Geospatial Analysis of Rickettsial Species

Rickettsia species are obligate intracellular, arthropod-borne bacteria with a potential to cause multiple diseases including Rocky Mountain spotted fever (RMSF). Fleas, mites, and ticks serve as vectors for Rickettsia, but ticks are the primary vector of interest. RMSF and other rickettsial diseases have continued to gain importance in both human and veterinary medicine as RMSF is the most common tick-borne disease within the United States according to the Lyme and Tick-Borne Disease Research Center. A statewide citizen science project was utilized to determine the prevalence of Spotted Fever Group (SFG) Rickettsia in Arkansas. This project yielded results in 64 of Arkansas’s 75 counties. Results were utilized to determine prevalence in each of the represented counties, and then compiled into a geospatial representation of the data. It was determined that 34.32% of the ticks sampled were carriers of one or more rickettsial species. As the samples were divided by county, multiple counties were shown to have concerningly high exposure risk for SFG Rickettsia. There were six species of ticks represented throughout this study with Amblyomma americanum being the most common. There were also six species of Spotted Fever Group Rickettsia found within the samples. The small portion of ticks that underwent further analysis to determine the specific rickettsial species present, indicated that Rickettsia amblyommatis is likely the most common SFG Rickettsia in Arkansas

Artificial neural networks in geospatial analysis

Artificial neural networks are computational models widely used in geospatial analysis for data classification, change detection, clustering, function approximation, and forecasting or prediction. There are many types of neural networks based on learning paradigm and network architectures. Their use is expected to grow with increasing availability of massive data from remote sensing and mobile platforms

Geospatial analysis and living urban geometry

This essay outlines how to incorporate morphological rules within the exigencies of our technological age. We propose using the current evolution of GIS (Geographical Information Systems) technologies beyond their original representational domain, towards predictive and dynamic spatial models that help in constructing the new discipline of "urban seeding". We condemn the high-rise tower block as an unsuitable typology for a living city, and propose to re-establish human-scale urban fabric that resembles the traditional city. Pedestrian presence, density, and movement all reveal that open space between modernist buildings is not urban at all, but neither is the open space found in today's sprawling suburbs. True urban space contains and encourages pedestrian interactions, and has to be designed and built according to specific rules. The opposition between traditional self-organized versus modernist planned cities challenges the very core of the urban planning discipline. Planning has to be re-framed from being a tool creating a fixed future to become a visionary adaptive tool of dynamic states in evolution

Geospatial Analysis of Rickettsial Species in Arkansas

Rickettsia species are obligate intracellular, arthropod-borne bacteria with a potential to cause multiple diseases including Rocky Mountain spotted fever (RMSF). Fleas, mites, and ticks serve as vectors for Rickettsia, but ticks are the primary vector of interest. RMSF and other rickettsial diseases have continued to gain importance in both human and veterinary medicine as RMSF is the most common tick-borne disease within the United States according to the Lyme and Tick-Borne Disease Research Center. A statewide citizen science project was utilized to determine the prevalence of Spotted Fever Group (SFG) Rickettsia in Arkansas. This project yielded results in 64 of Arkansas’s 75 counties. Results were utilized to determine prevalence in each of the represented counties, and then compiled into a geospatial representation of the data. It was determined that 34.32% of the ticks sampled were carriers of one or more rickettsial species. As the samples were divided by county, multiple counties were shown to have concerningly high exposure risk for SFG Rickettsia. There were six species of ticks represented throughout this study with Amblyomma americanum being the most common. There were also six species of Spotted Fever Group Rickettsia found within the samples. The small portion of ticks that underwent further analysis to determine the specific rickettsial species present, indicated that Rickettsia amblyommatis is likely the most common SFG Rickettsia in Arkansas

Geospatial Analysis of Land Use Change in Way Kuripan Watershed, Bandar Lampung City

The purpose of the study is to analyze land use and land cover change impact on Way Kuripan discharge. Six scenarios of land use and land cover changes at Way Kuripan watershed area of 53.54 km² was developed based on geospatial analysis with Geographic Information System. Peak discharge is calculated by using rational method. From the six scenarios simulation, scenarios 1, 2, 3, and 4 maintained the protected areas of 80.15%. Land use and land cover changes done by changing areas from vacant land and agricultural areas to be residential, industrial and government office areas. From the analysis, peak discharge of scenarios 1, 2, 3, and 4 change slightly which are between 11.19% and 23.46%. These results are in contrast to scenarios 5 and 6, in which scenario 5 keep the protected areas about 53.35% while in scenario 6 left the protected areas around 30%. Those protected areas changed into residential areas. The result showed that in scenario 5, the peak discharge changed about 66.29%. While in scenario 6, it changed about 107.19%. It can be concluded that the existence of protected areas in Way Kuripan Watershed was very important role to reduce the peak discharge values

Supporting Cancer Prevention Strategies Using Geospatial Analysis on HRSA data

This paper uses develops a methodology to geospatially analyze factors associated with disparities in cancer rates

Geospatial Analysis and Internet of Things in Environmental Informatics

Geospatial analysis offers large potential for better understanding, modelling and visualizing our natural and artificial ecosystems, using Internet of Things as a pervasive sensing infrastructure. This paper performs a review of research work based on the IoT, in which geospatial analysis has been employed in environmental informatics. Six different geospatial analysis methods have been identified, presented together with 26 relevant IoT initiatives adopting some of these techniques. Analysis is performed in relation to the type of IoT devices used, their deployment status and data transmission standards, data types employed, and reliability of measurements. This paper scratches the surface of this combination of technologies and techniques, providing indications of how IoT, together with geospatial analysis, are currently being used in the domain of environmental research.Comment: Applying Internet of Things Technologies in Environmental Research Workshop, Proc. of EnviroInfo 201