Tuesday, November 27, 2012

Lab 8 - Station Fire Map

The "Station Fire" is the wildfire started from August 26th, 2009, in Angeles National Forest near Los Angeles. It was 100% contained on October 16th, due to moderate rainfall in the San Gabriel Mountains. Caused by arson, the wildfire influenced 160,577 acres of land and is the largest wildfire in the modern history of Los Angeles County. 209 structures destroyed, including 89 homes, and two firefighters were killed during the accident. The neighbors affected were angry about fire station’s failure to control the fire within the 48 hours from origin.

The first two maps I produced present the progression of the fire. The first map presents the fire with roads and high ways of grey lines, and the second map presents the fire with retail centers of black dots. The fire is presented in a series of red of polygon. The darkest red presents the land influenced by the fire on Aug 28th, and the larger circles successively represent that on Aug 29th, Aug 30th, Aug 31st, and Sep 1st. We can see that the fire was completely manageable in the first two days, but because of the failure to control it, the fire expanded dramatically on the third day.

We can see from the first two maps that the origin of fire is a place with few retail centers and roads, which indicates that people are seldom living there and that may explain why the fire was discovered late and the fire station doesn’t put all the efforts into the fire suppression. However, as the fire expanded, it started to influence neighborhoods in the vicinity of the forest. Residences were complaining and that drew mass media’s attention. And because of the death of the two firefighters, homicide investigation was brought in.


The third map shows the fire with fire hazard severity zones in DEM slope version. PRC 4201-4204 and Govt. Code 51175-89 direct the California Department of Forestry and Fire Protection (CDF) to map areas of significant fire hazards based on fuels, terrain, weather, and other relevant factors. These zones, referred to as Fire Hazard Severity Zones (FHSZ), then define the application of various mitigation strategies to reduce risk associated with wildfires. State Responsibility Area (SRA) was originally mapped in 1985 and has not been updated since, except with respect to changes in SRA boundaries. Local Responsibility Areas (LRA) were originally mapped in 1996, and also has not been updated since. We can see from the map that the station fire area wasn’t included in the severity zone, and that may explain the lack of fire suppression ability then. The fire reminds us that the disaster zoning should be revised regularly and the real-time satellite monitor should be brought in.

In conclusion, although the wildfire could be largely due to the unusual weather, there’re still lots of reasons why fire stations and government should be blamed. The disaster response and policy in California should be reconsidered to avoid the similar case from happening, and we also should pay attention to the post-disaster recovery.

Reference:
[1] Fire Hazard Severity Zone Re-Mapping Project, California Department of Forestry and Fire Protection, 2008
[2] CalFire incident information, Incident Information System, 2009
[4] Station Fire Recovery, United States Department of Agriculture, 2011
[5] US Senate Station Fire Investigation, 2009

Note: references are from government and media internet, with unknown writers.



Tuesday, November 20, 2012

week 8 census mapping


The following census maps are created using the United States Census Bureau data in 2000. With ArcGIS mapping, the data make sense better than just figures.
1. Population percent of African-American people in America
It is quite obvious that black people tend to live in the southeast part of America, from Virginia down through the Gulf Coast to eastern Texas. The concentrated black population areas coincide with the slavery counties, which indicates that most black people there might be the descendants of the slaves imported from Africa. The so-called "Black Belt" area is known for its high crime rates and concentrated poverty.


2. Population percent of Asian-American people in America
Asian-American population is much less than African-Americans, and they are highly concentrated in California, Seattle and New York. Asian-Americans tend to live in urban areas, where it will be convenient for doing businesses. California is the most settled area, because its geographical location--it is convenient for immigrants who travelled by boat into America to settle down. And of course, the historical reasons like Acts against Asian immigrants and quota regulations also contribute to the old family tie in California, where some undocumented immigrants firstly come from US-Mexico border.


3. Population percent of other races people in America
Other races category is an aggregated population that includes Latinos,multi-racial people and so on. They tend to live in the west of America, which indicates that most of them are not from Europe; and they also tend to live near borders, which indicates that most of them might be immigrants from Latin-America.

4. Conclusion
These maps can be used together with other theme maps to judge the coincidences and relationships among different factors. For example, we can compare the African-American population percentage map with the slavery maps, and we can see why black people tend to live in south America.

Clearly different races have different geographical living and working tendencies. We can see from the map that minority groups tend to live in a concentrated form and that maybe explained by racial discrimination and segregation. We still have a lot to do to develop a mix-race society and more public attention should be drawn to concentrated minority living areas with high rates of crime and poverty, and more education supports should be brought into these areas.


Overall impression of GIS
I have got the elementary idea of how ArcGIS works and I've learned the basic ways to operate the system. GIS offers a good way to visualize data and analyze them. The abstract figures are turned into beautiful maps which are much easier for people to understand, and it's easier for people to make sense of different factors if they put different layers together and see the coincidences. 

From the first impression of selecting interesting maps (lab 1), to create our own maps using ArcGIS under the help of TAs and tutorials (lab 4), visualize them in different projections (lab 5) and even get a 3D version (lab 6), I've learned not only where those functions are located in the system interface, but also the ideas of presenting data in a neat and clear way. This class is a good start to learn more about GIS, and it's also an interesting one.

Tuesday, November 13, 2012

week 7 - DEM



I selected the shore area in California as my interested map. I’m interested in the area basically because I want to see how ArcGIS present sea level information and I think the comparison between mountains and sea could be fun. The extent information is as following:

Extent
Top   38.0016666667o
Left   -123.001666667o
Right   -121.998333333o
Bottom   36.9983333333o


And the information about the geographic coordinate system is as following:
Spatial Reference
GCS_North_American_1983


1. A shaded relief model of the area using a hillshade model layered above a
color-ramped DEM:

2.  A slope map of my location:
 3. An aspect map of my location:
4. A 3D image of my location:


Wednesday, November 7, 2012

week 6 lab - map projections

Conformal Projection



The distance between Washington and Kabul:

Lambert Conformal Conic
Line measurement (Planar)
Segment: 7,234.594867 Miles
Length: 7,234.594867 Miles

Line measurement (Geodesic)
Segment: 6,934.478105 Miles
Length: 6,934.478105 Miles

Line measurement (Loxodrome)
Segment: 8,112.060673 Miles
Length: 8,112.060673 Miles

Line measurement (Great Elliptic)
Segment: 6,934.483772 Miles
Length: 6,934.483772 Miles

[We can notice that the distance between Washington and Kabul measures the same under geodesic and great elliptic measurement methods.]

Transverse Mercator Complex
Line measurement (Planar)
Segment: 8,056.146836 Miles
Length: 8,056.146836 Miles

Line measurement (Geodesic)
Segment: 6,939.629965 Miles
Length: 6,939.629965 Miles

Line measurement (Loxodrome)
Segment: 8,154.936147 Miles
Length: 8,154.936147 Miles

Line measurement (Great Elliptic)
Segment: 6,940.273134 Miles
Length: 6,940.273134 Miles

Equidistant Projection


The distance between Washington and Kabul:

Equidistant Conic
Line measurement (Planar)
Segment: 6,857.024332 Miles
Length: 6,857.024332 Miles

Line measurement (Geodesic)
Segment: 6,934.478105 Miles
Length: 6,934.478105 Miles

Line measurement (Loxodrome)
Segment: 8,112.060673 Miles
Length: 8,112.060673 Miles

Line measurement (Great Elliptic)
Segment: 6,934.483772 Miles
Length: 6,934.483772 Miles

Azimuthal Equidistant
Line measurement (Planar)
Segment: 9,476.289268 Miles
Length: 9,476.289268 Miles

Line measurement (Geodesic)
Segment: 6,952.220838 Miles
Length: 6,952.220838 Miles

Line measurement (Loxodrome)
Segment: 27.200859 Miles
Length: 9,486.93483 Miles

Line measurement (Great Elliptic)
Segment: 6,921.222224 Miles
Length: 6,921.222224 Miles

Equal Area Projection


The distance between Washington and Kabul:

Lambert azimuthal equal area
Line measurement (Planar)
Segment: 6,241.499534 Miles
Length: 6,241.499534 Miles

Line measurement (Geodesic)
Segment: 6,898.234813 Miles
Length: 6,898.234813 Miles

Line measurement (Loxodrome)
Segment: 8,068.046878 Miles
Length: 8,068.046878 Miles

Line measurement (Great Elliptic)
Segment: 6,903.044743 Miles
Length: 6,903.044743 Miles

Cylindrical_Equal_Area
Line measurement (Planar)
Segment: 10,108.051114 Miles
Length: 10,108.051114 Miles

Line measurement (Geodesic)
Segment: 6,934.478105 Miles
Length: 6,934.478105 Miles

Line measurement (Loxodrome)
Segment: 8,112.060673 Miles
Length: 8,112.060673 Miles

Line measurement (Great Elliptic)
Segment: 6,934.483772 Miles
Length: 6,934.483772 Miles

Comments:
Map projection is the way to transfer the 3 dimensional the Earth into a 2 dimensional map. Basically there’re three categories of map projections: conformal, equidistant and equal area. Conformal projection maps such as Lambert Conformal or Transverse Mercator maps preserve angular relationships, which makes conformal maps good for navigation. Equidistant projection maps such as Azimuthal Equidistant or Equidistant Conic maps preserve distance from the center or origin of the map to all other places. The trait enables us to measure distance between two places accurately. Equal area projection maps such as Cylindrical Equal Area or Lambert Azimuthal Equal Area maps preserve relative sizes of geographic features, which give us a better idea of the relative sizes of regions and avoids misunderstandings.

Conformal maps are widely used in daily lives, especially the Mercator projection map. Conformal maps were used to be a navigational tool for sailors in the Age of Discovery. However, while the angular relationships are preserved, conformal maps change sizes and shapes of areas so that people develop a misleading idea about the relative sizes of regions. For example, it seems on the map that Alaska is bigger than Brazil, but the truth is just the adverse. Alaska appears bigger because it’s close to the North Pole and distances and area are exaggerated near the poles.

Equidistant maps present all points on the map are at proportionately correct distances from the center point, and that all points on the map are at the correct azimuth (direction) from the center point. It is useful for showing airline distances from center point of projection and for seismic and radio work. However, distances and directions to all places are true only from the center point of projection, and distortion of areas and shapes increases dramatically away from center point. For example, distance between Washington and Kabul in Equidistant Conic and Azimuthal Equidistant projection maps differs greatly from other projection maps and the real distance, even in equidistant maps themselves, different measure methods’ results vary dramatically.

Equal area projection maps accurately represent the relative size of areas, but it does not accurately represent angles. For example, unlike conformal or equidistant maps, Alaska and Brazil in equal area maps represent the right relative sizes, thus equal area maps are used to display information in the online Map Maker application by the National Atlas of the US, and the European Environment Agency recommends its usage for European mapping for statistical analysis and display. However, equal area maps cannot be used as navigation tool since they distort angles.

There's no "perfect map", we choose map based on our ultimate goal. Sometimes we may need more than one projection so that we can get more accurate details that tell us what's the real world like.










Friday, November 2, 2012

week 4 assignment - ArcGIS







To get a clearer view of the final proposal, please click here.

1. Potentials of ArcGIS
When I was creating my maps, I was constantly surprised and amazed by the powerful functions ArcGIS had. I could see clearly how layers overlap layers and the final presentation of all layers was incredibly neat, clear, and beautiful. The final results made sense and enabled us to analyze the situation powerfully.

ArcGIS is an extraordinary program for visualization. You can simply translate tons of data--tables, bar graphs, line charts, etc.—into all kinds of graphs that are easy for people to understand. As we all know, most people's brains work better to process graph information, and people usually react to graphs first (than to words and numbers), so in this information explosion age, it's the best way to assist people processing large amount of data information. It can also make the information processing interesting and fun, since there’re a lot of interesting maps created by ArcGIS. ArcGIS enables us to put many layers to one graph so that we can understand the relations between a bunch of factors by looking for coincidences of them. That’s very useful for a lot of fields such as criminology, population studies, transportation planning, and so on.

2. Pitfalls of ArcGIS
Apparently it’s not easy for beginners to use the program, even if we follow a tutorial. I was having a hard time understanding and setting up all the stuff and I have to say that the bugs of the programs constantly prevented me from finishing the steps successfully. The interface of the ArcGIS seems that it hasn’t been updated for years, and it’s not so user-friendly because I was struggling to find the exact locations of buttons and tool bars I needed.

Because of the complexity, ArcGIS prevent a lot of people from using it in their daily life. Actually there’re some simple functions that are quite useful for everyday life, and people will have fun in creating their own interesting maps, so I guess ArcGIS can learn from the Window Office software, which, although each of them contains plenty of functions and it’s very hard to master them, they have a friendly User Interface and that enables non-professionals to use them easily. In this sense, I think ArcGIS should learn for Neogeography, considering more for users and adding more user-centric functions such as sharing and commenting online.