<p><strong>Abstract.</strong> Infection with tropical parasitic diseases has a great economic and social impact and is currently one of the most pressing health problem. These diseases, according to WHO, have a huge impact on the health of more than 40 million people worldwide and are the second leading cause of immunodeficiency. Developing countries may be providers of statistical data, but need help with forecasting and preventing epidemics. The number of infections is influenced by many factors - climatic, demographic, vegetation cover, land use, geomorphology. The purpose of the research is to investigate the space-time patterns, the relationship between diseases and environmental factors, assess the degree of influence of each of the factors, compare the quality of forecasting of individual techniques of geo-information analysis and machine learning and the way they are ensembled. Also we attempt to create a generalized mathematical model for predicting several types of diseases. The following resources were used as a data source: International Society for Infectious Diseases, Landsat, Sentinel. The paper concludes with the summary table containing the importance of individual climatic, social and spatial aspects affecting the incidence. The most effective predictions were given by a mathematical model based on a combination of spatial analysis techniques (MGWR) and neural networks based on the LSTM architecture.</p>

Abstract. Medical geography and medical cartography can be denoted as classical application domains for Geographical Information Systems (GISs). GISs can be applied to retrospective analysis (e.g., human population health analysis, medical infrastructure development and availability assessment, etc.), and to operative disaster detection and management (e.g., monitoring of epidemics development and infectious diseases spread). Nevertheless, GISs still not a daily-used instrument of medical administrations, especially on the city and municipality scales. In different regions of the world situation varies, however in general case GIS-based medical data accounting and management is the object of interest for researchers and national administrations operated on global and national scales. Our study is focused onto the investigation and design of the methodology and software prototype for GIS-based support of medical administration and planning on a city scale when accounting and controlling infectious diseases. The study area is the administrative territory of the St. Petersburg (Russia). The study is based upon the medical statistics data and data collection system of the St. Petersburg city. All the medical data used in the study are impersonalized accordingly to the Russian laws.

Modern natural language processing technologies allow you to work with texts without being a specialist in linguistics. The use of popular data processing platforms for the development and use of linguistic models provides an opportunity to implement them in popular geographic information systems. This feature allows you to significantly expand the functionality and improve the accuracy of standard geocoding functions. The article provides a comparison of the most popular methods and software implemented on their basis, using the example of solving the problem of extracting geographical names from plain text. This option is an extended version of the geocoding operation, since the result also includes the coordinates of the point features of interest, but there is no need to separately extract the addresses or geographical names of the objects in advance from the text. In computer linguistics, this problem is solved by the methods of extracting named entities (Eng. named entity recognition). Among the most modern approaches to the final implementation, the authors of the article have chosen algorithms based on rules, models of maximum entropy and convolutional neural networks. The selected algorithms and methods were evaluated not only from the point of view of the accuracy of searching for geographical objects in the text, but also from the point of view of simplicity of refinement of the basic rules or mathematical models using their own text bodies. Reports on technological violations, accidents and incidents at the facilities of the heat and power complex of the Ministry of Energy of the Russian Federation were selected as the initial data for testing the abovementioned methods and software solutions. Also, a study is presented on a method for improving the quality of recognition of named entities based on additional training of a neural network model using a specialized text corpus.

<p><strong>Abstract.</strong> Road traffic infrastructure plays a key role in emergency management. It allows to evacuate people from the affected area in the shortest possible time, as well as to organize rapid emergency response. However, disasters often cause the destruction of roads, railways and pedestrian routes, which can significantly affect the evacuation plan and availability of facilities for emergency services, which increases the response time and thereby increases the losses. Therefore, it is very important to quickly provide emergency services with necessary post-disaster maps, created on the principles of rapid mapping. Change detection based on geospatial data before and after damage can make rapid and automatic assessment possible with reasonable accuracy and speed. This research proposes a new approach for detecting damage and detecting the state and availability of the road network based on the satellite imagery data, unmanned aerial vehicles (UAVs) and SAR using various methods of image analysis. We also provided an assessment of the resulting combined mathematical model based on neural networks and spatial analysis approaches.</p>

Abstract. The paper discusses a problem of complex data application when accounting erosion network elements to study soil runoff and soil material redistribution on arable slopes. It is needed to estimate and account contribution of microrelief landforms to the sediment (washed out soil material) redistribution on arable areas to enhance accuracy of estimation of the soil runoff and accumulation. However, microrelief landforms are hardly detected on topographic maps and plans used traditionally in land management. For example, temporary streams formed in plowing furrows (in the case of along-slope plowing) can be detected only when survey and soil sampling data are attracted, or (partially) using remote sensing data.Due to such a context, we discover integrated analysis of map data (digital maps represented and processed in GIS environment), data of gamma-spectrometric analysis of the soil samples, and very high resolution satellite imagery, which is aimed onto detection of the role of stable and dynamically changing microrelief landforms in soil material redistribution.