My passion for the environment started young, and over the years has grown into a strong commitment towards creating a more sustainable food system. Ever since I tasted the sweetness of a freshly picked strawberry, I knew my heart was in food. This semester, I used GIS analysis to evaluate the use of B.C’s Agricultural Land Reserve (ALR). Through ArcMap, I acquired, parsed, filtered, represented, and refined spatial data to critically assess the use of the ALR and its role in food security.

Accomplishment statements: 

Lab 4:

Gained working knowledge in downloading and importing spatial and tabular data to analyze housing affordability in Vancouver and Montreal.

Lab 5:

Effectively performed data visualization: acquired, parsed, filtered, represented, and refined spatial data to assess the environmental impact of a proposed ski resort.

Lab 5: 

lab5map

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MEMO: Environmental assessment of the Garibaldi at Squamish project

TO: British Columbia Snowmobile Federation

This memo regards the environmental assessment of the Garibaldi at Squamish project that proposes to construct a ski resort on Brohm Ridge in Squamish, BC. The proposed resort would include 124 ski trails and 23 lifts along with housing accommodations and commercial complexes. Although the resort has the potential to bring more employment and tourism to the area, there are some major environmental consequences and concerns to building such a large operation on Brohm Ridge.

In order to perform this analysis, I acquired data on the park and project boundaries, elevation, road accessibility, old growth management areas, ungulated winter ranges, fishery habitat, and red listed ecosystems. I organized the layers of data and filtered them by clipping them to the project boundary. It is important to analyze the elevation of the project area because the ski resort is climate dependent and needs snow in order to run. Previously, Whistler cited a report that states, “climatological considerations rule out reliable skiing on the lower 555m of vertical”. In order to assess the areas of the project that is below 555m I reclassified the digital elevation map by using the spatial analyst tool in ArcGIS. I then calculated the percentage of the project area that is under 555m by using the attribute table of the reclassified layer and looking at the statistics of the Shape_Area field and divided this by the total project area. Similarly, I calculated the percentages of project area that are environmentally protected areas: old growth management areas, ungulated winter ranges, fishery habitat, and red listed ecosystems.

The percentage of project area that is under 555m is 30% of the land. The percentage of total environmentally projected area is 56%. We can see that over half of the land is protected for environmental conservation and one third of the land is deemed “unreliable for skiing” because of its elevation. These results show that this project is not environmentally viable. Although the resort would boost jobs in Squamish, warming climate trends increase the resort’s vulnerability to economic loss once there is not enough snow to ski.

In my opinion, the two greatest environmental concerns to the project development are: unreliable skiing due to the high percentage of land in low elevation areas, and the detrimental impact of development on ecosystem conservation. One way to mitigate the risk of poor snow conditions in the lower areas of the resort is to make snow. Many resorts across Canada, including Whistler, use snow-makers increase their snow base. A consequence of this method is that it requires a lot of water to make snow. Making snow would not aid the resort nor the community of Squamish in adapting to climate change and conserving water resources that will become more scarce with time. Even if the project were to be developed in the higher elevations (>555m) there is a large river network that development would threaten. The best way to mitigate the impact of development on ecosystems would be to not develop. It is important that all of these factors be taken into account when assessing the viability of the project. I suggest that the BCSF remain opposed to the project as it poses a large threat to protected ecosystems, nor does it seem economically viable.

Personal thoughts on the Garibaldi at Squamish project proposal:

I personally do not think that the project should proceed because it does not take into account the striking reality of global warming and the unpredictability of precipitation. Also, when looking at the slope of the terrain one can see that there isn’t a large variety of slope grades and might not be a thrilling place for skiers to travel. Furthermore, the development of the land will be a threat to the conservation of wildlife in the area. I voiced these concerns in my memo to the British Columbia Snowmobile Federation.

Quantitative Data Classification:

Classification Methods- Housing Cost Map

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This map displays the data for the housing cost in Vancouver in four different methods of data classification: equal interval, manual breaks, natural breaks, and standard deviation. We can see that each method of classification displays the data differently. The equal interval method takes the range of data and creates breaks to divide the range into equally sized classes. The equal interval method takes into account the outliers in the data, the values at the extremes, but does not take into account the distance between the data points. This creates issues when there are outliers at the extremes and then a majority of points that are observed within a close distance. In the map of Vancouver we can see that the equal interval method skews the data so that it appears that the majority of the houses are affordable because there are some outliers at the very expensive end of housing costs. The natural breaks method of classification is a more “error free” way of classifying data because it takes into account the distance between data points and places the breaks in relation to that distance. The draw back to using this method of classification is that the legend is complicated to read because the numbers are not always nicely rounded. The standard deviation method classifies the data by the standard deviation from the mean. The standard deviation is an interesting map that reflects the expense of housing in comparison to the median cost of housing, but the draw backs are that many people would not be able to understand the purpose of the map nor its legend.  The manual breaks method allows for the geographer to set their own breaks in the data. It is important for the geographer to be considering all of the ethical implications of the display of data, and to choose the most “error free” breaks. 

Housing affordability:

affordability map

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Affordability measures the ratio of median income to housing costs. This is a more meaningful way of assessing housing affordability than just looking at housing costs because it factors into account the ability of the buyer to purchase a home of a certain cost. The 11th Annual Demographia International Housing Affordability Survey determined the following affordability rating categories: Severely Unaffordable 5.1 & Over, Seriously Unaffordable 4.1 to 5.0, Moderately Unaffordable 3.1 to 4.0, Affordable 3.0 & Under. The purpose of these ratings is to monitor the affordability of our cities and alert policy makers of significant decreases in affordability. This survey  is to be trusted because it covers 378 metropolitan markets in nine countries and is recommended by the world bank and the United Nations for assessing affordability. Affordability an indicator city’s livability but not the complete indication of whether or not a city is livable. Affordability does not factor into account things like employment availability, poverty rates, access to quality health care and education. These factors must also be included in determining the livability of the city.

Lab 1:

Gained practical working knowledge of ArcGIS, and evaluated the integrity of different maps and sources of data.

Lab 2:

Managed landsat data, remote sensing imagery, to create a composite image and analyzed the change in topography before and after a large volcanic eruption.

Lab 3:

Used spacial and tabular data to create a map and performed graphical analysis to assess the risk of a tsunami on areas in the city of Vancouver.

LAB 3 MAP

Click on above link to view map.

 Percentage of the city of Vancouver’s total area under tsunami danger:

11.34% of the city of Vancouver’s total area is under danger from a potential tsunami. I determined this percentage by using the attribute tables of the danger intersection layer and the Vancouver land use layer. These attribute tables contain a shape area column which I summed by using the statistics tool to get the total areas in each layer. I divided the total area in the danger zone by the total area of Vancouver to determine the percentage.

Healthcare and educational facilities within the Vancouver tsunami danger zone:

Facilities: Vancouver health

• False Creek Residence
• Villa Cathay Care Home
• Broadway Pentecostal Lodge
• Yaletown House Society

Facilities: Vancouver education

• Emily Carr Institute of Art and Design
• Henry Hudson Elementary
• False Creek Elementary
• St Anthony of Padua
• Ecole Rose Des Vents

I determined this answer by using the select by location tab and selected features from the vancouver_health and the vancouver_education target layers. I then selected the danger intersection as my source layer. I chose my spatial selection method to be “are within the source layer feature”. I then looked at the attribute tables  of the vancouver_health and the vancouver_education layers and identified the selected locations. We can see that there are four health facilities and five education facilities that are in the tsunami danger zone. 

How to fix misaligned and improperly referenced spatial data: A Guideline

1). Open ArcCatalog and preview your data information. You can do this by simply right clicking on each of the layers, navigate to their properties, then scroll down to the spatial reference section and compare the information.

2). Take note of the coordinate reference system of each of the files. This will be found next to the bold Spatial Reference header. (Ex: Canada_Lambert_Comfornal_Conic)

4). Add each layer to the map by dragging the layer into the Table of Contents, or by clicking the add data button at the top of the toolbar.

3). Look to make sure that all information is displayed properly on the map. If there are any files that have missing spatial reference they might not appear on the map. If this happens, follow steps i. and ii. to realign the layers.

               i).  Contact the data provider and ask what coordinate system the data is in, once you know  this information you can apply it to your data so that it aligns with the map. You can do this  by following step ii. 

              ii). Launch ArcCatalog and go to the missing layer’s properties . Under the XY coordinate  system tab find the coordinate system that the data provider used and apply it to the layer.

5). If you are wanting to do a specific analysis on the data provided, you can project different layers so that they are in the same coordinate system. You should choose the coordinate system depending on what type of analysis you are wanting to perform. If you want to project a layer you can take the next step (i.) to modify the data and create a new version of the data with a different coordinate system.

             i). Launch the ArcToolbox and navigate to Data Management Tools. From there find the    Projections and Transformations and click on Project. From there you can input your data set  that you would like to modify under the input data set. Then the modified layer would be entered into the output data set. You can change the coordinate system of the output through sections in the Output Coordinate Systems box.

Advantages to using remotely sensed Landsat data for geographical analysis:

Remotely sensed Landsat data is data collected by sensors on a satellite that detect reflected solar radiation and process it to create a visual image of the area. The data is collected on a 16-day cycle. This type of data is very useful for geographical analysis because the images can be used to observe changes of land areas over time. Often times, these changes can not be observed by the naked eye, and so this tool gives a useful new perspective when looking at geographical changes over time. For example, Landsat data also allows us to select different wavebands of radiation that we normally wouldn’t see to show different features of the area observed. Another advantage to using remotely sensed Landsat data is that the images are always available in a digital form, meaning that the data will always be compatible with a computer. Another advantage is that using remotely sensed data allows us to survey and monitor land that we otherwise might not have access to. For example, we can observe the land change after the eruption of Mount St. Helen’s, when otherwise it would have been too hazardous to survey.