in ArcToolbox, once more, navigating Analysis Tools > Overlay > Intersect. Then I entered Vancouver danger zones layer and healthcare locations layer in the “input features” field to find the healthcare facilities that fall into the Vancouver danger zones and I got the following from the attribute table of the new layer:
- FALSE CREEK RESIDENCE
- YALETOWN HOUSE SOCIETY
- VILLA CATHAY CARE HOME
I did the same for education and the following were the result:
- ST ANTHONY OF PADUA
- ECOLE ROSE DES VENTS
- FALSE CREEK ELEMENTARY
- EMILY CARR INSTITUTE OF ART & DESIGN (ECIAD)
- HENRY HUDSON ELEMENTARY
Here are some of the main skills I acquired from this lab (and also in this course):
- Applied my basic knowledge of GIS software in geographic analysis of real-world problems considering data integrity and ethical implications
- Identified misaligned and improperly referenced data and repaired the problem
- Created a map performing basic geographic analysis accompanied by standard map elements
When data is projected into a different coordinate system, in addition to distance, area, angles and direction of the map are deformed. To prevent this problem, we should make sure that the downloaded data are aligned and properly referenced. This helps further actions and analyses to run smoother. To do so, we first check to see if the coordinate systems, datum and units of all files are matching. These properties can be found by right-clicking on the file in ArcCatalog then going to ‘properties’. Most of necessary information is under ‘Spatial References’.
HOW TO FIX THE PROBLEM
To fix this problem in GIS, we launch ‘properties’ from ArcCatalog (like explained previously) and under ‘XY Coordinate System’ tab choose the relevant coordinate system and click ‘OK’. However, if spatial analysis needs to be done, the appropriate action is to project a layer. Projecting a layer transforms data and creates a new layer with a different coordinate system. To do this, first we add the file we are working with to Table of Contents. Then launch ArcToolbox from toolbar then navigate to Data Management Tools > Projections and Transformations > Project. A window will pop up. The ‘Input Dataset’ field should be filled with the name of the file and for the ‘Output Dataset’ field find the official/common projection. Finally add the new layer to the map.
WORKING WITH REMOTELY SENSED LANDSAT IMAGERY
Sunlight is used in landsat imagery as the energy source to measure responses of objects and surfaces on earth. These images are not just pictures but contain many layers of data collected along visible and invisible light spectrum. These images are used to look at earth’s surfaces including types of vegetation, water areas and etc. They’re advantageous in a sense that they provide repetitive observations of the Earth since 1972, which allows analysts to compare images of different times and determine changes that are occurring.