Site Analysis

This lab consisted of preforming spatial analysis to find the easiest route

for access to a new proposed school in the town of Stowe, Vermont. The town of Stowe,

Vermont has had an increase of population due to many families with children moving in

due to all the recreational activities it offers. The great influx of children is causing

overpopulation in the current schools and therefore a new school is being proposed.

Our first step is to find the best location for this new school. We must first gather our

data and open arc-catalog to use the spatial analyst tool functions. Once we have

displayed our data we can begin to manipulate the data to do a suitable analysis in the

model-builder feature under the spatial analyst tools.

Model builder allows us to display our data in an organized manner and later

calculate and run functions such as distances, etc. Model builder also allows us to

import and change information from raster to vector data-sets so we can make better

use of them in arc-map. We will locate the most suitable place for the new school based

on our land-use, elevation, recreational sites, schools, slope, and relative distances.

Since we do not have all these data-sets, we will have to use model builder to derive

some such as the slope and distances between points. After deriving this information we

will be able to give them a value from 1 to 10 according to their influence on the best

new location. After doing so, we will end up with a map displaying the suitable locations

for the new school. After carefully viewing the most suitable locations and selecting the

one spot that is best, we will then have to find the best access route to get to the new

school.

In order to find the most accessible way to the new school we must preform a

cost distance analysis using the cost distance tool. To preform the cost distance

analysis I will use the cost and source data-sets. I will assign values to the land types

according to its accessibility, for instance water will be 10 while barren land will be 2

because it is easier to build a road through there. When completed, we will end up with

a raster data-set of the least costly route which you will then have to convert to a poly

line feature.

Once all these steps are complete I can proceed to making my final map. In my

final map I have added the location of the new school site, along with the new route. We

can see the types of land the new route traverses along with the nearby roads. We can

see that this new route uses the shortest distance through the roads to save people

time and it will also save money because no new extensive road work will have to be

done. Spatial analysis in terms of suitability analysis and cost analysis is very useful

when planning out new locations for both public and private places. It also allows us to

find the most efficient and accessible way to get from one point to another. Although

spatial analysis has many advantages, it can also cause some problems because it

cannot cover environmental things such as land depths, or account for what may

happen if that area is changed. For instance, if a gas company was looking for a place

to add a gas station they may find a good location but it may have an underground

water system which can be ruined if the gas station was placed there. Another example

is if a mall was looking for a place and the best suitable place was in a section of the

forest because of space, but if the forest is changed it can bring problems to the animals

and ecosystem. We must also consider how up to date the data is that we are using to

create the analysis because things do change over time. Despite these factors,

suitability analysis seems to be good for planning and development.

Spatial Interpolation

This lab uses the process of spatial interpolation and its possible uses. As part of the lab I used two methods of spatial interpolation to help in the comparison and assessment of precipitation level changes between the season to date and the normal precipitation. Spatial interpolation helps us find out the unknown values of rainfall with the already known values. I decided I would use the method of Inverse Distance Weighted (IDW) and Kriging since both follow different paths when interpolating. IDW uses points to calculate weights and distances for the unknown areas of precipitation we are trying to calculate. Kriging has a more statistical approach using relationships in samples to calculate

the weighted average. Although both help us find the unknown values with those that we do, they give us different results due to the different methods they preform.

The first map shown displays the results of the IDW. Here we can see gradual changes from high to low going towards the West. The second map shows us the results of the Kriging method and how the changes are more gradual since it has slight change. When comparing the overall changes, there is more change in the Kriging method than in the IDW method probably because there is more change in the Season to Date map compared to the Normal Precipitation map, thus establishing a greater change when taking the difference between the two. Overall we can see how there tends to be more rain in the eastern part of the county than in the western portion, and there are about equal amounts of rain

going North and South from there. We can also see that there has been less rain this year than average possibly signifying a drought because there are a lot of areas in green. This may, however, change once we reach the wet months closer to the winter. 

Spatial Analysis

This tutorial went through methods of how to work through a research question. In this case, the objective was to find potential wastewater plant sites. We had to evaluate several characteristics such as elevation and distance to nearby areas to avoid flood problems, etc.

Geocoding

This lab consisted of using our new geocoding skills in a research question. I decided to

research the 25 closest libraries to my elementary, Third Street Elementary School, and

then compare the amount of libraries to the population of children 5-17 in that area. I

first made a spreadsheet of the 25 closest Los Angeles public libraries closest to 201 S.

June St. 90004. I then went through the process of geocoding and overlaying the points

in my map on the streets layer. I noticed that there were a lot more libraries towards the

East but not as many in the West. I then decided to obtain some census information to

see the demographics of the area. I mapped amount of children ages 5-17 per polygon

and discovered that there are more children living in the East than there are living in the

West. Although there are more libraries in the East because there are more children in

the East, the majority of the libraries are in the Northern region, but more children are in

the Southern region. This may have to do partly with the income of the area. Areas

more South tend to be of lower income than those in the North. Geocoding allowed me

to display where each library is located along with labeling each library.

Cartography: Natural Disasters

This is a final map of some natural disasters in the US. After obtaining my shapefiles I was able to manipulate the data through the joins and relates feature in Arcmap to better display and understand the statistics. Here we can see that almost every region of the US suffers from some sort of natural disaster whether it is earthquakes and volcanoes or hurricanes and tornadoes.

Importance of Normalization: Worldwide Alcohol Consumption

This lab consisted of mapping worldwide alcohol consumption data. The first of the two maps shows the worldwide total consumption of alcohol in millions of liters per country. The second map is normalized per person in each country, calculated in liters per person. Each map displays different colors per class, varying from lighter colors for the lower values to darker colors for the higher values. I used the “Color Brewer” website (http://colorbrewer2.org/) for the first map and chose a 5 class, sequential, multi-hue CMYK scheme which ranges from yellow, to green, to blue. The second uses the standard colors which arcmap had set for the Jenks classification. I decided to keep it since it ranges from a yellow, to an orangish-tan, to brown, the typical colors of beer, an alcoholic drink. Both color schemes have colors greatly distributed to clearly show the differences between classes which can be important when looking at a world map with countries varying in size. 

In creating my maps, I had to choose a classification to map the data in a way that makes sense. When choosing a classification, I took the type of data and the type  of distribution into account. I decided that the type of classification that would best distribute the intervals in each class was the Natural Breaks aka Jenks classification because “the purpose of natural breaks is to minimize differences between data values in the same class and maximize differences between classes.” (Slocum: Data Classification, pg. 84)  Jenks is typically a good type of classification for many types of distributions and data types and helps tell a “good” story of the information being mapped, this is probably why arcmap has it as the default classification. 

Taking these things into consideration, I can conclude that both maps are true for what they are trying to display, but they can also be misleading depending on the information the author is trying to convey to the reader. For example, if someone were to look at the first map and tried to figure out which country drinks the most, they would assume it was China because it is the darkest country on the map. This is true because as a whole, they drink the most alcohol, but this is only because they have a big population. However, if we take a look at the second map we will see that China is not the darkest country on the map anymore. This is because the second map is a normalized map of alcohol consumption per capita. The same can be said for India due to its big population. Personally I prefer the second map because I believe it tells a better story of alcohol consumption per country. When people think of alcohol consumption, they usually think of how much alcohol is being consumed per person not so much the country because we generally do not really know how big or small the population is per country. Another reason I’d prefer the second map is because it is easier to understand liters/person as opposed to the millions of liters/country in the first map. Not everyone enjoys math which would make the first map’s units a nightmare to try to visualize. Overall, the second map is just easier to understand and probably easier to relate to when trying to understand the data. 

Graphic Design: Brazil

All text and information from Brazil obtained from Wikipedia

http://en.wikipedia.org/wiki/Brazil