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.