Land Use Identification through Social Network Interaction

Jesus S. Aguilar-Ruiz, Diana C. Pauca-Quispe, Cinthya Butron-Revilla, Ernesto Suarez-Lopez, Karla Aranibar-Tila

Research output: Contribution to journalArticlepeer-review

Abstract

The Internet generates large volumes of data at a high rate, in particular, posts on social networks. Although social network data have numerous semantic adulterations and are not intended to be a source of geo-spatial information, in the text of posts we find pieces of important information about how people relate to their environment, which can be used to identify interesting aspects of how human beings interact with portions of land based on their activities. This research proposes a methodology for the identification of land uses using Natural Language Processing (NLP) from the contents of the popular social network Twitter. It will be approached by identifying keywords with linguistic patterns from the text, and the geographical coordinates associated with the publication. Context-specific innovations are introduced to deal with data across South America and, in particular, in the city of Arequipa, Peru. The objective is to identify the five main land uses: residential, commercial, institutional-governmental, industrial-offices and unbuilt land. Within the framework of urban planning and sustainable urban management, the methodology contributes to the optimization of the identification techniques applied for the updating of land use cadastres, since the results achieved an accuracy of about 90%, which motivates its application in the real context. In addition, it would allow the identification of land use categories at a more detailed level, in situations such as a complex/mixed distribution building based on the amount of data collected. Finally, the methodology makes land use information available in a more up-to-date fashion and, above all, avoids the high economic cost of the non-automatic production of land use maps for cities, mostly in developing countries.

Original languageEnglish
Article number8580
JournalApplied Sciences (Switzerland)
Volume12
Issue number17
DOIs
StatePublished - Sep 2022

Bibliographical note

Funding Information:
This work was supported by the Andalusian Plan for Research, Development and Innovation, by Grant PID2020-117759GB-I00 funded by MCIN/AEI/10.13039/501100011033, Spain, and by Grant IBA0021-2017-UNSA funded by the Universidad Nacional de San Agustín de Arequipa, Perú.

Publisher Copyright:
© 2022 by the authors.

Keywords

  • classification
  • land use
  • natural language processing
  • social networks data

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