When doing this lab the actual lab was incredibly easy, but accumulating the data and processing it was the difficult part, which seems to be the common trend in GIS.  In doing the maps though i discovered just how different IDW and Splining truly are.  IDW attempts to generalize the entire map, creating a common thread throughout. Splining seems to overestimate values.  The differences in the two styles is very apparent when looking at the northeast corner of the map.  In IDW, where generalizations are made and values tend to lean toward the middle, the precipitation is believed to be between 12 and 16 inches a year, where as the Splining method values the area to be below 4 inches a year becuase it overestimates and leans toward extremes.  The massive difference between the two values shows just how inconsistent the entire method is, and how being able to display multiple methods can help realize what the true values possibly are.
     Another major issue is the bias towards areas with more points, which makes sense.  When there are more values it is easier to make assessments, but it also creates problems, for example the large discrepancy with the northeast corner of the map.  With IDW, because a majority of the values were in the 12-16 range, it valued any area without too many points to be in that general area.  Spline seemed to favor the nearest point in making the decision.  All in all, the major discrepancies make it necessary to both collect as much data as possible and understand the differences between the methods.
IDW- generalizes
Spline- overestimates
areas with few receptors will be over-generalized.  bias towards areas with multiple points.
compare and contrast styles of idw and spline

Fire Probability

 

     This was probably one of the most straightforward and  simple labs we have had this quarter, if everything had gone smoothly.  I was able to get the fuel layer from http://frap.cdf.ca.gov/data/frapgisdata/download.asp?spatialdist=2&rec=fmod, the elevation layer from  http://seamless.usgs.gov/website/seamless/viewer.htm, and the fire zone coverage layer from the the UCLA website for geography 7.  That was the easy the part. What normally would have only taken me a few hours to do the tutorial and then my own analysis ended up taking multiple hours because of all of the problems I encountered.  I first had difficulty converting the elevation map into percent.  I got ridiculously high values that I might have been able to work with but were not in fact correct and may have tripped me up at later points in the project.  To fix this I had to convert the elevation layer from the projection it was in to UTM zone 11 which proved to be the most difficult part of the entire venture. The file may have been to large, but every time I attempted to convert the layer, ArcGIS simply shut down.  This happened multiple times on multiple computer.  I eventually had to reload the file and attempt the entire process over from scratch.  

     Once I was finally able to convert the file and actually start the project, I had difficulty finding the metadata to reclassify the fuel values.  Because the values were not simply given to me as in the tutorial, I also had to more or less guess at what the values should be and hope that the values were correct.  Fortunately i was able to find a separate layer online that had already classified the values, and my own interpretation was fairly similar, giving me confidence that I had adequately valued the fuels.  Once I had the values all accumulated I ran into even more problems in attempting to use the Raster Calculator and determine the ultimate risk of fire.  The first couple of times I attempted to find the values, they did not come out as classified values but were rather discrete, which was not correct and did not give me anything to work with.  After manipulating a few aspects I was finally able to get everything together and finish the lab.  All in all this should not have taken me more than 3 hours to do everything, but because of the multiple obstacles it took more than twice as long as that.

     Ultimately all of the issues probably taught me more about ArcGIS than the actual lab did.  I had to deal with so many issues on my own that I learned two things.  First, how to deal with those specific issues in the future should they arise.  The second, and more important revelation, is that I can in fact figure out some of these problems on my own.  The fact that I do in fact have a bit of a GIS toolbox is reassuring, even if it is still rather basic.  I hope that in the future similar projects will not take quite as long, but if they do at least I can be assured that I will probably learn valuable skills in the process.  

     

Landfill Placement Analysis

                 

     The ability to assess the suitability of certain locations allows for difficult decisions to be looked at from a more concrete point of view as opposed to making guesses on what would most likely be the most desirable site.  By using cut and dry numbers the choice is removed and replaced by an obvious answer.  The maps allows for subjectivity to be removed and an objective decision to be made.

    The weighted values option also makes a huge difference, especially in this case.  Not all variables are equal, and in this case, when the weighted option was developed it made a completely different map.  Certain variables are much more important than others, especially when choosing the location of a hazardous waste site that could negatively effect whatever location was chosen. 

   The weighted options made a huge difference in the development of the map, which is a major improvement.  As we saw with the article, the waste site is incredibly hazardous, causing birth defects and cancer in the surrounding areas.  The safety of those around the area is a large component in choosing such a huge project.  The weighting of options could save people's lives who would have been put in jeopardy if the project had not decided that certain components were more valuable than others.

    Also, the multiple variables going into the project makes this a complex and difficult project.  The raster calculator makes it much simpler and quicker to assess the potential of every single point within the entire county.  When there are such major issues with landfills, as they take up space, lower the value of the area and affect the health of the inhabitants in the area, the ability to include and process so many different aspects makes the project much less scary, as the consequences of choosing incorrectly could be incredibly detrimental.  

    Overall, this program makes the work much easier.  Every variable is accounted for, every possibility is assessed, and the choice becomes much less risky when viewed from every angle.  Ultimately, the ability to choose the safest possible location could save money, resources, and possibly even lives.

Medical Marijuana

 

     By creating this ordinance, a large number of dispensaries are found to be outside of compliance, which will force many of them to close, making this decision a negative one.  Due to time constrictions I was unable to accumulate a large sample of dispensaries, instead getting only a few major ones. While it is difficult to tell from this scale, many do in fact fall within the forbidden buffers upon zooming in.  This means that many of them would have to shut down due to the new law, as they fall within the buffers, seriously restricting those that rely on the them for medicine  Los Angeles is so densely populated and there is so little free space, that it is almost impossible to follow restrictions such as this, as the map clearly shows.  The few available areas within the city that do not fall under this law are difficult to access and much less populated.  .

   It appears from reading the article that the issue is not with the dispensaries themselves, but the ones that do not follow other laws about how they may distribute their products.  If all of the regulations are being followed, marijuana should not fall into the hands of children, who are not legally allowed to purchase from the clubs.  The problem instead appears to be with a few clubs who are not following regulations and allowing the product to either go to those who do not need it for medical usage but only for recreation, and the fact that it is becoming an activity in the clubs late at night as opposed to being used for medicine.

    Stricter restrictions need to be put on the distribution of the product, but the placement of them should not be an issue.  If the 1000 foot ordinance does come to fruition, it will cause many of the clubs to shut down unnecessarily and negatively effect many of their patients.

Iraq

Geocoding

 

   Geocoding allows for precise locations to be created within a map, allowing for spatial distributions to be better assessed.  By placing exact locations onto the map, trends become much more clear, and issues are easier to solve.  In this case, the problem with the Los Angeles public transit becomes an issue that is easier to grasp through geocoding and ARCGIS.

     In Los Angeles, the public transportation system is a mess.  It is incredibly slow and inefficient.  This is due mostly to the fact that it is so crowded and developed, no new development would really be possible.  But there is always room for improvement, and by mapping out the metro station stops we can see where there are potential gaps or places where new stations could do the most public good.

     By creating a buffer around each station, holes in the infrastructure becomes even more evident than they might have before.  I created a two mile buffer, which is quite large, in all honesty it should be smaller.  By making it smaller the holes would be even more evident, showing places where the stations are simply not accessible.  If population data had been made available, it would be good to see where the highest populated areas lacking a metro are located, and how that problem could be alleviated.

 Geocoded Data

	FID_	Status	Score	Match_type	Side	Match_addr	ARC_Street	Station_Na	Line	Address
	M	100	A	L	660 S FIGUEROA ST	660 S Figueroa St	7th St/Metro Center	Red	660 S Figueroa St
	M	100	A	R	6801 HOLLYWOOD BLVD	6801 Hollywood Blvd	Hollywood/Highland	Red	6801 Hollywood Blvd
	M	100	A	L	6250 HOLLYWOOD BLVD	6250 Hollywood Blvd	Hollywood/Vine	Red	6250 Hollywood Blvd
	M	100	A	L	5450 HOLLYWOOD BLVD	5450 Hollywood Blvd	Hollywood/Western	Red	5450 Hollywood Blvd
	M	100	A	R	5350 LANKERSHIM BLVD	5350 Lankershim Blvd	North Hollywood	Red	5350 Lankershim Blvd
	M	100	A	L	3891 LANKERSHIM BLVD	3891 Lankershim Blvd	Universal City	Red	3891 Lankershim Blvd
	M	100	M	L	301 N VERMONT AVE	301 N VERMONT AVE	Vermont/Beverly	Red	301 N Vermont Ave
	M	100	A	L	1015 N VERMONT AVE	1015 N Vermont Ave	Vermont/Santa Monica	Red	1015 N Vermont Ave
	M	100	A	R	1500 N VERMONT AVE	1500 N Vermont Ave	Vermont/Sunset	Red	1500 N Vermont Ave
	M	100	M	R	800 N ALAMEDA ST	800 N ALAMEDA ST	Union Station	Red	800 N Alameda St
	M	100	A	R	101 S HILL ST	101 S Hill St	Civic Center	Red	101 S Hill St
	M	100	A	L	500 S HILL ST	500 S Hill St	Pershing Square	Red	500 S Hill St
	M	100	A	R	3191 WILSHIRE BLVD	3191 Wilshire Blvd	Wilshire/Vermont	Red	3191 Wilshire Blvd
	M	100	A	L	395 N ALLEN AVE	395 N Allen Ave	Allen	Gold	395 N Allen Ave
	M	77	A	R	5150 POMONA BLVD	5150 E Pomona Blvd	Atlantic	Gold	5150 E Pomona Blvd
	M	100	M	L	901 N SPRING ST	901 N SPRING ST	Chinatown	Gold	901 N Spring St
	M	100	M	L	230 S RAYMOND AVE	230 S RAYMOND AVE	Del Mar	Gold	230 S Raymond Ave
	M	100	M	R	4780 E 3RD ST	4780 E 3RD ST	East L.A. Civic Center	Gold	4780 E 3rd St
	M	100	A	L	95 FILLMORE ST	95 Fillmore St	Fillmore	Gold	95 Fillmore St
	M	100	A	L	3545 PASADENA AVE	3545 Pasadena Ave	Heritage Square	Gold	3545 Pasadena Ave
	M	77	M	R	151 N AVE 57	151 AVE 57	Highland Park	Gold	151 AVE 57
	M	100	A	L	210 S INDIANA ST	210 S Indiana St	Indiana	Gold	210 S Indiana St
	M	100	A	R	340 N LAKE AVE	340 N Lake Ave	Lake	Gold	340 N Lake Ave
	M	77	M	L	370 W AVE 26	370 AVE 26	Lincoln/Cypress	Gold	370 AVE 26
	M	100	M	R	200 N ALAMEDA ST	200 N ALAMEDA ST	Little Tokyo/Arts District	Gold	200 N Alameda St
	M	100	M	R	4520 E 3RD ST	4520 E 3RD ST	Maravilla	Gold	4520 E 3rd St
	M	100	M	L	1831 E 1ST ST	1831 E 1ST ST	Mariachi Plaza	Gold	1831 E 1st St
	M	100	A	L	125 E HOLLY ST	125 E Holly St	Memorial Park	Gold	125 E Holly St
	M	100	A	R	905 MERIDIAN AVE	905 Meridian Ave	Mission	Gold	905 Meridian Ave
	M	100	M	L	1311 E 1ST ST	1311 E 1ST ST	Pico/Aliso	Gold	1311 E 1st St
	M	100	A	L	149 N HALSTEAD ST	149 N Halstead St	Sierra Madre Villa	Gold	149 N Halstead St
	M	100	M	R	2330 E 1ST ST	2330 E 1ST ST	Soto	Gold	2330 E 1st St
	M	49	M	R	4800 MARMION WAY	4600 MARMION WAY	Southwest Museum	Gold	4600 Marmion Way
	M	52	M	L	9998 GRANDEE AVE	10100 GRANDEE AVE		Blue	10100 Grandee Ave
	M	100	M	R	108 N LONG BEACH BLVD	108 N LONG BEACH BLVD		Blue	108 N Long Beach Blvd
	M	100	M	R	598 N LONG BEACH BLVD	598 N LONG BEACH BLVD		Blue	598 N Long Beach Blvd
	M	77	A	L	20220 S SANTA FE AVE	20220 Santa Fe Ave		Blue	20220 Santa Fe Ave
	M	100	M	R	1290 N LONG BEACH BLVD	1290 N LONG BEACH BLVD		Blue	1290 N Long Beach Blvd
	M	77	A	L	1920 S ACACIA AVE	1920 Acacia Ave		Blue	1920 Acacia Ave
	M	77	M	R	275 S WILLOWBROOK AVE	275 WILLOWBROOK AVE		Blue	275 Willowbrook Ave
	M	39	M	R	9501 GRAHAM AVE	8615 GRAHAM AVE		Blue	8615 Graham Ave
	M	53	M	L	7226 GRAHAM AVE	7225 GRAHAM AVE		Blue	7225 Graham Ave
	U	0	A			331 W Washinton Blvd		Blue	331 W Washinton Blvd
	M	100	M	L	128 W 1ST ST	128 W FIRST ST		Blue	128 W First St
	M	100	A	L	498 PACIFIC AVE	498 Pacific Ave		Blue	498 Pacific Ave
	M	100	M	R	1798 N LONG BEACH BLVD	1798 N LONG BEACH BLVD		Blue	1798 N Long Beach Blvd
	M	100	M	L	1236 S FLOWER ST	1236 SOUTH FLOWER ST		Blue	1236 South Flower St
	M	100	M	L	767 E WASHINGTON BLVD	767 EAST WASHINGTON BLVD		Blue	767 East Washington Blvd
	M	77	M	R	1700 E SLAUSON AVE	1700 SLAUSON AVE		Blue	1700 Slauson Ave
	M	75	A	R	4421 LONG BEACH AVE W	4421 Long Beach Ave		Blue	4421 Long Beach Ave
	M	100	A	R	3420 N PACIFIC PL	3420 N Pacific Place		Blue	3420 N Pacific Place
	M	75	A	R	1945 LONG BEACH AVE W	1945 Long Beach Ave		Blue	1945 Long Beach Ave
	M	77	A	R	2750 W AMERICAN AVE	2750 American Ave		Blue	2750 American Ave
	M	52	M	R	11701 WILLOWBROOK AVE	11611 WILLOWBROOK AVE		Green	11611 Willowbrook Ave
	M	77	A	R	11667 AVALON BLVD	11667 S Avalon Blvd		Green	11667 S Avalon Blvd
	M	100	M	L	11500 AVIATION BLVD	11500 AVIATION BLVD		Green	11500 Aviation Blvd
	M	77	A	R	11901 CRENSHAW BLVD	11901 S Crenshaw Blvd		Green	11901 S Crenshaw Blvd
	M	100	A	L	700 S DOUGLAS ST	700 S Douglas St		Green	700 S Douglas St
	M	100	M	R	2226 E EL SEGUNDO BLVD	2226 E EL SEGUNDO BLVD		Green	2226 E El Segundo Blvd
	M	100	A	L	11500 S FIGUEROA ST	11500 S Figueroa St		Green	11500 S Figueroa St
	U	0	A			11230 S Acacia St		Green	11230 S Acacia St
	M	77	A	R	12801 N LAKEWOOD BLVD	12801 Lakewood Blvd		Green	12801 Lakewood Blvd
	M	100	A	L	11508 LONG BEACH BLVD	11508 Long Beach Blvd		Green	11508 Long Beach Blvd
	M	100	A	L	555 N NASH ST	555 N Nash St		Green	555 N Nash St
	M	100	A	R	12901 HOXIE AVE	12901 Hoxie Ave		Green	12901 Hoxie Ave
	M	100	A	L	5301 MARINE AVE	5301 Marine Ave		Green	5301 Marine Ave
	M	100	A	R	11603 S VERMONT AVE	11603 S Vermont Ave		Green	11603 S Vermont Ave
	M	100	A	L	660 S FIGUEROA ST	660 S Figueroa St	7th St/Metro Center	Purple	660 S Figueroa St
	M	100	A	R	101 S HILL ST	101 S Hill St	Civic Center	Purpe	101 S Hill St
	M	100	A	L	3510 WILSHIRE BLVD	3510 Wilshire Blvd	Wilshire/Normandie	Purple	3510 Wilshire Blvd
	M	100	A	R	3775 WILSHIRE BLVD	3775 Wilshire Blvd	Wilshire/Western	Purple	3775 Wilshire Blvd
	M	100	M	R	800 N ALAMEDA ST	800 N ALAMEDA ST	Union Station	Purple	800 N Alameda St
	M	100	A	L	500 S HILL ST	500 S Hill St	Pershing Square	Purple	500 S Hill St
	M	100	A	L	660 S ALVARADO ST	660 S Alvarado St	Westlake/MacArthur Park	Purple	660 S Alvarado St
	M	100	A	R	3191 WILSHIRE BLVD	3191 Wilshire Blvd	Wilshire/Vermont	Purple	3191 Wilshire Blvd

 

Wastewater Plant