scholarly journals PSYCHODIDACTIC PRINCIPLES OF EDUCATIONAL GEOGRAPHIC MODELING

2019 ◽  
Vol 0 (19) ◽  
pp. 166-175
Author(s):  
Liubov Vishnikina ◽  
Tetiana Yaprynets
Keyword(s):  
Stroke ◽  
2018 ◽  
Vol 49 (4) ◽  
pp. 1021-1023 ◽  
Author(s):  
Michael T. Mullen ◽  
William Pajerowski ◽  
Steven R. Messé ◽  
C. Crawford Mechem ◽  
Judy Jia ◽  
...  

2019 ◽  
Vol 8 (9) ◽  
pp. 376 ◽  
Author(s):  
Zhi-Wei Hou ◽  
Cheng-Zhi Qin ◽  
A-Xing Zhu ◽  
Peng Liang ◽  
Yi-Jie Wang ◽  
...  

One of the key concerns in geographic modeling is the preparation of input data that are sufficient and appropriate for models. This requires considerable time, effort, and expertise since geographic models and their application contexts are complex and diverse. Moreover, both data and data pre-processing tools are multi-source, heterogeneous, and sometimes unavailable for a specific application context. The traditional method of manually preparing input data cannot effectively support geographic modeling, especially for complex integrated models and non-expert users. Therefore, effective methods are urgently needed that are not only able to prepare appropriate input data for models but are also easy to use. In this review paper, we first analyze the factors that influence data preparation and discuss the three corresponding key tasks that should be accomplished when developing input data preparation methods for geographic models. Then, existing input data preparation methods for geographic models are discussed through classifying into three categories: manual, (semi-)automatic, and intelligent (i.e., not only (semi-)automatic but also adaptive to application context) methods. Supported by the adoption of knowledge representation and reasoning techniques, the state-of-the-art methods in this field point to intelligent input data preparation for geographic models, which includes knowledge-supported discovery and chaining of data pre-processing functionalities, knowledge-driven (semi-)automatic workflow building (or service composition in the context of geographic web services) of data preprocessing, and artificial intelligent planning-based service composition as well as their parameter-settings. Lastly, we discuss the challenges and future research directions from the following aspects: Sharing and reusing of model data and workflows, integration of data discovery and processing functionalities, task-oriented input data preparation methods, and construction of knowledge bases for geographic modeling, all assisting with the development of an easy-to-use geographic modeling environment with intelligent input data preparation.


GCB Bioenergy ◽  
2010 ◽  
Vol 2 (5) ◽  
pp. 248-257 ◽  
Author(s):  
HENRIETTE I. JAGER ◽  
LATHA M. BASKARAN ◽  
CRAIG C. BRANDT ◽  
ETHAN B. DAVIS ◽  
CARLA A. GUNDERSON ◽  
...  

Burns ◽  
2018 ◽  
Vol 44 (1) ◽  
pp. 201-209 ◽  
Author(s):  
Carlee Lehna ◽  
Stephen Furmanek ◽  
Erin Fahey ◽  
Carol Hanchette

GI_Forum ◽  
2015 ◽  
Vol 1 ◽  
pp. 51-60
Author(s):  
Andreas Eisl ◽  
Andreas Koch
Keyword(s):  
Land Use ◽  

2020 ◽  
Author(s):  
Svetlana Malkhazova ◽  
Dmitry Orlov ◽  
Irina Bashmakova

<p>This research aims at the solution of environmental problems related to sustainable and economically efficient development of the North, which could enhance the quality of life and health of the population in the changing Russian Arctic. The medical geographic modeling of spatiotemporal patterns of naturally determined diseases is based on the detailed database covering the Arctic zone of Russia. The role of factors affecting the spread of diseases is unequal, with the climatic factor regarded as the most significant at all levels of territorial differentiation. At the highest (national) level, this factor determines the latitudinal zoning, which, in turn, determines the existence conditions of disease hosts and vectors and, ultimately, the foci of diseases. At regional level, the effect of climate is traced in monthly mean temperatures, temperature extremes, precipitation, snow depth, length of no-frost period, etc. Changes of these characteristics influence the poikilothermic (cold-blooded) arthropods, as well as the pathogens spending a part of their life cycles in the arthropods’ organisms. Another important factor is related to water resources, particularly, water-table height and ecological state of water bodies. Comparative analysis of hydrological and hydrochemical data, and their total impact on morbidity rates in terms of pathogenicity eco-indices, can serve as an additional tool for detecting the critical infection areas and population early warning. The original methodology is applied to evaluate the actual medical environmental situation, to forecast possible spatiotemporal changes in morbidity, including due to the most virulent infections, and to elaborate recommendations to public health authorities on planning the preventive and health-improving activities in the Arctic.</p>


1964 ◽  
Vol 5 (10) ◽  
pp. 19-33 ◽  
Author(s):  
Yu. G. Saushkin ◽  
G. T. Grishin ◽  
M. N. Stepanov ◽  
S. I. Ivanov ◽  
V. P. Novikov
Keyword(s):  

Author(s):  
Peter G. Furth ◽  
Adam B. Rahbee

A discrete approach was used to model the impacts of changing bus-stop spacing on a bus route. Among the impacts were delays to through riders, increased operating cost because of stopping delays, and shorter walking times perpendicular to the route. Every intersection along the route was treated as a candidate stop location. A simple geographic model was used to distribute the demand observed at existing stops to cross-streets and parallel streets in the route service area, resulting in a demand distribution that included concentrated and distributed demands. An efficient, dynamic programming algorithm was used to determine the optimal bus-stop locations. The model was compared with the continuum approach used in previous studies. A bus route in Boston was modeled, in which the optimal solution was an average stop spacing of 400 m (4 stops/mi), in sharp contrast to the existing average spacing of 200 m (8 stops/mi). The model may also be used to evaluate the impacts of adding, removing, or relocating selected stops.


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