Currently in the field of transport geography, the spatial evolution of electrical networks remain globally understudied. Publicly available data sources, including remote sensing data, have made it possible to collect spatial data on electrical networks, but at the same time a suitable data structure for storing them has not been defined. The main purpose of this study was the collection and structuring of spatiotemporal data on electric networks with the possibility of their further processing and analysis. To collect data, we used publicly available remote sensing and geoinformation systems, archival schemes and maps, as well as other documents related to the Moscow power grid. Additionally, we developed a web service for data publication and visualization. We conducted a small morphological analysis of the evolution of the network to show the possibilities of working with the database using a Python script. For example, we found that the portion of new lines has been declining since 1950s and in the 2010s the portion of partial reconstruction reached its maximum. Thus, the developed data structure and the database itself provide ample opportunities for the analysis and interpretation of the spatiotemporal development of electric networks. This can be used as a basis to study other territories. The main results of the study are published on the web service where the user can interactively choose a year and two forms of power lines representation to visualize on a map.

It is cost effective and environmentally rational to develop renewable energy source in sthe territories of decentralized energy supply, since it allows replacing diesel power generation. Yakutia has a high potential for the development of solar energy and 64 % of its area is in the zone of decentralized energy supply. The features of web mapping—interactivity, multiscale and availability—are useful for assessment of big multi-temporal data on solar resources at the regional scale. Web mapping shows one parameter for Yakutia—all sky insolation incident on a horizontal surface per day per square meter. The parameter is used to assess the potential of solar energy resources in the territory. Initial data comes from the Surface meteorology and Solar Energy archive of the NASA Prediction of Worldwide Energy Resource—SSE NASA POWER project. It includes monthly and annual average global grids for each year from 1984 to 2018. The web application allows users to map the data by grid points, by administrative units and by watersheds. Users can interactively set a period with an accuracy of a month and a territorial division for drawing up a map. The web-based mapping application allows users to create about 50,000 web maps in total. The number based on query combinations available to the user. A large amount of data used for web mapping requires the development of an information system for the dynamic delivery of data at the user’s request. Data preprocessing algorithm helps efficiently aggregate data “on-the-fly” for various territorial units. The DeckGL cartographic library gives high-performance visualization of big spatial data in the browser. We use PostgreSQL and Flask software to develop the information system. Web mapping is useful for the assessment of solar energy resources specifically at the regional level. Interactive tools provided by the web-based mapping applications deepen the analytical content of the cartographic work. There are notable changes in the data preparation through the design of web-based mapping applications in comparison with regular maps.

Backbone power lines in Russia have a complex spatial structure. There are no systematized and topologically consistent spatio-temporal data about them. however, the study of their evolution requires not only data mining, but also a comprehensive design of the structure of the spatio-temporal database. The structure should provide effective data storage, be convenient for filling the database and editing data, provide the ability to reconstruct the network for a given period and apply spatial analysis methods. Open sources like power grid operator reports, schemes and programs of power grid development, public cadastral map, information from Situational and Analytical Center of the Ministry of Energy and very high spatial resolution remote sensing data are the main data sources. Users do not have direct access to the database but refer to it using queries. Interaction with the database is carried out through Application programming Interface (API). This allows downloading data from the database as well as embedding them into external systems, for example, connecting analysis tools to them, creating cartographic web applications with this data. Data preprocessing is performed in python using the Arcpy module, the database is created with PostgreSQl, the API works on PostgREST. Consistent multi-temporal spatial database serves as the basis for analyzing the structural features of electrical networks, makes it possible to visualize the history of the development of the power grid of the territory in an interactive web-based mapping application, allows to apply geoprocessing tools and special network analysis tools. The detailed study of the evolution of backbone power grids is crucial in long-term strategies for the development of the power grid. Abroad, studies of the evolution of electrical networks usually operate with a schematic graph of a network without reference to real spatial geometry, therefore, there is no problem of designing the structure of spatio-temporal database. yet, ignoring topomorphological relationships in the network leads to the loss of information about electrical networks, which leads to a loss in the quality of spatial analysis.

Электрические сети как предмет изучения географии транспорта на данный момент плохо освещены в отечественной и зарубежной литературе. С появлением высокодетальных космических снимков появилась возможность их использовать для сбора пространственных данных. Несмотря на попытки автоматизации распознавания ЛЭП на снимках, самый надёжный способ получения информации по-прежнему – визуальное дешифрирование. В рамках исследования проведён сбор пространственно-временной информации о магистральных электрических сетях на территории московской энергосистемы. Были использованы общедоступные данные дистанционного зондирования картографических веб-сервисов, а также архивные снимки с американских спутников Keyhole. В работе приведён пример изменения одного участка сети по снимкам за 1973 и 2018 годы. Сбор данных осуществляется с помощью Google Earth Pro и ArcGIS, анализ данных — скриптами модуля arcpy, подготовка данных к публикации – скриптами R, публикация данных в виде WMS (Web Map Service) выполняется с помощью QGIS Server. При анализе данные хранятся в базе геоданных ESRI, для публикации переводятся в открытый формат Geopackage Основной результат исследования доступен по адресу https://powerlines.one.

A unique spatio-temporal database of the backbone electric networks of the Moscow power system was previously based on various information sources and published as a cartographic web service. In this study, we consider some mapping possibilities based on calculated parameters, including network analysis methods. To represent the data correctly for each studied year from 1936 to 2020, we have developed algorithms for verifying data integrity, as well as for automated creation of a topologically correct network model. Bringing the network to a topologically correct form implies the snapping of the end vertices of the lines to the point objects of the power system, the elimination of hanging dangles, as well as the elimination of self-intersections. The integrity check is carried out in three stages: 1) coordination of the time frame for the existence of network segments; 2) checking the connectivity of each power line for each time slice; 3) checking the connectivity of the entire network as a whole for each year. The age of the network, betweenness centrality, electric grid centrality, closeness centrality in this paper are taken as an example of local parameters, i. e. indicators confined to specific elements of the network (edges or vertices). In addition, we have considered a global indicator characterizing the network as a whole—the average shortest path in the network, which can be calculated in three ways: without taking into account the weight, taking into account the length of the lines or taking into account its capacitance characteristics, depending on voltage.