Lab 8: Mapping the Station Fire in ArcGIS

Introduction

The 2009 station fire that occurred in Los Angeles from the 29^th August till the 2^nd of September was one of the largest and deadliest fires to occur in California for many years. The Los Times reported that 'The fire churned through more than 42,500 acres of forest, from the edge of metropolitan Los Angeles up to pine-clad ridges and down toward the Mojave desert.' and that More than '12,500 homes were threatened' (Los Angeles Times, 2009). The fire itself was difficult to contain according to the United States Department of Agriculture (2009), due to the rugged terrain and accessibility. On this basis, I decided to create a map that provided a management strategy for containing the fire through the use of road and airports, and prioritising certain areas based on the population density.

Thematic Map 1

Thematic Map 2

Thematic Map 3

Thematic map uses

As aforementioned, the objective of my map was to develop a management strategy for containing the fire. Upon researching the fire control units available to the Los Angeles Fire Department, I noticed that they use helicopters for both mapping the fire and fire containment through dumping water (Inciweb, 2009). I plotted LA air fields and large bodies of water to identify which airport was the best to launch a fire containment strategy. The helicopters available to the LAFD have a range of over 400 Miles (LAFD, 2012) which I factored in when choosing the airport. From the map its clear that the airport (highlighted by the purple ring) in Thematic map 3 is the best for launching the helicopters due to the location to the station fire/s, as well as the nearby body of water. 

Secondly, I plotted major highways and hospitals, again helping decide which roads to take when on the ground to the nearest health point. Lastly, I added population density, indicated by the small grey dots. Its clear that on the south side of the fire, there is dense population so preventing it from developing towards the south was essential.

The Pros and Cons of the thematic map

The map offers a huge amount of visual analysis, finding the best airport to use in terms of its location to the station fire and to a body of water is easy to see. Furthermore, you can see the road network, which is layered on top of the station fire allowing for the shortest route to be viewed. Finally, you can identify the hospitals easily – the red spots. If I were to complete the Lab again, I would like to add buffer zones to each airport as well as live traffic information to the road network. Finally, I would like to make the map interactive, allowing for labels to be added such as what the airports name is, how large the body of water is, and the information to appear on the fire points.

Conclusion

The station fire eventually went out on 2nd September 2009. It engulfed 160,577 acres of land but was 100% contained (CA.gov, 2012) thanks to the efforts of both ground and air fire control units.  These fires happen every year in California due to its climate being very dry - particularly in the summer and mapping in this way will help plan, manage and prevent fires in the future.

Reference:

CA.gov. "Station Fire General Information." N.p., 2010. Web. 13 Dec 2012. [http://cdfdata.fire.ca.gov/incidents/incidents_details_info?incident_id=377].

Inciweb. "InciWeb the Incident Information System: Station Fire." N.p., 2012. Web. 13 Dec 2012. [http://inciweb.org/incident/1856/].

LAFD. "Los Angeles County Fire Department." N.p., 2012. Web. 13 Dec 2012. [http://www.fire.lacounty.gov/airwildland/AirWildlandAirOps.asp].

Los Angeles Times. "Station fire claims 18 homes and two firefighters." N.p., 2009. Web. 11 Dec 2012. [http://articles.latimes.com/2009/aug/31/local/me-fire31].

United States Department of Agriculture. "Station Fire Initial Attack Review." N.p., 2012. Web. 14 Dec 2012. [http://www.fs.fed.us/fire/station_fire_report.pdf].

Lab 2: USGS Topographic Maps

1. Beverley Hills Quadrangle

2. Canoga Park, Van Nuys, Burbank, Topanga, Hollywood, Venice, Inglewood

3. 1995

4. North American Datum of 1927, North American Datum of 1983, National Geodetic Vertical Datum of 1929

5. 1 : 24,000

6. a) 5 cm : 120,000 cm (scaled) - 120,000 cm : 1,200 m

    b) 5 in : 120,000 in (scaled) - 120,000 in : 1.89394 mi

    c) 1 mi : 63,360 in - 63,360 in : 2.64 in (scaled)

    d) 3 km : 300,000 cm - 300,000 cm : 12.5 cm (scaled)

7. 20 feet

8.a) Public Affairs Building: 34*4'22'', 118*26'15'' ; 34.072778*, 118.4375*

    b) tip of Santa Monica Pier: 34*0'37'', 118*30' ; 34.010278*, 118.5*

    c) Upper Franklin Canyon Reservoir: 34*7'20'', 118*24'45'' ; 34.122222*, 118.4125*

9. a) Greystone Mansion: 580 ft ; 176.784 m

    b) Woodlawn Cemetary: 140 ft ; 42.672 m

    c) Crestwood Hills Park: 680 ft ; 207.264 m

10. UTM zone 11

11. lower left corner: 3763000 N, 361500 E

12. 1,000,000 m²'

13.

14. 14* N

15. North to South

16. 

Lab 1: Interesting Maps

For this weeks lab I selected three maps I found interesting and added my own thoughts and comments.

Source: http://images.vizworld.com/wp-content/uploads/2009/12/LaphamMap081609.png

This map, taken from vizworld.com, shows the spread of diseases around the world dating back to the 1500's. I found this map particularly interesting as it shows specific examples along with dates and where the disease spread too and from. Smallpox is shown by the blue, leprosy by the red and malaria in yellow. Mapping in this way is extremely useful as it could contribute to disease control in the future.

Source: http://bioval.jrc.ec.europa.eu/products/gam/download/accessibility.png

This map, taken from the EuropeanCommission, shows the travel time to major cities around the world. This map is very unique, and shows a vast amount of information. The time travel ranges from 0 hours to 10 days. Its interesting to see that in highly populated areas such as Europe, the travel time is very low – whereas in rural areas the travel time is significantly higher.

Source: http://ibgeog2009.wikispaces.com/Economic_Interactions

This map taken from IB Geography shows the Gross Domestic Product per capita around the world. But this map has a twist – its a cartogram so space is distorted in order to convey the information. Its interesting to see that North America, Western Europe, Japan and South Korea have the largest GDP per capita - while Africa, India and China have a relatively low GDP.  

Lab 7: Census 2000

For this weeks lab, we compiled three different maps showing the distribution in percentage of different races across the US. Seen below are the maps, showing Asian, Black and some other race in the US.

Map 1 - U.S. Counties with Black Population, 2000

Map 2 - U.S. Counties with Asian Population, 2000

Map 3 - U.S. Counties with Some other Race, 2000

Map 1 shows the distribution of Blacks across the US. Its clear that the vast majority of black people live in the South and South Eastern parts on the United States – Alabama, Mississippi, Arkansas, North and South Carolina. This distribution can be attributed to the historical background of the region. There are however other areas of interest such as the west coast (California) where there is a medium density of Blacks.

Map 2 shows the distribution of Asians in the US. Here we see a more balanced spread across the US as a whole, but there is a high percentage of asians on the west coast, again California. Additionally, the east coast sees a medium - high percentage of Asians – Rhode Island, New Jersey, and Maryland.  

Finally, Map 3 shows the population of Some other Race which is highly concentrated in the South West of the US. Although the race isn’t specified, its likely to be hispanic due to the high percentage near the Mexican/US boarder. California has a high percentage of some other race, as does New Mexico, Arizona, Colorado and Texas. Other areas include Washington State. 

The map series shows a lot of potential. Maps like these are very useful in showing the distribution of races across a particular area and allows further explanation and analysis. Its an easy and effective way to convey data.

GIS has once again shown to be a powerful analysis tool. When looking at the raw data taken from the census site I had no idea where the high or low percentage of different races were concentrated. But with the use of GIS, mapping the data meant identifying these patterns was clear and easy. Taking the raw numerical data and processing it onto a map was good to see. The only problem I did encounter was joining the excel spreadsheet – I mislabelled one of my columns. As an overall assessment, it was a good task showing me how to join excel files and performing spatial analysis, but if I were to do the task again I would use data from the 2010 census.  

Lab 6: DEM's in ArcGIS

For this weeks lab we produced four different maps using the same data. Each map showed a different characteristic – Hill shape, Slope, Aspect and a 3D render of the particular area we were working with.

The potential of this lab work is shown by the amount of extrapolated data that can be taken from just one data source. With this lab we were given one set of data and produced four different maps all of which displaying different aspects – hill shape, slope, aspect and a 3D model. This illustrated that ArcGIS has once again proven to be a very powerful analysis tool. Layering, selecting different colours and editing the map to make it presentable was fairly easy. Its also easy to see the real world application of this task which made it more enjoyable to complete. Finally, this task was, I feel the first time I felt like I knew what I was doing and what each step would produce before I completed it.

The consequences and pitfalls to this task are that if you didn’t have previous experience with ArcGIS then this task would be very difficult. Furthermore, as I mentioned earlier the data has come from one source which may be inaccurate – meaning that the four maps that I produced could be inaccurate. To counteract that problem you could compare the data to other data sources. Finally, the data was sourced for us, and we didn’t have a chance to find an area to work on that we knew meaning that there wasn’t as much of a personal touch to the task in comparison to others. Even so it was an enjoyable task.  

Lab 5: Projections in ArcGIS

Map projection is about taking the globe, a 3 dimensional object and converting it into the 2 dimensional world. However when making these conversions, distortions are inevitable. There are three broad categories of map type to choose from, Equidistant (preserves distance from some standard point or line), Equal Area (preserves area) and conformal (preserves shape). There are many different types of map projections within each category, but the use of these maps depends entirely on the purpose. Each category has its disadvantages and no map is perfect.

As aforementioned, Equal area map projections preserve area and help retain the actual shape of area's on the earths surface relative to the map. I used the Hammer and Bonne (mistakenly labelled) projections to create these equal area maps. They are useful for studies that are attempting to represent the concentration of an attribute in an area - for example a dot density map. The Hammer map would be better at showing this than the Bonne map as this map is heart shaped and may not be as clear.

(Bonne Map (Bottom) mistakenly labelled 'Plante Carree'

On the other hand, Conformal map projections have all the lines of longitude and latitude lines intersecting at right angles and consequently do not retain area or distance. The examples I've used are the Mercator and the Stereographic projection. Both maps have huge distortions. With the mercator, you can see that as you move with latitude countries get larger – Greenland looks vastly bigger than its true size. The same can also be said with the Stereographic projection, which greatly reduces the size of Africa. Conformal maps, however are extremely important in complex analysis as well as in many areas of physics and engineering.

Finally, Equidistant map projections preserve the true distance from map to ground surface. The examples I've used are the Equidistant conic and Plate Carree. They again come with their disadvantages. As you can see, both over emphasise the area of land forms and distortions get more pronounced with latitude. However, equidistant maps are useful for navigational purposes.

Map projections are essential to our understanding of the world. The Mercator map is the dominant world map and previous to this lab I had not realised how large Africa was. Furthermore its clear that maps can also come with a political agenda as shown the with Cold war and the Soviet union – The USA wanted to over emphasise the size of Russia in order to portray a larger force than was actually there. This reaffirms my belief that understanding the basics of map projection is essential when deciding on which map projection to use for a piece of work or project.

Lab 4: Introducing ArcMap

In this weeks Lab we used the program ArcGIS 10.1 in order to complete a number of tasks. Using ArcGIS we mapped out the noise pollution caused by a proposed airport expansion zone, noting the population density, land use and schools in the surrounding areas.

In comparison to last weeks lab using Google Maps, there are several disadvantages to ArcGIS. Firstly, I found that there was a steep learning curve with a lot of different features available to me. Despite having a guide, I found that there was a lot of trial and error, even performing the most basic of tasks such as adding a tittle was made difficult for the first couple of tries. This may have been down to the fact that the instructions that we had were for ArcGIS 9, not ArcGIS 10.1. This again is a drawback as it seems that with every new version you have to re-learn some of the basic functions of the program. Finally, ArcGIS is very expensive and not readily accessible to everyone. I was limited to working on the lab computers and not on my home PC. With Google Maps I could work on it from anywhere with an internet connection.

Despite its pitfalls, ArcGIS excels in other areas. It was easy to display complicated patterns and had the ability to turn on and off layers which was particularly helpful in spotting trends and patterns. As well as this, adding new layers was easy and smooth, there was no 'lag' or delay. Furthermore, it was much less restrictive in comparison to google maps and allowed for greater personalisation and flexibility when handling and displaying data.

Despite its drawbacks, ArcGIS appears to be a powerful tool in displaying data as well as finding relationships and patterns. The objective nature of the task also adds credit to its findings.