Category: Uncategorized

Housing affordability:

Map of housing affordability in Vancouver and Ottawa                                  – MAP > affordabilityvo

As seen in the map above, there are ratings of affordability which were used from the The 11th Annual Demographic International Housing Affordability Survey. These ratings determine affordability ratings and puts them into according categories: Affordable 3.0 & Under, Moderately Unaffordable 3.1 to 4.0, Seriously Unaffordable 4.1 to 5.0, & Severely Unaffordable 5.1 & Over.

 

Affordability is measuring the ratio of median income to housing costs. It is a better indicator of housing affordability than housing cost alone because it accounts for the factors of a home-buyer’s affordability as well as available budget for purchasing a home.

As previously mentioned, the housing affordability rating categories are: “Affordable 3.0 & Under”, “Moderately Unaffordable 3.1 to 4.0”, “Seriously Unaffordable 4.1 to 5.0”, & “Severely Unaffordable 5.1 & Over”. These ratings were determined by the 11th Annual Demographic International Housing Affordability Survey. The purpose of this rating system is to convey the rates of affordability in cities and monitor patterns, highs, etc. It can also alert policy makers and governments when there are large decreases in affordability, which can be significant to the state or market. This survey can be trusted because it used in 378 metropolitan markets as well as the fact that it is endorsed by the UN and major world banks. It is a good universal scale for affordability.

Affordability is not a good indicator of a city’s livability because it does not really look into things such as poverty rates and employment availability. It also doesn’t account for the access one has to quality healthcare or education. In order to determine the livability of a city, these factors as well as many others, must be included so that a proper livability assessment can be made.

 

 

Quantitative Data Classification:

Maps of different classification methods – MAP > dataclass

---

As a journalist, I would choose to use the equal interval method of data classification for my reader audience. The reason why is because the equal interval method will divide the cost of housing into classes that contain an equal range of values. Due to the fact that only a certain number of houses are significantly more expensive than most of the other houses, the equal interval classification method will be subject to isolate these houses and allocate them to a class of their own. As a result, the map will visually illustrate a very small part of one class which is the category containing the most expensive houses. This will bring the reader’s attention to this specific area as they are reading the map.

As a real estate agent, I would choose to use the manual breaks method of data classification for prospective home buyers near UBC.  The reason why is because the manual breaks method creates a map of housing cost that has the ability to fit the needs of the prospective home buyers. If my prospective home buyers do not have much money to spend and are riding on a low budget, I would select manual breaks of smaller range at the lower end of the housing cost spectrum. This way, I would be able emphasize and clearly depict the difference in cost between such houses in the low end and high end of prices. Nevertheless, if I have wealth prospective home buyers that have a large budget and could afford more expensive housing, I would select manual breaks of smaller range at the higher end of the housing cost spectrum in order to clearly distinguish and too emphasize the difference in cost between these houses. In all, this will allow my prospective home buyers to make better decisions on both ends, and therefore I would able to generate as much home sales as possible.

In my opinion, as a journalist, this equal interval classification method carries an ethical implication for the purpose that it is not fully representative of the unequal distribution of the datasets, and therefore the map may misinform or deceive the reader. Furthermore, as a real estate agent, the ethical implication that comes with using the manual breaks classification method is that I am the one who can biasly decide what the break values are, as well as having the option to choose what I can accentuate and de-accentuate. The manual breaks method would allow me the possibility of manipulating the break values to my advantage, if I had an intention to mislead my buyers of course.

Overall, however, I believe that it is better to use Manual Breaks, rather than Natural Breaks, in a manual breaks classification, the GIS analyst can simply define classes and can insert breaks manually into the dataset to categorize them into the different classes. This way, the map can be as “error free” as possible, and consider all potential ethical implications. In comparison, a natural breaks classification is not ideal because it classifies data based on natural groupings inherent in the dataset, as its algorithms mathematically “select” what these natural groupings are. Sometimes, it is hard to read this map’s method classification because the numbers are not nicely rounded.

Accomplishment Statements for each lab:

---

Lab 1: In this Lab, I learned the basic concepts of ArcGIS and acquired practical working knowledge in how to use a variety of data-sets in multiple contexts in order to perform a geographic analysis. This will help me in the future when it comes to finding a job or pursuing my career.

---

Lab 2: After finishing this Lab, I became more confident and comfortable with working on the Arcmap program. I explored the various uses of vector and raster data in ArcGIS and i learned how to better evaluate geospatial data. In all, I now know how to create a map that properly depicts both vector and raster features in a map.

---

Lab 3: Following the completion of this lab, I have gained a good knowledge of how to produce a map by using spatial and tabular datasets. As a result, I created a map that displays the areas in the city of Vancouver that are at a potential risk of a tsunami if one was to occur.

 

---

Answer from Question 7:  What percentage of the city of Vancouver’s total area is under danger? Explain the method used to determine this percentage.

I came to the conclusion that 26.2% of Vancouver’s Total Area is in DANGER!

-I Right clicked Vancouver_Danger layer, then selected Attribute Table, right clicked Shape_Area, and selected Statistics.

-Next, I looked at the Sum of all of the DANGER shape areas (Area in Danger = 34357203.85 m² = 34.36 km²)

-Then, I right clicked on Vancouvermask and then selected the Attribute Table.

-Hence, I found the total Vancouver Area shape area under the Shape_Area column (City of Vancouver Area = 131020600.02 m² = 131.02 km²)

-In order to find the percentage, I divided the sum of all of the shape areas in the Vancouver_Danger layer by the total shape of the Vancouvermask layer (Percentage of Vancouver’s Danger Area = [34.36 km²]/[ 131.02 km²] = 26.2%)

-Thus, 26.2% of Vancouver’s Total Area is in DANGER!

 

---

Answer to Question 8:  List the healthcare and educational facilities within the Vancouver danger zone, if any explain how you came up with your answer.

Education facilities under Danger:

ST ANTHONY OF PADUA, ECOLE ROSE DES VENTS, FALSE CREEK ELEMENTARY, EMILY CARR INSTITUTE OF ART & DESIGN, HENRY HUDSON ELEMENTARY, HERITAGE 3R’S SCHOOL, VANCOUVER MONTESSORI SCHOOL, ST JOHN’S INTERNATIONAL, ST FRANCIS XAVIER, & INSTITUE OF INDIGINEOUS GOVERNMENT.

Healthcare facilities under Danger:

FALSE CREEK RESIDENCE, YALETOWN HOUSE SOCIETY, VILLA CATHAY CARE HOME, BROADWAY PENTECOASTAL LODGE, & COAST WEST COMMUNITY HOME.

In order to find these facilities, I used the overlay tool in ArcToolbox, going to Analysis Tools > Overlay > Intersect. Then I entered the layers of ‘Vancouver danger zones’ layer and the ‘healthcare locations’ layer—as well as the ‘education locations’ layer—in the field box called “input features” and I found the healthcare and education facilities that fall into the Vancouver danger zones. I opened the Attributes table to see the names of the facilities that were affected.

 

---

PDF – Map

In general, special data is sometimes subject to being misaligned or improperly referenced. Usually, the most common properties that are affected when spatial data is projected into a different coordinate system consist of distance, shape, area, or direction of the data.

Moreover, the Projection-on-fly process is a practical feature of the program which changes map projections and allows the alignment of spatial coordinate systems for display and mapping. It is unlikely to be used when printing a map that requires a different look/style than the original dataset. But nevertheless, it is a good process to use when wanting to change the projection for the layer into a common spatial reference system, specifically when performing spatial analysis.

In comparison to the Projection-on-the-fly process, using ArcToolbox Project and Transformation commands allows for a different way of projecting a layer and actually modifies the data to create a new version of the data layer with a different coordinate system. Therefore, it is not possible to just go to the properties of a layer and change the information; rather, a special tool in ArcCatalog has to be invoked in order to perform the transformation.

Furthermore, it is more advantageous to use remotely sensed Landsat data for geographic analyses. One of the main reasons for this is the complexities that result from the mixed pixel problem, which proves to be a significant issue/dilemma when it comes to Landsat data projections.  Specifically, the effect that the mixed pixel problem could have when representing an area is that it only displays individual cell information, which brings up the issue of how each data structure treats objects. The cells are individual spatial units in raster models unlike the way that the vector model where it makes the object is its own entity and all the pertinent information can be accessed at once. The other dilemma with this is the raster category seems to be different in certain areas relative to the vector model. In other words, a pixel represents one value for the entire area of the overall pixel.

After finishing Lab 2 for our GEOB 270 class, I have gained comfort with working on the Arcmap program. Also I am more familiar now with using vector models and raster models, as well as being able to differentiate the two.

I have created my Geob 270 Portfolio Blog successfully! Get ready to read some great blog posts!