Working through GIS labs and the final project allowed me to challenge myself with time and stress management. Although these pressures did get the best of me at times, I was able to produce a series of maps that I am extremely proud of. Something new I learned about myself is that in addition to working hard when forced into “survival mode,” I have high standards for myself that are hard to avoid. This is both a weakness and a strength that I will need to better balance in the future given that my strongest effort will not always yield my best results.

MEMO
To: Grace Newton, Director of the British Columbia Snowmobile Federation (BCSF)
From: Nancy Pham, Natural Resource Planner
Date: November 17, 2017
Re: Environmental Assessment Examination Results

Good afternoon Grace! As per your request, I have reviewed the recommendations and criticisms concerning the Garibaldi at Squamish Ski Resort. Upon a detailed examination of the environmental assessment conducted for the project, I found that the BC Environmental Assessment Office and the Resort Municipality of Whistler had valid concerns over the viability of this proposed project. Major concerns brought up by these two parties included the lack of information regarding potential effects on vegetation, fish and wildlife, as well as the consideration of reliable skiing given that snow abundance is insufficient below 600 meters above sea level.

My analysis and conclusions are based on the attached maps (Garibaldi at Squamish and Hillshade Garibaldi) outlining areas of concern within the project boundaries. These maps were created with ArcMap using secondary data sources from DataBC and the UBC G-Drive Database. Upon parsing all the relevant information, the data was filtered to meet the project boundaries and mined to reflect the concerns of interested parties. Using a digital elevation model, I was able to determine which areas within the project boundaries were below 600m to assess Whistler’s concern over the reliability of snow abundance for skiing. This data shows that 31.78% of the proposed project area lies below the recommended elevation for skiing. The data chosen to reflect potential impacts on vegetation, fish and wildlife include habitats of ungulates and fish, as well as red-listed ecosystems and old growth forest management areas. Combined, the area of these concerns amounts to 53.69% of the total project area. Below, you will find a table outlining all the areas of concern and the proportion of project area they occupy.

Taking into consideration the effect of climate change, snow abundance in the project area will likely decrease in upcoming years, limiting the suitable winter conditions for skiing and other winter sports. With that being said, Whistler’s concern over the viability of the project is validated when we consider that nearly a third of the project area will not yield a sufficient amount of snow and that those areas that do yield enough will likely decrease in area as climate change effects increases. Regarding the potential impact on vegetation, fish and wildlife, this project will cause widespread damage to the surrounding ecosystem thereby threatening already sensitive habitats and livelihoods. With over half the project area posing serious threats to ungulates, fish, threatened species and old growth forests, it is my recommendation that you continue to oppose the project.

The Garibaldi at Squamish Ski Resort will inflict serious environmental impacts, so the best approach towards mitigation would be to withdraw the project altogether. Based on this examination, the greatest environmental concerns related to this project would be its introduction of anthropogenic waste (construction, noise, pollution, traffic etc.) to a sensitive mountain ecosystem as well as its direct effects on vegetation, fish and wildlife habitat. Considering that there are six red-listed ecosystems within the project boundaries, this project will likely cause extirpation of these already threatened species. Regarding old growth forests, the destruction of their environment will not likely see reclamation for many generations since these trees have been growing for hundreds of years without this degree of disturbance. Based on the sheer size of project area that encompasses areas of concern, these impacts will not be mitigatable. Allowing these plants and animals to thrive in their existing environment is the best approach to avoiding irreversible damage.

I look forward to hearing from you in the near future, and please do not hesitate to inquire upon more information regarding the proposed project.

Best,

Nancy Pham, Natural Resource Planner Retained by BCSF

Self Reflection:

Similar to my role as a natural resource planner, I do not think this project should move forward. Even before creating these maps and assessing percentages, areas and so on, you could guess what kind of ecological impacts a massive ski resort would impose. This is already enough for me to say no. not that I am anti-development, but I find the project wildly unnessary given that we already have the resort municipality of whistler so close by. Furthermore, I’ve studied the proponents of the project in the past, and they are known for environmental degradation and subsequently not caring when someone sues them. Ethically, it would be best to cease the project, for the sake of the environment given that it has the inherent right to exist and not be disturbed.

Data classification is power – you have the power to manipulate information to better perceive your goals whether it be ethical or appropriate to do so.

Being a journalist, my intention with making these maps was to expose the ‘truth,’ meaning there is no bias in my judgements. Since my data is not normally distributed, I would not choose standard deviation as my classification method. The data set is not uniform which means equal interval is insufficient as well. Manual breaks allow for the greatest subjectivity since I am the one choosing the intervals displayed. In this case, picking representative breaks would be challenging to not seem partial. I would choose natural breaks since this method looks for the best arrangement based on the distribution. Best arrangement is determined by the Jenkins natural breaks method, which looks for significant changes in the histogram as ideal breaks for classifying data. For a journalist, this is the preferred method because it represents the data best without over/understating any census tracts. With all this being said, in the event that I were creating maps for comparison, I would use manual breaks to best represent disparities between datasets.

If I were a real estate agent with intentions of selling homes near UBC, I would choose equal interval since it displays the neighborhoods around UBC similarly to the rest of the city. “Why buy a house in east Vancouver when there are homes similar in price in the west end? Just look at this map showing how similar they are!” would be my selling line. This method misrepresents reality because this area is actually the most expensive within the City of Vancouver. This raises ethical implications for cartographers who have to choose how data is represented. Data classification is inherently subjective since somebody has to make the choice on the preferred method, which can steer the map reader in different directions when it comes to comprehending the information. There has to be caution in manipulating data since the choice for classification methods is made with the map purpose/objective in mind.

We are measuring affordability by normalizing median dwelling cost by median household income. This measures how “affordable” a house is based on household income. It’s a better indicator than merely stating housing cost because it considers how much income a household can pull in to maintain the cost. Affordability has a different definition depending on who’s using the term. We can map 5 million dollar homes and make it look unaffordable to map viewers, but chances are, whoever owns that 5 million dollar home is able to afford it – deeming it “affordable” by this standard.

The categories we used to differentiate our normalized figures was developed by the Demographia International Housing Affordability Survey which calls the method of classification “median multiple.” Affordability is divided among 4 categories which can be trusted given that we are assessing the ratio of cost to income, not merely income. By using a proportion, the same indicator can be applied across every house.

I would say affordability is a great indicator for livability, although it is not the only factor that should be looked at. Being able to live somewhere means being able to sustain yourself, your family and your home. If you are unable to afford housing in Vancouver for example, you might not find the city so livable. But if you enjoy mountains, water, greenscapes and mild weather, you might overlook housing costs (like many people do), and decide this city is very livable. You might have to rent forever, but if that’s ideal for you given what Vancouver has to offer, then you would consider Vancouver livable.

How to fix misaligned and improperly referenced spatial data:

To align your layers, you first have to check your coordinate/projected coordinate systems through ArcCatalogue. By right clicking>properties, you can view the current system and make changes to match other layers. If this step is ignored, layers will not line up or look true to its actual extent. The purpose of your map will likely dictate what coordinate/projected system you need. If it is a small-scale map, like Vancouver, then UTM is your best bet. If it is Canada being mapped, then the projected system Albers Conic would be ideal. Another option would be visiting the “project” tool in the toolbox. This will change the projection used throughout all layers. Right clicking our data frame>properties will also give you the option of applying the same system across all layers. To avoid having to project on the fly, you would use the “transform” tool to modify data and create a new version of the layer with a different coordinate system. Properties to consider during this process are distance, shape, area and direction – you must decide what properties are needed to be preserved in order to display your data effectively.

Landsat Data:

This method of data visualization allows you to avoid common problems with using raster and vector data like the mixed pixel problem. Instead of increasing resolution in your layer, using Landsat data would fix the problem of generalizing data since every point has a value predetermined by radar signals. Although there are restrictions for Landsat data, like how the sun plays a large role in retrieving it, using this energy-induced method of data visualization is still beneficial if you are looking to monitor something over time. In lab 2, we analyzed landsat data of Mt. St. Helen’s before and after the volcano had eruption. New lakes had formed which were picked up with landsat radars, allowing us to view landuse changes as a result of the natural disaster.