Spatial Interpolation

Spatial interpolation is a process whereby with the use of known data we are able to predict values for areas where we do not have data for reasons such as shortage of funding, lack of access or missing data. Using spatial Interpolation techniques I designed a total of six maps for the County of Los Angeles. Using data from the counties Water Resources website, http://ladpw.org/wrd/Precip/index.cfm, I was able to create an excel sheet that contained the known rainfall totals for both the normal rainfall totals and the season rainfall totals based on stations around Los Angeles County. With this information I then created maps using two different Spatial Interpolation techniques; Inverse Distance Weighting (IDW) and Spline, which are more exact techniques. With maps created for both normal and season rainfall totals in each of these spatial interpolation techniques I was able to use the raster calculator to determine the difference between the normal rainfall and the season rainfall to create two new maps.

As you can see on the IDW Normal map the heaviest areas of rainfall are in the eastern central part of the state with very precise definition. In the Spline Normal map below, you see the higher levels of rainfall in the same area, but with larger areas, less defined. In the Season maps again you see more defined areas of rainfall in the IDW map and more general in the Spline map. We observe the comparison maps created using the raster calculator we see more variation in the IDW map than in the Spline, however they is a general pattern that they share with above average rainfall in the eastern portion of the county and less in the west.

When creating these maps I had to consider which spatial interpolation technique to use. I opted for the IDW and the Spline methods as they are considered more exact. When using the IDW method I opted to use the default power of 2 and the variable radius type. I considered my data after evaluating the locations of the stations and since some areas of the county had sparse data, I opted to use number of points for my evaluation and used the default of 12. When using the Spline method, I tried the default regularized type however the data seemed extreme, so I tried the tension type which gave me more reasonable results. Based upon the final results using these two spatial interpolation techniques, I would tend to use the Inverse Distance Weighting as I feel like it gave more specific results.

In this week’s lab we were to create a slope/fuel hazard model for the area within and surrounding the Station Fire. The Station Fire of 2009 was the largest wildfire in modern Los Angeles County history. Located 15-20 miles north of downtown Los Angeles, the Angeles National Forest bore the brunt of the flames. With fire suppression being the norm, there was plenty of fuel for the fire that was a result of arson. Beginning on August 26, the fire quickly spread over the following days and eventually was responsible for the death of 2 firefighters, the destruction of 209 structures and over 160,000 acres of once forested land was burned.

The purpose of this map is to incorporate slope data with land cover data to determine the areas with the highest threat of fire. I first acquired a digital elevation model (DEM) from USGS, which was used to create a slope in percent that was then calculated and modeled. I reclassified this data into ten ranges. The land cover (fuel) data, acquired from atlas.ca.gov, was reclassified into 10 categories, based upon the highest threat of fire (flammability). These two layers were used to create a slope/fuel model using the raster calculator. I then added additional information such as major roads and the perimeters of the 2009 Station Fire.

Upon analyzing the completed model it is apparent that the area within the previous Station Fire does in fact have a higher slope/fuel hazard. However, I also noticed that to the east of the fire there appears to be an even higher risk of potential fire. I must ask myself if the method I used for classifying the vegetation types might have been flawed. The vegetation shapefile used did not have the same breakdown of vegetation as the breakdown shown on the tutorial that I used as a guide. Thus, it is possible that my reclassification was incorrect. I would seek out a different shapefile that aligns better with the National Fire Protection Association’s NFPA1144 if I were to create this map again, and then compare it to this completed map.

Lab 5 Landfill Suitability Analysis

California has some of the most stringent environmental policies in the nation. The difficulty being, that it is hard to enforce these policies. In addition to the environment protections, the human population must be considered as well. Kettleman City, in the Central Valley is the home of a 1,600-acre landfill that has raised concerns for the local community, and now this small town is gaining political attention and support. At question is whether Chemical Waste Management, who is intent on expanding this site, has been neglectful in providing a safe environment for those exposed to some of the toxic waste within and running off the landfill. A thorough study must be made to ensure that the community does not suffer potential harm as a result of the landfill being expanded. A Suitability Analysis is one way to locate the best available location and potential expansion for a landfill. 

In this week’s lab we created and converted grid layers to produce an analysis model for a fictional county in Montana, Gallantin. We used the Spatial Analyst to assist in order to mask our area of study so we could better focus on the area of interest. We produced 5 individual maps identifying relevant factors of the area of study, as well as a final analysis incorporating the factors into one map.

We produced a map indicating the slope of the study area, which is relevant for potential runoff. The second map created buffers around the landfills in the area of study, of which there were two. Map three, indicated the soil drainage for the study area. The fourth map shows buffers around streams in the area. Map five, shows the land cover for our study area. Each of these factors play an important role in determining the best area suitable for a landfill with minimal impact on human life, which is our goal.

Using the raster data we created, we were able to produce our final analysis process map. We incorporated each of these factors, with weighted values placed on elements, to create a map that would provide information necessary to locate the best areas for a landfill. It is through careful analysis with consideration of key factors in relationship to landfills that the potential of human health and wellbeing can be preserved.

I enjoyed this lab, as through some of the repetitive functions it became second nature producing the maps showing the different factors involved in locating an acceptable place for something such as a landfill. Bringing all of these factors together into a final analysis was rewarding. ArcGIS is an invaluable tool for presenting visual aide in an easy to understand format.

Mid Term

Not in my backyard! I approve the legalization of marijuana for the tax revenues it would produce and accept the fact that for some, it brings needed relief to those suffering sever pain. However, I do not think that a mere 1000 foot buffer is sufficient enough distance from our children. It is quite apparent from the map above that schools are quite numerous in the city of Los Angeles. Upon a closer look (map below) it is obvious that much of Los Angeles is residential and it is difficult to find areas that fit within this buffer zone. I feel that areas prone to more industrial areas would prove to be safer on the whole.
In the area just to the west of the Santa Monica Blvd. label in the lower map shows a dispensary with a school within it's buffer zone, and yet another one further to the west. I recognize the honest need for alternative methods of pain relief, but does it need to be this close to a school with children? Marijuana is considered a "gateway" drug, and whether you support that theory or not, some of those who choose to use it are not always the sort of people you want around children. I feel a better alternative would be to allow dispensaries to operate in more commercial areas or industrial parks, places where children are not likely to be exposed to some of the less than attractive aspects of marijuana. Further GIS mapping could locate areas with a more significant buffer zone between children and marijuana.

Digitizing Iraq

I am a visual person and love playing with color. I enjoy playing around with the layout options and deciding how best to present information through GIS. I elected to keep this map simple, not too many colors, not too much clutter. Digitizing has been one of my favorite parts of GIS thus far. I'm certain some would find it tedious, but I enjoyed the challenge.
The Iraq map was a nice project. Not only did it get me familiar with the digitizing process, but also how to label attributes. Once I digitized the boundary for Iraq I used the Cut Polygon Features option to outline each of the provinces. Identifying each of the cities helped me become familiar with some of the cities I have heard of for the past 8 years since we have been in Iraq, but never bothered to identify their location.
The most difficult part of digitizing this map in particular was dealing in and around Baghdad. It got a little messy in that area and defining the flow of the river was difficult even magnified at 600%. The only other problem I ran into was that the scale bar was obviously not true to the map. Jida informed me that I would have to do some finite projecting to correct this. I would like to learn this when I have the time. Jida also instructed me on how and when I could use the Auto-Complete and Modify Features options.
Though I am not fully proficient with the commands and at times struggle with basic computer language, I am glad I have been able to complete, as best I can, the labs. I have enjoyed learning how to use GIS. I really wish I had the luxury of having access to the program at home on my own computer, not just remotely or during school hours.