Sunday, June 25, 2017

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:

Tuesday, June 6, 2017

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 Policy30(1), S264-S285.

Friday, May 26, 2017

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 Geographer69(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 Geographer69(1), 126-137
Summary of :Scale-Economy Conditions for Spatial Variation in Farm size
By Sent Visser
Jimmy Brymer
Image result for farming
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.


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 Journal151-12. 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
Image result for farming

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.


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

Sunday, April 30, 2017

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