Lab 6 Two Georgetowns: Izzy's Top Parks in Georgetown, Texas

Georgetown is a hidden gem for outdoor loving and adventurous people. About 30 minutes from Austin, this town has all the outdoor commodities but a fraction of the population. From swimming, hiking, to laying out in the sun, these parks have it all. On this map we have Izzy Lackner's top 15 parks of Georgetown, Texas. Izzy goes to Southwestern University and is a resident of Georgetown and has spent 2 years marking down the best places to be outside. These parks are her personal favorites for outdoor fun. The parks labeled in blue are perfect for swimming since they are by water. The parks labeled in black are not recommended for water activities by Isabella Lackner.
The exact location of each park was plotted by the longitude and latitude.  Each park is no more than 3-4 miles from another park.  
I marked Southwestern University with a different color font to show a central location for any one using this map.  I go to Southwestern University and this shows the distance from where  me and other college students live in proximity to the parks. 

Spatiotemporal analysis and the swine flu

When an outbreak of the H1N1 swine flu started in Mexico and the U.S. it was quickly declared to be a pandemic. The World Health Organization or WHO, reported a total of 429 deaths world wide and over 90,000 serious cases. There were limited studies that have focused on global scale analyses of pandemics. The authors of “utilizing spatiotemporal analysis of influenza-like illness and rapid tests to focus swine-origin influenza intervention” did just as the title states. They used spatiotemporal analysis in order to study patterns that emerged from the swine flu outbreaks in order to assist the prevention of future outbreaks. From the data that they could access, they found that the swine flu was very prevalent by the US-Mexico border. The maps below show the cases of illnesses and their location.

 

By using spatiotemporal analysis, the authors were able to gain an understanding of how swine flu traveled and where outbreaks were likely to occur. In the future it will be helpful to use this technique in real time in order to stop pandemics from spreading and predict what areas are at the highest risk.

Wilson, J. G., Ballou, J., Yan, C., Fisher-Hoch, S. P., Reininger, B., Gay, J., ... & Lopez, L. (2010). Utilizing spatiotemporal analysis of influenza-like illness and rapid tests to focus swine-origin influenza virus intervention.Health & place, 16(6), 1230-1239.

I have acted with honesty and integrity in producing this work and am unaware of anyone who has not. Jolene Klenzendorf

Environmental variability and fish population density

Using fine-scale GIS data to assess the relationship between intra-annual environmental niche variability and population density in a local stream fish assemblage

Knouft, J. H., Caruso, N. M., Dupre, P. J., Anderson, K. R., Trumbo, D. R., & Puccinelli, J. (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(3), 303-311.

Varying population densities has primarily been attributed to the suitability of species to inhabit varying habitat. Knoufr et al. (2011) uses GIS to understand how intra-annual variation in habitat effects niche characteristics and population density of fish species in a stream. This was done during four time periods which were July 2007, October 2007, January 2008, and April 2008. These were chosen because of varying flow rates and temperature throughout the year. The two most important units of measure in this study are niche breadth (habitats occupied by the species) and niche position (the difference between the habitat occupied by the species from overall habitat). These were correlated with seven different abiotic habitat parameters which were dissolved oxygen, benthic flow rate, midwater flow rate, surface flow rate, depth, lower canopy cover (riparian vegetation <3 m in height) and sediment. Habitat parameters were collected at 83 or more locations along a 675-m sample site. Locations were divided based on river habitats which can be divided into riffles, runs and pools based on water flow patterns. They georeferenced each sample location and imported them as shapefiles that contained the 7 habitat parameters. They ended up with 5 measures of habitat variability because benthic flow rate, midwater flow rate, and surface flow rate were highly correlated and could be combined to produce an average flow rate. Figure 1 (below) shows the distribution of the five habitat parameters within the stream. Variation in population density among species was successfully predicted for samples in October and January.

I have acted with  honesty and integrity in producing this work and am unaware of anyone who has not. Bianca Perez