Lauren Sullivan's Cartography Blog

Bizarre Map Competition

2010-04-14T10:32:00.000-07:00

This map was my entry into the Bizarre Map Competition, hosted by SDSU. Compare mine to the top ten and/or winner at http://bizarremap.sdsu.edu/.

Geog 168 Final Project: Slope Instability Modeling in San Diego County

2010-03-12T19:31:00.000-08:00

Lauren Sullivan
3/18/2010
Research Brief

Introduction

Landslides are one of the most destructive forms of geological hazard (Fernandez 2003). Several different types have the ability to ravage various terrain and each type has differing requirements in order to initiate the slide. Yet, despite the risk, the Index to Landslide Maps provided as a courtesy by the State of California on their website indicates that most of the maps referenced were made about 20 years ago. Because GIS allows us to map quite easily what might have been difficult when the State made the last set of Landslide Hazard maps, an updated analysis is now not only necessary, but much more feasible than when the last maps were commissioned.

The California Geological Survey defines several major types of Landslide Maps: Landslide-Inventory Maps, Landslide-Hazard Maps, Landslide-Risk Maps and Landslide-Zone Maps. Landslide-Inventory maps are essentially maps of past landslides, while Landslide-Hazard maps attempt to effectively predict where new landslides will occur. Landslide-Risk maps focus on the potential for landslides to destroy residents or their property, while Landslide-Zone maps merely define and depict areas in general that have a higher landslide potential. For the purposes of this project, I chose to create Landslide-Hazard Maps. This category has two further divisions, Landslide-Susceptibility maps and Landslide-Potential maps, which differ in defining areas of landslide potential based on native aspects of the area (slope, soils, etc.) versus the potential for a landslide due to an inducing event, such as an earthquake or heavy storm. Because the data that I could acquire for this project had a time limit and needed to be publicly (and somewhat easily) available, I decided to combine these map types to make an overall Landslide-Hazard Map for San Diego County.

There are many great articles currently available that discuss the benefits of GIS in landslide hazard analysis. Researchers use a variety of different methods, but many have found that, even after using a complex analysis with up to 17 different input layers (Remondo et al. 2003), only a few different datasets determine most of the probability of a landslide (Fabbri et al 2003), with layers such as geology, surface soil material and depth, and land use playing a secondary role in the determination of areas prone to sliding. Knowing this, I gathered my data to use for my analysis. I imagined that areas with the steepest slopes, heaviest rainfall and the areas affected most by recent wildfire would be most susceptible to landslides.

Methods

I used six different layers in order to analyze landslide risk in San Diego County accurately. I first located a DEM dataset for the County, and used it to make a new layer to depict slope. Next, found the locations of fault lines within San Diego, and buffered them to 1 kilometer, which I believe are areas most likely to experience the effects of such close proximity to faults. Though I would have loved to find a more accurate figure, I had a hard time looking it up so I estimated instead. These fault lines are relatively small and there are many of them in one central area, so my reasoning was that those close enough to be affected were probably within a kilometer of any fault. My third layer was a rainfall layer that I obtained from the SanGIS website for the County. Layer four was the areas of wildfires in the County. Because most of San Diego vegetation is chaparral, which is notorious for growing in soil that becomes highly impermeable following a wildfire, I thought this aspect was important enough to warrant a separate layer analyzing it, even though other researchers did not include it. My fifth layer was a dataset that classified all of the San Diego County soil types by erodibility: Slight, Moderate, or Severe. Surprisingly, much of the county soils were in the Severe category. My last layer was areas of geohazards in San Diego—locations of past landslides, areas prone to soil liquefaction, coastal bluffs (likely to collapse from erosion) and other slide-prone formations. This layer assisted me by providing the necessary data on previous landslides, which was a major portion of every analysis by other landslide researchers.

After converting necessary layers to a raster format, I also reclassified several layers as necessary. First, I created a slope raster that focused on slopes greater than 30 degrees. Though sliding does occur in slight or moderately steep slopes that are less than 30 degrees, I needed to create a threshold and used the definition from Endo et al., who defined generally hazardous slopes as those greater than 5 meters in height and a slope of 30 degrees. I also reclassified the soil erodibility layer and fire layer so that pixel values for areas with no data became zero. The layers analyzed in the Raster Calculator must be of the same extent (which I did not realize) so I learned how to assign values to areas that were not previously included in the analysis. This, for example, explains which much of the desert area of San Diego in the main Landslide Analysis map appears pixellated and much darker, indicating a low landslide probability. While the desert not only had fewer, lower slopes and less rainfall, making it a poor candidate for landslides, but it was also missing data—such as the soil erodibility layer. I did not worry about this lack of data, however, as it only occurred in the desert where it appeared that few landslides took place anyway.

After correcting the layers, I used the Raster Calculator to evaluate the following expression:
2 * ([Fault Zones] + (2 * [Fire]) + (3 * [Prone to Sliding]) + (0.5 * [rain]) + (3 * [Slope]) + [Soils])

While other researchers clearly believed in the use of GIS to evaluate landslide probability, none of the research I dug up appeared to use this method of evaluation, so when it came to writing the Raster Calculator equation I was left to estimate based on common sense. Ultimately, the range of values for each of my layers was in landslide index units. For example, for soil erodibility, Slight = 1, Moderate = 2, and Severe = 3. The calculator essentially added all these indices (weighted) together. I determined that the highest-weighted layers (areas prone to sliding, slope and rainfall (because it had relatively high values for more inundated areas)) were most important in determining slope instability from reading the relevant research or from the California Geological Survey’s website.

Results

My final output map from this analysis is the map of Slope Instability in San Diego County. Areas of highest slope instability appeared to be along the beaches near La Jolla and Point Loma as well as select slopes inland, particularly around Palomar Mountain. It is also clear that several gorges in the Ramona/Julian area and between Ramona and Palomar Mountain have a high probability of collapsing or sliding. These areas have steep slopes, get lots of heavy rainfall, have a high soil erodibility and have recently burned (last 5-10 years). However, coastal areas prone to landslides are unstable for other reasons. Areas near Torrey Pines and La Jolla are centrally located over San Diego fault lines, have steep slopes, erodible soils, and are prone to liquefaction or sliding from a sandier soil. These areas have had landslides before. Though the areas of the map are red for different reasons, they are both prone to landslides.

I would not say my map is completely free from error, but I do think that it does a good job of representing areas where landslides might occur. Regarding its limitations, I would say that I would attempt to classify the slopes differently next time. Debris flows, which are a more common problem in mountainous areas like the San Gabriel Mountains, require steeper slopes than the kinds of landslides that take place along the coast. I think my map does underestimate to a small degree the hazard of landslides near the coast because the slopes are not as steep there and in comparison they appear less hazardous. In reality, these slopes do not need to be as steep as the mountainous areas to provoke a landslide, because the soils near the beach have very little clay helping them to bind together, which makes it difficult for the soil to break away in a slide. I would also investigate more thoroughly the use of aspect in landslide analysis. Several other analyses used slope aspect (which direction the slope faces) in their calculations but never disclosed exactly how aspect affects the slope instability. I could have integrated aspect into my own analysis but did not know exactly how the direction of some slope faces contributes to landslide probability more than others.

Conclusion

Overall, I believe my maps do an adequate job of predicting areas of potential slope instability, both on the coast and inland in San Diego County. Mountainous areas with steep slopes, heavy rainfall and a recent history of fire are prone to fast runoff and, when devoid of vegetation, the soil can easily slip from the mountainside. Though I believe that this analysis would be useful in any area with as diverse of topography as San Diego County, I would not hesitate to change the analysis based on either new data or a better understanding of the current information.

References

Barlow, J. and S. E. Franklin. 2007. Mapping Hazardous Slope Processes Using Digita Data. In Li, J., Zlatonova, S., and Fabbri, A. (eds.) Geomatics Solutions for Disaster Management. Lecture Notes in Geoinformation and Cartography Series. Berlin: Springer.

California Geological Survey. 2007. "California Geological Survey -- Landslides." http://www.conservation.ca.gov/cgs/geologic_hazards/landslides/Pages/Index.aspx

Endo, K., et al. 2001. Slope failure disaster prevention activities in local government using GIS. Proceedings, ESRI International User Conference. San Diego: July 9-13. http://proceedings.esri.com/library/userconf/proc01/professional/papers/pap996/p996.htm (Accessed 14 March 2010)

Fabbri, A. G., et al. 2003. Is prediction of future landslides possible with a GIS? Natural Hazards. 30: 487-499

Fernandez, T., et al. 2003. Methodology for landslide suceptibility mapping by means of a GIS. Application to the Contraviesa Area (Granada, Spain). Natural Hazards. 30: 297-308

Remondo, J., et al. 2003. Landslide susceptibility models utilising spatial data analysis techniques. A case study from the Lower Deba Valley, Guipuzcoa (Spain). Natural Hazards. 30: 267-279

Quiz #2

2010-03-10T10:30:00.000-08:00

1. China, India, United States, Indonesia, Russia, Brazil, Pakistan, Japan, Bangladesh, Nigeria

2. Nigeria, Guinea, Egypt

3. Grenada, Suriname, Guyana, Trinidad and Tobago, Uruguay

4. 15 rivers are part of the Amazon river system (Amazon, Guapore, Japura, Madeira, Madre de Dios, Purus, Putamayo, Rio Branco, Rio Juruena, Rio Maranon, Rio Negro, Rio Teles Pires, Tapajos, Ucayali, Xindu)

5. 60 countries are within 500 km of the two rivers:
Leninobod
Jalabad
Zareh Sharan
Turgay
Zhezkazgan
Taldykorgan
Kyzylorda
Almaty
Bishkek
Talas
Karakol
Nukus
Shymkent
Dashkhovuz
Urgench
Naryn
Tashkent
Namangan
Andizhan
Osh
Gulistan
Fergana
Dzhizak
Navoi
Bukhara
Samarkand
Kashi
Chardzhev
Karshi
Dushanbe
Ashgabat
Kulob
Qurghonteppa
Mary
Termez
Feyzabad
Taloqan
Konduz
Mazar-E Sharif
Sheberghan
Aybak
Baghlan
Meymaneh
Mahmud-E Eraqi
Charikar
Qal eh-ye
Asadabad
Bamian
Mehtar Lam
Kabul
Chaghcharan
Mayda Shahr
Srinagar
Peshawar
Baraki Barak
Islamabad
Rawalpindi
Gardez
Ghazni
Dzhambul

6. About ~ 452,300,000 (452,297,220 to the nearest 100,000)

7. The least populous landlocked country in the world is Vatican City. The most populous landlocked country is Ethiopia.

8. Poland, Czech Republic, Slovakia, Austria, Slovenia, Hungary, Romania, Croatia, Bosnia and Herzegovina, Yugoslavia

9. I would say Tuvalu is the country with the fourth-smallest area in the world if you are not counting the Parcel or Spratly Islands (which seems to be a little unclear). If you are counting these islands, then Monaco is the fourth-smallest country in the world. (Vatican City, Monaco, and Nauru are 1, 2, and 3 respectively in the first case; Vatican City, Paracel Is, and Spratly Is. are 1, 2, and 3 in the second case)

10. Cameroon, Central African Republic, Libya, Niger, Nigeria, and Sudan border Chad.

Bonus: Asuncion (capital of Paraguay)

Lab 7 - Spatial Interpolation

2010-03-09T16:40:00.000-08:00

While 2010 is an El Nino year--and the weather that has hit the southern coast of California this season appears to be unusually wet to most residents--the maps above, which show rainfall throughout Los Angeles County, suggest otherwise. The year has been mostly normal, with some slightly wetter weather in only a few selected areas. Though there has been some increase in rainfall in areas of Los Angeles according to the map showing the difference in rainfall (top right, Season Total - Season Normal), most of that difference has been relatively small and limited to only certain areas. In fact, much of the county still sports deficit in precipitation as compared to normal. Perhaps because Southern California has been experiencing a drought for the past several years, this season has seemed very wet, but in terms of average rainfall since records have been kept for this area, the 2009-2010 season has confirmed that at best, Los Angeles is almost making its precipitation average for this year and at worst, many of the rain gauges are well below where they "should" be.

I believe that the kriging technique is best for this type of data analysis. This is because I noticed that it gave consistent results, and did not drop any gauge data or "smooth" over it an unusual data value, though this could be seen as a negative attribute. I also felt that the kriging technique had more accurate results in the map focusing on the difference in rainfall from the Season Total to the Season Average. The other technique I used, Inverse Distance Weighted technique, was very good at smoothing the map but also seemed to almost homogenize the central Los Angeles and San Fernando Valley area even though I am relatively sure that these areas had at least some differences between the values. Both techniques seemed to do a fairly good job in creating a map of the season's total/average, however. I included the elevation map as a test of the interpolation techniques as well as to give the viewer an idea of where the rainfall usually is--since more rain always falls in the mountains, I included it as a spatial reference and to check the precipitation maps for accuracy.

I had real trouble using the splining technique for this lab so I decided to focus on the two other techniques instead. Splining gave me consistently inaccurate and seemingly ridiculous results, with maps that didn't seem to show real connection. I attempted to change the number of surrounding points used to base the interpolation several times, but it didn't seem to alter the output much. However, changing the number of surrounding points using the other two techniques changed the results for the better. Since there weren't that many points with useful information, I thought using the default of 12 surrounding points to estimate a rainfall value for any given location wouldn't be very accurate--there are less than 70 rainfall gauges on the map. So instead, I used either 4 or 5 surrounding points to interpolate the rainfall values. This way, only the closest points were used and values in the mountains, for example, weren't polluted by points in the desert, which are relatively close by but have very different values for rainfall.

National Geographic Map Contest

2010-03-09T16:33:00.000-08:00

My submission to the AAG's and National Geographic's student map competition. The quality of the JPEG isn't nearly as good as my actual submission, which was a pretty heavy PDF. In particular, the export to JPEG via ArcGIS made the quality of the accompanying pictures poor. Winners will be announced at the 2010 Association of American Geographers' annual meeting in April. For more information click here.

Geog 168 Lab 6 - Suitability Analysis II

2010-02-22T14:02:00.000-08:00

Above, clockwise: Elevation, Fuel Risk, Final Fuel/Elevation Model, and Fuel Type. Work from the exercise for Lab 6:

I created my fire map with the intent to focus on two major aspects of fires: fuel and elevation. The location of the Station Fire was a typical mixed shrub-and-forest environment that was not only incredibly dry, but was also at a higher elevation overall than a fire might be along the coast or in a suburban area. In the exercise fire model, we worked with raster images for vegetation type, fuel, and slope. However, I decided to incorporate elevation as part of my fire model because my experience backpacking taught me that in dry years, campfires (and sometimes hiking altogether) are prohibited in areas of high elevation due to the risk of forest fires. Fires are more difficult to extinguish at higher elevations because its usually rugged terrain and alpine environments take much longer to regenerate than others because the growing season is shorter. These environments might also be relatively isolated--not many areas of Los Angeles have native conifer trees, and plants and animals adapted to these environments are much more vulnerable if an area is destroyed. I also worked with elevation directly instead of calculating the slopes of the area because--while slope is a very important factor in fighting fires--I had more trouble exporting a slope raster from my DEM than in the lab exercise. I don't know exactly why, but I concluded that I had more than enough information on land cover, fuel hazard, and elevation profile to make a meaningful map.

First I downloaded land cover and elevation data. I retrieved the elevation data from the USGS Seamless server, and the land cover data from the California Department of Forestry and Fire Protection. While I thought that the land cover data was important (coloring on the Fuel Type map indicates which types are worse for fires), however, I was more interested in the risk associated with that fuel. For example, tree stands that are ablaze are much more hazardous than a fire in a meadow. Grass fires light quickly, but burn "cool" because there is little fuel on the ground. Live hardwoods in relatively thin stands can survive these fires. Conifers engulfed in flames, however, burn long and hot. Burrowing animals might not survive, and the area is completely devastated. To get a good idea of locations where high elevation (and therefore more delicate environments) meet high-risk fuels, I decided to use the raster calculator to make a model.

I first reclassified the elevation data into about 7 classifications, in increments of 500 feet. Next I reclassified the land cover data as well--twice. The first time I consolidated similar land cover classifications so that it would be easier to read. For example, Class 1 "Grass" and Class 2 "Sparse pine/meadow" were grouped together. The result is the "Fuel Types" map in the lower left corner of the map sheet. I should note that these land cover classes (retrieved from the California Dept of Forestry/Fire) were organized by degree of hazard, so I changed the colors for these land cover types appropriately (e.g., Grass is a lighter color than Brush). Next I reclassified the raster further. I gave urban, desert, barren and other non-forest environments a low value because they are less likely to burn as fast or furious as a forest fire. I separated the rest of the classes by their degree of hazard, giving more hazardous fuel types (conifer stands) higher values. Next I used the raster calculator to bring the rasters together. My calculation was (Elevation_Reclassification) + (2*(Land_Cover_Reclassification)) + (2*(Fuel_Risk)). The fuel risk raster (in the upper right corner) was obtained from the same website as the land cover raster and accounted for slope and other factors (such as proximity to water sources) in assessing the risk of fuels in this area. I thought that fuel type and fuel risk were more important to determining overall fire risk than elevation, so I gave each twice as much weight in the calculation as the elevation raster.

The result is the Elevation/Fuel Risk model in the lower right corner of the map sheet. As a final touch, I added the roads to the area because I thought it was worthwhile for the viewer to know that the most hazardous areas for a fire in La Cañada Flintridge were actually some of the closest to the local roads. I also added a general elevation map to the map sheet, and hillshade to two maps so they looked more polished. Finally, I added a small map of LA County in case the viewer didn't know the context of the fire. I didn't have much trouble with this lab but I think if I could have obtained the slope it might have altered my fire model. I also think that knowing which areas burned most recently might have added to the map, but I didn't want to make it too complex.

Geog 168 Lab 5 -- Suitability Analysis

2010-02-11T17:46:00.001-08:00

Suitability analysis techniques in GIS are incredibly useful in finding the appropriate site for new buildings or services. In relation to the current debate over expansion of a landfill near Kettleman City, CA, suitability analysis could be just as helpful by aiding the visualization of the landfill, its proposed expansion, and the problems in the town.

Kettleman City is facing several issues that could be assisted by suitability analysis: most notably, the expansion of the landfill, the prevalence of birth defects in the town, and the contamination of public drinking water. The first, landfill expansion—as illustrated by the newly-finished lab—involves several evaluation factors, such as distance from current landfills, distance from water sources, soil analysis, land cover analysis, etc. Kettleman City might evaluate some of these variables, but given that the town is struggling with possible contamination of public water sources with arsenic, they will likely also analyze the distance between the landfill and the city wells, and the distance between the landfill and some of the instances of birth defects in the town.

Because Kettleman City is such a small town, it is difficult to discern whether or not there is a real problem with birth defects in the population or if the number of birth defects is just fluctuating naturally. In addition, because there are so few births each year, just one additional birth defect in a single year appears to be a huge jump in the number of birth defects per 100,000 live births. Mapping instances of birth defects in proximity to the landfill could reveal whether or not the toxic waste dump site plays a part in what townspeople say is a rise in the number of defects. Because the water is contaminated with arsenic, the potential connection between the water and the birth defects, as well as the water and the landfill, must also be evaluated. If city wells are very close to the landfill, and if the landfill is consistently being fined for failing to monitor PCBs—a known carcinogen—then possible contamination of the wells is a real problem.

Suitability analysis in this case is crucial to carry out before any decision on the landfill expansion is decided. Because the city is located in a primarily agricultural district, it is necessary to determine whether or not agricultural runoff is the primary cause of the arsenic contamination and/or the birth defect problem (if it is a problem). The city should undertake a suitability analysis for the landfill and its expansion, but should additionally evaluate the other possible sources of arsenic leaching into the wells, such as the nearest farms.

Finally, it is likely that a possible water treatment plant in the city to treat the contaminated water will need a suitability analysis for its placement as well. The water treatment plant could be evaluated for factors such as distance from the landfill, distance from arsenic-contaminated wells and fields, and distance from the city residents. Suitability analysis would be highly useful in assisting both Kettleman City and the state to determine if the landfill expansion, water contamination (and proposed water treatment plant) and birth defects are linked, and additionally how to site the new buildings to keep these problems to a minimum.

Geog 168 Lab 4 -- Digitizing

2010-01-27T16:00:00.000-08:00

Geog 168 Lab 3 -- Geocoding

2010-01-26T16:22:00.001-08:00

The topic I chose for this lab was red light cameras--cameras placed at intersections that are triggered to go off by cars entering the intersection after the light has turned red. Geocoding complements this topic well because many drivers would like to be aware of whether or not red light cameras have been installed in their city, and if so, where they are. The fine for running a red light is about four hundred dollars in most areas of San Diego County and since most people do not drive with the intention of running a red light (rather, it is a split-second decision made just before entering the intersection), it is reasonable to assume that drivers would be more cautious around them if they knew where they were. Geocoding makes locating and identifying the intersections of red light cameras easy, so that a map may be made of all areas.

To know where the camera is you must be able to identify the intersection it resides in. However, I decided to additionally map red light cameras by municipality within San Diego County, for two reasons. First, there are less than 100 red light cameras in all of San Diego County, and they are all located within incorporated cities. Second, while it might appear that the cameras are relatively spread out across the map, once the municipal boundaries are shown, it becomes clear that the cameras are only in specific cities. Some cities have elected not to install the cameras. Escondido, El Cajon, Oceanside, Poway, Chula Vista and the City of San Diego are the main cities with cameras at their major intersections. In contrast, Lemon Grove and Carlsbad, for example, have no cameras.

Geocoding is especially helpful in this case because mapping the exact locations of the red light cameras allows drivers to identify areas where there are several red light cameras clustered together. Additionally, this map might also be useful for the company selling the cameras. Carlsbad, which has many major roads with high speed limits and multiple lanes, has zero red light cameras, even though sister cities Oceanside and Encinitas do. This map might help catalyze an investigation to determine why.

Geocoding is a useful mapping tool that, using standard addresses, can give you instant locations on a map of several objects or places all at the same time, allowing you to analyze the objects in relation to each other. Because we don't refer to most places by to their latitude and longitude coordinates, this is an essential tool as it is the converter necessary for mapping if your input is the standard referencing system of location: address.

Data:
street city state ZIP

10th Ave and A St San Diego California 92101
54th St and Montezuma Rd San Diego California 92115
Aero Drive and Murphy Canyon Rd San Diego California 92123
Balboa Ave and Ruffner St San Diego California 92111
Black Mtn Rd and Mira Mesa Blvd San Diego California 92126
Camino Del Rio S and Mission Center Rd San Diego California 92108
Fletcher Pkwy and N Magnolia Ave El Cajon California 92020
Garnet Ave and Mission Bay Drive San Diego California 92109
Grand Ave and Mission Bay Dr San Diego California 92109
West Grape st. and N Harbor drive San Diego California 92101
Harbor Dr and S 32nd st San Diego California 92113
Imperial ave and 60th st San Diego California 92114
Magnolia Ave and E Madison Ave El Cajon California 92020
Miramar Rd and Miramar Mall San Diego California 92121
Montezuma rd and Collwood blvd San Diego California 92115
Towne Centre Dr and La Jolla Village Dr San Diego California 92122
10th ave and F st San Diego California 92101
Lake shore dr and jackson dr San Diego California 92119
Mira Mesa Blvd and Scranton Rd San Diego California 92121
El Camino Real and Del Mar Heights Road San Diego California 92130
Convoy st and Clairemont Mesa blvd San Diego California 92111
Mira Mesa Blvd and Westview Pky San Diego California 92126
Washington st and Cleveland ave San Diego California 92103
Rosecrans Street and Nimitz Blvd San Diego California 92106
Balboa Ave and Kearny Villa Road San Diego California 92123
S 28th Street and Harbor Drive San Diego California 92113
Genesee Ave. and N. Torrey Pines Rd. San Diego California 92121
43rd St. and University Ave San Diego California 92105
Camino De La Reina and Qualcomm Way San Diego California 92108
h street and broadway Chula vista California 91910
E Main st and N Mollison Ave El Cajon California 92020
Broadway and N Mollison ave El Cajon California 92021
Fletcher Parkway and N Marshall Avenue El Cajon California 92020
Fletcher Parkway and Navajo Road El Cajon California 92020
E Washington ave and s Mollison ave El Cajon California 92020
W Washington Ave and El Cajon Blvd El Cajon California 92020
Jamacha rd and e Main st El Cajon California 92019
Fletcher Parkway and Garfield Avenue El Cajon California 92020
Greenfield dr and n 2nd st El Cajon California 92021
n 2nd st and e Madison ave El Cajon California 92021
w Valley Pkwy and s Tulip St Escondido California 92025
n Escondido Blvd and w Valley Pkwy Escondido California 92025
S Juniper St and E 2nd Ave Escondido California 92025
S Escondido Blvd and W 2nd Ave Escondido California 92025
N Fig St and E Lincoln Ave Escondido California 92026
San Pasqual Valley Rd and Oak Hill Dr Escondido California 92027
E Valley Pkwy and N Broadway Escondido California 92025
W Valley Pkwy and Auto Park Way Escondido California 92029
W Valley Pkwy and Centre City Pkwy Escondido California 92025
El Camino Real and Leucadia Blvd Encinitas California 92024
Encinitas Blvd and El Camino Real Encinitas California 92024
Olivenhain Rd and El Camino Real Encinitas California 92024
Camino del Mar and Del Mar Heights Rd Del Mar California 92014
Camino del Mar and Via de la Valle Del Mar California 92014
Otay Lakes Road and Bonita Road Bonita California 91902
Otay Lakes Road and Allen School Ln. Bonita California 91902
Otay Lakes Road and Avenida Del Rey Chula Vista California 91910
Otay Lakes Road and Ridgeback Road Chula Vista California 91910
Lomas Santa Fe Dr and Highway 101 Solana Beach California 92075
Via de la Valle and Highway 101 San Diego California 92014
solana hills dr and lomas santa fe dr Solana Beach California 92075
Camino Del Norte and Pomerado Road Poway California 92064
Highway 67 and Poway Road Poway California 92064
Pomerado Rd and Ted Williams Parkway Poway California 92064
Poway Road and Community Road Poway California 92064
Poway Road and Pomerado Road Poway California 92064
Scripps Poway Parkway and Community Road Poway California 92064
Scripps Poway Parkway and Pomerado road Poway California 92064
Twin Peaks Road and Community Road Poway California 92064
Canyon Dr and Oceanside Blvd Oceanside California 92054
College Blvd and Waring Rd Oceanside California 92056
College Blvd and Frazee Road Oceanside California 92057
College Blvd and Oceanside Blvd Oceanside California 92056
Mission Ave and Canyon Dr Oceanside California 92054
Oceanside Blvd and Crouch Street Oceanside California 92054
Oceanside Blvd and Melrose Drive Oceanside California 92056
College Blvd and San Luis Rey Mission Expy Oceanside California 92057
N Emerald Drive and W Vista Way Vista California 92083
Genesee ave and Park Mesa Way San Diego California 92111
S Melrose Drive and Hacienda Dr Vista California 92081
Mesa Dr and College Blvd Oceanside California 92057
S Santa Fe Ave and Vista Village Dr Vista California 92084
S Santa Fe ave and Escondido ave Vista California 92083
Sycamore Ave and Shadow Ridge Dr Vista California 92081
Vista Way and S El Camino Real Oceanside California 92056

Geog 168 Lab 2

2010-01-19T20:12:00.000-08:00

Geog 168 (Intermediate GIS) Lab 1

2010-01-06T13:29:00.000-08:00

NAAMP - A Route Map

2009-10-23T10:02:00.000-07:00

Over the summer I was fortunate enough to land an opportunity making maps for the North American Amphibian Monitoring Program at the Patuxent Wildlife Research Center in Laurel, Maryland. One of my assignments was to turn route paths that were used to survey frogs into digital maps that could be exported to a PDF format and emailed to volunteers. Routes were originally marked on a paper map and/or were mapped in an outdated version of Street Atlas USA, software by DeLorme that was replaced with ArcGIS 9.3. Major lessons I learned this summer? GIS classes somehow forget to mention that in the real world you have to find your own data and figure out how to make it work in GIS. Also, the ESRI Support Forums are indispensable. I also discovered the many, many uses for ArcMap extensions.

Detail maps showing two halves of another route:

Modeling Yosemite Using ArcScene

2009-05-26T16:11:00.000-07:00

Made a DEM (digital elevation model) of Yosemite Valley. Sighting is from the east. Was a little tough to pick out El Cap and Half Dome at first (hint: Half Dome looks really thin).

Mapping Trends in the LGBT Community

2009-03-18T11:06:00.000-07:00

These maps were for a final project in my Cartography class at UCLA. The first (click on each to view larger images) shows incidence of discrimation due to sexual orientation (by county) with distribution of LGBT households. The second shows gentrification from 1990 to 2000 in two metropolitan areas: Boston and the San Francisco Bay area, and points out neighborhoods in which LGBT household income has either risen or declined in that period. The third is a very basic Google Earth map of relatively well-known LGBT neighborhoods across the United States. You can make more complex maps in Google Earth, but with this assignment I was just getting my feet wet a little bit.

The GeoWeb & Neo-geography

2009-03-02T13:26:00.001-08:00

Google Map: Los Angeles Unified School
District High School API Scores

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"API" stands for Academic Performance Index, and it is essentially a ranking system; a way to measure school performance in the state of California. API scores are determined for individual schools based on two main standardized tests: the CST, or the California Standards Test, and the CAHSEE, the California High School Exit Exam. The CST measures the ability of the school's pupils to meet the learning requirements of a student in their grade in areas like reading comprehension and math. The CAHSEE is an exam which public high school students must pass in order to graduate, regardless of whether or not they have fulfilled all of their high school's other requirements.

The API is on a scale from 200 to 1000. The state deems that all public schools should attempt to target an 800. In our sample, which consisted of all the standard high schools in LA Unified, the mean score was 659. Only 8 percent of the schools we mapped had an API score of 800 or above.

Dots, Symbols and Shades

2009-02-25T12:00:00.001-08:00

These maps are a look at calorie consumption (and cereal production and consumption) around the world using a couple of different symbologies.

The John Muir Trail

2009-02-23T13:14:00.000-08:00

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This is a rough look at where the John Muir Trail travels, from Mt. Whitney to Yosemite National Park. Google doesn't keep track of the trail when it leaves National Parks so I had to just guess between Kings Canyon and Yosemite. The trail is a total of 212 miles long and I will be hiking it in September of this year.

GPS 1.0: Maps Within Maps

2009-02-18T13:52:00.001-08:00

Principles of Design I : Ten Maps of Laos

2009-01-31T20:10:00.001-08:00

The purpose of this assignment was essentially to be creative and focus on map design. These ten maps all had to be in black and white (no gray allowed), with one chunk of text from Wikipedia worked into the mix somehow.
The text of each map reads:
Laos is a landlocked country in Southeast Asia and the thickly forested landscape consists mostly of rugged mountains, the highest of which is Phou Bia at 2,817 m (9,242 ft), with some plains and plateaus. The Mekong River forms a large part of the western boundary with Thailand, whereas the mountains of the Annamite Chain form most of the eastern border with Vietnam. The country is one of four in the opium poppy growing region known as the "Golden Triangle".

Mapping Distributions

2009-01-27T21:59:00.000-08:00

This week's lab had me mapping distributions, and with its relative freedom compared to the previous lab I decided to focus on data from Australia, from where I have just returned on a year abroad. I downloaded census data from the Australian Government's Bureau of Statistics website, deciding to focus on population distributions of Aboriginal Australians. Australia has a confusing system of local government that is essentially without counties, so I decided to focus on both states and their capital cities (there are only about 8 states). For my first map I decided to look at populations of indigenous Australians within states and capital cities. As many Aboriginal communities are in the Northern Territory, I was not surprised to see that both the territory and its capital, Darwin, had high percentages of Aboriginals compared to the other cities and states. Melbourne in particular had very few indigenous communities, with Aboriginal people making up only 0.39% of the city's population.

My second map is a little more complex, but indeed another interesting way to examine the population distribution of indigenous Australians. I know generally how many Aboriginals make up the populations of each state and city, but which state has the highest overall population? While the Northern Territory had a high percentage of indigenous people, it's a large state with relatively few people overall--it's a stereotype that all people with indigenous heritage live out in the bush. My second map focused on indigenous populations in relation to the total number of Aboriginal people in Australia. The dot-scatter method shows clearly that like the overall Australian population, most indigenous people live on the east coast, with high concentrations in cities like Brisbane and Sydney.

My final map is probably the most difficult to understand, but hopefully also the most interesting. My last map deals with Aboriginal population distributions between cities and regional areas of states. Before I discuss my analysis, I must first explain that Australia is a uniquely populated country. Because the country's population is quite low for its total area, there are really few to no cities that can compete with the the size of those that are the capitals of each state. Thus, through figuring out the statistics for whom resides in cities, it by default allows me to generalize a little as to who resides in the country--and I wondered: in New South Wales, for example, are most of the Aboriginal people there living in the city? Or are they more likely to prefer country life? So I made a map. I divided the Aboriginal population in a capital city by its state Aboriginal population to find the percentage of indigenous people living in that city, which is what is represented by the blue circles. There is an outlier on this map, however; the Australian Capital Territory is the Australian equivalent of Washington, D.C., and therefore most people in this "state" also live in the city. There is really very little area outside of its capital, Canberra. However, I was pleasantly surprised by the rest of the cities' results: hardly any Aboriginal people in the Northern Territory reside in Darwin. And while Map 2 showed that many indigenous people live in Sydney, according to Map 3 that's only 25% of the number that live in the rest of New South Wales.

This week's mapping exercise wasn't often the most straightforward, but my efforts shed quite a bit of insight into the population densities and distributions of Aboriginal people across Australia.

The New York Times Election Map: A Reproduction

2009-01-14T17:00:00.000-08:00

This week's lab involved imitation and improvement: first, a test of my ability to match the New York Times' election map. My first map is my attempt. You can find the original link to the NYT's map here. The next part of the challenge was surpassing the NYT map by improving it. It always bothered me that election maps often appear as though the Republican has a huge lead simply because the red states have so much area. Thus, even though these states have few electoral votes due to their lower populations than states on the coasts, they look like they have a lot more clout than they really do. I decided to try improving the map by adding a smaller map in the corner that showed state areas as a function of electoral votes. I first found the percentage of electoral votes granted to each state (California, for example, has 10.22% of the votes of the Electoral College). Then I used that percent to figure out how much "Electoral Area" that state would have in the US (so CA would have 10.22% of the total area of the US). I then figured out what ratio of the state's original area would be the "Electoral Area" and sized each state accordingly. For example, because D.C. and Alaska have the same number of electoral votes, 3, they should have the same area on the new map. However, D.C. only has an area of about 66 sq. miles, whereas Alaska has around 600,000 sq. miles. So D.C. needed an increase of 1739% of its original area, while Alaska had to be scaled down to about 18% of its original size. This addition to the map shows clearly how important the New England region is to those candidates seeking the presidency, while physically larger states like Montana, Nevada and New Mexico are less critical.

Race in the United States, by County

2009-01-13T12:33:00.001-08:00

Click on maps to enlarge.

The three maps that I chose to focus on were relatively straightforward census data. I only chose data that in which those surveyed identified themselves as one race--none of these maps represent data of people who identified with two or more races. For example, the last map is a representation of the percent of the population that identifies as white only. The first map shows Asian populations, by percent, for each county in the contiguous U.S. The highest concentrations of people identifying of Asian race were only found in counties near big cities, such as Houston, Washington, D.C., and New York City. However, areas and cities on the West Coast had especially higher populations. As many first-generation immigrants from the Pacific Rim entered the United States via Angel Island and settled in San Francisco or other cities in California or on the West Coast, this is expected. Indeed, most of the inner United States is green, indicating that Asians make up less than just one or two percent of the population in these areas. This map has seven classifications for a few reasons. Firstly, much of the U.S. has small Asian populations, and there is variation worth pointing out that one cannot do with few classifications. The entire West coast shows an obvious trend towards higher percentages--lots of yellow and orange. However, the bits of orange in the rest of the country really only point out counties around major cities. Without enough classes, similar percentages (e.g. between two percent and 0.5 percent) would be shown as all the same color, and we would look at this map thinking there were no Asians between San Francisco and New York.

The second map shows how much of county populations identified as American Indian or Alaska Native. Unlike the first map, this map shows opposite trends. The highest concentrations of Native Americans seem to be in the Midwest and mountain regions, mostly around Oklahoma, the Dakotas, Montana and the Four Corners region. With the exception of Oklahoma jutting out, there is almost a clear line between the east, which has few counties with American Indian populations over 0.5 percent, and the west, where many counties fall in the higher categories at anywhere from ten to ninety percent. Counties with at least ten percent are likely to have an reservation residing in them, or several. Oklahoma might have relatively high populations as well because during the 1800's many Native Americans were forced to move from their homelands to territory in that state. Around Oklahoma, the use of six classes allows us enough precision to see how some American Indians have moved from that state to other areas nearby.

The final map shows the percentage of county populations that identify as white. Most of the Northern U.S. counties, from coast to coast, have white populations that exceed eighty-five percent. Further, most of the Northeastern U.S. has white populations that exceed ninety-five percent! However, in the Southeast, this is not the case: many of these states have several counties with white populations less than fifty percent. Much of the Southwest has whites clocking in at less than seventy percent of the population as well. There are several reasons why this may be the case. First of all, many of the first immigrants to the United States were from Europe, and settled in or around the Northeast--New York City, Chicago, et cetera. However, in the Southeast, where slavery ruled until the Civil War, we have a much larger black population. And in the Southwest, immigration from Latin America has driven up the population of Hispanic non-whites. The relatively high proportion of whites to non-whites in most counties justified only using five classes in this case, as relatively few counties have under about thirty percent.

My Favorite Map

2009-01-06T23:25:00.000-08:00

My favorite map would have to be this interesting and "handy" map of San Francisco Bay, borrowed from the awesome guy at http://strangemaps.wordpress.com and originally published in the 1938 Cartoon Guide to California by Reg Manning. I like it because it's unusual, and while it might not be a map from the digital age, it's a fun and interesting look at how maps can be unconventional.