Intelligent Control Systems in Urban Planning Conflicts: Social Media Users’ Perception

Nailia Gabdrakhmanova,Maria Pilgun

doi:10.3390/app11146579

Nailia Gabdrakhmanova, Maria Pilgun

Open Access

https://doi.org/10.3390/app11146579

Copy DOI

Abstract

The relevance of this study is determined by the need to develop technologies for effective urban systems management and resolution of urban planning conflicts. The paper presents an algorithm for analyzing urban planning conflicts. The material for the study was data from social networks, microblogging, blogs, instant messaging, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the construction of the North-Eastern Chord (NEC) in Moscow (RF). To analyze the content of social media, a multimodal approach was used. The paper presents the results of research on the development of methods and approaches for constructing mathematical and neural network models for analyzing the social media users’ perceptions based on their digital footprints. Artificial neural networks, differential equations, and mathematical statistics were involved in building the models. Differential equations of dynamic systems were based on observations enabled by machine learning. Mathematical models were developed to quickly detect, prevent, and address conflicts in urban planning in order to manage urban systems efficiently. In combination with mathematical and neural network model the developed approaches, made it possible to draw a conclusion about the tense situation around the construction of the NEC, identify complaints of residents to constructors and city authorities, and propose recommendations to resolve and prevent conflicts. Research data could be of use in solving similar problems in sociology, ecology, and economics.

Highlights

This paper is an extended version of the paper presented at the 14th InternationalSymposium INTELS ’20, which will be published after the conference
This paper presents the results of research on the development of methods and approaches to the construction of mathematical and neural network models for analyzing the perception of social networks users on the basis of their digital footprints, as well as for the rapid identification, prevention and resolution of urban planning conflicts necessary for effective urban systems management
By using state-of-the-art methods to derive differential equations from data, this paper investigates the applicability of such methods to sociological modeling on social media data

Summary

Introduction

This paper is an extended version of the paper presented at the 14th InternationalSymposium INTELS ’20, which will be published after the conference. One of the relevant tasks is to analyze social media users’ perception of certain situations, events and phenomena using new technologies. The main source of information, in this case, is digital footprints, among which unstructured linguistic data are of importance. Neural network technologies open up new possibilities for studying the specifics of perception of a situation on large volumes of verbal data. Various aspects of linguistic representations in artificial neural networks have been presented in studies over recent years [2,3,4], and the speech behavior of Facebook users enabled creation of new personality constructs [5]. Various aspects of perception are presented in numerous works, for example, speech perception [6], and Bayesian analysis of data from cross-linguistic studies on color and memory perception [7], etc. It should be noted that the new autoregressive language model GPT-3 with 175 billion parameters marked a new stage in the field of natural language processing technologies [8]

Objectives

Methods

Results

Discussion

Conclusion