Thursday, March 18, 2010

Final Project


















Introduction
According to an renowned biologist the biodiversity on Earth is going through a “immense and hidden” tragedy that requires the scale of response that is now involved with climate change(Guardian UK).Since the last century 183 known species have become extinct. Including the focal species of the Tasmanian Tiger, the Caribbean Monk Seal, and the Toolache Wallaby. However, these are only the known species and this number seems to be , according to The International Union for Conservation, considerably less then what is actually occurring. The problem is that these species are disappearing
faster then we can study, understand, or even find them. A large portion of protecting these species and a lot of unknown species is protecting the habitat they frequent. One of the most important and diverse biological communities is a forest. These habitats shelter thousands of known species and thousand of unknown species, species that as the saying goes... “could be the next cure to cancer”. Yet the only way to find this potential species is to consider the consequences of our actions as humans on this planet and find ways to decrease our footprint.
Habitat destruction, fragmentation, and introduction of native species are Hawaii's principle threats to biodiversity. Development and expansion of human populations fragments and deteriorates important habitats. Most of this however, is happening outside the park boundaries. If we can analyze the current surroundings and decide what habitat poses the most need, we can look to protect these lands from further degradation. With Just .2% of the U.S land area, Hawaii has about 25% of the U.S endangered species. Habitat destruction along with the spread of invasive species have been heavy contributors to biodiversity loss.(OTA 1993)

The purpose of this project is to examine the Hawaiian islands and do a suitability analysis to try and determine potential areas for conservation. One of the factors for this analysis is distance away from roads. This factors also helps in determining a distance away from populated areas as they have more roads then rural areas. In this analysis we want the potential reserve to be at least ½ mile away from the nearest major road. Another factor I am looking at is the location of dump sites within the islands, however most of these site are ocean related factors I am still creating a buffer of 2 miles from the nearest dump site. The next factor is land cover, I reclassified land cover as will be talked about more in the Methods section, on a scale from 1-5. 5 being primarily forest land and the most suitable area to place a potential reserve. Another factor that I took into consideration was the density of threatened and endangered plants. On the same classification scale as the land cover the area with the highest density of threatened and endangered was deemed a 5 or most suitable to be protect by a potential reserve. Finally, I created a map for each island that shows the most suitable places for a potential reserve after each of the above factors have been weighed and added together through the raster calculator.

Methods

After finalizing my idea of running a suitability analysis potential conservation areas in Hawaii, most of the the data came from Hawaii's GIS site. This site is one of the most comprehensive GIS data site I have come across, all the data is organized in categories of physical features/ base maps, political boundaries, natural resource and environmental layers, hazard layers, and finally coastal layers.

The factors I took into account where hill shades, roads,dump sites, land cover,proximity to other reserves, and density of threatened and endangered species. The first step in creating a suitability analysis for potential reserves in the Hawaiian islands was to create buffers. I initially made a multiple ring buffer for the roads of ½ mile and 1 mile. I also created a buffer from dump sites of 2 miles

After creating the buffers I converted all my shape file in raster data through the spatial analyst toolbar, convert feature to raster. This process was a slow one because every conversion took at least 5 min for the computer to process, it was a lot of hurry up an wait for these action to happen.

I used the raster calculator then to infer values for the roads buffer and dump buffer, then I reclassified Land Cover 1.Urban and Built up land was considered 0 suitability.2. Barren Land was considered 1 in suitability 2. Water and Wetlands were considered 2 in suitability 3. Agricultural Land was considered a 3 in suitability 4. Range land was considered a 4 in suitability 5. Forest Land was considered a 5 in suitability . On a scale from 1-5, 5 is considered the most suitable land cover to potentially put a new reserve.

I also reclassified Density of Threatened and Endangered Species. Where the values of Very High , High , Medium, Low, and Non or Little species corresponds with the suitability number of 1-5. Again, 5 is the most suitable location.

Finally I reclassified, the distances of the road buffers and the dump site buffers into values of 1-5 again. 5 being the farthest site from roads or dump sites and slowly getting closer to these points as the values go down and the site becomes less suitable.

Results

Hawaii: "The Big Island"
The map of the Big Island shows the suitability analysis for the whole island in the main frame. The suitability of the locations on the island are classified from 1-10. 1 being the least suitable site for a potential reserve that most likely corresponds to areas of high population density and therefore urban or built-up areas. The score of 10 is the most suitable and is designated by the areas in red. These areas most likely correspond to areas with low population density, low road density, and areas of Forest land. In the inset map of the class 10 or Red areas in Hawaii shows a small area outside of already reserved land. It seems then that it might be possible to create another reserve in the red area, because it is close to other reserves to help circumvent fragmentation as well as being the most suitable area after accounting for all the factors.

Oahu: "The Gathering Place"
The maps of Oahu is the same concepts as said above. To expand, the legend's color classification scheme is purposefully accentuating the areas that score 7 or higher on the suitability analysis. This is because I wanted to target the best possible site for potential reserves, and the sites that need the most attention first. These are areas that have a high density of threatened and endangered fauna species and are in one of the most important biomes: Forest. All of these factors hopefully state that the areas in Red, Purple, Green, and Blue are the ones that need immediate attention and would be the first sites to designate as reserves or parks. In the inset map of Oahu's potential sites, the red area below Kaala Natural Reserve and above Waianae Kai Forest Reserve is the potential site for the reserve. If both the two said reserves would expand their boundaries slightly this area would be captured.

Molokai:" The Friendly Isle"
Molokai is split into two main geographical areas.The western half is very dry due to sheep grazing a poor management practices. The eastern side is a rising plateau that is covered mostly with lush forest. This split is well defined in the map. The Eastern plateau is well covered in reds, purples, greens, and blues. Most of the current reserves encapsulate the areas in purple, however as shown in the inset map there are two area of "most suitable" land that is not currently designated as a reserve. These two red area would be good candidates for a potential reserve

Maui: " The Valley Isle"
Maui experienced rapid population growth in 2007 when the town of Kihei was one of the most rapidly growing town in the U.S because of this there were controversies of whether to continue the rapid real estate development of the island. In 2009 however, the county approved a 1,000 unit development in South Maui (medb.org). This is just one example how a sizable area can be converted from potential reserve sites to a housing development. There is a lot of suitability class 8 that would be good candidates for potential reserves in South Maui. In the inset map there is a lot of purple class 9 suitability and a few dot of class 10 suitability that would benefit from the West Maui Forest Reserve expanding its boundaries North.

Kauai:"The Garden Isle"
Kauai's is a very mountainous island. the highest peak is Kawaikini at 5,243ft. The second is Mount Waialeale near the center of the island. On the East side of Mount Waialeale is considered one of the wettest areas on Earth. This fact is shown well in the map. The East side of Kauai has a ton of Red, Purple, Green, and Blue areas. However, most of the current reserves are also situated on the East side. As shown in the inset map the Southern Central area is relatively neglected when it come to reserves. it would be beneficial to capture some of those areas in reserves

Conclusion
All in all, there are many potential reserve sites on all of the Hawaiian Islands. However, some of the shortcomings of this project was that I did not look at planned developments. And, even though I looked at proximity of roads as an indication of population density in further research I would have plotted heavily populated areas. Without the constraint of time, I would have also gone on to look at what exactly is within the potential areas I mapped and see if it is really possible to create a reserve there, and what the factors would be in creating it.

Wednesday, March 10, 2010

Quiz #2

You have until 11.05AM to download the quiz.zip archive and use the world, cities and rivers shapefiles to answer the following questions. Each correct answer is worth 2 points, post your answer to your blog.



1. Rank order the ten most populous countries of the world.

1. China

2. India

3. United States

4. Indonesia

5.Brazil

6. Pakistan

7. Japan

8. Bangladesh

9. Nigeria

10. Mexico

2. Identify the three most populous countries in Africa.

1. Nigeria

2. Guinea

3. Egypt

3. Rank order the five countries of South America with the lowest population.

1.Uruguay

2. Paraguay

3. Bolivia

4.Ecuador

5. Chile

4. The Amazon river system consists of how many rivers?

15

5. What cities are within 500km of the Amu Darya and Syr Darya rivers?

Leninobod,Jalabad,Zareh Sharan,Turgay,Zhezkazgan,taldykorgan,kyzylorda,almaty,

bishkek,talas,karakol,nukus,shymkent,dashkhovuz,urgench,naryn,tashkent,namangan,a

ndizhan,osh,gulistan,fergana,dzhizak,navoi,bukhara,samarkand,kashi,chardzhev,karshi,dushanbe,ashgabat,kulob,Qurghonteppa,Mary,Termez,Feyzabad, Taloqan,Konduz,

6. To the nearest 100,000 what is the total population of countries within 300 kilometers of Iran (not including Iran)?

387060970 ish

7. Identify the most and least populous countries of the landlocked countries of the world.

Least= Vatican City, Most=Ethiopia

8. Identify the all countries within 300 kilometers of Veszprem, Hungary.

Slovenia, Slovakia, Bosnia And Herzegovina, Croatia, Czech Republic, Austria, Hungary, Yugoslavia, Romania, Poland


9. Which country of the world has the fourth-smallest area (island territories like Guam do not count)?

tuvalu

10. What countries border Chad?

Niger, Central African Republic, Libya, Egypt, Sudan, Nigeria, Cameroon


BONUS: Rearrange the first letter of the capital cities for the following countries to obtain the name of another capital city:

Burkina Faso, Mauritania, Guinea, Mongolia, Costa Rica, Pakistan, Kazakhstan and Chad

G , F ,G,M,G,P,P,F

Tuesday, March 9, 2010

Interpolation





Discussion

The last El Nino season recorded started in September 2006 and lasted until July 2007. Since then The US has been experienceing a drought until finally now in 2010 we are facing another El Nino. So far however this seems to be a weak El Nino year with the maps showing that rainfall to date this season is almost only at the normal values or even in some areas below normal. However, this may be only because the data for the season ended in March, the entire data set is needed to really determine wether or not this season is an El Nino year or not.

As one can see in the IDW maps the heaviest rainfall seems to have occured in the Eastern central area of LA county. This is consistent in both the Season Total and Season Normal maps. The North Western corner of LA county as seen in the Difference map shows an area that has recieved much less precipitation this season then it normally would.
In the Spline maps where elevation is a negligable factor the dryest areas for this season seem to be the North Eastern area of LA county, this data is also consistent with amount of percipitation expected in a Normal Season. Although most of the maps are fairly similar to each other there are definatly some areas that recieved less precipitation then normal.

The individual rainfall stations store data from specific areas but since we do not have rain gauges every square mile or so we must use interpolation techniques to gather data for the rest of the area.The two types of interpolating techniques I used were Inverse Distance Weighted (IDW) and Spline. The IDW technique works well when a set of points are dense enough to be a representative sample of the whole area. For this lab I judged that we did in fact have a high enough density of rain fall station point to use the IDW interpolation technique. The next technique, Spline, estimates the values using a function that minimizes the curvature of the surface. It predicts “ridges and valleys” in the data set a creates sort of rubber sheet over the data to neutralize these curvatures. It is hard for me to say which technique is better, they each have their strengths and weakness, however it is important to look at the outputs from each of the techniques because they both tell us something unique and valuable.

Tuesday, February 23, 2010

Fire Mapping








Discussion

This project continues to show how GIS can be used to allieviate and determine hazards areas like the Station Fire. It address the relationship between slope and fuels to assess how vulnerable an area is to fire. To begin creating my own fire assement maps I downloaded perimeter, DEM, vegetation, and other supplimental data such as roads and county shapfiles. The DEM data was downloaded via the USGS Seamless Server, and the U.S Forest Service provided the vegetation data. Most of the supplemental data was obtained from the Census Tiger Line Shapefiles.

After downloading the data, I created the hill shade and the slope from the DEM file I downloaded. I then proceeded to convert the vegetation data into raster format and reclassify it with similar classifications as table 2 in the tutorial. Shrub, Conifer, and Mixed wood were the highest risk to fire, with Urban, agriculture, and barren land being the least suseptible to fire. This is shown in the Fuel Risk map shown in the bottom right hand corner of “Fire Assements Maps for the Station Fire and Surrounding Areas”. To create the Slope/Fuel Risk map I used the raster caluclator to add the fuel risk to the percent slope. This map shows that as the slope increase so does the risk of fire, and since the flat ground is usually asscoiated with a populated area with no trees, vegetation tends to increase with slope as well.

One problem I encoutered with this map was initally aquiring all the data, putting it in one place, and sorting through it to see what is needed. Another problem I ran into was deciding exactly what to portray through the map, the project was vague and therefore we could go in any direction we wanted. My goal was to make a map that was simple but not boring, complex but not overwhelming.Finally, one technical problem I had was getting the slope percent to work, even though I kept choosing the percent button within the interface it kept spitting up decimal numbers,I tried many different avenues but kept coming up with the same result.
Through this map and the spatial analyst of GIS we now understand the potential for fire in the Station Fire area. This map and maps like these can help address some of the potential risks that arise in certain areas. Although somewhat frustrating this lab helped me to understand the data in a completely different way.

Fuel Classifications:

Herbacious

1

Light

Shrub

2

Medium

Conifer

3

Medium

Mix

4

Heavy

Hardwood

5

Heavy

Barren, Water, Agriculture, Urban

0

Non-Fuel




Wednesday, February 17, 2010

Landfill Suitability Analysis




Discussion
In the debate of birth defects in relation to proximity to the landfill in Kettleman City, CA. U.S senators, Fienstein and Boxer called for a halt on the plans to expand the landfill until all the results are in on the investigations of birth defects in the area. The residents of this area demanded further study of the “ impacts of decades-long exposure to pollutants, including smog and particulates, pesticides used in fields, arsenic in the water and the hazardous wastes processed at the landfill” ( LA times article). In issues such as these GIS techniques can help to investigate.

In this week's lab we ran Suitability Analysis for potential sites for new landfills in a fictional country. A suitability analysis looks to identifying areas and locations for given land use possibilities. This analysis uses the characteristics of land, water, and soils of the country to determine the proper location for this landfill. It also considers factors such as wilderness areas,and heavily populated areas that would be considered in a real world situation.

For the issue in Kettleman City, it would be in the county's best interest to conduct a suitability analysis that interpreted the suitability of the area that the landfill would be expanded to. However, there are some underlying issues that would need attention before conducting a suitability survey of the expanded landfill. First, there is the debate that the original landfill is already causing health issues. Some argue that safety violations within the facility have occurred and therefore the facility is not fit to expand when it cannot even regulate it' s now smaller facility. The residents of the area are concerned that the birth defects that have occurred over a 22 year period are related to the proximity and exposure to the landfill and its pollutants. Officials concluded that the birth defects were not higher then expected in relation other communities. For this debate it would be good to map out birth defects across the country and their relationship to power plants, and landfills.

However, one problem with GIS and map making is that interpretation is often left up to the viewer. If you are mother who just lost their child to birth defects and then find out that a landfill is nearby it is easy to jump to conclusions and claim the landfill is the culprit. Also, GIS may show trends that are not really there and vice versus, some of these techniques are interpretive and depending on the map maker may vary. One, final problem with GIS is that it does not interpret which factors matter more, is it better to put a landfill on a slightly elevated site with non-permeable soil or is it better to put the site on flat land when the soil is more permeable? These are questions that the interpreters of the maps must answer, and unlike a simple math equation the answers are not always clear. Such as the situation with Kettleman City, birth defects are nothing to take lightly, and the proposed expansion of the landfill should be halted until all possible investigations are held. The GIS suitability analysis would be a step in the right direction in making sure that all future landfills, power plants and things of that nature are located the best possible places with the right soil, away from water tables, land elevation, slope of elevation, etc...

Finally, I think that this article on Kettleman City is a bit unreasonable. The article, as well as the residents jump to the conclusion that the landfill is the cause of bad drinking water and also cause of birth defects. It is hard to draw these conclusions without scientific proof. However, since this issue mustn't be taken lightly it would be wise to halt the expansion of the landfill until all appropriate analysis are done including a GIS suitability analysis








Wednesday, February 3, 2010

I am in favor of the LA city councils decision to set new rules for the dispensaries. The 1,000 foot buffer around schools will curtail the explosion of the medical dispenaries that having been popping up like weeds lately.However, this may also lead to something like the "sex offender effect" that areas with less schools, parks,and libraries will become overpopulated with theses dispensaries. In conclusion, I agree that there needs to be rules for theses dispensaries such as closing their doors after 8pm or regulating them as far away as possible, however we must take into consideration the issue that since LA county is full of schools that we may regulating these places out of site but not out of mind as these dispensaries will only be somebody else's problem. The NIMBY effect.

In favor of LA City Council decision of medical marijuana dispensaries

Friday, January 29, 2010

Lab 3- Geocoding


Write Up:

This past weekend I traveled from Los Angeles to Ann Arbor, Michigan for a water polo tournament. Along my way I took longitude and latitude readings of my various locations. These locations include airports (LAX and DTM), the hotel, restaurants, pools (UCLA and University of Michigan) and various other points.

I took a total of 55 GPS readings, from my iphone, over a 72-hour period. I then converted the longitude and latitude readings from degrees, minutes, seconds to decimals. Lastly, I used geocoding to plot these 55 locations in ArcGis on a USA state map shape file.

Once I plotted these points into ArcGis I was able to assess their accuracy using street maps. It was mind boggling for me to find out that my iphone could plot my locations with such accuracy. In most cases the coordinates where right on and some varied by just hundreds of feet. This tremendous accuracy exemplifies the extreme value that geocoding and gis has in the tracking of criminals or the monitoring of peoples movements. My GPS location was plotted in mere seconds and have the potential to monitor my movements every time my iphone “pushes” for data.

This is the third time that I have travelled to the University of Michigan for water polo. It would be interesting if I had taken GPS readings the previous two trips or if I took them next year to compare the places I visit and the paths of my travels. Taking these points would allow me to visit my favorite places again and avoid ones that I was not fond of. Geocoding and GPS provide the tools necessary to accomplish such tasks with tremendous ease and accessibility.


Points:

WayPoint

Long(Y)_neg

Latitude (X)

1

-118.449187

34.066241

2

-118.447944

34.074173

3

-118.44797

34.071795

4

-118.404316

33.949311

5

-83.341207

42.233522

6

-83.742473

42.238388

7

-83.763117

42.249438

8

-83.732921

42.25064

9

-83.700931

42.351594

10

-83.741122

42.267953

11

-83.763117

42.249438

12

-83.341352

42.23341

13

-83.35981

42.203381

14

-83.356784

42.204612

15

-83.356784

42.204612

16

-83.375808

42.22177

17

-83.370642

42.222353

18

-118.381138

33.932722

19

-118.401117

33.939771

20

-118.445485

34.066766

21

-118.440018

34.07295

22

-118.447976

34.06631

23

-118.448483

34.075707

24

-118.442577

34.079189

25

-118.447735

34.066971

26

-118.44797

34.071795

27

-118.44517

34.060728

28

-118.439819

34.072789

29

-118.440018

34.07295

30

-118.444273

34.067088

31

-118.448483

34.075707

32

-118.44797

34.071795

33

-118.442745

34.071279

34

-118.444273

34.067088

35

-118.446344

34.061629

36

-118.441783

34.071187

37

-118.4476639

34.07030556

38

-118.44805

34.06307778

39

-118.4474583

34.06168056

40

-118.4421222

34.05566944

41

-83.74868056

42.26587778

42

-83.74312222

42.24198333

43

-83.74894167

42.24280278

44

-83.73949444

42.25764444

45

-83.73831111

42.23920278

46

-118.4379639

34.07369444

47

-118.4494444

34.06548889

48

-118.4499222

34.06494722

49

-118.4527306

34.07407778

50

-118.451375

34.07418889

51

-118.4651667

34.063325

52

-118.4546111

34.07644167

53

-118.4555861

34.07574722

54

-118.4550056

34.07031667

55

-118.4560056

34.06616944