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.

Reference:

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 Epidemiology, 10(1), 5-13.

Web Access:

http://www.researchgate.net/publication/12655552_Peek-Asa_C_Ramirez_MR_Shoaf_K_Seligson_H_and_Kraus_JF_GIS_mapping_of_earthquake-related_deaths_and_hospital_admissions_from_the_1994_Northridge_California_earthquake_Ann_Epidemiol10_5-13

https://en.wikipedia.org/wiki/1994_Northridge_earthquake

https://en.wikipedia.org/wiki/Earthquake

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: http://nationalmap.gov/3DEP/.

Snyder, G. I. (2013). The benefits of improved national elevation data.Photogrammetric Engineering and Remote Sensing, 79(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. 

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.





Sources:
http://earthquake.usgs.gov/earthquakes/pager/events/us/2010rja6/index.html
http://voices.nationalgeographic.com/2012/07/02/crisis-mapping-haiti/
http://www.esri.com/news/releases/10_1qtr/haiti.html

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. GeoInformatics, 15(3), 46-47.

Topography-based Analysis of Hurricane Katrina Inundation of New Orleans

During the relief efforts of Katrina in 2005, response teams in low lying New Orleans relied on geospatial data to predict the most inundated parts of the city. Lidar, which was the geospatial technology used to attain the inundation data, is a high-resolution, high-accuracy elevation data, which proved valuable for the development of topographic-based products crucial in the immediate days following the storm. Because of its high level of spatial detail and vertical accuracy of elevation measurements, USGS scientists were able to give estimates on flood water volume, areas of extreme flooding, etc.

Because of its high detail and accuracy, lidar is an excellent mapping technology for use in low-relief hurricane-prone coastal areas. Possibly lidar could be applicable for use in other disaster relief efforts that involve a geospatial aspect. 

Gesch, D. (2005). Topography-based analysis of Hurricane Katrina inundation of New Orleans. Science and the storms: The USGS Response to the Hurricanes of.

When a powerful and deadly hurricane makes landfall somewhere, geospatial data can be very useful. In 2005, a category five hurricane known as Katrina made landfall in New Orleans and affected much of the infrastructure and people in the city. Lidar data has been used to determine the land surface elevation of a place and was used in Louisiana in 2002 after an oil spill. As a result it was available three years later and provided high-resolution elevation data for New Orleans after Katrina made landfall and was extremely helpful in how people would respond to its aftermath. The lidar data was needed in order to determine the magnitude of flood waters in specific areas around the city.  It allowed people to make estimates of the floodwater volume as well which were needed so that people could anticipate the amount of time it would take to get rid of the floodwater in the city. People were more knowledgeable on how to respond to a flooded area with this data information. In addition, people can use this information to determine how flooding might impact an urban environment. This data will also be helpful to people when planning on building infrastructure or reconstructing so they can be more ready for these types of disasters in the future.

Gesch, D. (2005). Topography-based analysis of Hurricane Katrina inundation of New Orleans. 

           Science and the storms: The USGS Response to the Hurricanes of.

This study shows the latest developments of the Normalized Difference Vegetation Index (NDVI) in ecology. This data has been used to show the distributions and abundance of herbivores and non-herbivores. Since about 1981 the importance of different temporal and spatial lags on population performance can be assessed by the understanding the population dynamics.  This was previously thought to most useful in temperate environments.  Models can be used to reconstruct old patterns in vegetation in the effects of future environmental change on biodiversity. Since then, the NDVI has been an essential tool for past and future population and biodiversity consequences of change in climate, vegetation phenology and primary productivity.

Pettorelli, N., Ryan, S. J., Mueller, T., Bunnefeld, N., Jedrzejewsk, B., Lima, M., & Kausrud, K. (2011). The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology. Climate Research, (46), 15-27.

Solar Radiation Models and Temperature Data

Pinde Fu and Paul Rich’s article concerns solar radiation. They made isolation maps from digital elevation models in order to apply the model for spatial interpolation of different topography aspects in the world. The maps are best utilized in forest and agriculture. Often, geographical information is not accurate that is readily available. These solar radiation models are cost-effective as they do not cost much to build and do not require an insolation monitor station. The models made by Rich and Fu are developed for ARC/INFO GIS platforms. Temperature is part of the information used by the solar radiation models. Weather stations are not very accurate as there are not enough per square mile to get accurate temperature all of the time. In using a solar radiation model, a more accurate temperature can be measured. Many other systems have been used to interpolate temperature, however, they often have miscalculations due to not factoring geography features into their systems. Rich and Fu intend to prove that high spatial resolution maps can provide a better temperature prediction. Together they determine temperature maps for a study based on an isolation model. The then outline how this process is completed.
    In order to begin the research process Fu and Rich decided to study at the Rocky Mountain Biological Laboratory in Gunnison County, Colorado.  Eleven Hobo soil temperature senors were buried in many different locations around Gunnison County, Colorado. They then logged temperature by each hour. After the data was collected, Fu and Rich began creating insolation and temperature maps to display their findings. The insolation data was inserted into the TopoView model. Most weather stations do not record soil temperature, so this study is valuable. The results determined that soil temperature varies with position in the topographic landscape. It was concluded that during the summer high spatial resolution for temperature was represented. When snow is present the spatial resolution lowers. This study is valuable because using solar radiation models for soil temperature calculation is just one of the abilities. The study mentions that water balance is also available to study with insolation data and solar radiation models.

This model displays soil temperature (in degrees Celsius) with a legend that has the different temperatures in relation to color. The lighter the color on the legend the higher the temperature at 20cm depth. These soil temperatures were collected daily with a minimum and maximums.

Fu, P., & Rich, P. M. (2002). A geometric solar radiation model with applications in agriculture and forestry. Computers and electronics in agriculture, 37(1), 25-35.

GIS Data Being Used to Determine How Population Density in a Local Stream Fish Aggregation Relates to the Intra-Annual Environmental Niche Variability

GIS Data Being Used to Determine How Population Density in a Local Stream Fish Aggregation Relates to the Intra-Annual Environmental Niche Variability 

            Studying the variation in species has been one of the most important tasks for the advancement of ecological discoveries. It gives the ability to track changes in species while taking the environmental factors into consideration (Anderson, Caruso, Dupre, Knouft, Puccinelli, & Trumbo, 2011). Quality and quantity of habitats can be determined by how healthily a species is surviving; a habitat in bad condition causes the species to be in bad condition, as well. GIS is used to examine these ecological behaviors. In the case of the stream fish being examined, we are looking at the “relationship between intra-annual habitat variation and species’ niche characteristics and the subsequent influences on variation in population density” (Anderson, Caruso, et al., 2011). By niche characteristics it means the characteristics of the habitat that the species best adheres to. A species with a lower niche requirement is more likely to have a dense population due to its ability to fit in multiple habitats (Anderson, Caruso, et al., 2011).

            “Research was conducted in Labarque Creek, a second-order tributary of the Meramac River in Jefferson County, Missouri” (Anderson, Caruso, et al., 2011). Examination was done four times over the course of a year, once for each season. Specifically 30 June–2 July 2007 (which can be seen in Fig. 1), 29–30 October 2007, 14–15 January 2008, and 26–27 April 2008. This was done seasonally because the environment changes due to the differences in temperature, weather, and water flow. The data that was obtained involved stream flow rate, dissolved oxygen, and species of fishes. Stream flow rate varied between seasons. Flow rate was low in July and October, but high in January and April.  Dissolved oxygen, while at a sufficient level, varied from day to day. This is because there are natural factors that affect the dissolved oxygen levels, such as temperature. There were 25 different species of fish caught, but only eleven were caught during every sampling period. Because of this, these eleven species were the only ones where their data was used, which can be seen in Table 1.

            The data gathered in the study suggests that the effects of the changing seasons on habitat availability are detrimental towards determining the “variation in population abundance among species” (Anderson, Caruso, et al., 2011). The results of the study were also found to be contradictory to previous findings. Particularly, “the extent and distribution of available habitat is a strong predictor of variation in population density among species, but only during colder periods within a seasonally variable environment, with the understanding that our results are based on a single location” (Anderson, Caruso, et al., 2011). This is most likely due to the study occurring in a small place, in this situation a creek, rather than a widespread area like a river. Competition most likely increased during the colder seasons, causing a lower population density (Anderson, Caruso, et al., 2011). Also, predation as a factor was not taken into account. Increased and decreased predation during different seasons could have altered the results if tested for. Regardless, the study was beneficial towards using habitat availability as a predictor of variation in population density (Anderson, Caruso, et al., 2011).

Source:

Anderson, K., Caruso, N., Dupre, P., Knouft, J., Puccinelli, J., & Trumbo, D. (2011).                        

            Using fine-scale GIS data to assess the relationship between intra-annual environmental niche  

            variability and population density in a local stream fish assemblage. Methods in Ecology and  

            Evolution, (2), 303-311. doi:10.1111