Friday, November 13, 2015

Vegetation mapping methods have dramatically evolved over the years. Instead of field mapping and photo-interpretation, there are now more accurate and efficient ways to map vegetation across a landscape. One of the most common ways is by observing and analyzing the special distribution of certain vegetation and specific environmental variables. This is called predictive vegetation modeling.

In order to accurately make this vegetation model, one must need maps of environmental variables and spatial information about the specific vegetation that is being emphasized. The relationship between the environment and vegetation can either be observed or further analyzed. The results of the map is either a static or equilibrium model. The static models are the most prevalent and constructable. They measure the temperature, precipitation, elevation, elevation-derived terrain variables, and surface composition of the area being studied. These models are useful to draw conclusions about where the different types of vegetation is most likely to be located.  

 Miller, Jennifer, and Janet Franklin. Modeling the Distribution of Four Vegetation Alliances Using Generalized Linear Models and Classification Trees with Spatial Dependence. San Diego: Ecological Modeling, 2002. Print. 

Wednesday, November 4, 2015

Gtography-Bakeries in Georgetown

This map shows the bakeries that can be found in Georgetown. There are 6 depicted on this map and the majority are on the West side of town.

Tuesday, September 29, 2015

Voter Migration and the Geographic Sorting of the American Electorate

        In the United States, most citizens can be divided into two categories: Republican or Democrat. We can determine this by looking at majority votes towards politicians and which ones are in office. States can be divided into red (Republican) or blue (Democrat) states. In the map below, we can see how the United States is divided in political party preference, with the colors regarding the party affiliation of the governor. However, the party affiliation of an area can sometimes be contradictory towards an individual's preferences. This can cause a migration to an area that an individual finds more suitable for his or her political views. 

Party Control of Governors' Offices (December 2014)
Blue: Democratic Governor   Red: Republican Governor   Yellow: Independent Governor
(Areas in grey boxes in bottom left are US territories)

        In the study by Cho, Gimpel, and Hui, the migration patterns due to party affiliation of citizens of the United States were examined. In 2004, 2006, and 2008, seven states were examined to determine how this migration affected the "political landscape" of each state. These seven states were New Jersey, Maryland, Delaware, and Pennsylvania in the East; and California, Oregon, and Nevada in the West. These states were chosen "for their adjacency, because they register voters by political party, and, importantly, because they maintain accessible, high-quality voter registration records" (Cho, Gimpel, and Hui, 2014). Using these records the migration patterns of the voters could be followed. When looking at the areas that the individuals migrated to, they tended to go to areas where it was more politically favorable towards them. Republicans moved to where Republicans would benefit and Democrats moved to where Democrats would benefit. Many factors come into play when referring to favorable areas for an individual and his or her party, such as "racial composition, income, population density, and age" (Cho, Gimpel, and Hui, 2014). According to the study, income and economic status were the most motivational incentives. Many other factors come into play, as well, but harder to gauge because they could be personal reasons to an individual, that is to say, not entirely political. While showing definitive results, the study does not represent all of America, only seven states. Because of this, the data must be taken with a grain of salt.

Resources: Cho, W., Gimpel, J., & Hui, I. (n.d.). Voter Migration and the Geographic Sorting of the     
        American Electorate. Annals of the Association of American Geographers, 856-870.   
        Retrieved September 29, 2015.

GIS and Earthquakes

GIS Mapping of Earthquake-Related Deaths and Hospital Admissions from the 1994 Northridge, California, Earthquake

Earthquakes pose a serious risk to human health and the public’s safety. Earthquakes have the potential to destroy entire cities and kill thousands of people in just the matter of a few minutes. The article by Peek-Asa et al. (2000) was a study done on the 1994 Northridge, California earthquake that devastated the city. The history of that deadly earthquake is described below for better understanding of the author’s experiment.

The basic background of earthquakes has to be taken into account in order to comprehend the methods used in this study. Earthquakes are tremors and shaking in the earth’s crust caused by seismic activity, which is the sudden release of energy.
In regards to the Northridge quake, it was located in California in an earthquake prone area. While the earthquake had a duration of only 10-20 seconds, it had a moment magnitude of 6.7. This was the highest ground acceleration ever instrumentally recorded in a North American urban area. The tremors were felt as far away as Las Vegas, Nevada, which was about 220 miles away from the epicenter. The epicenter was located in the San Fernando Valley, about 20 miles northwest of the downtown area of Los Angeles.
There were several thousand aftershocks after the main quake, some of which were quite large still. The death toll was 57 people, while there were more than 5,000 injured. Furthermore, the Northridge quake amounted to approximately $13-$40 billion in property damage.

First and foremost, earthquakes are extremely unpredictable and there is little warning when one is about to occur. The authors of this study desired to study the spatial relations between the injuries sustained by people and the seismic activity and location of the earthquake. Considering earthquakes pose such a massive health threat, the authors found that there was significance in researching the relations of seismic hazards and building damage to the risk of injury of a person.
To accomplish this, fatal deaths and those injured and admitted to hospitals were identified and pinpointed. Then, all injury locations were charted on map of the area using GIS methods and software. Subsequently, injuries were analyzed in regard to the distance from the epicenter of the earthquake, as well as other factors such as the proportion of damaged buildings in the area, and peak ground acceleration.

The results from the Peek-Asa et al. (2000) study were that injury severity was inversely related to the distance from the epicenter (i.e. more injuries occurred in areas closer to the epicenter, and less injuries occurred farther away from the epicenter), and in addition, increased with cumulative ground motion and building damage. However, the study did not show that injury severity and incidence were completely predicted by the building damage and the seismic hazard.
They also predicted that outside factors such as age and the activity of the person during the earthquake could have affected the severity of injury (such as driving a car). The figure below shows the injury locations in regards to how intense the quake was in that specific area, as well as the proportion of damaged residential structures. Furthermore, Peek-Asa et al. (2000) found that injuries of all severities occurred over a wide range of distances from the epicenter of the quake. They discovered that rescue efforts cannot be solely focused on the immediate damage zone.


Peek-Asa, C., Ramirez, M. R., Shoaf, K., Seligson, H., & Kraus, J. F. (2000). GIS mapping of earthquake-related deaths and hospital admissions from the 1994 Northridge, California, earthquake. Annals of Epidemiology10(1), 5-13.

Web Access:

Monday, September 28, 2015

The Benefits of Improved National Elevation Data

National elevation data is extremely useful in areas such as flood hazard mitigation, agricultural productivity, infrastructure and energy development, resource conservation, and national security.  The National Digital Elevation Program (NDEP) was created to meet the needs of the government and industry for digital elevation models.  The program includes numerous federal agencies such as the USGS, the Census Bureau, and numerous agencies within the Department of the Interior.  In general, elevation data updates come for areas every 30 years, while the technology grows at a much faster pace.  At the time of this writing, the elevation data needs of the United States were not being met, so a task force was created to assess the potential for improving the national elevation data.  The National Enhanced Elevation Assessment (NEEA) was conducted in 2011 to assess the current needs for improved elevation data, assess the costs and benefits of improving data, and evaluate new models.
The benefits of improved data are many, and their significance cannot always be captured by a dollar value.  For example, improved elevation data can eliminate the need for survey crews when constructing new roads, which eliminates deaths to survey crews that occur yearly.  A larger-scale example occurred in Washington, where improved elevation modeling helped discover a fault near the Tacoma Narrows that led to an over $700 million bridge repair.  As recently as 2014, President Obama declared that the National Digital Elevation Program would be used as part of the Climate Action Plan to locate which areas will be most affected by climate change.  Improved data can also be used for siting wind farms, directing agricultural runoff, and constructing efficient oil and water pipeline paths.  The research of the NEEA also showed that technology is at a stage of growth where it makes sense from a cost standpoint to engage in updating the digital elevation models.

The assessment determined that the benefits of improving the national elevation models outweigh the costs by a large factor.  There are several different levels of elevation data quality that can be used, however, and each quality level comes with a corresponding level of benefits that can accrue at each level of precision.  Each quality level except for the very highest comes with a net benefit to the US, and at ratios greater than 4:1.  Figure 1 shows the relative image quality of the highest three quality levels, and Figure 2 shows the cost/benefit analysis of quality levels ranging from highest to lowest levels of improvements.  The assessment ultimately led to the creation of the 3D Elevation Program (3DEP), which is now in the process of being implemented.  Federal and state agencies work together along with others to improve the elevation using light detection and ranging (LIDAR) and interferometic synthetic aperture data (IFSAR) which is used specifically for data in Alaska.  Data will be collected on 8 year cycles, and annual benefits from a fully funded program would be $690 million.  The 3DEP receives $50 million annual now, and needs an additional $96 million annually to be fully implemented.  This relatively small investment could lead to huge savings over time, especially in case of disasters.  Improved elevation data leads to better emergency flood mitigation plans, better preparedness for impacts of climate change, and increased operating efficiency and capacity.  Watch for annual improvements in the coming years from 3DEP.  The program’s website is:

Snyder, G. I. (2013). The benefits of improved national elevation data.Photogrammetric Engineering and Remote Sensing79(2).

The deforestation of areas of land affects the state of the streams and affects the amount of water in the atmosphere. Over half of the native vegetation has been removed in the watershed of the Araguaia River in east-central Brazil.

Without as much vegetation, there is less evapotranspiration which means there is more moisture in the ground rather  than in the air. Most of the deforestation is due to the high demand for agricultural uses. This land is more useful to a person trying to make a living when they can grow crops and raise cattle. Despite the economical advantages to using land for agricultural purposes, the ecosystem has been designed to have dense vegetation and it is unnatural to change one of the ecosystems most identifiable and important characteristics. Water runoff, river discharge, erosion and sediment fluxes are the most common hydrological, geomorphological, and biochemical issues coming from the mass deforestation.

 Coe, Latrubesse, Ferreira, & Amsler. (2011). The effects of deforestation and climate variability on the streamflow of the Araguaia River, Brazil. Springer Science Business Media. 

Friday, September 25, 2015

GIS as a Disaster Management Tool

In 2010, Haiti was struck by a magnitude 7.0 earthquake that killed between 220,000-316,000 and caused tremendous damage to homes and businesses on the island, making it the most deadly natural disaster in the last decade. In the immediate aftermath of the earthquake, Haiti's communication network was destroyed and actionable information was not being communicated effectively.

The USGS, branches of the U.S. Military and FEMA created maps of the earthquake using GIS images to demonstrate where the strongest effects were felt, and later, where the greatest casualties were taken.

The graphics below illustrate how GIS can assist decision makers in appropriating resources during emergencies with the greatest efficiency possible.


GIS is making jumps in big data, APIs from popular apps like Flickr provide big data with geographical context. This data is known as Volunteered Geographic Information (VGI) and can be a valuable information base for real time geodemographics for user profiling. This big data comes with obstacles in validity and reliability that require more testing to improve. What is big data? Along with mobile phone tracking their users, Social media applications such as Facebook, Twitter, and Flickr are used to collect large amounts of data about their consumers, this is big data. Mobile media advances have enabled the collection of big locational data about anyone, anywhere and at any time. Paired with GIS databases companies can use geodemographics for analyzing and visualizing their target consumers and create lucrative sales regions for their goods
This is a map of the tourist density and flows calculated from the Flickr Database.

VGI is created outside the professional practices of the GIS sector but uses a GIS base in its technology. Because VGI is relatively new there are critics such as many GIS practitioners who are concerned with certainty, accuracy and inferior map quality. But due to the clear potential of VGI leads to an acceptance by many practitioners. Hopefully VGI continues to develop and can be used to further help businesses and the community. 

Fischer, F. (2012). VGI as Big Data: A new but delicate geographic data-source. GeoInformatics15(3), 46-47.