Voter Migration and the Geographic Sorting of the American Electorate

This study shows that party ID voters relocate based on destination characteristics and constraints but also prefer to locate to destinations with people who share their political party’s ideology. While aiming to address the corresponding migration of individuals with locations that fit their political preferences the authors do take to consideration that geographic patterns may occur due to social processes and environments. They also note that relocation decisions are usually and primarily made due to constrained choices which are usually economic. However, if there is a desire for individuals to move to places that suit them socially and economically then the same can go for politically.
The authors find that 30% of those surveyed in their data take partisanship to be an important factor to their desired place of living. Liberal residential settings are typically urban, whereas conservatives prefer suburban and rural areas. For example, democrats are found to prefer densely populated areas more than Republicans.
According to the article republicans tend to sort rather than mix where as democrats don’t mind mixing, sometimes democrats will relocate to areas that are more conservative than their origin but this is probably because the most democratic locations are in big cities that have several social problems. The same negative features can go to the most republican places as well which are rural and have limited employment opportunities. Republicans prefer moving to more republican locations which keeps increasing the further the distance is. On the other hand Democrats can have similar preferences as republicans except their preferences are not as strong as the preferences republicans have. This can be seen in figure 1 which shows the migration in and from Portland, Oregon.

Cho, W. K., Gimpel, J. G., & Hui, I. S. (2013). Voter Migration and the Geographic Sorting of the American Electorate. Annals of the Association of American Geographers, 103(4), 856-870. doi:10.1080/00045608.2012.720229

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

This article explores the relationship between the individual voter turn outs and the city district's voting outcomes. Particularly the authors who conducted the study wanted to answer their question on whether the neighborhood effect influences the individual voter turnout in local politics. It is important to note that the key subject word "neighborhood effect" in the article is defined as where social interaction within residential communities affects people's voting choices and behaviors.
In pursuing their case study the authors choose College Station as a sample because the city's voting districts are fairly close to neighborhood communities. The authors concluded that the neighborhood effect is heavily influenced by the distribution of voters and that the neighborhood effect is related to the issues and the publicity of the issues.
To obtain their results the GIS based spatial analysis and the address matching tools mapped individual voter distribution and voting results at the local level for referendums. Arc GIS's Ripley K function and Getis-Ord's GI* was able to find the relationship between the distribution of actual voter turn-out and the neighborhood effect.
In figure 1 each point represents an individual voter and the positive z values signal high spatial clustering where as negative z values show low spatial clustering. Figure 1 shows the spatial distribution of the actual voter turn out done through the use of address matching and voting results in the local political level. The authors remarked that the neighborhood effects had a clear impact on voter turn out especially on a controversial issue that is important to voters.

This study proves that people who talk together vote together; in other words inter-personal communications and organizationally based interactions are part of the neighborhood effect which influences voting behavior. The study shows that the voter turn out’s spatial distribution influences the geographic patterns seen in voting results.

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(2), 159-173. doi:https://doi.org/10.1016/S0962-6298(01)00054-3

New York City: GIS Measures and Field Observation

This article maps out certain criteria that effect the lives of residents of New York, New York. The disparity the article focuses on is chronic disease related to lack of physical activity in poor neighborhoods of New York and non poor neighborhoods. 
It is true that the low-income neighborhoods of New York would be predisposed to physical activity because of their walkability, a phrase which here refers to intersecting factors such as land-use mix, and high population density). Yet, these opportunities for physical activity are not utilized because they are complicated by further economic disparities such as high crime rates, and lack of aesthetic appeal. It is difficult to decide if the the authors' conclusion would best be described as oblivious or hackneyed: that in order for low-income residents to utilize the walkability their neighborhoods , the architecture must be made more aesthetically pleasing, and crime must be reduced. Upon a cursory reading of this article, the authors' methods may seem straightforward and logical. When looked at on a more macro scale, however, reading the article becomes uncomfortable. That lack of physical activity despite walkability was the central issue seemed simple enough. Yet, the factors leading to lack of physical activity were not plugged into the greater socio-economic contect. To phrasing of the article makes the lack of physical activity in low-income neighborhoods of New York appears to prioritize physical activity, or lack-there-of, over the factors which caused it. Crime rates in low-income neighborhoods are often high, in part due to racial-biases embedded in law enforcement. I have often found that the aesthetics of high-income neighborhoods are often high quality due to scenarios such as: a corrupt city council members take donations from wealthy residents, and therefore delegate more of the city's funds to fixing the roads of wealthy streets, or creating historical markers.
In summation, the authors' attempt at mapping out a way to benefit impoverished New Yorkers was not successful. I do not know that such an attempt could be successful without treating racism and classicism with proportionate gravity.








Neckerman, K. M., Lovasi, G. S., Davies, S., Purciel, M., Quinn, J., Feder, E., ... & Rundle, A. (2009). Disparities in urban neighborhood conditions: evidence from GIS measures and field observation in New York City. Journal of Public Health Policy, 30(1), S264-S285.

GIS Methods and Multi-Temporal Remote Sensing Data for Improved Landslide Hazard Mapping in Southern Kyrgystan

This article discusses how multi-temporal remote sensing data and landslide information sources combined with GIS can be used to make a multi-temporal landslide inventory for South Kyrgyzstan. This would allow for more precise tracking of landslide activity and accurate landslide hazard assessments. Tracking landslide triggering factors, such as precipitation and seismicity, GIS and and remote sensing data were used to predict and pinpoint the areas most likely to have landslides.




Figure 3 and Figure 4 show how spatial mapping was used to identify the origins of landslides and then track the affected areas in South Kyrgyzstan.

Land Changes Fostering Atlantic Forest Transition in Brazil: Evidence from the Paraíba Valley.

In the wake of the modernization happening in Brazil a forest transition is happening in Paraiba Valley. In order to analyze this phenomenon the history of this land must be looked at. The forest cover over Paraiba Valley traditionally showed a period of afforestation followed by a period of deforestation. As the Brazil experienced industrialization the opening of plantations, specifically eucalyptus, was seen across the Paraiba Valley. In the wake of Brazil's globalization the eucalyptus plantations decreased in production and some even abandoned.

To analyze this forest transition a cross-temporal map was made of the Paraiba Valley over 1985 to 2011. Using data from Landsat-5 TM, Rapid Eye, and field data forest cover changes were able to be seen and analyzed. The article focus on the deforestation and the cumulative gross rate of forest gain; at fist glance it is seen that stable forests are declining while at the same time gross rate of forest gain is increasing greatly. However, upon further observations of the classes used, it is seen that degraded pasture or used land is the "forest" that is growing as a response to the eucalyptus plantations being abandoned.

In conclusion it was seen that this forest transition, from stable forest to degraded pasture land, can be attributed to the socioeconomic constraints experienced by Brazil. The industrialization led to the clearing of forests while years later, once abandoned, nature begins to grow back showing net forest gain despite obvious deforestation.

Bicudo da Silva, R. F., Batistella, M., Moran, E. F., & Lu, D. (2017). Land Changes Fostering Atlantic Forest Transition in Brazil: Evidence from the Paraíba Valley. Professional Geographer, 69(1), 80-93

Characterizing and Predicting Traffic Accidents in Extreme Weather Environments.

This article concerns itself with the making of a traffic accident predictive model based on the Doppler data of Fairfax County, VA after an extreme weather event, in this case snowstorms. This will effectively estimate where hot-spots for accidents occur while increasing predictive capabilities with every input of data. Before analyzing the Doppler data, the systematic processes of climate change, urban migration, and aging infrastructure must be accounted for.

Figure 2 Ground Doppler weather radar visualizations of the 2011 Washington, DC, metropolitan area storm. (Color figure available online.)  

The analysis of the traffic accident patterns was carried across frequency of accident, speed limit where accident occurred, and the zone that the accident occurred. By using a kernel density smoothing method in combination hypothetically increasing the number of accidents leads to the predictive model of accident likelihood in urban Fairfax county.

Conclusions showed a strong correlation between accidents in residential zones. Speed limit analysis showed that up to 800 accidents sections between 35-45 mph held the most accidents; past 800 accidents the sections there were <=25 mph saw the majority of accidents. From this the County of Fairfax has been able to reduce urban hazards as well as be more prepared for inclement weather.

Medina, R. M., Cervone, G., & Waters, N. M. (2017). Characterizing and Predicting Traffic Accidents in Extreme Weather Environments. Professional Geographer, 69(1), 126-137

Summary of :Scale-Economy Conditions for Spatial Variation in Farm size

By Sent Visser

Jimmy Brymer

This article looks at farming and how it relates to economics of scale. The article starts by discussing how there is no great body of evidence to support this idea there is an optimal farming size. He then further supports this by doing a repression model that support this idea. Since this is true he then moves forward to prove that how farming of scale works with distance to market and soil fertility affects how big the farm is. With farms closer to market and having more fertile being smaller and having less production close allowing them to be smaller but farms that are further away from market and having less fertile soils having to be bigger.

Bibliography

Visser, S. (1999). Scale Economy Conditions for Spatial Variation in Farm Size. Geographical Analysis: An International Journal of Theoretical geography, 27-44.

Malaria diagnosis and mapping with m-Health and geographic information systems: evidence from Uganda

This article discusses how m-Health ("mobile-health", referring to the use of mobile and other wireless technology in healthcare) technologies and GIS can be used to improve access to medical services in Uganda's rural regions, where malaria prevalence is high and a significant portion of the population is not able to receive proper treatment. Once carried out successfully in Uganda, it is likely that these methods can be adjusted and used to scale in other countries around the world to reduce a variety of diseases. Using GIS, this study investigates where in Uganda malaria affects the largest number of people and where the application of m-Health protocol based on the available mobile network would have the highest impact. It also stresses the importance of continued diffusion of information and communication technologies (ICT) that would provide cheap, efficient, and geo-referenced data transmission for timely and effective response to disease outbreaks in all regions, but particularly rural regions.

Figure 2 below shows the number of malaria cases per year per hectare in Uganda, and figure 3 shows the area covered 2G and 3G mobile network coverage and the area covered by 3G mobile network coverage alone. Figure 4 shows the number of potential m-Health cases that could be covered by increased mobile network accessibility and the implementation of m-Health strategies.







As seen in Figure 3 and 4, the implementation of m-Health strategies could have a significant impact on accessibility to treatment in remote populations, particularly in Uganda's west and central-southeast regions. After analyzing which regions suffer the most from malaria outbreaks, mobile network creation and improvement efforts can be made in these specific regions to vastly improve access to  low-cost, reliable and safe diagnostic protocol, as well as reduce overcrowding and contamination potential in health facilities.

Larocca, A., Visconti, R. M., & Marconi, M. (2016). Malaria diagnosis and mapping with m-Health and geographic information systems (GIS): evidence from Uganda. Malaria Journal, 151-12. doi:10.1186/s12936-016-1546-5

Summary of Technological Change and The Spatial Structure of Agriculture

Summary of

Technological Change and

The Spatial Structure of

Agriculture By Sent Visser

By Jimmy Brymer

This article written in 1980 makes an interesting argument that although with all the advancement of society’s advancements that distance to market still matters for the amount of agriculture still being practiced. The author points to Von Thunen’s theory on this which he say is still relevant. To prove this point he used sample counties from big agriculture areas as samples such as areas in Colorado and Kansas. With these samples he entered them into a regression model that looked at agricultural intensity, the capital-labor ratio, and capital productivity increases over time from distance to the market. The regression model found that agriculture intensity does increase overtime and distance from the market. It also found that the further away from the Market there was no change in the Capital-labor ratio. Finally the fourth regression found that there was a decrease in the capital productivity overtime as you go fourth away from the market area with this being unaffected by advancements in technology. This he argues supports his claim that is rule still holds true.

Bibliography

Visser, S. (1980). Technological Change and the Spatial Structure of Agriculture. Economic Geography , 312-319.

The Global Impact of Land-Use Change

This article is about why there has been an increase in land-use and changes in land use, as well as the effects this has on climate change. The main questions the authors set to address were:
1. How are land-use changes contributing to global environmental changes?
2. What social-economic factors determine land use, and how will they change?
3. How does land use modify processes that influence global change?
The article was published over 20 years ago now, so some the terms and information have been improved upon between the time of publishing and now, but it is still an interesting look at the way social and natural sciences can be used together to better understand the cause and impact of land use change. The authors took a good look at various land use practices and how they are influenced by people in different social contexts. The authors used this chart to better understand the relationship between people, socio-economic factors, and land use change:

Ojima, D. S. “The Global Impact of Land-Use Change.” BioScience, vol. 44, no. 5, Global Impact of Land-Cover Change, 1 May 1994, pp. 300–304. JSTOR

Local food practices and growing potential: Mapping the case of Philadelphia

In their article 'Local food practices and growing potential: Mapping the case of Philadelphia' Kremer and DeLiberty set off by discussing the current state of the food system in the US. They write that because the system of food production has grown and become so removed from most people's everyday lives, "rating locally is no longer and obvious possibility to individuals living in major urban areas in the US." To map the local food system in Philadelphia the authors first interviewed various participants in the local system such as farmers, relevant government officers, etc. The mapping focus here was to use spatial analysis to show the locations of various types of local food, as well as their accessibility to people throughout the city. The authors concluded that because these local food systems are still developing and there is a lot to be learned from them it is important to continue to analyze them to see if they are making a valuable impact in communities, especially since most local food is generally marketed towards upper-middle class populations that may not need help with access.

Kremer, Peleg, and Tracy L. Deliberty. “Local Food Practices and Growing Potential: Mapping the Case of Philadelphia.” Applied Geography, vol. 31, no. 4, 2011, pp. 1252–1261.

GTography

As an environmental studies student interested in outdoor activities, I wanted to find out where the best hiking trails in Georgetown are located. This map shows several of the highest rated hiking trails in the Georgetown, TX area. Many of these trails are located around Lake Georgetown, along the San Gabriel River. There are many trails in Georgetown, so no matter where you're located in the city there are great places to hike nearby with beautiful foliage and various species to see.

GTography- PJ Quinters

This map shows all the chicken restaurants in Georgetown, Texas. As you can tell there are multiple places to fill your chicken needs. From fast food chicken places like Raising Cane's who only sells chicken, To places like Buffalo Wild Wings were there are a few other options. One thing is for sure, and that is Georgetown, Texas sure does love its chicken.

Late Night Dining in Georgetown, Texas

In the map above, I mapped out nine restaurants that are open after 10pm in Georgetown, Texas. I picked restaurants that are close to Southwestern's campus so that this can be helpful for Southwestern students. Late night fast food is becoming an important culture and economic aspect of life. As we continue innovating as a society, people tend to stay up later, trying to get more things done. This also is very important for a college town, because college age kids tend to be up late into the night. Late night restaurants are also important if they are close to a big highway, such as I-35, as some people travel late at night, and may get hungry and stop for food.

Gtography Haley Nicola Bars vs Coffee Shops

Topography and Analysis of Inundation from Hurricane Katrina in New Orleans

This article discussed the use of geospatial data such as elevation data to estimate flooding in New Orleans. Light detection and ranging (lidar) remote sensing is a tool that is used to map this area. This data was used in response to Katrina to analyze inundation. Lidar data was collected in 2002 by the Louisiana Oil Spill Coordinator’s Office and made publicly available.

The data recorded in 2002 was valuable after Katrina to map the extent of flooding throughout New Orleans. The lidar elevation data was used to estimate the volume of floodwater in order to predict the amount of time required to remove it. The depths were relative to the elevation of water that was recorded by a gage on Lake Pontchartrain. 

The application of topographic data with gage data for mapping was very useful when information was needed. Elevation data is valuable to assess flooding in cities and may aid in planning and reconstruction of infrastructure in the future.

Gesch, Dean. "Topography-based analysis of Hurricane Katrina inundation of New Orleans." Science and the Storms: the USGS Response to the Hurricanes of (2005): 53-56.

The Prevalence of Obesity and Diabetes in Mexican-Americans

This article examines the influence of socioeconomic status on obesity among the Mexican-American community. A study was conducted along the United States-Mexico border town Brownsville, Texas to track the obesity rates in citizens. The study was conducted to observe whether obesity was related to socio-economic status. Data was collected from 810 people in the area. The sampling used census data to find a diverse variety of participants for the study in terms of socio-economic status. The census used 4 tracts and divided them into 4 strata that classified income. Sampling was then retrieved from the first (lowest) and third socioeconomic strata. The median household income was $17,830 for the lower socioeconomic strata and $31,747 for the higher socioeconomic strata. The study conducted to observe obesity among the recipients with measurements of BMI, waist circumference, insulin levels and blood glucose.

GIS was used in this experiment through census data and geocoding of the houses of the recipients of the study. GIS was also used to visualize the distribution of income among households and the density of where samples were derived.  Maps were then formed with this data.

The studies showed that more than half of the participants were morbidly obese and did not show a variation between both socioeconomic strata. The most significance difference between the socioeconomic strata was the amount of undiagnosed diabetes. 1 in 10 of the participants had diabetes that they were unaware of in the lower socioeconomic strata. When the first strata was compared with the fourth strata(highest socioeconomic status), results showed that there diabetes was among 20% of the lower SES and only 10% in the higher SES. Additionally, diabetes and undiagnosed diabetes was more prevalent among people aged 55 to 64 in the lower SES.

In conclusion, the study showed that obesity did not vary between strata; however, diabetes was significantly more prevalent among lower people with lower socioeconomic status. This information is important because it brings attention to the rate of diabetes in this specific area and represents a group of people that likely have little or no access to healthcare.

AR, Salinas JJ, et al. "Socioeconomic Status and Prevalence of Obesity and Diabetes in a Mexican American Community, Cameron County, Texas, 2004-2007." Center for Disease Control and Prevention 7 (2010).