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Models of Geospatially Referenced People Distribution as a Basis for Studying the Daily Cycles of Urban Infrastructure Use by Residents
City services and infrastructures are focused on consumers and are able to effectively and qualitatively implement their functions only under conditions of normal workload. In this regard, the correct organization of a public service system is directly related to the knowledge of the quantitative and qualitative composition of people in the city during the day. The article discusses existing solutions for analyzing the distribution of people in a territory based on data collected by mobile operators, payment terminals, navigation systems and other network solutions, as well as the modeling methods derived from them. The scientific aim of the study is to propose a solution for modeling the daily distribution of people based on open statistics collected from the Internet and open-web mapping data. The stages of development of the modeling software environment and the methods for spatial analysis of available data on a digital cartographic basis are described. The proposed approach includes the use of archetypes of social groups, occupational statistics, gender and age composition of a certain territory, as well as the characteristics of urban infrastructure objects in terms of composition and purpose. Solutions for modeling the 48 h distribution of city residents with reference to certain infrastructure facilities (residential, public and working) during working and weekend days with an hourly breakdown of the simulated values were created as a result of the study. A simulation of the daily distribution of people in the city was carried out using the example of the city of Volgograd, Russian Federation. A picture of the daily distribution of city residents by district and specific buildings of the city was obtained as a result of the modeling. The proposed approach and the created algorithm can be applied to any city.
Models of Geospatially Referenced People Distribution as a Basis for Studying the Daily Cycles of Urban Infrastructure Use by Residents
City services and infrastructures are focused on consumers and are able to effectively and qualitatively implement their functions only under conditions of normal workload. In this regard, the correct organization of a public service system is directly related to the knowledge of the quantitative and qualitative composition of people in the city during the day. The article discusses existing solutions for analyzing the distribution of people in a territory based on data collected by mobile operators, payment terminals, navigation systems and other network solutions, as well as the modeling methods derived from them. The scientific aim of the study is to propose a solution for modeling the daily distribution of people based on open statistics collected from the Internet and open-web mapping data. The stages of development of the modeling software environment and the methods for spatial analysis of available data on a digital cartographic basis are described. The proposed approach includes the use of archetypes of social groups, occupational statistics, gender and age composition of a certain territory, as well as the characteristics of urban infrastructure objects in terms of composition and purpose. Solutions for modeling the 48 h distribution of city residents with reference to certain infrastructure facilities (residential, public and working) during working and weekend days with an hourly breakdown of the simulated values were created as a result of the study. A simulation of the daily distribution of people in the city was carried out using the example of the city of Volgograd, Russian Federation. A picture of the daily distribution of city residents by district and specific buildings of the city was obtained as a result of the modeling. The proposed approach and the created algorithm can be applied to any city.
Models of Geospatially Referenced People Distribution as a Basis for Studying the Daily Cycles of Urban Infrastructure Use by Residents
Danila Parygin (author) / Alexander Anokhin (author) / Anton Anikin (author) / Anton Finogeev (author) / Alexander Gurtyakov (author)
2024
Article (Journal)
Electronic Resource
Unknown
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