Category: Projects

Upon the completion of GEOB 270 (Introduction to Geographic Information Science), I have developed various transferable skills of utilizing the ArcMap and ArcCatalog programs in analysis of real-world geospatial problems and situations. Each of the  labs consecutively provided a deeper insight and understanding of the software, which also progressively raised the difficulty of labs. However, this allowed me to explore GIS out of my comfort zone as well as gain the perseverance to fulfill all the requirements of each lab to my potential. All in all, these valuable new skills I have obtained will inevitably be a detrimental addition to my skill-set as they are very practical and desirable in many occupations.

1. Summarize the project:  your team’s project goal?

The project goal was to assess the land use of the Agricultural Land Reserve (ALR) provided by the British Colombia provincial government and whether the reserve is appropriately used with the Nanaimo area. In specific, the ALR is a policy that protects agricultural land to strengthen provincial food security. Upon implementation, the ALR was seen as progressive legislation that prioritized food production for future food security. In recent years, there have been increasing threats to the ALR from urbanization and land development.

2. How did you organize your team (project management)?

In terms of project management, my team worked as a whole in the retrieval of data for our project. Afterwards, we divided the analysis of the project area by Biogeographical analysis and Social analysis, which two people worked as a team for each section. For communication and sharing of files, we utilized Google Drive, which was very efficient.

3.What were some of your most valuable contributions (as an individual) to the group? And are there people you should thank for really standout work on your team?

I think that one of my most valuable contributions to the group was my interest in GIS analysis and the overall ArcMap program. I genuinely interested in assessing geospatial properties and using that in relation to real life problems/situations. This interest motivated me to be proactive with other members who were at times struggling with using ArcMap and finding solutions to use ArcMap functions to its potential for our project area. I contributed by performing a lot of the analysis and aided my group members when they had questions with how to use ArcMap.

4. What are some interesting things you learned as a result of the process?

Of the many interesting things I’ve encountered in this project, the one that stands out the most is finding the slopes of the project area and isolating the layer with a slope that is greater than 30 degrees (which is too high for agricultural production). Through this obstacle, I’ve cemented what I’ve learned before in labs in the operation of DEM’s (Reclassify, Clip, Raster to Polygon) as well as new skills/tools such as using the Slope tool and Mosaic to Raster tool. Since, our project area fell between two UTM zones of 92G and 92F, many of our files had to be merged together and then clipped to our project area. When combining DEM’s, the Merge tool would not suffice, so I’ve learned to use the Mosaic to Raster tool, which is incredibly useful for future reference. I’ve also learned the importance of organization when downloading data and how to properly manage a geodatabase which was crucial in the production of a neat and tidy map. This helped my group compile all of our maps to create our final shapefile.

Accomplishment Statement:

Learned to effectively perform data visualization (acquired, parsed, filtered, represented, and refined spatial data) to assess the possible environmental impact of a proposed ski resort.

 

 

Within this lab, I was presented with a scenario of assessing the potential environmental impacts of the proposed Garibaldi project in the Squamish area as a resourced planner hired by the British Columbia Snowmobile Federation (BCSF). In order to complete this assessment,  there was an analysis performed using the map of the project area displaying: the elevation requirement of the land in order for snow to exist and ecologically substantial areas which maybe disturbed by the construction of the project. Moreover, the following report was conducted to explain the results of the analysis.

Map: lab5

MEMO: Environmental assessment of the Garibaldi at Squamish project

TO: British Columbia Snowmobile Federation

The following memo assesses the suitability for the Garibaldi project (mountain resort) to take place in Brohm Ridge, 15 kilometers north of Squamish on Highway 99. This project would include 124 ski trails and 23 lifts, plus resort accommodation and commercial developments. This memo will use ArcGIS to analyze the environmental impacts which would occur along with the construction of this project.

 

Firstly, obtaining the information and data is required, which includes the project/park boundaries, ungulated winter ranges, fish habitat, road accessibility, red listed ecosystems, and elevation. After acquiring the data, I inputted them into ArcGIS as layers and clipped each layer to the project boundary making sure that each dataset constituted of only the required project area. In order to determine how much area is usable in the project area, I needed to combine all these factors and divide it by the overall area of the project. Since the elevation of 555 meters is important to the location in creating and sustaining a cold enough environment for snow to prevail affecting its ability to support skiing, I calculated the percentage of the project area that was under 555m by first reclassifying the DEM (digital elevation map) that was under 555 meters using the spatial analyst tool. Following that, I converted the layer from a raster to a polygon which allowed me to view the statistics of the Shape_Area field in the attributes table of the layer and divide that by the total project area (which is also found in the attributes table). The rest of percentages of environmentally protected areas (old growth management areas, fishery habitat, ungulated winter ranges, and red listed ecosystems) within the project area were determined using similar methods. Since there are many layers with areas/polygons that overlapped, I used the union tool as well as the dissolve tool to combine the entire overlapping polygon into one larger one, making it easier to determine the total area that is environmentally protected. I then used this value and divided by the overall project area.

 

Thus, the calculated percentage of the project area that is below 555 meters is 29.9%, which is not able to use for skiing because of its low elevation. Moreover, the percentage of total environmentally protected land stands at 53.4%. This means that more than 80% of the overall project area is unusable proving the large scale of impact that the project would cause on the environment within the project area.

 

In my perspective, the two greatest environmental concerns to the project development are the large amount of climatologically deemed ‘unusable’ land due to low elevations as well as the effect on the environmentally protected lands which make up more than half of the project area. Although some areas with elevations close to but still lower than 555 meters can still be salvageable through the use of artificial snow, it is very expensive to maintain these machines and they are not very environmentally friendly which requires the water to create. The best decision is not develop the project within this area all together as the environmental impacts seems costly and outweighs the brief economic benefits of the resort.

 

Personal thoughts on the Garibaldi at Squamish project proposal:

Personally, according to the analysis conducted, I believe that this project should not be allowed to proceed. From an environmental impact perspective, there are all sorts of indications of damages to the existing ecosystems and wildlife due to the limited amount of viable ski slopes in the project area. Moreover, because of the existence of much larger nearby ski resorts in Whistler, there will not be much of a demand for a this Garibaldi ski area.

 

Accomplishment Statement:

Obtained knowledge in downloading and importing spatial and tabular data which was used to assess and compare housing affordability in Vancouver and Montreal.

Quantitative Classification: dataclass

The following map shows the how the housing cost in Vancouver is displayed via the four different methods of data classification: Equal Interval, Manual Breaks, Natural Breaks, and Standard Deviation. Each method varies in its display of the data. Natural Break displays the most “error free” way of classifying data which would display the realities of extremely high housing costs in Vancouver. Within natural breaks, class breaks are identified that best group similar values and that maximize the difference between classes. The features are divided into classes whose boundaries are set where there are relatively big differences in the data values (ESRI). On the other hand, Equal Interval classification method is a method that would make the data appear more affordable and therefore, more attracting to home buyers. The Equal Interval method divides the range of attribute values into equal-sized subranges. Although the equal interval method takes into account the outliers in data and the values at extremes, it does not for account for the distance between data points. Hence, some problems regarding ethical implications of the display of data may arise due to the generalization of ranges of values in each class. However, as a geographer, the most “error free” method is desired, which displays data reflecting the truth and realities of processes within a certain area, which in this case, the natural break method is preferred. Aside from these two methods, standard deviation displays the expense of a house with the median cost of housing, which may confuse its reader with its legend. Manual breaks, allows users to set their own breaks.

Housing Affordability: affordabilityVM

Within this lab, affordability is used to measure the ratio of median income of citizens to housing cost within the city. Using affordability, as opposed to just using housing cost alone, is a much better because it takes into account the financial ability of these buyers to the price of a home. This means that affordability would eliminate any discrepancy between a larger city where standards of living are higher to a smaller one with standards of living that are lower. The following housing affordability rating categories that are provided by “11th Annual Demographia International Housing Affordability Survey” include:

• Severely Unaffordable (Corresponding to a Median and Multiple value of 5.1 and higher)
• Seriously Unaffordable (Corresponding to a Median and Multiple value of 4.1 to 5.0)
• Moderately Unaffordable (Corresponding to a Median and Multiple value of 3.1 to 4.0)
• Affordable (Corresponding to a Medial and Multiple value of 3.0 and Under)

This rating is most likely trustworthy because it is highly suggested by the World Banks as well as the United Nations for assessing affordability, covering 378 metropolitan markets in nine countries, hence determined through trends in very large data sets. Through comparing Montreal and Vancouver’s affordability, we are able to compare the correlation between income and housing costs, however, if more factors were taken into account (such as poverty rates, employment opportunities), this map would be much more complete.

 

 

 

 

Accomplishment Statement: 

Using tabular and spatial data sets, a geographic analysis was performed on the City of Vancouver to assess the risk of danger of a tsunami on different areas. Calculations of the amount of area that would be affected was also carried out.

Map of Lab 3: 

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

The percentage of area under tsunami danger is about 12%. The percentage was obtained by taking the sum of  the areas of Vancouver_Danger layer and Vancouver_landuse layer from the attribute tables using the Statistics tool and dividing the sum of the area of the danger layer by the sum of the area of the landuse layer.

Healthcare and educational facilities within the Vancouver tsunami danger zone:

Education facilities affected: 

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

Healthcare facilities affected:

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

I was able to determine this answer through using the Clip overlay tool which I inputted the layers Vancouver_health (health layer within the proximity of Vancouver) and Vancouver_education (education layer within the proximity of Vancouver). For the clip features, I put Vancouver_Danger layer in. This produced two layers which only displays the educational and health facilities within the tsunami danger zone in Vancouver. I was able to then access and identify the select locations through the attribute table of each corresponding layer.

 

Accomplishment statement:

Further explored two important applications within ArcGIS: ArcCatalog and ArcMap. ArcCatalog is effective in  previewing, documenting, and organizing data. Moreover, use of ArcCatalog can help with understanding the importance of coordinate systems and projections. Fixing misaligned and improperly referenced data is also achievable through ArcCatalog. Composite images can be created by using remote sensed data and analyzing the different bands.

A guideline for fixing misaligned and improperly referenced data: 

1. In order to verify that all ArcGIS shapefiles has the same spatial data properties, you will need to first preview each shapefile in ArcCatalog to check if all data are similar (right click on each file on ArcCatalog and go to Properties).
2. Make sure the coordinate reference system is the same for each file, which is found next to the bold Spatial Reference header. (Ex: Canada_Lambert_Comfornal_Conic)
3. Drag files into Table of Contents as layers. If there are any different projections, convert all layers into the common projection first before analysis.
4.  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.
   1. You will need to find out what the coordinate system of the data is from the data provider in order to apply it your data so that it aligns with the map
   2. Using ArcCatalog, find the coordinate system that the data provider used and apply it to the layer. Under the section ‘XY coordinate system’ you can then select the Geographic Coordinate System you would like for the layer. Units of measurement and datum can also be adjusted in the properties menu.
5. The new layer could now be projected and added to the map as it can be aligned to the rest of the data.

The advantages to using remotely sensed Landsat data for Geographic Analysis

Since remotely sensed Landsat data is on a 16-day cycle by sensors on a satellite reflected by solar radiation, it creates a highly detailed visual image of the area. Landsat data would also be useful for geographical analysis because of it’s ability to observe change of land over time as well as different wavebands of radiation which are usually unseen by the naked eye. For example, Landsat data would be incredibly useful for researching the global recession of glacier ice as glaciers retreat throughout the globe. Specifically, if landsat data was used to monitor the retreat  of glaciers in Glacier National Park, that could prove to be very useful in explaining the impacts of global climate change. You would need to precisely select various times within the year and different seasons and monitor the glaciers at those dates every year at those specific dates and times in order to be able to gauge what the change in the movement of glaciers are. For example, a team of researchers from Ege University in Turkey and NASA’s Goddard Space Flight Center analyzed four decades of Landsat satellite data to monitor the major decline in glacial area  in Mount Suphan. There, they saw discovered that the glacier that previously resided there has lost more than 75 percent of its initial area since the 1970s. Thus Landsat data can provide quantitative and accurate analysis on the recession of glaciers between various periods of time by examining the detailed imagery at those particular dates and times.

Accomplishment statement:

Obtained practical training with basic functions in the software ArcGIS. Explored GIS applications to effectively describe spatial data and evaluate data integrity as well as ethical implications.