GIS II

In an attempt to project the population growth of the areas surrounding the schools in the year 2020, census data comparisons were conducted from years 2000 to 2010 and population change between that 10 year span was continued through 2020 to achieve a projected population change. Each census block was calculated based on its population in 2000 compared to the population in 2010. This was done by joining census info from 2000 to the current 2010 census attribute table. The field calculator was then used to find the difference in population, then calculate the percentage change by entering the proper mathematical query (PERC_CHNG = ([POP_CHANGE] / [Pop_2000]) * 100).

This is considered deductive reasoning, as we are using data that is already gathered and available to the user and is assuming the population growth/decline rate is continual enough to be accurate for the most part. Several factors can play into population change over a span of 10 years, from economic growth/decay to migration trends. By looking at just the last 10 years of population change and no other factors to predict the next 10 years, we are taking a basic approach in determining future population numbers, but can get an overall accurate general picture of how trends will go.

By conducting these calculations the following numbers were obtained:

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Arts & Technology Academy AVERAGE GROWTH RATE: 5.8%

Projected 2020 School-Age population: 6,759

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Buena Vista School AVERAGE GROWTH RATE: 10.1%

Projected 2020 School-Age population: 6,696

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Yujin Gakuen AVERAGE GROWTH RATE: 10.5%

Projected 2020 School-Age population: 8,772

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Charlemange AVERAGE GROWTH RATE: 7.8%

Projected 2020 School-Age population: 2,770

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The below maps are meant to be used in comparison and relation to the Final Lab Part 2&3 maps located here:

Final Lab-Part 3

Caleb Stevens

3-8-16

 

The potential student numbers for each school area were generated from US census data. Ages 5 to 14 were selected and extracted from a larger dataset, and once extracted, were clipped by each 6 minute drive-time once the geoids were calculated. By converting the census block data do geoids, we error on the side of smaller numbers in terms of school-age population size. And although these areas overlap one another in certain areas, we can treat these 6 minute drive-times as separate because of the difference in schools (Japanese, Spanish, French emersion schools and an arts school). This can also be looked at as an issue. Perhaps a parent outside the drive time area wants to send their child to a specific school, in which case, they would not be accounted for in these maps and datasets. Overall though, taking these both into consideration, it is believed the numbers presented are for the most part accurate.

 

 

Caleb Stevens

Lab 4 (GEOG 482)

2-22-16

Tsunami Zones of Oregon: Why It’s Important to Know

The Oregon Coast is a popular destination for tourists and settlers alike. As more and more people choose to relocate to the 33rd United State, the more attention it receives toward its vulnerability to natural disasters. A main catastrophic event that threatens thousands of Oregon residents is the possibility of a major tsunami striking the coast. Oregon lies just east of the Juan de Fuca/ Pacific plate subduction zone, which is due for a major earthquake within the next 50 years, geologists predict. Such an earthquake would cause a tsunami and, depending on the severity of the seismic event, would most likely cause major damage to the communities and towns along the Oregon coast. The more information we can gather about the possible tsunami zones and affected areas along the coastline will better prepare us for when the event does occur. Things like elevations, slopes, roads clear of the tsunami zones, and population densities can give us an educated assumption of how a future tsunami could unfold in a given city. By conducting these studies, we can determine where is the best location to set up relief spots and shelters for people who may lose their homes. GIS, and more specifically Multi‐Criteria Evaluation, is an extremely vital tool when it comes to these analyses because it can take into consideration multiple factors that will play a role in the areas overall safety and accessibility, and by taking separate factors and having the ability to apply relatable functions to said factors, we can determine spatially what areas are most ideal for tsunami relief.

The methods used to conduct this analysis relied heavily on a few pieces of data that were available. These few datasets could then be combined to calculate ideal locations for tsunami relief areas. Digital elevation models were used to calculate the slope of the area being studied. By deriving the slope and steepness of the local terrain, we are able to eliminate a lot of location right off the bat. Since these areas with high slope in the coastal mountain ranges will be susceptible to landslides after a seismic event, these areas need to be avoided at all cost. Another useful dataset was provided by DOGAMI and allowed us to map the areas along the coast known as “Tsunami Zones”. It essentially is a buffer of the water bodies along the coast that would most definitely swell and cause flooding in the event of a tsunami. Census information was also used to calculate the population densities of the selected areas. This is helpful in mapping where the relief zone should be because the higher the population density, the higher the need for disaster relief and therefore, a relief shelter should be where the most people are located. Finally, roads that were clear of the tsunami zone were used, provided by ODOT. Because transportation around the area affected is essential (for medical emergencies, supply distribution, etc.), roads that would not be condemned by the tsunami were viewed as more desirable and played a factor into where the relief location was located. By using these characteristics of the local area, we can create models which can be used and implemented in other areas around the world where similar data is available to the user who is trying to conduct such research and preparedness.

(Screen shots of GIS Model used in calculating the most ideal tsunami relief location.)

The first map is an overview of the state of Oregon and the areas of vulnerability to tsunamis mapped in white, known as the “tsunami zones” of the coastal region. By using State land and county information, paired with the tsunami zone shapefile, we can calculate the potential amount of land that would be covered by a tsunami if it were to strike. A similar operation is used in calculating the amount of State highways would be directly affected by a tsunami. And lastly, by using US Census information on each county and city that contains land on the coast, we are able to calculate the amount of civilians that would be affected directly by the tsunami zone provided by DOGAMI. The table below displays the overall land, highways, and populations that are currently residing on land within the “tsunami zone”.

(Map 1)

 

A closer look at the area of Coos Bay, Oregon was conducted and areas of recommendation for a tsunami relief zone were determined. By calculating the ideal conditions on a scale from 1 to 10 (10 being the most ideal, 1 being the least), of the areas slope, nearness to but not within the tsunami zone, population density, and nearness to unscathed highways, we were able to map the most desired locations to place a tsunami relief shelter (See map 2 for Desired locations within Coos Bay area). The point starred on map 3 is an area with high population density, is on a highway that is out of the tsunami zone yet leads to the center of town, has a very low slope, and is close to but not within the tsunami zone. This location is ideal because of its close proximity to those affected by the tsunami, yet is far away enough for it to be a safe place. The Bi-Mart that is located within this ideal zone also contains a large parking lot where shelters could be installed if need be.

(Map2)

(Map3)

 

 

CONCLUSION:

The research conducted yielded satisfactory results in the fact that it built a successful model that can be implemented wherever tsunami relief zones need to be determined/mapped. Different weights for each characteristic in the model can be altered and edited in order to pertain more to the specific location of focus. The more spatial methods and research that are conducted such as this, the better understanding and preparedness communities will have in areas of natural disaster risk like the Oregon coast. By assessing the towns slope, population density, affected roads, and distance from the tsunami zone, we were able to map the most ideal locations to conduct tsunami relief in a time of despair and need for those affected.

 

 

Sources:

http://www.oregon.gov/DOGAMI/Pages/index.aspx

http://www.cityofseaside.us/emergency-preparedness/earthquake-and-tsunami-preparedness

 

Caleb Stevens

Lab 3 Answers

 

1. The DEM layer shows the elevation of the area in shades (Darker shades being low elevation, lighter shade being higher elevation). The cell size is 32.798 x 32.798 Pixels. This is given in the Layer Properties window under the Source tab.
2. Re-Classifying the water in this way is important because this will be telling the Cost-Distance analysis that water should be “impossible”, or extremely costly to travel through, and will treat it more like a barrier than a surface that is travelable for the mushroom picker.
3. Other data that could be used in this analysis is vegetation cover of the area. With the more heavily vegetated areas making it more difficult to travel through, it would add a higher cost-surface to said areas. I think slope/ water are definitely important in terms of gaining a somewhat accurate representation of the cost-surface, but without knowing the vegetation cover, somewhere with no water and a shallow slope could be covered in briars, making it extremely costly to travel through.

4.The attribute table of the Observer Points is essentially telling me what pixels are representing what is visible from Lookout 1, 2, and both (Each row in the attributes table represents lookout 1, 2 and the third row represents areas visible by both). Along with this attribute table, I can deduce which colors represent which lookout by the hillshade surrounding the lookouts. (Hillshade and slopes opposite the lookout that would be hidden by other peaks and slopes facing the lookout are in fact not colored.

5. I would recommend lookout 1 to the Forest Service because of its areas of visibility in respect to the path the mushroom picker may have taken. There is more area nearer the Least Cost Path than lookout 2, and therefore, would be a better option in terms of chances of seeing smoke signals/ SOS signals being made by the hunter.

5. The overview map gives a nice representation of the elevation and terrain with the hillshade. I would focus mainly on the valleys around the starting point, and  the destination point. Because the hunter probably wasn’t focusing on scaling any high elevation hillsides. But if he was lost, it may be in our best interest to search some peaks in case he/she wanted to get a better view of his/her surroundings. With the Cost Distance Map, elevation and water were taken into account to calculate the cost of traveling across the land. This doesn’t take into account the vegetation characteristics of the land, and therefore might not give a clear representation of the actual path the hunter took. That being said, elevation would play a big factor in the route the hunter took and is a good basis to go off of. The Viewpoint map leave a bit to be desired. There are not a lot of places that are visible from either lookout that are near the least cost path. Although Lookout 1 is definitely superior, it may be the lesser of two evils. If we are using a peak to have a lookout, we might as well choose a peak more centrally located in respect to the hunters supposed route.

 

Basemap/ Overview Map

 

 

Cost Dist. Map

 

 

Viewpoints Map

Lab 2 Answers

Caleb Stevens

 

1. We discovered in Lab 1 that different projections will move points around on your map. By ensuring that the datum in the GPS is the same as the map we are adding them to, we can assume they will be accurately displayed spatially.
2. The Campus data layers are in a “GCS_North_American_1983_HARN”, whereas the Lights_New is “GCS_GRS_1980(IUGG, 1980)
3. I set the buffer for the call boxes to 100 ft. This seemed like a reasonable distance for someone to have to run to in a timely manner if something was going wrong. It would only take the average person a few seconds to run that distance.
4. In order to figure out what walkways were outside the callbox buffer AND the light buffer, I would use the Intersect Tool and use those two layers (walks_nolight & walks_no_callbox) as the input. The output result would give me a layer where the two layers intersected.
5. The areas nearest major streets (Alder, University, Franklin) seem adequately lit and supplied with call boxes. The areas in the center of campus, mainly between University St. (Gerlinger Hall) and the Art Museum and the Knight Library are both ill lit AND don’t have a call box nearby. I would suggest putting a few lights along those walkways, along with a callbox somewhere centrally located within that “dark zone”.
6. Some issues are that the criminal data didn’t specify whether or not some of the crimes were in the east hall or west hall of certain buildings like Bean (Robert Sharp) Hall Complex or Living-Learning Center for instance. Since these buildings in the Attributes Table of the “Buildings” layer specify North or South, I simply assigned a N or S to the crime data.
7. I think a better history of crime that has occurred on campus would be beneficial and even comparing data prior to the installation of emergency call boxes with the amount of crimes that are committed WITH the call boxes would be an interesting thing to look at if said data is in fact available. When using buffers to determine what areas are “safer”, you are unable to see the actual area and its characteristics. The buffer may paint a false picture. Yes, there’s a lamp there, but are there trees/ buildings that create shadows/ darker than usual places. And corners of campus that are less traveled may be prone to theft/ assault that a buffer might not be able to depict.

 

Overview Map

 

Map of lights

 

Map of Callboxes

 

Map of crimes

Projections of the United States

 

 

 

Graduated Color Scheme

 

 

Graduated Symbol Scheme

 

 

 

Dot Density Scheme