Southwestern: A Virtual Tour

In the rapidly advancing technological age, virtual environments are becoming increasingly prevalent.  Video games and virtual reality have put the users in the middle of the action to immerse them in a completely simulated setting.  Is this just a phase?  Or, will virtual reality be a major component of spatial learning and understanding for future generations?  Before, Southwestern University’s virtual tour is comprised of photos and brief snippets of information.  By using and working with Google, the user can navigate the campus similarly to Google Streetview.  Now, it is a virtual environment, made up of 360-degree panorama points where the user can look up, down, and all around the SU campus from their computer or smartphone.  This innovative use of the Google platform to work with a virtual tour of campus has not been done in this capacity before and would test the limits of this tool and of virtual environments in general.

The Effect of Damming on the Blanda River Flow Regime

Iceland is one of the only countries in the world that can claim to have a completely clean energy portfolio.  The demand for hydropower is still increasing, and Iceland has dammed many of its rivers to meet this need at the expense of their riparian ecosystems.  The regulation of flow regimes and creation of reservoirs has had a significant impact on the ecosystems in and around the rivers of Iceland.  The Blanda River has been utilized for hydropower since the early 1990’s, and will be used to model the effect of the dam on the downstream flow regime using Digital Elevation Modeling and Flow Accumulation.  By using these models along with river flow data, we will be able to analyze the effects felt as a result of the dam.

Oldest Churches in Georgetown

Proper Systems of Land Use: Farming

Source: 

Global Consequences of Land Use
Jonathan A. Foley et al. Science 309, 570 (2005);
DOI: 10.1126/science.1111772 

When thinking about Land Use, the general population tends to think more on a scale of deforestation, habitat destruction, logging, and farming. Things that are relatively brought up or seen in your daily activities. In this study about Land Use, it is concluded that "subsistence agriculture, clearing tropical forests, intensifying farm production and expanding urban centers" is changing the makeup of the worlds landscapes and at the expense of the environment. 

An example of this is found through comparing different types of land and the trade offs of human activity on these land types. 

In these diagrams we find that while maintaining a Natural Ecosystem is beneficial to the ecosystem, we clash with the ecosystem as we do not benefit financially and do not benefit from the land we considered to be "not used". In an Intensive Cropland, we essentially destroy the natural landscape and soil makeup. There is no natural regulation other than possibly restarting a new crop every year. If you look at the Cropland with Restored  Ecosystem Services, it is depicted that there is a mutual abundance of natural and human based eco-services. 

While food and crop production has been beneficial in supporting the human population, particularly in the U.S, there is a correlation with intensified farming and environmental damage. eg: Increased use of fertilizer correlates to poor water quality, soil erosion and overgrazing leads to complete loss of arable land. 

The continuance of uninformed farmers, ranchers, and land owners not practicing sustainability with the land the practice on will ultimately result in a more widespread disaster of climate change. 

Abstract: Food Miles Behind Nepali Coffee

Nepal’s coffee sector has grown dramatically within the past decade and is predicted to continue expanding in the coming years. The demand for Nepali coffee in both local and international markets is increasing; Nepali coffee is steadily establishing a firmer position in the world coffee market. While some of the coffee farmers that comprise Nepal’s coffee sector operate independently, many have organized themselves into coffee cooperatives to share resources, expand market access, and secure higher premiums for their beans. The effects of these cooperatives often extend beyond coffee farming to other domains of members’ lives. With a predicted increase in coffee production and number of coffee cooperatives, it is worthwhile to evaluate how cooperatives are reshaping the lives and livelihoods of their members. By using GIS mapped out the food miles behind Nepali coffee from the farmers, to the cooperative, to the storefront, mainly using data coordinates. This research provided insight regarding the challenges for coffee farmers and the effort that is behind every cup of Nepali coffee.

Georgetown City Park Locations Compared to Median Household Income Values

Want to go outside and play? Find a park, Georgetown is full of them. In fact, there are 30 city parks total.  Referring to the Georgetown Parks website, I plotted the location of 28 out of 30 city parks. Then, I used Fact Finder on the Census website to map out reported median incomes in Williamson County. There is a higher density of parks in the lowest income bracket in Georgetown. The middle brackets have few parks. The upper middle class has many parks separated by at least a mile. The upper class subdivision does not have any public parks.

Notice, each income level is distinctly separated in Georgetown. The highest income level lies farthest away from the downtown, while the lowest income levels are nearest the downtown. Parks may be more prevalent in lower income areas because there is a higher need, whereas upper class subdivisions tend to have larger private yards. More, the low income sector in Georgetown also overlaps the historic district. Perhaps, the parks were built first and as development spreads north, there are fewer parks being built.

This map focuses on the amount of elderly, ages 65 and up in Georgetown, TX. Each dot indicates 5 elderly. From the map, one can see the area of Sun City, located down Willams drive, south Willamson county down I-35, and east near Hutto have dense amounts of elderly in the area. 

GTography: Breakfast Tacos in Georgetown, Texas

Texas is known for serving delicious breakfast tacos, especially in the Austin/Georgetown area. This map displays all of the restaurants that serve breakfast tacos in Georgetown. I used yellowpages.com to find the addresses, uploaded them onto arcgis.com, and created a map displaying the locations. The map shows an interesting clustering of restaurants right off of highway I-35, and also on the main streets that lead to I-35. The clustering in these areas probably occur because people would rather stop somewhere on route to their workplace, rather than go completely out of their way, when commuting to work. 

Abstract: Correlations Between SAT Scores and Potential Influential Factors

High school students must take the SAT and/or ACT before applying to college in the United States. SAT and ACT scores are considered important in determining a student’s acceptance to their desired school. However, some may argue that these standardized tests may be biased, and influenced by factors other than a student's abilities in math, reading, and writing. For example, does the parental status of the student (e.g. divorced, married, single) have any correlation with the scores? In this project, the Texas Education Agency's annual report on college admissions testing for high school seniors in public schools was used to create a visual representation of SAT combination reading and writing scores across the state. In order to create a map with an uninterrupted range of scores, versus simply an accumulation points, the data was interpolated. Statistical analyses compared the scores to important factors such as race and ethnicity, household income, parental status, etc. These analyses revealed significant correlations between SAT scores and the above factors, suggesting that the scores may not be an accurate assessment of student's math, reading, and writing scores. 

Land use in Belize

In this article the author explores deforestation in Belize and the impact that road making has on the landscape and development of the country. Roads are what allow access to remote forests in the country and they do not always lead to the most efficient use of the land. The author suggests that is GIS methods were put into use by the people making the road then the land could be use more efficiently. By studying the land through the use of GIS it is more likely that road planners would have a better idea of what resources these roads may lead to so that trees are not needlessly cut down in vain. By planning out the use of land spatially and in the context of the environment, rather than just clear cutting a road, there can be a cut down on unneeded loses.

By following the model of von Thiinen and the equations that can better determine the cost and benefits of land use we can put value to the land so that a plot of land is used to its best economic and environmental potential. Methods like this and GIS can be used to better plan out the use of land not only in Belize but really anywhere there is land to be developed.

Chomitz, Kenneth M., and David A. Gray. "Roads, Land Use, and Deforestation: A Spatial Model Applied to Belize." Oxford University Press, n.d. Web.

Abstract: Ecuador's Rose Farms

Behind Colombia, Ecuador is the world's leading exporter of cut roses. In addition, they export numerous other cut flowers. The United States of America is the number one consumer of Ecuadorian roses, and the rose floriculture provides significant economic impact in both Ecuador and the U.S.A. This project's aim is to provide a visual of rose farm concentration in Ecuador and provide market information on the quality of roses as it relates to altitude. A map such as this does not exist, and as such, this map will provide a social service to the rose growers in Ecuador. ExpoFlores, an organization that manages the social and environmental impact and image of the Ecuador's flower farms, provides a list of farms. From this list, I will create my own specific list of rose farms. Luckily, most farms have a website;however, most do not list the address, but the town. Since the exact locations of farms are not always provided, I will use Normalized Digital Vegetation Difference (NDVI) technology through ArcGIS online to locate the farms.

Digital Elevation Models (DEM) will be used to determine the altitude of the farms. Rose growers in Ecuador claim roses grow taller, stronger and straighter with higher altitudes. Once I have the farms location and altitude plotted, I will refer to sights such as FlorEcuador and ExpoFlores to see if the quality of the rose does indeed correspond to the altitude. If the DEM model proves to be too time intensive, I will then plot all flower farms to create a map which models concentration of rose farms compared to other flowers.

Industrial Evolution in Austin, Texas: From Environmental Hazards to a High-Tech Explosion (Abstract)

Grady Sampley

Abstract:

As the Capital of Texas, Austin has always been a major epicenter and leader for  Texas economic and cultural progression. Generally, Texas has been known for their large-scale  manufacturing industry wherein the petrochemical sector takes the lead. This highly industrial  economy and culture carries along with it a variety of environmentally hazardous byproducts.  Austin has also historically absorbed this kind of large-scale manufacturing industry as will be  shown in this project via GIS applications using the Toxic Release Inventory (TRI); however, as it  has advanced into modern times, Austin has moved towards a more innovative, creative, eco-friendly, and high-tech culture and economy. This budding cultural and economic change has resulted from the influx of young and educated populations who are mainly attracted to Austin’s unique and recently fostered progressive reputation which, in turn, prompted the explosion of its high-technology industrial sector generally due to the abundance of suitable potential employees. To show this explosion of high-tech industry within the Austin area my project uses the “Directory of Austin-Area High-Tech Firms” produced by The Greater Austin Chamber of Commerce in unison with GIS mapping applications. Through the utilization of both TRI and this directory, my project will illustrate the evolution of Austin’s Industrial landscape from a large-scale manufacturing industry – along with its environmentally hazardous byproducts – towards a much more clean, creative, and innovative high-tech industry. With these illustrations, my project analyzes the positive and negative implications of this industrial evolution on Austin’s socio-economic landscape.

Mexican-American Socioeconomic Status and Obesity

Source: 

Fisher-Hoch SP, Rentfro AR, Salinas JJ, Pérez A, Brown HS, Reininger BM, et al. Socioeconomic status and prevalence of obesity and diabe- tes in a Mexican American community, Cameron County, Texas, 2004-2007. Prev Chronic Dis 2010;7(3)

In this article, the risk for obesity and diabetes is examined in a small county on the U.S-Mexico border in Texas. The study is partial to Mexican-Americans, as the county is predominately made up of Mexican-Americans. Randomly selected and studied persons aged 35-64 year olds are looked at along with their socioeconomic status and the correlation to diabetes risk along with obesity. The final sample, after going through multiple cuts to make the sample more accurate, was made up of all hispanics with 68% being female. 

Variables such as, BMI, waist circumference, fasting blood glucose and insulin were taken into account. They then took this information and compared it too the annual household income, using GIS "to visualize spatial distribution" by different income quartiles. The findings were phenomenal, as this county was found to have a large uninsured racial minority population. People in the lower income stratum were found to be more likely to have undiagnosed diabetes, as the risk increases with age. Oddly enough, there were no differences between socioeconomic status with prevalence of obesity. 

Conservation of Viperids in North-West Africa Using Ecological GIS

When one thinks of the wilderness of Africa, one of the first concerns that comes to mind is the presence of venomous snakes.  Scientists are also extremely interested in the locations of these snakes, and in conserving them and their habitats.  They are now beginning to implement High-Resolution GIS imagery into their methods of determining the environments of these snakes.

As you can see in this first image, the use of raster data is crucial in the determination of the landscape of various regions in this section of NW Africa.  This map also includes vector data of the points at which the various species of snakes are located.  These two types of data are combined to create the map below, which shows the different species of snakes along with the likely overlap of species in the various regions.

The knowledge of the density and variety of viperids in the various biomes of NW Africa is instrumental in their conservation.  Knowing the variety and density of viperids in each type of habitat allows conservation biologists to tailor the conservation programs to the individual scenarios that occur.

1. • Brito et al., 2011
   • J.C. Brito, S. Fahd, P. Geniez, F. Martínez-Freiría, J.M. Pleguezuelos, J.-F. Trape
   • Biogeography and conservation of viperids from North-West Africa: an application of ecological niche-based models and GIS. Journal of Arid Environments, 75 (11) (2011), pp. 1029–1037

Links in Diabetes Rate Among Mexican American

 

Mexican Americans are more likely to have diabetes than any other ethnic group in the United States. Many believe that this is because the disease is genetic but may in part be due to the lower economic status of this ethnic group. Many cannot afford the decent health care that can identify early warning signs of diabetes and they do not get treatment right away.

 

 

 

Using GIS we can conclude that many of the clusters of diabetes form near the border to Mexico. The correlation between higher risks of diabetes and these areas is due largely to the socioeconomic status. Many Mexican Americans living along border towns are not wealthy and therefore cannot afford proper medical care. It can be seen that with better medical care comes less cases of diabetes among Mexican Americans. People who can afford the better medical care will be healthier and it shows among this particular group of people. This data can be useful for government agencies wishing to release funds for better medical care among struggling communities.

 

https://lms.southwestern.edu/file.php/5109/Literature/Wilson-2010-OBESITY_MEXICAN_AMERICANS.pdf

 

Swine Flu 2010

When the Swine Flu (H1N1) outbreak occurred in 2009 many people feared this outbreak. But by using GIS techniques scientists could be able to better watch out for an outbreak and know how to react when such a case happens. In Brownsville Texas GIS data is used to track past information about outbreaks of diseases from previous years. They can use this data to identify clusters where the disease may have sprung from or where it began spreading. If a certain disease is spreading rapidly in one specific area consistently, there may be some reason for this.

 

After you get a general feel for where and how severely specific influenza type diseases like ILI, RIDT, and S-OIV spread in a town like Brownsville, you can compare this to a new disease like Swine Flu and get a general idea for where your front line defenses should be. By mapping out specific danger zones you can actually be better prepared for when a major disease hits a city. When Swine Flu hit the city of Brownsville the city officials had a better idea of where care for the disease would be necessary and that way they were better prepared.

 

https://lms.southwestern.edu/file.php/5109/Literature/Wilson-2010-SWINE_FLU.pdf

Austin 2002 Species Distribution

 

Scientists are starting to use geographic information systems (GIS) to track growth and extent of plant life in a specific area. This information can be used to understand and predict the trends in certain species of plants. They can visually see growth, decline, or migrations that might be occurring and use that information to get a general idea of how a species is competing.

 

In order to get a good understanding of the environment there are three models of information that need to be taken into account, the ecological model, a data model, and a statistical model. The ecological data is information that can be taken from the field and tested to find results. The data model is the numerical value of the ecological findings and the statistical model measures the level of error and determines the significance. We can now see trends and find possible reasons for a change in a species and natural ecosystem of this specific area. Discrepancies between process model and statistical model will demonstrate our lack of knowledge and may also indicate the way forward. Different environmental factors affect specific areas differently.

 

https://lms.southwestern.edu/file.php/4373/Literature/Austin-2002-SpeciesDistribution.pdf

 

 

GIS employed in California's Supplemental Nutrition Assistance Program (SNAP)

Stone, M. (2011). Enhancing the delivery of supplemental nutrition assistance program education through geographic information systems. Journal of Nutrition Education and Behavior, 43(4, Suppl 2), S148-S151. doi: 10.1016/j.jneb.2011.01.009

Link: http://www.nursingconsult.com/nursing/journals/1499-4046/full-text/PDF/s149940461100042x.pdf?issn=1499-4046&full_text=pdf&pdfName=s149940461100042x.pdf&spid=24335937&article_id=849514

The California Department of Public Health and the USDA has began an initiative known as The Network for a Healthy California (known as Network) to educate individuals who are eligible for the Supplemental Nutrition Assistance Program (SNAP). In order to be eligible, the person's household income should be below 185% of the federal poverty level.

The Network implements GIS technology to geographically identify areas of California that contain large numbers of eligible individuals. GIS is used to identify eligible census tracts, low-resource schools, and community sites (i.e. unemployment housing or food banks).

Network users also use GIS to create layers of varying information, from the California Fitness-gram scores to the number of fast-food restaurants available to students within a half-mile of school. Currently, the Network has 122 layers available for spatial analysis.

The maps that result from these spatial analyses are used to influence policy makers decisions and engage the neighborhoods that are identified.

Obesity and Diabetes Trends in Cameron County, Texas

Fisher-Hoch, Susan et al (2010). Socioeconomic Status and Prevalence of Obesity and Diabetes in a Mexican American Community, Cameron County, Texas 2004-2007, Preventing Chronic Disease: Public Health Research, Practice and Policy. 7 (3), 1-10.
 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2879985/


This study relied on data from the Cameron County Hispanic Cohort (CCHC) to determine the prevalence of obesity and diabetes in Mexican American populations. Specifically, the study examined the affect of socio-economic status on obseity and diabetes rates. The CCHC now numbers 2,000 people aged 35-60 years. Of these, 810 people were randomly selected for the study.

Income was divided into four stratas. Researchers targeted the first strata (the lowest earning) and the third strata (a higher earning group). The lower earning strata had a median income of $17,830 or less; higher earning, $24,067-$31,747. There were more participants representing the lower strata than the higher.

Two nurses and four field workers, all bilingual and bicultural, conducted the study. They had each participant fast for ten hours prior to examinations and rescheduled the exam if they did not fast. During the exam, they measured weight, height, body mass index (BMI), blood pressure, waist circumfrance, insulin levels and blood glucose levels. BMI was used as a measure of obesity.

Results showed that over half of the participants in both income stratas reported a BMI in the obese range. 57% in the lower income strata were obese; 55.5%, in the higher.  Less than a half of participants reported insurance, and only five percent reported Medicaid coverage. More women than men were able to participate in the study, because the men tended to work hourly wage jobs.

The study concludes that income is a direct influencing factor contributing to obesity and diabetes. This study is the first of its kind to focus exclusiviely on Mexican Americans in a border city. Results coincide with already published studies which show that the rate of diabetes in Latino communities is twice that of non-Hispanic white communities.

Impacts of Urbanizaiton on the Environment in China

Source: 

Wong, Q.  2001.  A remote sensing–GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China.  International Journal of Remote Sensing, 22(10): 1999-2014.

The Zhujiang Delta region of China is one of the largest areas of economic concentration in Southern China, and as such has experienced rapid urbanization during the years since the implementation of China’s economic reform policies.  It is also one of the richest agricultural regions.  With the rapid urbanization process changing the land-use patterns of the region, and keeping in mind the Urban Heat Island phenomenon, in which urban areas tend ot have higher surface temperatures than surrounding areas, Wong attempted to use satellite imaging and GIS analysis technology to study the pattern of urbanization and determine its effects on the surrounding environment. 

Wong identified 7 types of land use using LANDSAT images of the region for both 1989 and 1997, and then quantified the changes land use between the two with a matrix.  These images were then overlaid in a GIS system to represent the data on land use changes in a single image detailing the overall changes.  Another layer, containing data on roads in the region, was added to the GIS system and a buffer was added to that data to determine the correlation between urbanization and the distance from major roadways. 

 

After that, radiant surface temperature data from each year, obtained from LANDSAT TM thermal infrared data, was added to the GIS system and an algorithm was used to determine the change between the 8 year period.  Combining these elements in the GIS system allowed for the spatial analysis of the correlation between urbanization trends and increases in surface temperatures between 1989 and 1997. 

 

 

This allowed Wong to conclude that not only has increased surface temperature correlated to increased urbanization, but also that greater urbanization has occurred in rural areas and in closer proximity to roadways.  It also presented interesting data regarding two other land uses.  Surface temperature increases were also correlated with barren land use as well as non-traditional horticultural lands, though to a lesser extent.  Higher temperatures were also found in certain water bodies, which Wong hypothesized might be a result of increased sediments and other particles in the water from greater urbanization and industrial run-off.  This demonstrates other potential uses of GIS analysis in determining impacts of urbanization and land use change on the environment.

Scale in GIS: An overview

F. Goodchild, Michael (2011).  Scale in GIS: An overview.  130 (2011), 5-9. 

This review article discusses the relevance of scale, as a disciplinary problem, within GIS and geomorphology.  Three general problems of scale are discussed, then two methods for conceptualizing phenomena, raster vs. vector data, problems specific to GIS & modeling, & lastly, methods of formalizing scale within various disciplines.

The first problem of scale is semantic.  Scale is used in 3 senses in science:

1)   Cartographers refer to the representative fraction as the parameter that defines the scaling of the earth to a sheet of paper

a.     ratio between map/ground

b.     defines level of detail, positional accuracy

c.      this is undefined for digital data

2) Extent of study area

3) Resolution 

The second problem (resolution is finite):

1)  The earth’s surface is infinitely complex & could be mapped to molecules/smaller

2    2)   Praxis determines the most largest (usually most relevant) features

a.     This makes me wonder why the largest features are usually most relevant

The third problem (resolution in processes):

a) If the process is significantly influenced by detail smaller than the spatial resolution of the data, then the results of analysis/modeling will be misleading (this is the problem of downscaling which geostatistics seeks to alleviate through the use of an inferred correlogram)

a.     This leads me to wonder if there are any processes that require all sorts of different levels of scale

      b) Most theories are scale-free

a.     can’t tell whether the model’s error (all models are imperfect) is due to spatial-resolution effects or the model, or both

There are two methods for conceptualizing geomorphological phenomena, which are scale-independent theoretical frames:

1) Discrete objects

a.     the world’s surface is empty like a table top (it is empty in the sense of it being simply flat, not empty empty or without a coordinate system) except discrete things

b.     things can overlap

c.      good for biological organisms, vehicles, buildings

d.     represented as points, lines, areas, volumes

e.     so these would be better able to map interactions between things (even if those things are not things per se), where in the continuous-field this would be lost/aggregated/generalized, esp. raster models?

f.      maybe we can make a partial analogy between this and substance ontology?

       2) Continuous-field

a.     phenomena expressed as mappings from location to value/class, so every location in space-time has exactly one value of each variable

b.     topography, soil type, air temperature, land cover class, soil moisture

c.      process ontology?

Both of these can be raster or vector data, but raster can’t be laid on curved surfaces

      1)   Raster data has resolution explicit in the size of its cells

a.     smaller cells, more of them = better resolution

b.     the intervals between samples defines this

c.      in 3-d sampling the vertical dimension is often sampled differently than the horizontals leading to a differential in resolution

                                               i.     e.g. remote-viewed 2-d map + field photos of vertical dimension

      2)   Vector resolution is poorly defined and difficult/impossible to infer a posteriori from the contents of its data sets (as most GIS work is done on already present data sets)

a.     if the data’s taken at irregular points the distance between points may be used as resolution – how often is the data taken irregularly?

                                               i.     use the minimum, mean, max nearest-neighbor distance?

b.     when data’s captured as attributes of areas, it’s represented as a polygon/polyhedra

                                               i.     resolution is the infinitely thin boundaries, density of sampling of the boundary, within-area/volume variation that’s replaced by homogeneity, size of areas/volumes

                                              ii.     this creates the impression that vector data has infinite resolution

1.     at finer resolutions the numbers of areas/volumes increase and their boundaries are given more detail but they’re more homogeneous

GIS encounters a problem that applies to many types of geographic data

-measuring the length of a digitized line

-a vector polyline’s length is easily computed as the sum of straight-line segments

-if we assume each sampled point lies exactly on the true line, we will get an underestimate of the truth because no line has the points lie exactly straight

            -the underestimate is by an amount that depends on sampling density

-this applies to all natural features, slope, land cover

-this reminds me of stats (line regression) and calc (instantaneous velocity)

-the issue of zooming out and losing detail/generalizing reminds me of “essentialism” from philosophy, & i’m sure the problem of definition comes up elsewhere in GIS (what variables to use, what sampling method)

-the modified area unit problem shows us that aggregation or intersections of mapping elements (data collection and state lines, for example) will change correlations in a manner that is not random variation from alternative samples

-mandelbrot tells us that the rate of info loss/gain is constant/predictable through scaling laws and exhibits power-law behavior and self-similarity 

             an image displaying self-similarity:

-geostatistics gives us spatial interpolation to refine the spatial resolution of a point data set artificially through methods such as spatial autocorrelation, correlograms, and variograms

when creating an inferred correlogram does one infer up in levels/”steps” till the desired resolution of the study?

-wavelet analysis (a subset of Fourier analysis) allows the decomposed field variables to vary spatially in a heirarchical fashion ­– this sounds very interesting

In light of all of this, I am currently wondering about the study of part-whole interactions in relation to resolution and vector/raster datasets.