Thursday, January 31, 2013

Amazon Basin Roads and Deforestation

Deforestation has become a widely recognized issue in today's more environmentally aware world.  Still, there is much to learn.  Many don't know that the main cause in many deforestation incidents is to construct and maintain roads.  A good amount of these roads aren't strictly for public use but are used by loggers to transport their quotas.  In the Amazon basin, the rapidly growing infrastructure is affecting both the high levels of biodiversity in the region and the agriculture.  In order to prevent illegal exploitation of logging, the Brazilian State has implemented concession logging thereby increasing the overall aggregate of deforestation. The map below shows the roads and their origins that travel through the designated logging areas.  It is obvious that the main purpose of these roads is to aid in the transportation of lumber. 
Errors  that the researchers have realized within the maps include over simplification of streams and roads, unreliable information due to different loggers mapping different roads, and over exploitation of certain spatial areas by over greedy loggers.  The use of the GIS software is definitely useful but it is only as powerful as the users.  If the information received isn't correct, there is no way that the map can be accurate.  Fortunately, if we assume most of the information is accurate for the most part, it is possible to create a spatial analysis of the roads in the Amazon used by the logging industry.  There is still a major problem of deforestation that will not stop.  It is a continual process where the building of roads leads to more destruction of biodiversity and deforestation and the need for more lumber leads to the need for more roads.  GIS can help determine where new roads need to be built and where the best lumber is but it is not working on the issue of deforestation. 

Source: NSF project (BCS-0243102) “Collaborative Research: Socio-Spatial
Processes of Road Extension and Forest Fragmentation in the
Amazon,” and from NASA project (NNG06GD96A) “Spatially
Explicit Land Cover Econometrics and Integration with
Climate Prediction: Scenarios of Future Landscapes and
Land-Climate Interactions.”

Are voter turnout levels effected by the community around you?

A review of A GIS-based spatial analysis on neighborhood effects and voter turn-out: a case study in College Station, Texas


Some consider it a privilege to vote, others consider it their duty, and still others don’t even vote at all. Researchers Danile Sui and Peter Hugill show that people of the same neighborhood often vote for the same candidates which is called a contextual effect, or neighborhood effect. Many studies have been conducted on this topic, but few have incorporated GIS and spatial analysis on the individual level.



                         Voter Turnout                                           Voting Results



















Sui and Hugill georeferenced, a GIS based address matching procedure, each voter and non-voter for 3 changes being done in College Station and found that nonvoters were generally clustered in certain neighborhoods, as well as the voters. The voters themselves also seemed to live in areas with other likeminded voters. The researchers were able to use individual level data, derived from address matching, to explore the applicability of GIS-based spatial analytical procedures and examine the impacts of actual voter turnout on the neighborhood effect. The research of Danile Sui and Peter Hugill reveals the spatial heterogeneity and complexity of neighborhood effects and can be used for future insights on voting sciences and mechanics.

Sui, D., & Hugill, P. (2002). A GIS-based spatial analysis on neighborhood effects and voter turn-out: a case study in College Station, Texas. Political Geography, 21(2) 159-173. doi.org/10.1016/S0962-6298(01)00054-3.

Tuesday, January 29, 2013

Locating Invasive Species

As drought sweeps across the United States and devastates arid and semiarid areas, invasive plants that use more water than native species are under fire. Saltcedar is considered the one of the worse offenders and grow along rivers in southern Texas and northern Mexico. In order to gather accurate data on the number and location of invasive species, remote sensing has previously been used in Texas and Florida. In order to find the most effective method for detecting saltcedar, the remote sensing types, high spatial resolution data, hyperspectral data, and moderate resolution-imagery were compared while studying a small area along the Rio Grande.

In preparation, researchers went on foot and logged locations of the desired species using GPS to create points and polygons and used their observations to create 16 classes of land cover.The five pixel based programs studied would place each pixel into one of the land cover classes and then further classify these into one of three categories: invasive species, native species and clear.Afterwards an assessment of the accuracy was done by overlaying the results with the gathered points and polygons. The Researchers found that hyperspectral data was the most accurate, but required more care when processing the data.

 Le Wang, José L. Silván-Cárdenas, Jun Yang & Amy E. Frazier (2012): Invasive Saltcedar (Tamarisk spp.) Distribution Mapping Using Multiresolution Remote Sensing Imagery, The Professional Geographer,
DOI:10.1080/00330124.2012.679440

Monday, January 28, 2013

Hunting effort and game vulnerability studies


Hunting effort and game vulnerability studies on a small scale: a new technique combining radio-telemetry, GPS and GIS

For centuries hunting has existed as a popular pastime (for both recreation and subsistence purposes) around the world. Yet, this practice affects the distribution, population, and behavior of game animals in many different areas. With respects to game management, it is important to understand the ways in which various harvesting strategies impact animal populations. Thus, it is important to have techniques that enable provide spatial data pertaining to both hunters and hunted populations. Recent technological advancements allow for the unique combination of GPS and GIS data in order to study special and temporal relations between hunters and game animals.

In this particular study, Brøseth and Pedersen combine GPS data from ptarmigan hunters in Norway with data from radio-tagged willow ptarmigan to identify factors affecting the bird's probability of survival in within an area of 30 square km. Hunts were conducted by 9 experiences hunters from the tenth through the twenty-second of September in 1997. GPS units were attached to the hunter's backpacks, tracking their movement throughout the hunting grounds. Each hunter marked the positions of harvested ptarmigans throughout each hunt. Those conducting the study captured ptarmigan within the hunting area during March and April prior to the hunts. They fitted the birds with necklace radio transmitters, which revealed valuable information about the birds and their habits (home ranges, etc.).





























*The image on the left depicts the lines taken by the hunters during this study, while the image on the right displays polygons revealing the home range of ptarmigans within the hunting area paired with lines used by hunters within these given ranges. It should also be noted that the circle indicates the lodge housing the hunters during this study.
            
During the 50 hunter-days hunters traveled a total of 818 km and harvested 135 birds (roughly 20% of the population). Logistic regression indicated that the birds that resided closer to the cabin experience higher hunting pressures and lower rates of survival (and vice versa). Although these results might seem obvious, the methods utilized in obtaining and analyzing data are fascinating to say the least. The combination of GIS, GPS and radio telemetry provides detailed visual information, which can greatly assist hunting and conservation efforts on a global scale.

Source:

Brøseth, H., & Pedersen, H. r. (2000). Hunting effort and game vulnerability studies on a small scale: a new technique combining radio-telemetry, GPS and GIS. Journal Of Applied Ecology, 37(1), 182-190. doi:10.1046/j.1365-2664.2000.00477.x

Socioeconomic Status and Prevalence of Obesity and Diabetes in a Mexican American Community, Cameron County, Texas, 2004-2007


 Is there a link between race and obesity?  Often questions like these can be a difficult subject to approach due to mixed emotions of the incorporation of race into the discussion.  While it is important to acknowledge that factors such as race, socioeconomic status, sex and others can often be linked to certain diseases, it is important to understand that just because one may be a part of different groups does not necessarily mean they will develop a particular disease.

 According to research in Cameron County, Mexican Americans are at increased risk for obesity and diseases linked to obesity.  The purpose of the study was to determine whether there is a relationship between economic status and health.  Scientists randomly selected 810 Mexican American individuals to study between the ages of 35 to 64 years old.  Using the subjects’ weight measurements and other markers of health such as glucose levels along with surveying the subjects socioeconomic status, results showed that people who hold higher socioeconomic status, with an annual income of $24,067 to $31,747, are less likely to have undiagnosed diabetes than people who hold lower socioeconomic status, with an annual income of $17,830 or less .  Unfortunately in Cameron County, the majority of Mexican Americans in the community hold lower socioeconomic statuses and report that they do not have health insurance.  Not having health insurance decreases people’s visits to doctors resulting in them not receiving the necessary health check-ups and healthcare that they need.   Less than one fourth of the participants in the study reported having health insurance.  1 out of 10 participants in the lower socioeconomic group found that they had undiagnosed diabetes. 

Below is a graph that displays the research findings in Cameron County revealing a correlation between age, socioeconomic status and percentage of participants with diabetes. 



The research findings in Cameron County suggest that the Mexican American community is struggling with a health battle consisting of obesity and undiagnosed diabetes.  Lack of resources and funding is continuing to hinder this population and in result is affecting their health.  With proper monitoring, medicine, daily exercise, and a well-balanced diet, diabetics can poetically live a long and healthy life; without proper diagnosis and treatment, further health problems can occur and life span can decrease.  

Fisher-Hoch SP, Rentfro AR, Salinas JJ, Pérez A, Brown HS, Reininger BM, et al. (2010). Socioeconomic Status and Prevalence of Obesity and Diabetes in a Mexican American community, Cameron County, Texas, 2004-2007. Prev Chronic Dis 2010;7(3). http://www.cdc.gov/pcd/issues/2010/may/09_0170.htm. Accessed January 27, 2013.

A GIS-based spatial analysis on neighborhood effects and voter turn-out: a case study in College Station, Texas.



It is typically assumed that the percentage of voter turnout in an area is at least minimally affected by local or neighborhood influence, whether that means local media coverage of a certain issue or varying opinions on what areas of the city or town need to be repaired, using local tax dollars, etcetera.  This study, by Danile Z. Sui and Peter J. Hugill, attempts to spatially illustrate and analyze the trends between voter turnout in an neighborhood and the different issues in each election, called the "neighborhood effect."

Sui and Hugill chose College Station, TX, the home of Texas A&M, for their data set. Because the city has very complex processes to initiate official political moves, such as creating city petitions and demanding recalls, but opperates on a relatively small scale, College Station has a very particular political environment perfect for a study examining local elections in a spatial manner. They chose to explore the results of three referenda on three different years, the first of which addressed the application millions of dollars in tax bonds being set aside to improve the city’s infrastructure (1995), the second a petition to halt the construction of a City Convention Center (1997), and the third an appeal to reopen one of the oldest streets in the city, that was closed by the City Council due to incessant traffic, all compared to the voting turn out in each voting precincts. These precincts were used as if they were neighborhoods, as they coincided with many of the main residential areas and are useful in depicting election results, although the results would not be as effective, or accurate, in larger cities.
In analyzing the data, the researchers matched addresses on voters and non-voters, then applied the voting results for each referenda to a map of the city, in order to compare the spatial distribution in voter turnout and the actual results of each. Sui and Hugill used a fairly complicated method to analyze this data called Ripley’s K-Function to determine “whether a given point pattern departs from randomness toward clustering or regularity” (Sui and Hugill 2002, 163). This is important in determining whether certain neighborhoods had any correlation with the voter turnout, if there was clustering, or whether it was primarily indiscriminate.  
The maps below illustrate the results from this study. In the maps on the left (i, iii, and v), each point represents a registered voter. It is clear that the first issue, the tax bond for repairing city infrastructure was the least controversial, with a low voter turnout and, as seen in figure ii, and passed with a majority in nearly every precinct (with the solid gray sections of the circles representing for the referenda and the white with dots against it in each precinct, seen in map ii). The distribution of these results further illustrates the fact that the areas that would receive these improvements had the most voter turnout, thus the most people who cared about such a relatively mundane issue turned out to vote. In map iii, there is a much higher voter turnout, as well as closer results in many of the precincts, seen in map iv. Evidently, the idea of College Station having a city funded convention center was quite controversial, as it was seen as a pure business expenditure paid for with citizen tax dollars to some, but an effective stimulate for economic growth in the area to others. The measure passed, but not with a large majority.


However, the 1991 issue was the most controversial, illustrated in v and vi. There is a clear concentration of voters in map v, apparently extending south from A&M’s campus. In closing Munson Avenue, citizens needing to go north, presumably mostly students and employees if the college, obviously felt quite strongly that the road needed to be open, probably because it offered more options, thus less traffic, to get where they needed to go.
Sui and Hugill determined that when there is a particularly high voter turnout in the precinct, there tends to be a very clear preference in the area for a particular result, which may be extremely different than the results in other precincts, clearly seen in the first and examples. However,  the difference between these two incidents is the actual voter turnout. There is certainly clustering of voters in the first example, but not to the same scale as the third, where there is a distinct clustering in very large numbers, illustrating the passion the certain neighborhoods felt for repealing the closure. Conversely, when voter turnout is more scattered, there is not obvious polarization of results, as seen in the even spread of voters and  nearly split results overall in the second example. These results are complicated and certainly do not show the full range of components affecting voter turnout, but the use of GIS in this particular geographic area to analyzed political results helps illustrate “the neighborhood effect,” where communities can  have a legitimate impact on voter turnout, and thus influencing the outcomes of particular issues acutely affecting members of these communities. 

Sui, D.Z., & Hugill, P.J. (2002). A GIS-based spatial analysis on neighborhood effects and voter turn-out: a case study in College Station, Texas. Political Geography, 21. Retrieved from https://lms.southwestern.edu/file.php/4373/Literature/Sui-2002-GIS_Voter_Turnout.pdf 

Sunday, January 27, 2013

Land Use/Cover Change in Central Tibet

There is current research being conducted in central Tibet to compare approximate amounts of cultivated land in 1830 to 1990.  In order to find these data, researches must use the tax decree that instigated the use of the kang.  A kang refers to an approximate weight of barley seed sown by an estate.  Only by calculating seed weight is it possible to determine an area of cultivated land.  Rather than determining precise rates of change, since the information is often ambiguous or inaccurate and the tax decrees have been inconsistent, the researchers will test several general hypotheses relating to general regional patterns of land use and coverage.  Obviously, not all cultivated land was controlled by estates or taxed but most of it probably was.

Unfortunately, there is much unknown data because Tibet remained fairly independent and did not keep records of this variety back in 1830. The distribution of land grants was also mapped for which approximate sizes are known.  The resulting grid cells were located to within 2-10 km of accuracy.

 By studying the changes historically in agrarian land use patterns, we can examine a traditional theme of cultural geography. In this manner, we are able to see how the people and the culture changed as well as the land. 

Some traditional central Tibetan districts are not included in the Iron Tiger land decree that established the kang.  Also, the Sakya monastery and its landholdings are excluded.  There are a few other areas excluded due to certain historical events.

Below is the map of central Tibet divided into groups by kang distribution.

Researchers are also able to find potential cultivatable land area because the kang only refers to the amount of seed sown in a given area.  The actual land size depends on other factors such as soil fertility and local climate conditions.  Using more recent county-level data we can figure out county boundaries within which kang tax records from 1830 most likely apply.  On average, Tibet's cultivated land increased by 59% over the 160 year period. 

This data exemplifies how environmental and economic history are often intertwined and can be used to effectively answer many questions about a certain region.


Source: The Geographical Journal, Vol. 167, No. 4 (Dec., 2001), pp. 342-357Published by: Wiley-Blackwell on behalf of The Royal Geographical Society (with the Institute of BritishGeographers)

Tweet Me Your Talk: Geographical Learning and Knowledge Production 2.0



     Can I Have Your Attention?

     Could our rapidly changing “online” culture be changing our brains? Studies have shown that the way we produce and translate geographical knowledge is changing, and some would say for the worse.
New neurological research has uncovered that the way the human brain collects information is able to be rewired. It’s been found that “our brains are flexible and can be modified at the cellular level depending on exposure and usage,” changing the way we learn and focus on information (Schuurman, 2).
     Compared to the past when neurological patterns supported the deep concentration needed to read long scientific articles and scan books, our culture is now favoring short, scattered methods of receiving information that is characteristic of online browsing. The type of reading done online has the tendency to have nonlinear patterns and focuses on keywords versus the article as a whole. Because of our constant access to the web’s boundless information, we under stimulate long-term memory, making our ability to use old knowledge to create new knowledge weak.  It has become a cycle of constant scanning but little real focus, described as “continuous partial attention” (2).
     But what does this mean for geographers? There are several negative effects of this type of learning has on the human brain and how we process information. Essentially, we are losing our ability to focus. Distractions are endless in our online culture, and each time we are distracted, it takes us ten to twenty times longer to recover. We are being overloaded with information, which lowers our ability to make rational decisions, stemming from our loss of critical thinking. The constant barrage of information websites, email, and news sources afflict on the average American is taking its toll on our minds. It’s keeping our brains from the “downtime” needed for our unconscious brain to help make decisions. This is a problem for geographers because “It makes learning less geographically specific and more homogeneous across space” (4).
     The way information is being presented affects our ability to learn as well. Scholarly article websites have started using crowdsourcing, or relying on views and promotion to make an article front-page and valid. This encourages passivity in choosing the right article for your task, taking the “search” element out of searching for information. The layout of PLOS One’s article is a novel example, listing page views and allowing for comments, what some would say are distractors from the article itself.


     This shows a movement toward simplification of content versus the advancement of the content itself, a trend that affects geographers and Americans alike. Sharing information has become a phenomenon that focuses on presentation versus content.
     How we evolve to learn and grow academically will affect the field of geography in unprecedented ways. Let us hope that this is a change that will ultimately be for the better, as more people are able to access the abundance of information that can be found in such a diverse field.




Nadine Schuurman (2012): Tweet Me Your Talk: Geographical
          Learning and Knowledge Production 2.0, The Professional Geographer,
          DOI:10.1080/00330124.2012.693873