Through the variety of topics in our both our laboratory and lecture sessions, this course has laid a solid foundation for me in GIS, both in terms of the science and theory, as well as the actual computer systems used to transform data and information into visual representations of spatial entities. Throughout the semester, I was challenged and rewarded over and over again, and gained an appreciation for the plethora of opportunity that lies within the field of GIS. I am excited to continue developing my skills in GIS, and to be able to apply these skills in a variety of contexts in the future.

The final component of this GIS course was a final project, aimed at providing a platform to showcase the culmination of knowledge gained throughout the semester.

The learning objectives were as follows:

1. Identify datasets appropriate for GIS analysis and develop ways to project and manipulate those datasets.
2. Conduct GIS analyses that will demonstrate knowledge of GIS analysis concepts and software tools.
3. Represent the results of analysis in maps and a report.
4. Follow a project proposal until the project is successfully completed.
5. Build teamwork skills.

Project Proposal

Our proposed project was centred around greenspace availability in Vancouver, which is consistently ranked one of the “greenest” cities in the world, as well as one of the most walkable. For our project, we aimed to analyze current greenspaces (public community gardens, public parks, and greenways) with a walkability buffer of 400m or 5-minutes walking distance, in order to identify areas lacking accessibility to these green spaces that need development of greenspaces in order to achieve the City of Vancouver’s goal of 100% walkability to greenspaces by 2020.

The following is the abstract of our project:

“With a goal of becoming the greenest city by 2020, an understanding of the areas lacking and needing greenspace within the city of Vancouver will be vital to achieving this target date. The key is determining areas of current greenspaces in Vancouver, the accessibility the public has to these greenspaces, and areas of high priority needing reachable greenspace. While many greenspace analyses have been done for the Vancouver area, our analysis represents not just accessible park spaces, but greenways as well as community gardens that are within a 400m walkable distance. To do this we use the ArcGIS software ArcMap to develop a map that identifies areas within Vancouver that lack greenspace access and prioritized these areas based on their land use classification. From this analysis we determined that areas totalling 9.96 km2 within Vancouver are lacking greenspace, more than half of this area was found to be high priority. Greenspace and its accessibility is vital to the health of cities and its inhabitants as they create spaces for the residents of urban landscapes to live, play, socialize, and connect with nature, all the while strengthening the sense of community.”

We created several maps to showcase our results alongside our analyses of our findings. These maps, along with our analyses, can be found here, in the final project report.

Project Management and Learning Points

Overall, this team was a pleasure to work with through all phases of the project. Together, the four of us created the various maps we wished to include in our analyses, while we pieced together the flowchart along the way. We took turns at the computer, but were all engaged and active in discussions the entire time, and did not take further steps without all being in agreement. For the report writing, we set up a Google Doc so that we could work on our sections individually but still remain “up-to-date” with how the others were doing. The abstract was a collaborate effort, written once we all had our sections finished. The bibliography, maps and figures, and flowchart were also all collaborate. We split the report 4 ways, with each of us writing about 700-800 words. In my opinion, every member of my group was dependable, focused, and excited about our topic, and we each contributed to the overall product equally.

I have previously been in group projects where the process has been a frustrating experience from start to finish. This was not the case for this project. We all brought skills to the table, and were all well versed in both the science behind GIS that we had learned throughout the course, as well as the practical applications of techniques from the various labs. For the most part, we used the techniques and functions that we had previously used throughout the semester, but used them to a considerably larger extent. Our maps required extensive fiddling with the data layers – clipping, buffering, dissolving, over and over it seemed. But these steps were necessary to transform our data to what it needed to be.

Obtaining our data was also convenient: much of it was already available to us through the UBC Geography department, and the rest we obtained from the Open Data Catalogue provided by the City of Vancouver. Our data was up-to-date, and our topic had a ton of information on it online. Overall, this project was a great way to bring together the bulk of what we had learned throughout the semester. It was inspiring and rewarding to see our initial idea and project proposal come to life with maps that we created from scratch, and in-depth analyses that would’t have been as feasible without having gone through all the labs and lectures.

For the fifth and final lab of the course, the objective was to perform an environmental impact assessment of the proposed Garibaldi at Squamish year-round mountain resort on Brohm Ridge after producing a map showing different areas within the project boundaries.

The learning objectives were as follows:

• Learn how to independently acquire spatial datasets online;
• Parse and filter data based on your analytical objectives;
• Evaluate and use different spatial analysis tools, based on your objectives;
• Further develop cartographic and written communication skills by producing a map and short memo summarizing your results for a non-technical audience.

Background:

The Garibaldi at Squamish project is the proposed development a year-round destination mountain resort on Brohm Ridge, which is located 15 kilometers north of Squamish on Highway 99. An application for a Project Proposal Certificate under the Environmental Assessment was submitted by the proponents of the project, Northland Properties and the Aquilini Investment Group of Vancouver, in 1997. A series of addendums were made because the project proposal lacked information on the potential effects on wildlife habitat and vegetation. As of January 2016, the project has been tentatively approved, on 40 specific conditions. Garibaldi at Squamish includes 124 ski trails and 21 lifts, as well as resort accommodation and commercial developments. It is estimated to take 20 years to build, and is expected to provide 900 construction jobs and a further 2,500 jobs for its operation.

In May and June of 2015, a two-month-long community consultation was held, during which a 14-page letter opposing the development was submitted by the Resort Municipality of Whistler. In this document, economic and environmental concern was voiced because climatological considerations rule out reliable skiing at elevations lower than 555 meters of vertical. This value is probably higher now, estimated at 600m.

Scenario:

As part of the lab analysis, I was given a scenario in which I was a natural resource planner, retained by the project proponents. My task was therefor to evaluate the criticisms and concerns brought to light by the Resort Municipality of Whistler, and make recommendations as to what should be prioritized in order to proceed with the proposal. To do so, I created two maps and a memo.

Maps:

The first map shows the project boundary and all of the areas within it that will negatively effect the development of the ski resort. These include riparian areas, ungulate winter range, old growth management areas, red listed ecosystems, and areas below 600 meters of elevation.

For the second map, I created a 3D hill shade of the project area, and draped rivers, ungulate winter range, and old growth management areas over the raster layer.

These maps provide a visualization of the various components of the proposed project area, and how they might be affected by the development of a year-round mountain resort.

Memo:

Personal opinion:

In my personal opinion, this project should not be allowed to go through. Although I understand that such a ski resort could be beneficial in terms of tourism, economy, provide jobs, promote BC’s reputation as home to world-class ski resorts, etc., I believe that the risks associated with this project’s development are too high. Furthermore, while the economy and tourism could benefit from this new ski resort, what would happen when climate change further influences climatological patterns and snow fall is insufficient even above 600 meters of elevation? Is the solution then a “command-and-control” approach to nature, artificially making snow perhaps or building lifts to areas above 600 meters of elevation? Either way, drastic measures are being taken.

Many of the points I made in the policy memo as a natural resource planner hold true for my own personal opinion. Just by creating this map and examining it for the analysis portion of this lab, it seems clear to me that too much is at stake: the protected areas make up a significant portion of the proposed project area, and the viability of skiable terrain is not ideal currently, and can only be expected to get worse due to anthropogenic climate change. There are already a generous sampling of world class ski resorts in BC, and I strongly believe that the Brohm Ridge area should remain free of development, and that focus should be placed on maintaining ecosystems at all levels in the area, rather than threatening them with development.

As part of this lab, we were required to create 4 separate maps of the Vancouver census tracts, each with a different data classification method. Of the many methods of data classification that exist, the four most commonly used (and the ones that we used in the lab) are:

• Natural breaks
• Equal interval
• Standard deviation
• Manual breaks

The natural breaks method, as well as equal interval and standard deviation are automated methods within ArcMap. Natural breaks is the default method in ArcGIS and is based on natural groupings within the dataset with a default class number of 5. Standard deviation is based on statistical principles, grouping the values according to how much they vary from the average of the dataset. For manual breaks, the GIS user adds the class breaks manually, rather than the computer doing it automatically.

While each of these methods have pros and cons, there is no single method of data classification that can be deemed superior to the others. The one that is chosen to classify the dataset depends on the purpose of the data. When comparing datasets, for example housing affordability between Vancouver and Ottawa, as is the case here, it is important to use the same range of values in order to properly represent the data.

This map shows how these four different methods of data classification produce very different visualizations of housing affordability in Vancouver, even though the dataset is the exact same. The different visualizations of the same dataset could lead to different interpretations of the housing costs associated with census tracts across Vancouver, and could prove detrimental to the intention of the map.

Ethical implications associated with the choice of data classification method: 

The method of data classification that is used to portray specific data through a map can influence how the maps look. As such, data classification techniques can be subjective, as a specific method may be chosen knowing that it will produce specific results, effectively manipulating the outcome of the map so that the mapmaker’s goals are tailored to.

In this lab concerning housing affordability, I was asked which classification methods I would use for two different scenarios: (1) if I was a journalist putting together maps of housing cost in Vancouver, and (2) if I was a real estate agent preparing a presentation for prospective home buyers in the area near the University of British Columbia.

First of all, there are two main ways of describing housing cost in an area: median and average. The median cost of housing in an area can give a good idea of the price of real estate in that area, as well as a picture of how a certain area has been performing over time. Median cost of housing also reflects the sample size that is used, and looking at median prices over time can also provide an indication of market trends, help estimate the prices of properties, and whether or not a certain location is within price range. However, in order to get a complete picture of the market in a certain area, the median prices must be considered in conjunction with other factors. Using the average cost of housing can yield skewed results, as a significant outlier in price – either on the high or low end – can lead to misrepresentation by including these outlying values.

If I were a journalist, I may choose the equal interval method of data classification to divide the cost of housing into an equal range of values. This would effectively allocate the small fraction of exceptionally expensive houses into a class of their own, and on a map, it would appear as though only a small fraction of the homes in Vancouver are exceptionally expensive, where in reality, nearly all of Vancouver is severely unaffordable. Choosing to represent housing cost by the equal interval data classification method could thus lead to an inaccurate representation of the distribution of housing cost, and lead individuals to believe that a greater area of Vancouver has lower housing costs than actually is true. Furthermore, choosing average housing cost over median housing cost to portray the data could also have ethical implications, as using average housing cost could skew the results and provide a housing cost value that does not efficiently represent the cost of homes on the lower end of the price spectrum.

If I were a real estate agent, I might consider to display the housing data on the map using median housing cost, because the average housing cost near UBC is most of the highest averages in the city due to the extremely expensive homes in the area. By using the median housing cost, the value I could show prospective buyers would be lower since it takes into account the less expensive homes in the city. By doing this, ethical implications certainly arise as I purposely would choose median housing cost in attempt to have a lower cost of housing to report to the prospective buyers in the area. In terms of what data classification method I might choose as a real estate agent, using manual breaks, however, the fact that I would be choosing which values to set the manual breaks at produces ethical implications, since the values I choose could lead to very different representations of the data.

This lab was centered around housing affordability, and required retrieval of Canadian census data from reliable online sources.

The learning objectives were as follows:

1. Develop a working knowledge of Canadian Census Data:

• Downloading Spatial and Tabular Census Data
• Join tabular data to spatial layers
• Visualizing housing data
• Terms of Canadian Census Data collection

2. Understand quantitative data classification, and creating a map to illustrate the difference between four methods of classification:

• Natural breaks,
• Equal interval,
• Standard deviation; and
• Manual breaks

3. Work with ratios to compare datasets, and normalizing data to determine housing affordability.

4. Create maps of GIS analyses results.

What does affordability measure?

Affordability is calculated as a ratio of household income to the cost of an item, and is thus a measure of an individual’s ability to purchase a specific item, such as a house, relative to their income. As such, affordability provides a much better indicator of housing affordability than looking at housing cost alone. For example, while a house may be affordable to an individual who makes a good living every month, that same house may be very unaffordable to an individual with a lower salary. Housing affordability is directly relative to income. In Vancouver, for example, most homes are unaffordable, due to the fact that the average individual’s income does not match the high prices of homes.

Housing affordability rating categories:

The various housing affordability rating categories, as set forth by the Demographia International Housing Affordability Survey are:

• Severely unaffordable
• Seriously unaffordable
• Moderately unaffordable
• Affordable

In order to assess housing affordability, this survey uses the “Median Multiple”, which is the median house price divided by gross annual median household income. The Demographia International Housing Affordability Survey assesses the housing affordability rating categories in combination with the median multiple values. The affordability of housing in a specific region can thus be quantified as follows:

• Severely unaffordable (Median Multiple of 5.1 and over)
• Seriously unaffordable (Median Multiple of 4.1 to 5.0)
• Moderately unaffordable (Median Multiple of 3.1 to 4.0)
• Affordable (Median Multiple of 3.0 and under)

With a Median Multiple value of 3.0, the median house price is two times more than median household incomes, which is considered affordable. With a Mean Multiple value of 5.0, for example, the median house price is five times more than median household incomes, which is considered seriously unaffordable. The higher the Median Multiple value, the less affordable a house is relative to income.

One of the maps I created for this lab compares the housing affordability of Vancouver and Ottawa. As can be seen on the map, the affordability varies considerably.

Affordability as an indicator of a city’s liveability: 

Looking at housing affordability alone does not provide a sufficient indication of a city’s liveability, as there are many other factors to be taken into consideration. Liveability encompasses social and environmental factors as well as economic, for example, and other indicators such as safety/crime, international connectivity, climate, quality of infrastructure, urban design, business conditions and opportunity, tolerance, public transportation, walkability, and access to nature are also important to consider when looking at the liveability of a city. Furthermore, affordability does not provide any indication of the quality of a home. In Vancouver, for example, there are many houses of very poor quality, but due to their location, they are still very expensive because of the land and city they are situated in alone. Affordability does hold significance when considering the liveability of a city, but should be looked at in conjunction with these numerous and varied environmental and social indicators, as the liveability of a city ultimately entails the total quality of life that an individual will experience living in a certain place.

In these first three lab sessions of the course, I have already learned a lot of skills: both in terms of the variety of functions available in the GISystem ArcMap, as well as analytical skills in assessing the results and features of the map. I feel as though I have begun to create a solid foundation that I can continue to build on for the remainder of the course.

Accomplishment Statements:

In Lab 1, I researched various GIS applications posted on the Internet, for example a map depicting tsunami wave heights after the earthquake in Japan, and analyzed the integrity and ethics of the data and map representation to gain awareness of the various ways in which data sources and techniques can be used differently to produce maps for certain objectives.

In Lab 2, I learned how to combine layers with different coordinate systems by using the projection-on-the-fly method, as well as the ArcToolbox and transformation commands in ArcGIS: both of which allowed me to properly line up the layers on my map to avoid error introduced my misalignment.

In Lab 3, I used a variety of spatial and tabular datasets in both raster and vector formats to calculate statistics and create a map of Vancouver that specifically highlighted areas that are in danger of a tsunami, and identified the various land uses, education and health centers, and roads that are within this danger zone.

In Lab 4, I acquired census data from Statistics Canada and created a geodatabase in ArcMap to produce maps using different types of data classification methods to compare housing affordability between Vancouver and Ottawa.

Lab 5, I completed the seven steps of data visualization (acquire, parse, filter, mine, represent, refine, interact) and used an overlay analysis to union together all of the layers containing protected areas in my environmental impact assessment.

In this lab, the focus of the work shifted to spatial and tabular datasets, both raster and vector. The scenario that was presented to me was one in which I had been hired by the GIS department in the City of Vancouver, after certain geologic events that had occurred recently around the world led to the need to create a map highlighting the areas in Vancouver that are at particular risk of a tsunami, should one occur. For the sake of simplicity, this lab considers the immediate danger zone from an anticipated 15-meter wave to be mainly low-lying areas, meaning at or below 15 meters and that lie up to 1 kilometer from the shoreline. For the purpose of this lab’s analyses, this area is considered the “danger zone”.

The learning objectives were as follows:

1. Review data for geographic analysis.

2. Perform basic geographic analysis to determine areas for a possible tsunami.

• Perform buffer proximity analysis.
• Reclassify raster layers.
• Convert raster to vector data files.
• Combine vector data layers with polygon overlay tool intersect.

3. Perform geographic analysis to extract Vancouver data affected by possible tsunami.

• Combine vector data layers with the polygon overlay tool
• Perform a proximity analysis using select by location.
• Extract datasets with the polygon overlay tool

4. Calculate statistics (areas, lengths) of Vancouver land use and roads affected by potential tsunami.

• Create summary tables by area of land use.
• Create lists of facilities affected.
• Create summary tables of road infrastructure affected.

5. Add a layer of potential signage points.

• Learn how to create a new feature class, explaining the different types (point, multipoint, etc.)
• Introduce basic editing of features and tables (change values on individual table cells, modification/creation/deletion of features)
• Introduce the concept of snapping parameters for more accurate positioning of a new feature.

Specific information that I was required to analyze was: (1) the percentage of the City of Vancouver’s total area that was within the “danger zone” and thus at risk of being hit by a tsunami; and (2) the healthcare and educational facilities that were located within this danger zone.

Percentage of the City of Vancouver’s total area in the Danger Zone

In order to calculate the percentage of the City of Vancouver’s total area within the danger zone, the following formula was used:

(Area of land within danger zone) / (total area of the City of Vancouver) * 100%

Thus, these two values were needed. To obtain these values, a series of functions were preformed in ArcMap.

1. First, I exported a new layer, “Vancouver_landuseDanger” by intersecting “Vancouver_landuse” and “Vancouver_Danger”. This new vector layer created with the intersect tool incorporates both conditions for areas susceptible to a tsunami: the areas within a 1km buffer, and the areas below 15 meters of elecvation.
2. I then opened the attribute table of “Vancouver_landuseDanger” and used the Summarize function on the categories of landuse in order to generate an output summary table showing the total area for each landuse zone that are in the Danger Zone.

This is the output summary table:

3. To find the total area of Vancouver, I opened the attribute table of the “Vancouver_landuse” layer and used the Statistics funtion. This gave me a sum of all areas of all landuse types in Vamcouver, which is equal to 131020600.022758 m2.
4. With the values for the formula now obtained, I applied them:

Percentage of Vancouver area in the Danger Zone =

15876532.766953 / 131020600.022758 x 100% = 12.12%

Healthcare and educational facilities within the Danger Zone

To identify the healthcare and educational facilities within the danger zone, I created two separate layers with the Overlay and Intersect tools: one for education centers within the danger zone, and one for health care centers within the danger area. By viewing these two layers as well the Vancouver Danger layer, I was able to view only the health and education centers within the danger zone. I then examined the attribute tables to find the specific facilities in the danger zone.

Educational facilities in the danger zone:

• St. Anthony of Padua
• Ecole Rose des Vents
• Heritage 3R’s School
• Vancouver Montessori School
• False Creek Elementary, Emily Car
• Henry Hudson Elementary
• Institute of Indigenous Government.

Health care facilities in the danger zone:

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

The product of this lab was a map of Vancouver with the danger zone delineated, showcasing the road network and land use types affected. It can be viewed here.

The second lab of the course focused on coordinate systems and spatial data modeling.

The learning objectives were as follows:

1. Manage and preview data:

• Gain familiarity with the interfaces of ArcMap and ArcCatalog – the two basic applications used throughout this GIS course.
• Learn how to preview, manage, and explore the properties of spatial data.

2. Understand coordinate systems and projections:

• Learn the proper procedures for creating, saving, and storing a map document properly.
• Understand the importance of reference systems in spatial data.
• Learn how to manage and change projections.
• Learn how to repair misaligned and improperly referenced spatial data.
• Learn the effect that various projections have on geometric properties.
• Establish best practices when working with spatial data and reference systems.

3. Compare and contrast spatial data models:

• Understand the differences that exist between vector and raster data models.
• Learn the effect that data models have on the visualization and processing of spatial data sets.
• Learn how to add fields to an attribute table and how to perform simple calculations.

4. Work with remote sensing imagery:

• Gain an understanding of how remote sensing operates.
• Learn to display remote sensing images.
• Learn to create a composite image using the different bands from satellite data.

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Misaligned and/or improperly referenced spatial data:

When dealing with geospatial data that is obtained from different sources, problem can arise with misalignment and/or improperly referenced data.

Projections are always based on a geographic coordinate system, and are a reference to a flat surface. Map projections transfer the spherical Earth onto a two-dimensional surface, effectively approximating the true shape of the Earth, presenting error in the spatial data and leading to distortion of some aspects, depending on which of the many projections is used. The four basic geometrical properties of distance, shape, direction, and area cannot all be held true simultaneously on a map projection, which leads to some of these properties being held true, while others are distorted. If a map is put together with layers using different projections, subtle differences can exist in terms of their alignment and reference coordinates, which can cause inaccuracy. Although misaligned or improperly referenced spatial data can occur, it can also be prevented through the use of a Geographic Information System, such as ArcGIS.

It is important that the projection of the various data-frames match the projections used for the area of the study, which can be checked by examining the properties of the various files, where information on coordinate systems, projects, datum, and units of measurement are available. In ArcMap, a function called “projection-on-the-fly” allows the user to combine layers with different coordinate systems, and automatically aligns them with each other without changing the spatial coordinates during the process. Another option is to actually modify the data by creating a new version of the data layer with different coordinate systems, which is done through a series of Transformation commands.

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Advantages of remotely sensed Landsat data for geographic analysis:

Landsat is a remote sending program that, since 1972, has been continually scanning the earth and providing high quality imagery of its surface. It measures the response of objects and surfaces on earth, using sunlight as its energy source. While a total of eight Landsat satellites have been launched since 1972, there are only two still in operation: Landsat 7 and Landsat 8. These satellites can scan the same area of the earth every 16 days, and the images captured are made freely available to the public.

There are several advantages to using the high quality images provided by Landsat satellites, for example: the still photos taken of the entire globe, every day, in moderate resolution have been archived since 1972. Furthermore, Landsat data and imagery are available for free. And finally, Landsat imagery contains many layers of data, which allows users to manipulate the images (Fort Collins Science Center). For over 40 years, Landsat images have provided data for applications in fields such as agriculture, cartography, geology, forestry, regional planning, and education.

Here is an example of a Landsat satellite image used in this lab. It was taken over Mt. St. Helen’s in 2002, and shows the drastic changes to the landscape after the eruption in 1980.

Fort Collins Science Center. Landsat imagery: A unique resource. URL: https://www.fort.usgs.gov/landsat-study. Accessed: April 12, 2016

This was the first of five labs that I was assigned as a component of Geob 270 – Geographic Information Systems. The purpose of the lab was to review examples of GIS applications, as well as complete an online tutorial from the Environmental Systems Research Institution (ESRI). ESRI provide this basic online course for individuals who may be interested in exploring the field of Geographic Information Systems (GIS). Through a series of lessons, videos, tutorials and exercises, this course provided me with an introduction to basic GIS concepts, and also familiarized me with the ArcGIS interface. This first lab would serve as the foundation of the next four labs.

The learning objectives were as follows:

1. In relation to GIS software:

• Demonstrate a basic use of GIS software, namely ArcGIS, by completing the online Introduction to ArcGIS provided by ESRI.
• These basic uses included displaying map features, adding data to a map, manipulating data tables, creating a map, and properly saving the map and its associated data files.

2. In relation to GIS applications:

• Explore GIS applications posted on the Internet.
• Describe spatial data and geographic analysis for a GIS map.
• Discuss the data integrity and ethical implications for a GIS map.

Upon completion of the online ESRI course, I was awarded a Certificate of Completion.