Lab 8: Census 2000/2010

The first map (below) shows the percentage of the total population that Blacks make up for each county. Here it can be seen that there are higher concentrations of Blacks in the southeast portion of the United States. This group represents people that have origins in any of the Black racial groups of Africa. So this includes people who noted their race or races as "Black" or "African American" or wrote down that they were Nigerian or Haitian on the census. In the year 2000, the total population for the United States was 281,421,906 and there were 34,658,190 Blacks, meaning that Blacks accounted for 12.3 percent of that total population.

The second map (below) shows the percentage of the total population that Asians make up for each county. This race is defined as people who have origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent. So this includes people who noted their race or races as "Asian Indian," "Chinese," "Filipino," "Korean," "Japanese," "Vietnamese," or "Other Asian" on the census. This also includes people who wrote down that they were Burmese, Hmong, Pakistani, or Thai. In the year 2000, there were 10,242,998 Asians and they accounted for 3.6 percent of the total population in the United States. There tends to be a higher concentration of this category on the west coast of the United States, especially in California.

The third map (below shows the percentage of the total population that Some Other Race makes up for each county. This category in the Census 2000 was created to represent people who were unable to identify with the five Office of Management and Budget race categories. So this included people who identified themselves as Moroccan, South African, Belizean, or were of Hispanic origin. People who were of Hispanic origin noted down things such as Mexican, Puerto Rican, or Cuban. In the year 2000, this category comprised of 15,359,073 people and accounted for 5.5 percent of the total population in the United States. There is a higher concentration of this category along the southwest to the south central portion of the United States, which makes sense because this region is closest to Mexico and South America where people of Hispanic origin come from.

My Census map series show how Census 2000 data can be used with ArcGIS to show population characteristics in 2000. Using ArcGIS with this type of data makes it possible to perform a powerful analysis on population because the geographic extent of the census has grown as new states and territories have been added. ArcGIS can monitor and display the change in the population as it grows. Also the type of information that is collected from the people has also expanded to cover a wide variety of topics (such as income) making it possible to analyze and map different aspects of the population to understand its changing characteristics in this complex nation. It is necessary to understand the population and how it changes to obtain statistics to provide a basis for future planning. Census data can also be important in redrawing electoral boundaries so that the election of members of the House of Representatives is based fairly on population. Census 2000 is one of the largest collection of spatially referenced data in the United States and ArcGIS is a great tool that can help people interpret, map, and share the information derived from the census.

My overall impression of GIS is that it allows people to create and share information through maps. It allows people to present information in different ways by focusing on different aspects of a given subject, such as population within a county and the races that make up that population. People can perform a spatial analysis on an area and obtain important information quickly, such as surface slope (in degrees or percent). This is usually a factor that is taken into consideration where construction might take place. GIS also allows people to see how different things are associated and related to one another, such as slope angle and fire hazard. People can create maps to diagnose the condition and show the progression of disasters such as wildfires. This can help make important decisions regarding the best steps to take to resolve issues, such as the evacuation of people. GIS allows for the accurate measurement of long distances to be made by making it possible to project spatial data correctly. GIS can be used to create maps that help people plan things such as the construction of a new airport based on several different factors that need to be taken into account spatially. It can also be used to allow people to plan trips and share their experiences with other people who can also plan and take the same trip. Multimedia mash-ups like My Maps on google are a great resource for this. All in all, GIS democratizes the map-making world by making it possible for just about anyone to create their own map with their own point of view. However, there is the pitfall that people can use GIS and create biased or incorrect information. But most importantly, it allows people to carry out powerful analyses on spatial data which is necessary to learn more about people, the earth, and the complex interactions that occur among people and take place between people and the earth.

Lab 7: Mapping the Station Fire in ArcGIS

I wanted to create a fuel map for the Station Fire that occurred in Los Angeles County around August and September 2009. First, I obtained a Digital Elevation Model (DEM) for Los Angeles County from the National Map Seamless Server. Then I downloaded the projected shapefiles of the Station fire perimeters for different days from the LA County Enterprise GIS website. And I acquired the land cover or vegetation shapefile for Los Angeles County from the Fire and Resource Assessment Program (FRAP) website. I then started ArcMap and loaded the DEM of the LA County area. I changed the projection of the DEM so that the shapefiles for the perimeters of the Station Fire and vegetation or land cover would overlay with the topographic slope layer of the DEM later.

I created a hillshade for the newly projected DEM and went on to deriving a topographic slope in percent. The hillshade would allow the surface terrain to be more easily seen over the Los Angeles County area. Slope is important because fires spread quickly on steep slopes. Fires move upslope as they consume trees up to the canopy and spread to other vegetation as more heat and smoke rises and accumulates. I then reclassified the slope according to the hazard points based on the steepness assigned by the National Fire Protection Association (NFPA). So slopes with a range of 0-10 percent ranked a value of 0 (which is the lowest or smallest fire hazard). Slopes between 10-20 percent were ranked a value of 1, while slopes between 20-30 percent ranked a value of 2. A rank of 3 represents slopes between 30-40 percent and the highest rank of 4 is given to all slopes that are great than 40 percent.

I proceeded to load the vegetation or land cover shapefile and reclassified it by the cover types using points from 0-4 (where 4 represents the most hazardous fuel that is most likely to burn). I then converted the vegetation or land cover feature to raster to add it to the topographic slope in the raster calculator. By adding them, I was able to get a raster of the combined slope and fuel hazard of areas to determine where fires were most likely to break out. Such areas should contain the steepest slopes and the best fuels (cover types with the highest ranking hazard points). Then I went on to add the perimeter shapefiles for the days that the Station Fire occurred and overlayed them onto the reclassified fuels and reclassified slopes of the Los Angeles County area. The three perimeters of the area covered by the Station Fire occurred on August 29th, 30th, and 31st of 2009. These are shown on my maps (two below). On my first map, I overlayed the Station Fire perimeters over my combined slope and fuel hazard raster to create a fire hazard map. On my second map, I showed both reclassifications of the slopes and fuels for the Los Angeles County area with the Station Fire perimeters overlayed on top of them.

The two maps (below) indicate that fires are most likely to burn in areas with the steepest slopes and with the best fuels, which were mainly hardwood forests and conifer forests. These had high fuel rankings (or high fuel model codes), which made them big fire hazards. A mixture of these two produced the best fuel with the highest fuel ranking. Part of the vegetation in Los Angeles consisted of shrubs, which had a lower fuel ranking. However, the lowest fuel ranking went to herbaceous vegetation, which also composes a part of the land cover for Los Angeles County. The land cover types that received a fuel rank of 0 were agricultural, urban, and residential areas. This does not mean that fires cannot break out in these areas, but that they do not occur naturally. And if they do occur in these areas, people are highly inclined to prevent them or put them out. Other areas that had a fuel rank of 0 were areas that have water, are barren, or are covered by rocks or snow. This makes sense because these areas are least likely to burn and are not big fire hazards because they do not provide fuel for fires.

I encountered some difficulties in creating these two maps because at first I had a hard time overlaying the vegetation or land cover shapefile for Los Angeles County onto the topographic slope for the DEM covering this area. I realized that this was due to the fact that the DEM file is a raster file and does not originally have a projection. This same problem occurred when I tried to overlay the Station Fire perimeter shapefiles onto the topographic slope for the DEM in the beginning. So I had to go back and project the DEM (to UTM Zone 11 for the Los Angeles area) and then recreate a slope layer in percent. I also reprojected the shapefiles for the land or vegetation cover and the Station Fire perimeters to the same projection of the DEM so that these would match and overlay correctly with the slope layer of the DEM later. After that, I had to reclassify the vegetation or land cover types using hazard points on a smaller scale compared to the one assigned by the NFPA in the tutorial provided by ESRI. I found a website that gave me some indication of what cover types would be assigned with which hazard points or fuel model codes. Everything went smoothly after I overcame these challenges and the end products show interesting results.

Bibliography:

1) "FRAP - Fire and Resource Assessment Program." FRAP - Fire and Resource Assessment Program. State of California, n.d. Web. 20 May 2010.

2) "Geospatial technology for the citizens of Los Angeles County." Geospatial technology for the citizens of Los Angeles County. Los Angeles County Enterprise GIS, n.d. Web. 20 May 2010.
3) Hart, Christopher. "Wildland Fire Lessons Learned Center." Wildland Fire Lessons Learned Center. Arkansas Firewise Communities, 2 Jan. 2005. Web. 20 May 2010.
4) Price, Mike. "ArcUser Online." ESRI - The GIS Software Leader | Mapping Software and Data. ESRI, n.d. Web. 27 May 2010.
5) "The National Map Seamless Server." The National Map Seamless Server. U.S. Department of the Interior | U.S. Geological Survey, 1 Feb. 2010. Web. 20 May 2010.

Lab 6: Digital Elevation Model (DEM

The area I selected is a northern portion of San Diego County with an extent from top to bottom that stretches from 33.3547 degrees (decimal degrees) North to 33.0091 degrees North. The extent from left to right stretches from -117.0680 degrees West to -116.5586 degrees West. The projected geographic coordinate system of this data is NAD 1983 UTM Zone 11N (North American Datum 1983 Universe Transverse Mercator Zone 11 North). Most of this area is covered by slopes ranging from about 0 to 30 degrees while some areas exceed 70 degrees. The elevation of this area ranges from its lowest point at 90.55 meters high to 1990.89 meters high (highest point). The jagged and steep surface terrain of this area contains many hills and mountains with sides that face all directions and a few flat areas.

Lab 5: Map Projections

The two map projections that are conformal are the Mercator map and the North America Lambert Conformal Conic map. The distance between Washington, D.C. and Kabul, Afghanistan in the Mercator map is about 10,168 miles. The distance between the two cities in the North America Lambert Conformal Conic map is about 6,875 miles. The two map projections that are equal area are the Cylindrical Equal Area map and the USA Contiguous Albers Equal Area Conic map. The distance between Washington city and Kabul city in the Cylindrical Equal Area map is approximately 10,113 miles, while the distance between these two cities in the USA Contiguous Albers Equal Area Conic map is about 7,320 miles. The two map projections that are equidistant are the USA Contiguous Equidistant Conic map and the North America Equidistant Conic map. The distance between the cities of Washington and Kabul in the USA Contiguous Equidistant Conic map is about 7,278 miles while it is approximately 6,951 miles between these cities in the North America Equidistant Conic map.

Map projections are important because they allow maps to represent the Earth accurately. Maps need coordinates to give specific locations to areas on the surface of the Earth and can be given in different coordinate systems. There are geographic coordinate systems which are degrees of longitude and latitude. These can be represented in degrees, minutes, and seconds, or in decimal degrees. There are also projected coordinate systems from the projection of maps. A projection of a map is like putting the 3-D world onto a 2-D map. A datum, which is a 3-D frame of reference or model of the Earth, needs to be selected first and is used to define surface locations. It helps define the origin and orientation of points on the surface of the Earth. Then the ellipsoid that was fitted to the geoid of the Earth (where all locations on the Earth are leveled to it so it is a gravity model with an equi-potential surface) is mathematically converted and transformed to the flat plane of a map.

There are many types of map projections. Different types of projections preserve different properties onto maps. For example, local conformal map projections preserve local shapes and angles. The parallels and meridians intersect at right angles. This can be seen in the Mercator map and the North America Lambert Conformal Conic map, where the parallels and the meridians intersect at right angles. Equidistant map projections make the distance from the center of the projection to any other place on the map uniform in all directions. This is why the USA Contiguous Equidistant Conic map and the North America Equidistant Conic map have the north pole as the center of the projection and show how areas in all directions from this center are equidistant. Equal area projections make the areas on the map maintain the same proportional relationship to the areas on the Earth that they represent. This is why the continents in the USA Contiguous Albers Equal Area Conic map and the Cylindrical Equal Area map maintain about the same proportional relationship (in terms of area and size) to the actual continents on the surface of the Earth.

Map projections are useful but have distortions. There are discrepancies between map projections and this is the reason why the six different maps have a different distances between the two cities of Washington, D.C. and Kabul, Afghanistan. For example, the North America Lambert Conformal Conic projection preserves direction but distorts distance and area on the map of places on the surface of the earth. This is why the North America Lambert Conformal Conic map shows the continent of South America as being significantly bigger than the continent of North America, which is incorrect. Another example is how the Mercator map projection preserves the shape and direction of places on the Earth on maps but distorts areas on the surface of the Earth on maps. This is why the shape of the continents on the Mercator map have the right shape but are represented with the wrong area. However, map projections can help people make measurements and allow them to compare areas, shapes, distances, and directions of features on maps. Map projections are not very important when a map covers a small part of the Earth's surface (for example, street maps) because distortion is negligible at this scale. Projections are also not important when people are only interested in the relative location of features on a map.

Lab 4: ArcGIS Tutorial: Getting Started

At first, I did not realize how useful ArcMap would be in spatial analysis. I thought it was just a map-making program. After getting to work with ArcMap, I realized that it does have several potentials and that it is a great tool to use in GIS. However, the program's abilities also reveals several pitfalls in GIS that also need to be addressed. ArcMap is more than just a map-making tool, and although it took me a while to learn how to use it at first, there are definitely many ways to geographically represent places and factors associated with them in maps.

Learning how to use ArcMap was difficult at first. I was just following steps and did not think I could remember everything. So going through the tutorial a few times helped me cement basic concepts and remember the components and aspects of creating a digital map. I was really surprised to see how many things go into making a digital map. ArcMap is really useful because it allows you to map things that are not necessarily concrete objects in space, like the noise contours in the tutorial. This allows people to make more complex and detailed spatial analyses based on more than just the physical location of physical objects. It also allows people to focus in on the subject or area of interest and helps keep things organized. I never really thought about focusing on a small, specific area with boundaries, like the shape of a county before.

ArcMap also allows people to modify the maps they create at different scales. This is one of the advantages of making a map digitally because you can zoom in on details to work more easily with them. And it also allows people to work with different features in their maps separately (by making certain layers visible or invisible) which makes working with the data, like editing, a lot easier. Another spatial analysis tool ArcMap brings to GIS is its ability to create graphs, which puts numbers and the association of things into perspective. GIS has a lot of potential in its ability to combine and store a lot of data in an organized manner. After working with the tables in the tutorial, I have never realized how detailed and vast data can be. GIS allows users (with the additional tool of ArcCatalog) to create their own data (which can all be put into a geodatabase), which can be joined with other data because of its organizational structure. This makes it easier to work with different files and different data (with different attributes in tables) from different users. ArcMap also lets people make calculations with the attributes from the features in their maps to create new attributes. It even allows them to alter the way features are represented based on their attributes and change data (by deleting or creating a new value) by editing it through the editor toolbar so they can show exactly what they want to display in their maps.

With the many useful tools for spatial analysis that GIS offers through ArcMap, there are also pitfalls. Anyone can make a map, and their map will not necessarily be useful to another person. People make maps to suit their own needs. Also, with ArcMap, anyone can make a map showing what they are interested in, but their map may not be accurate. With all the editing tools in ArcMap, it is very easy to change or get rid of data that a person does not want to show in their map or which may affect the purpose or the point of their map. So things may not be correctly represented spatially (objects, like roads, may be missing) and values can be skewed or completely wrong. Hence, there are pitfalls as well as great potential in GIS. People need to look at maps with an appreciation but with a critical eye. And they must understand the value of a map (the time and effort it took creating it) and be able to use it wisely (where its purpose can be of most help in a given situation).

Lab 3 Lake Tahoe South Shore Summer Trip

My Map

View Lake Tahoe South Shore in a larger map

Commentary:

The pitfall of neogeography is that any one can make a map. A person does not necessarily have to be a professional cartographer. This means that the maps they create may not follow the standards or map making principles of cartography (for example, including a north arrow and a scale bar). Another pitfall is that the maps that are made by people may not be very accurate in terms of how things are represented spatially, where they are located, or what they are labeled as. However, neography does have potential because it allows more people to share more information.

People do not have to be professionals to show graphic representations of how they perceive the world spatially. They can choose to make maps on whatever topic or subject that interests them, whether it is where recreational parks are located or where their favorite restaurants are located. Neography has democratized the map making world because it allows anyone to create and distribute maps via multimedia mashups like "My Map" in Google Maps. The consequences of neography include allowing just about anyone to make a map and share it with the world. Some maps can be very useful to certain people, while not being useful to others. Since some maps can be more accurate than others, people should be aware of how reliable the source is for the map they use and be critical of its contents.

Lab 2

1. Beverly Hills
2. (1) Canoga Park, (2) Van Nuys, (3) Burbank, (4) Topanga, (5) Hollywood, (6) Venice, (7) Inglewood
3. 1966
4. Vertical datum (gives the height or the z coordinate) and horizontal datum (gives the x and y coordinates)
5. 1:24,000
6. (a) 1,200 meters; (b) 1.893939 miles; (c) 2.64 inches; (d) 12.5 centimeters
7. 20 feet
8.
(a) 34 degrees 4' (minutes) 26" (seconds) North, -118 degrees 26' (minutes) 20" (seconds) West = 34.074 degrees N, -118.439 degrees W
(b) 34 degrees 0' 27" N, -118 degrees 29' 59" W = 34.008 degrees N, -118.5 degrees W
(c) 34 degrees 7' 14" N, -118 degrees 24' 35" W = 34.121 degrees N, -118.41 degrees W
9. (a) 560 feet = 170.668 meters, (b) 134 feet = 40.843 meters, (c) 713 feet = 217.322 meters
10. UTM zone 11
11. UTM northing 3,763,000 and UTM easting 361,500
12. 1,000,000 meters squared
13. Elevation Profile










14. 14 degrees east
15. north to south
16. UCLA Map