Geographic modeling and simulation systems for geographic research in the new era: Some thoughts on their development and construction

Regionality, comprehensiveness, and complexity are regarded as the basic characteristics of geography. The exploration of their core connotations is an essential way to achieve breakthroughs in geography in the new era. This paper focuses on the important method in geographic research: Geographic modeling and simulation. First, we clarify the research requirements of the said three characteristics of geography and its potential to address geo-problems in the new era. Then, the supporting capabilities of the existing geographic modeling and simulation systems for geographic research are summarized from three perspectives: Model resources, modeling processes, and operational architecture. Finally, we discern avenues for future research of geographic modeling and simulation systems for the study of regional, comprehensive and complex characteristics of geography. Based on these analyses, we propose implementation architecture of geographic modeling and simulation systems and discuss the module composition and functional realization, which could provide theoretical and technical support for geographic modeling and simulation systems to better serve the development of geography in the new era.

Medical geographic modeling of spatiotemporal changes of naturally determined diseases under the changing climate and economic development of the Russian Arctic

<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>

From Manual to Intelligent: A Review of Input Data Preparation Methods for Geographic Modeling

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.

Identifikasi Faktor-faktor yang Mempengaruhi Angka Kematian Bayi di Provinsi Jawa Barat melalui Model Berbasis Regional

In geographic modeling, global models such as ordinary linear regression (OLR) model theoretically it provides quite reliable local information if there is not any spatial diversity by region. In other words, OLR model cannot describe the relations between variables in heterogeneous difference of each region. This study will consider a model that will be used to estimate or predict the infant mortality rate in the several regencies / cities in West Java Province. Because the response variable observed in this study is count data which is assumed Poisson distributed, geographically weighted Poisson regression model (GWPR) is used. A better model is used to analyze the data of infant deaths in each regency / city in West Java based on the AIC value, GWPR model has the smallest value (compared to Poisson regression model), in which there is an interesting and important difference from each regency/city about the factors that significantly influence the Infant Mortality rate in each region.